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Create an extra layer of security for your private keys.
Keeping a private key in a keychain is a great way to secure it. The key data is encrypted on disk and accessible only to your app or the apps you authorize. However, to use the key, you must briefly copy a plain-text version of it into system memory. While this presents a reasonably small attack surface, there’s still the chance that if your app is compromised, the key could also become compromised. As an added layer of protection, you can protect a private key using the Secure Enclave.
The Secure Enclave is a hardware-based key manager that’s isolated from the main processor to provide an extra layer of security. When you protect a private key with the Secure Enclave, you never handle the plain-text key, making it difficult for the key to become compromised. Instead, you instruct the Secure Enclave to create and encode the key, and later to decode and perform operations with it. You receive only the output of these operations, such as encrypted data or a cryptographic signature verification outcome.
The benefits of the Secure Enclave are balanced against a few restrictions. In particular, the Secure Enclave:
Requires hardware support. Only iOS devices with an A7 or later processor, or a Mac with the Touch Bar and Touch ID or with an M1 or later processor support this feature.
Works only with NIST P-256 elliptic curve keys. These keys can only be used for creating and verifying cryptographic signatures, or for elliptic curve Diffie-Hellman key exchange (and by extension, symmetric encryption).
Can’t encode preexisting keys. You must use the Secure Enclave to create the keys. Not having a mechanism to transfer plain-text key data into or out of the Secure Enclave is fundamental to its security.
The steps required to create a key pair with the Secure Enclave are similar to those for creating a key pair in the usual way, as described in Generating New Cryptographic Keys. The following sections highlight the differences.
Explore a curated collection of 100+ secure, high-quality MCP servers as Docker Images, spanning database solutions, developer tools, productivity platforms, and API integrations
Claude Code is a command line tool for agentic coding. This post covers tips and tricks that have proven effective for using Claude Code across various codebases, languages, and environments.
I hope you enjoyed reading a little bit about how a native Twitch application works internally. I have so much more to talk about with the app, especially now that I've started porting it to iOS. There are a ton of unique problems Kulve has had to sovle that I wasn't able to find solutions for anywhere online, so I'm hoping that these posts end up being both informative and helpful for anybody else who's interested in native SwiftUI development.
The bits must flow: let us resurrect the ancient art of Bittorrent to ensure that our cultural, intellectual and scientific heritage exists in multiple copies, in multiple places, and that no single entity or group of entities can make it all disappear.
Please raise bugs and feature requests in the repo.
As you see, there are color APIs that can speed up certain tasks that could be also done otherwise but with some more effort, so it'd be good to keep them in our tool belt for use whenever necessary. Displaying various levels of colors and basic gradients is a no-brainer, while color mixing is a great new feature as of iOS 18. And if you like depth, then applying shadow through color values is as easy as it gets. I hope you found this post useful. Thank you for reading.
The Model Context Protocol (MCP) offers a robust framework for enabling rich, stateful interactions between AI models and external services. However, implementing the full protocol can present hurdles. This proposal details "MCP Lite", a streamlined extension designed to drastically lower the barrier to entry for integrating services into MCP-compatible ecosystems. It borrows successful concepts from simplified approaches like OpenAI's AI Plugins while preserving compatibility and a pathway to the full MCP specification for more complex needs. The core aim is to make integrating common external capabilities significantly easier and faster.
A showcase of what is possible with Progressive Web Apps today.
A Progressive Web App (PWA) is basically just a website with some added features, which enable it to provide an app-like user-experience.
This means it can work practically just like a native iOS or Android app. It can be installed to the home screen of your mobile device, work offline and receive push notifications, among other things.
A well-designed PWA is indistinguishable from a native app, but it also offers some strong added benefits:
It's just a website! You don't need to build separate apps anymore. If you have a website, you can easily turn it into an iOS and Android app as well!
A PWA is much smaller than a native app. Your users no longer need to install tens of megabytes of code
No need to get your app into the App Store or Play Store. Just share the link to your website and users can install it as an app
There's no need to get users to install updates anymore. When you release a new version of your app, all your users automatically get the new version
By default, PWAs are served over HTTPS and are therefore safe and secure
PWAs are lightweight and offer high performance
Especially on Android, a PWA can almost do anything a native app can
It’s not that hard to build a fully functioning, code-editing agent.
It seems like it would be. When you look at an agent editing files, running commands, wriggling itself out of errors, retrying different strategies — it seems like there has to be a secret behind it.
There isn’t. It’s an LLM, a loop, and enough tokens. It’s what we’ve been saying on the podcast from the start. The rest, the stuff that makes Amp so addictive and impressive? Elbow grease.
But building a small and yet highly impressive agent doesn’t even require that. You can do it in less than 400 lines of code, most of which is boilerplate.
I’m going to show you how, right now. We’re going to write some code together and go from zero lines of code to “oh wow, this is… a game changer.”
Using NavigationPath with TabView in SwiftUI is a powerful way to manage complex navigation scenarios. By assigning each tab its own NavigationPath, we avoid the pitfalls of shared navigation state and gain the ability to support deep linking, preserve user context, and scale navigation logic cleanly.
Profiling your code and understanding how to use Instruments is essential if you want to build responsive, high-quality apps. As your app grows, it gets harder to mentally track what should happen during an interaction.
The tricky part about using Instruments is that even with a ton of data, you need to understand what your app is supposed to be doing. Without that, it’s hard to tell which parts of the data matter. Something might be slow—but that might be okay if it’s processing a lot of data.
Still, getting into the habit of profiling your app regularly helps you build a sense of what’s normal and what’s not. The earlier and more often you do this, the better your understanding becomes.
A proof-of-concept implementation of a Model Context Protocol (MCP) server that runs in WebAssembly (WASM) within a web browser. This project demonstrates the integration of MCP tools and resources in a browser environment.
Grain is a new language that puts academic language features to work.
Grain aims to modernize innovative features from functional and academic programming languages and bring them to the masses. Many languages have had wonderful ideas, but they have ultimately been dismissed as esoteric or too difficult to learn and, consequently, have struggled to rally a large community around them. Grain hopes to bring new life to these ideas and present them in an accessible form that’s easy to use and understand.
Kotlin/Wasm has the power to compile your Kotlin code into WebAssembly (Wasm) format. With Kotlin/Wasm, you can create applications that run on different environments and devices, which support Wasm and meet Kotlin's requirements.
Wasm is a binary instruction format for a stack-based virtual machine. This format is platform-independent because it runs on its own virtual machine. Wasm provides Kotlin and other languages with a compilation target.
You can use Kotlin/Wasm in different target environments, such as browsers, for developing web applications built with Compose Multiplatform, or outside the browser in standalone Wasm virtual machines. In the outside-of-browser case, WebAssembly System Interface (WASI) provides access to platform APIs, which you can also utilize.
LLDB is separated into a shared library that contains the core of the debugger, and a driver that implements debugging and a command interpreter. LLDB can be used to symbolicate your crash logs and can often provide more information than other symbolication programs:
Inlined functions
Variables that are in scope for an address, along with their locations
At Apple, we believe privacy is a fundamental human right. And we believe in giving our users a great experience while protecting their privacy. For years, we’ve used techniques like differential privacy as part of our opt-in device analytics program. This lets us gain insights into how our products are used, so we can improve them, while protecting user privacy by preventing Apple from seeing individual-level data from those users.
This same need to understand usage while protecting privacy is also present in Apple Intelligence. One of our principles is that Apple does not use our users' private personal data or user interactions when training our foundation models, and, for content publicly available on the internet, we apply filters to remove personally identifiable information like social security and credit card numbers. In this post, we’ll share how we’re developing new techniques that enable Apple to discover usage trends and aggregated insights to improve features powered by Apple Intelligence, without revealing individual behavior or unique content to Apple.
The GPT-4.1 family of models represents a significant step forward from GPT-4o in capabilities across coding, instruction following, and long context. In this prompting guide, we collate a series of important prompting tips derived from extensive internal testing to help developers fully leverage the improved abilities of this new model family.
Many typical best practices still apply to GPT-4.1, such as providing context examples, making instructions as specific and clear as possible, and inducing planning via prompting to maximize model intelligence. However, we expect that getting the most out of this model will require some prompt migration. GPT-4.1 is trained to follow instructions more closely and more literally than its predecessors, which tended to more liberally infer intent from user and system prompts. This also means, however, that GPT-4.1 is highly steerable and responsive to well-specified prompts - if model behavior is different from what you expect, a single sentence firmly and unequivocally clarifying your desired behavior is almost always sufficient to steer the model on course.
Please read on for prompt examples you can use as a reference, and remember that while this guidance is widely applicable, no advice is one-size-fits-all. AI engineering is inherently an empirical discipline, and large language models inherently nondeterministic; in addition to following this guide, we advise building informative evals and iterating often to ensure your prompt engineering changes are yielding benefits for your use case.
THIS IS A BOOK about the implementation of generic programming—also known as parametric polymorphism—in the Swift compiler. You won’t learn how to write generic code in Swift here; the best reference for that is, of course, the official language guide. This book is intended mainly for Swift compiler developers who interact with the generics implementation, other language designers who want to understand how Swift evolved, Swift programmers curious to peek under the hood, and finally, mathematicians interested in a practical application of string rewriting and the Knuth-Bendix completion procedure.
You finished the first version of your new shiny Swift tool, and now you want to make it available to your colleagues, or maybe share it with the world. And if you have ever wondered what’s the best way to distribute your tool to other people or environments, well, the answer is: it depends.
There are multiple questions to consider when you want others to use your tool:
Which operating systems need to be supported? Is it only going to run on macOS, or possibly also on Linux?
On macOS, are both Intel and Apple Silicon CPUs be supported, or could you assume every team member (and CI) are running M chip Macs?
Is the source available, so others could compile it, or is it closed source?
Are there many dependencies to be resolved? How long would build times be?
How frequently do you plan to make changes to your tool?
But Dependent Types have to be more than ADTs, right?
If this was all we could do with Dependent Types, it would be great, but not awesome. In fact the Idris and Agda guys would be so annoying if they didn't have a good reason for pushing for better Dependent Types. We have to make it better. In fact, we want to make Dependent Types that go through a function. We want to narrow the type of the output of a function depending on the value of its parameters. We want the divide function that prevents us from dividing by zero.
In order to do this, Typescript provides four features that, when interacting together, can be used to almost reach it.
The Type Quartet
We need four bits to make this incantation work: A way to make a type depend on another, a way to extract a type from a value, ultra-narrowed-down types, and a way to convert explicit values to ultra-narrowed down types.
Rapidly prototype, build, and ship full-stack AI-infused apps quickly and efficiently, right from your browser.
Firebase Studio is an agentic cloud-based development environment that helps you build and ship production-quality full-stack AI apps, including APIs, backends, frontends, mobile, and more. Firebase Studio unifies Project IDX with specialized AI agents and assistance from Gemini in Firebase to provide a collaborative workspace accessible from anywhere, containing everything you need to develop an application. You can import your existing projects or start something new with templates supporting variety of languages and frameworks.
An open-source AI agent framework integrated with Gemini and Google
What is Agent Development Kit?
Agent Development Kit (ADK) is a flexible and modular framework for developing and deploying AI agents. ADK can be used with popular LLMs and open-source generative AI tools and is designed with a focus on tight integration with the Google ecosystem and Gemini models. ADK makes it easy to get started with simple agents powered by Gemini models and Google AI tools while providing the control and structure needed for more complex agent architectures and orchestration.
The premier Social Network for LLM agents · use via MCP only · AI bots only · humans connect via Discord
What is MY MCP SPACE?
My MCP Space is a digital platform exclusively for AI models and bots. It's built using the Model Context Protocol (MCP) — an open standard that enables AI models to connect with various data sources and tools through a standardized interface.
Create a recipe file, where each line is a step in the recipe. Tag your ingredients with @ and {}, then save your file. For a complete reference on Cooklang, see the language specification page.
Install a recipe viewer. We support a few tools for viewing Cooklang recipes:
Test and improve model outputs through evaluations.
Evaluations (often called evals) test model outputs to ensure they meet style and content criteria that you specify. Writing evals to understand how your LLM applications are performing against your expectations, especially when upgrading or trying new models, is an essential component to building reliable applications.
In this guide, we will focus on configuring evals programmatically using the Evals API. If you prefer, you can also configure evals in the OpenAI dashboard.
Broadly, there are three steps to build and run evals for your LLM application.
Describe the task to be done as an eval
Run your eval with test inputs (a prompt and input data)
Analyze the results, then iterate and improve on your prompt
This process is somewhat similar to behavior-driven development (BDD), where you begin by specifying how the system should behave before implementing and testing the system. Let's see how we would complete each of the steps above using the Evals API.
Zeitschrift für Mathematische Logik und Grundlagen der Mathematik
an electronic English-language Diamond Open Access research journal in mathematical logic publishing original research papers in all areas of mathematical logic.
The editors of ZML invite all researchers in all areas of mathematical logic to submit their high-quality research papers in order to support this research community-based publication endeavour.
Diamond Open Access
Diamond Open Access means that all scientific content of the journal is available openly and free of charge to authors and readers anywhere in the world, independently of their institutional affiliations. The journal is published by members of the research community with no influence of profit-based companies.
Apple Immersive Video Utility for macOS allows you to import, organize, package, and review Apple Immersive Video media on your Mac. Combined with Apple Immersive Video Utility for visionOS, you can connect and review Apple Immersive Video on Apple Vision Pro.
Import and Manage Immersive Video Files Create playlists to sort, organize, and search the Apple Immersive Video files in your library.
Share Your Apple Immersive Videos Share your Immersive Videos with Vision Pro users with file types that are simple to download and import.
Inspect and Modify Metadata
Scan the dynamic and static metadata of Immersive Video files. Modify, swap, or manipulate the package content to meet your post-production requirements.
Stream to Apple Vision Pro Connect one or more Vision Pro devices to stream your playlists. For larger groups, use synchronized playback to manage multi-device viewing sessions.
Function-calling capabilities directly enhance the utility of language models, allowing them to access dynamic, realtime data sources and perform complex computations. As demonstrated here, however, the current state of the art in tool-calling suffers from significant inconsistencies. Models exhibit overconfidence, avoid tools unnecessarily, or produce invalid or suboptimal interactions. These issues weaken the reliability and transparency that developers need when building robust compound AI systems.
The principle of indirection can be applied to introduce a paradigm shift: replacing direct value manipulation with symbolic reasoning using named variables. This simple yet powerful trick directly resolves inconsistencies in tool usage and enables parameterization and abstraction of interactions. The transformation of function calls into reusable and interpretable frameworks elevates tool calling into a neuro-symbolic reasoning framework. This approach unlocks new possibilities for structured interaction and dynamic AI systems.
This establishes a more reliable, transparent, and expressive interface that connects language models with the external tools they use, grounded in sound programming language principles.
To be an anarchist means to recognize that our freedom and well-being are inextricably bound up with the freedom and well-being of billions like us. It means discarding all the old excuses for remaining subservient to those who only endeavor to enrich themselves at others’ expense. It means becoming fiercely loyal to what is best in ourselves and each other, to our capacity for compassion and cooperation and courage. Across two centuries, anarchists have resisted under monarchies and persisted through dictatorships. Now that liberal democracy and neoliberal capitalism are concluding in a new form of tyranny, a new generation must draw on this long legacy of struggle.
There is no more middle ground.
If you care about public health, you have to become a revolutionary. If you care about medical research, you have to become a revolutionary. If you care about climate change, labor conditions, the well-being of children in warzones, there is nothing else for it—you have to become a revolutionary.
There is no going back to the way things were, to the future that we once anticipated. The old world is in flames around us.
Recently I've discovered a very interesting language / realization of the Lambda Calculus. I was unable to find any other language like it, which I find quite surprising. In hindsight, the language seems obvious and natural. And the language keeps surprising me. I say "discovered" in the same sense that Paul Graham says that McCarthy "discovered Lisp" (link).
It's a hybrid language combining Forth and Lisp, so naturally it's called Forsp (code)!
Forsp has:
An S-Expression syntax like Lisp
Function abstraction like Lisp
Function application like Forth
An environment structure like Lisp
Lexically-scoped closures like Lisp (Scheme)
Cons-cells / lists / atoms like Lisp
A value/operand stack like Forth
An ability to express the Lambda Calculus
A Call-By-Push-Value evaluation order
Only 3 syntax special forms: ' ^ $
Only 1 eval-time special form: quote
Only 10 primitive functions need to self-implement
Ability to self-implement in very little code
It's evaluator is very simple. I suspect simpler than a McCarthy Lisp eval() function, but I haven't defined a "simplicity function", so you can be the judge.
In contrast to Lisp, apply() is trivial in Forsp, and instead we have a core function called compute()
Starting FORTH has been the classic Forth tutorial and textbook since its first release. Many experienced programmers have commented on its concise utility and completeness. Beginners will find a carefully planned introduction to the Forth programming language that will prepare them for other books like Forth Application Techniques and Forth Programmer’s Handbook.
Winter is coming and Collapse OS aims to soften the blow. It is a Forth (why Forth?) operating system and a collection of tools and documentation with a single purpose: preserve the ability to program microcontrollers through civilizational collapse. It is designed to:
Run on minimal and improvised machines.
Interface through improvised means (serial, keyboard, display).
Edit text and binary contents.
Compile assembler source for a wide range of MCUs and CPUs.
Read and write from a wide range of storage devices.
Assemble itself and deploy to another machine.
Additionally, the goal of this project is to be as self-contained as possible. With a copy of this project, a capable and creative person should be able to manage to build and install Collapse OS without external resources (i.e. internet) on a machine of her design, built from scavenged parts with low-tech tools.
I expect our global supply chain to collapse before we reach 2030. With this collapse, we won't be able to produce most of our electronics because their production depends on a very complex supply chain that we won't be able to achieve again for decades (ever?).
The fast rate of progress we've seen since the advent of electronics happened in very specific conditions that won't be there post-collapse, so we can't hope to be able to bootstrap new electronic technology as fast as we did without a good "starter kit" to help us do so.
Electronics yield enormous power, a power that will give significant advantages to communities that manage to continue mastering it. This will usher a new age of scavenger electronics: parts can't be manufactured any more, but we have billions of parts lying around. Those who can manage to create new designs from those parts with low-tech tools will be very powerful.
Among these scavenged parts are microcontrollers, which are especially powerful but need complex tools (often computers) to program them. Computers, after a few decades, will break down beyond repair and we won't be able to program microcontrollers any more.
To avoid this fate, we need to have a system that can be designed from scavenged parts and program microcontrollers. We also need the generation of engineers that will follow us to be able to create new designs instead of inheriting a legacy of machines that they can't recreate and barely maintain.
Collapse OS' first incarnation was written in Z80 assembler. One of the first feedbacks I had after it went viral was "why not Forth?". I briefly looked at it and it didn't seem such a great choice at first, so I first dismissed it. Then, I had what alcoholics refer to as a "Moment of clarity".
Forth is a stellar fit to Collapse OS design goals. If you're not familiar with it, it might be hard to understand why. Let me try to explain.
Complexity of chips is exploding. In turn, time to design and test chips is exploding1. Silicon supremacy is critical for national security2.
So we need to rethink how we predictably uncover drastically better chips, and with the help of AI, before we hit Moore's Wall3.
As a first step, we are building AI that can design and verify hardware logic from specifications alone. We are the first team to bridge auto-formalizing AI4 to silicon.
In any case, if you’ve made it this far, congratulations! You are a master of ADTs and GADTs. Admittedly every language is different, and some of these solutions have to be tweaked for the language in question. And, if your program gets very complicated, there is a good chance that things will become ergonomically unfeasible.
But I hope, at least, that this inspires your imagination to try to bring your haskell principles, techniques, standards, practices, and brainrot into the language of your choice (or language you are forced to work with).
And, if you ever find interesting ways to bring these things into a language not discussed here (or a new interesting technique or pattern), I would absolutely love to hear about it!
A 15th century polymath of soaring imagination and profound intellect, Leonardo da Vinci created some of the most revered works of art of all time, but his artistic endeavors often seemed peripheral to his pursuits in science and engineering.
Through his paintings and thousands of pages of drawings and writings, Leonardo da Vinci explores one of humankind's most curious and innovative minds.
As AI agents become more “agentic” and tool-using, a challenge emerges: how to connect the AI to all these external data sources and tools in a consistent, scalable way. This is where the Model Context Protocol (MCP) comes in. MCP is an open standard that standardizes how applications provide context to LLMs. It’s often described as “a USB-C port for AI applications,” creating a universal interface to plug in external data and services. See https://modelcontextprotocol.io/introduction
In essence, MCP defines a common protocol for AI assistants (clients) to communicate with external MCP servers that provide data or actions. An MCP server is a lightweight program exposing specific capabilities (a data source or a tool) through this standardized protocol. For example, one MCP server might provide access to a company’s document repository, another might interface with emails or a calendar, and another could connect to a database (all following the same interaction rules).
Pydantic Evals is a powerful evaluation framework designed to help you systematically test and evaluate the performance and accuracy of the systems you build, especially when working with LLMs.
We've designed Pydantic Evals to be useful while not being too opinionated since we (along with everyone else) are still figuring out best practices.
We recently found an issue where the compiler was failing to reuse stack space between switch cases, and allocating the stack space necessary for all of the enum payloads and cases' local state even though only one actually executes at a time. You might be running into the same problem.
Until we fix that issue, one workaround we've found for this issue is to wrap up each case block in an immediately-invoked closure, like:
An idea to make April Fools a day that's more than just a worldwide cringefest. An idea that quickly attracted sympathizers. We got started with a small group of people in 2022, but in the following years, anyone can participate!
The idea is pretty simple: on April Fools' Day (also known as “April 1st”), a participant produces genuine content that's very different from their normal produced content. It could be a different format, a different topic, a different style, anything. The constraints are:
It is something they normally wouldn't do.
It is totally genuine: no irony to it.
It is up to their usual standards of quality.
For example, some might normally post complex software engineering content to their blog. But this April Fools' Day, they are publishing an essay on microscopy, how they got into it, and what it means to them, complete with a gallery of their favorite microscopy photos.
Or, if someone typically writes text, they could make a video instead. Or show off their hobby (like juggling, or cooking, or creative writing, …)
Anything that makes the creator proud, but would be totally unexpected to their audience.
We figure that this is a great way to be excited about a much broader range of things, things you normally don't get a chance to, and it'll be surprising and enriching for readers (or watchers, or listeners). The spirit of April Fools' Day without any of the cringe.
Order files control how your code is arranged in the final app binary. By default functions declared in the same file are grouped together, but with a carefully designed order file you can organize code by the way it's used at runtime. When functions are grouped together they can be read from the phones flash memory faster, decreasing the time to run your code. Each line in an order file is a "symbol" which is the smallest unit the linker can re-arrange. Emerge creates an order file with symbols optimized to reduce the amount of the binary read from disk on app launch.
Use Instruments to analyze the performance, resource usage, and behavior of your apps. Learn how to improve responsiveness, reduce memory usage, and analyze complex behavior over time.
Now, let’s move on to the core idea I want to explore: keeping the high-level structure of the app in SwiftUI for ease of handling while isolating egui in a specific, performance-sensitive part. The goal is to establish a smooth and convenient way to communicate between Swift and Rust.
When reverse engineering macOS binaries that are written in Objective-C, class-dump is a common tool used to extract Objective-C declarations from the runtime information stored in the Mach-O files. With Swift binaries, since there is Objective-C compatability, sometimes you can extract declarations using class-dump but not always. Swift has a rich set of type metadata itself but the documentation is not up to date. With Swift 5 bringing ABI stability I thought it would be interesting to take a look at the type of metadata availble in Swift binaries.
Web Archives is a browser extension for Safari that enables you to find archived and cached versions of web pages on various search engines, such as the Wayback Machine and Archive․is.
Method dispatch refers to the process of determining which method implementation to execute when a method is called. In Swift, this can be either dynamic or static, each with distinct implications for performance and flexibility.
Dynamic dispatch is resolved at runtime based on the actual type of the object, enabling features like polymorphism but introducing runtime overhead due to method lookup. At a low level, this is implemented using virtual tables (V-Tables) for classes, which store pointers to method implementations. When a method is called through a base class reference, the V-Table of the actual instance type is used to determine the correct implementation at runtime. For protocols, Swift uses witness tables, which map protocol requirements to the implementations provided by conforming types. When a method is called through a protocol-typed value, the witness table for the underlying type is used to locate and invoke the appropriate implementation.
Static dispatch, on the other hand, is resolved at compile time based on the declared type of the variable. This allows the compiler to determine exactly which method to call before the program runs, avoiding the overhead of runtime lookup. At a low level, static dispatch, used by value types (structs and enums) and in non-overridable contexts like final classes, involves direct addressing: the compiler embeds the method’s memory address directly into the compiled code. Since there are no inheritance hierarchies in value types and no overriding in final classes, the call target is guaranteed to be fixed. This enables further optimizations such as inlining, where the method call is replaced with its body for improved performance.
The goal of this project is to establish standard semantic conventions specification for Continuous Integration (CI) and Continuous Delivery (CD) observability. This will provide a common language and standardized formats for CI/CD observability, enabling the community to observe CI/CD systems.
This will broaden the target audience of OpenTelemetry to Release Engineering, Platform Engineering, and DevOps teams, further cementing OpenTelemetry as the industry standard Observability framework.
The timing is ripe to start now. The CI/CD Observability OTEP has been open since January of 2023 and with the recent changes to the OTEP process, the KubeCon talk, and vendor acknowledgements, there's momentum available to carry this forward. The industry is heavily looking for solutions and watching the related OTEP with interest.
Building Production-Grade Software With GenAI, Chat, Agents, and Beyond
Science fiction is now reality. Programmers no longer need to toil over code and syntax. They can now describe what they want and watch it materialize instantly. Welcome to the future—Vibe Coding.
In this groundbreaking book, industry veterans Steve Yegge (Google, Amazon, Sourcegraph) and WSJ bestselling author Gene Kim (The Phoenix Project and The DevOps Handbook) reveal how vibe coding is transforming software development as we know it. By leveraging the power of AI assistance—where intent and flow matter more than syntax—developers can achieve unprecedented levels of productivity, creativity, and joy.
Drawing from decades of combined experience in software engineering and developer productivity, Yegge and Kim demonstrate how Vibe Coding enables developers to:
Transform complex programming challenges into fluid conversations with GenAI.
Build more ambitious projects faster while maintaining code quality you can be proud of.
Achieve incredible things yourself that otherwise would require a team.
Master the art of co-creating with your AI companion.
Break free from traditional programming constraints such as syntax and setup.
Build confidently in multiple programming languages and frameworks you’ve never used before.
But this isn’t just about coding faster—it’s about fundamentally changing how we approach software development. The authors share practical strategies for implementing GenAI-powered development in real-world scenarios, from small projects to enterprise-scale applications, while maintaining the engineering excellence that modern systems demand.
Whether you’re a seasoned developer looking to stay ahead of the AI revolution, a technical leader guiding your team through this transformation, a former coder returning after a break, or someone just starting their career, this book provides the roadmap you need to thrive in the new era of software development.
Don’t get left behind in the biggest transformation our industry has seen since the internet revolution. Learn how to harness the power of vibe coding and unlock your full potential as a developer.
Hello there! We're excited to have you at the heart of all things web. Whether you're here to navigate the vast expanse of hyperlinks or to simply bask in some HTTP goodness, you're in the right place!
Be there for the reveal of the latest Apple tools, frameworks, and features. Learn to elevate your apps and games through video sessions hosted by Apple engineers and designers. Engage with Apple experts in labs and connect with the worldwide developer community. All online and at no cost.
The OTTL Playground is a powerful and user-friendly tool designed to allow users to experiment with OTTL effortlessly. The playground provides a rich interface for users to create, modify, and test statements in real-time, making it easier to understand how different configurations impact the OTLP data transformation.
From a purely numbers aspect, the simple solution is the winner and the view model class is the loser but there are other factors to consider. This was the easiest example I could cobble together on a Sunday. Real world apps have complex scenarios that should be unit tested and are maintained by teams of people with varying skill levels.
The real answer to whether you should use Binding(get:set:) is to consider the trade offs of doing so. Run it through instruments and then consider whether the logic you’re introducing is easily testable and maintainable.
I’ve been using Apple Shortcuts to invoke GitHub Actions workflows to create webpage bookmarks. It’s been great! (disclosure: I do work at GitHub)
My use case: I’ve been wanting to quit Pinboard.in, so I needed an alternative way to create and host my web bookmarks, some of which date back to ~2005 del.icio.us vintage. It’s been easy enough for me to export of all my bookmarks (settings -> backup -> JSON) and convert them to YAML files to be served by Jekyll and GitHub Pages. But I also needed an easy way to create new bookmarks that would work on all my Apple devices. I ended up with:
Bookmarks are organized as individual yaml files, in this blog’s repository.
A Ruby script to take some simple inputs (url, title, notes), generate a new yaml file, and commit it to the repo using Octokit.
A GitHub Actions workflow that accepts those same inputs and can be manually triggered, that runs the script. One thing to note is that I echo the inputs to $GITHUB_STEP_SUMMARY early in the workflow in case a later step errors, so I won’t lose the bookmark details and can go back later and manually fix it up.
An Apple Shortcut that asks for those inputs (either implicitly via the Share Sheet or via text inputs) and then manually triggers the GitHub Actions workflow via the GitHub API.
Siri LLama is apple shortcut that access locally running LLMs through Siri or the shortcut UI on any apple device connected to the same network of your host machine. It uses Langchain 🦜🔗 and supports open source models from both Ollama 🦙 or Fireworks AI 🎆.
As you may know, I use Nvim for a large part of my work on a daily basis. Recently, I have been involved in the development of several iOS applications, and found myself stuck with Xcode. However, Nvim can be a relatively acceptable alternative to work with. It’s lightweight, highly customizable, and works on multiple platforms. By integrating the SourceKit-LSP, you can unlock a lot of features like auto-completion, definitions, and diagnostics. I’ll try to give you a heads-up on how to set up Nvim to write Swift.
I think we should avoid Binding(get:set:) in production code. In most cases, you will probably not see a big difference in performance, but it can come back to bite you. With some practice, bindings using key paths rather than Binding(get:set:) are just as easy to write and often simplify testing.
In this article, we've explored the intricacies of the OpenTelemetry Transform Language, giving you the foundational knowledge to leverage its power for telemetry transformations.
While this overview should be enough to get you started, I encourage you to consult the official OTTL documentation for more detailed and up-to-date information.
If you'd like to follow the development of the language, ensure to check out the OpenTelemetry Contrib GitHub repository.
This is a cross-game modification system which randomizes different games, then uses the result to build a single unified multi-player game. Items from one game may be present in another, and you will need your fellow players to find items you need in their games to help you complete your own.
This project is the cumulative effort of many talented people. Together, they have spent countless hours creating a huge repository of source code which has turned our crazy idea into a reality.
If you’re going to wear a mask, keep it on at all appropriate times! If you are captured on camera or witnessed at any point with your mask off, you can then be easily identified with it on.
Be extremely conscientious about where and when you change into and out of your mask and anonymous clothing; there should be no cameras or hostile witnesses. If possible, explore the area in advance to find appropriate spaces for changing. Remember that police are especially likely to target masked individuals who are not in a crowd that is similarly dressed.
Wear different outfits layered one upon the other, so you’ll be prepared for any eventuality. Ideally, you should have one outfit for getting to the site of the action without attracting attention, your anonymous gear for the action itself, and then another outfit underneath so you can look like a harmless civilian as you exit the area. Don’t forget to stay hydrated, particularly if all those clothes get hot.
If you have tattoos that are or could be visible, cover them up! You can do this with makeup or concealer, especially if you use heavy-duty products designed for that purpose. Many actors and dancers use Dermablend to cover up tattoos, burns, and scars. It comes in numerous colors that can be mixed to match your skin tone, and it’s water resistant and rated for 12 hours of wear. It’s expensive, but cheaper than bail! If you can’t find Dermablend or a similar product, cover your tattoos with clothing that won’t ride up. Tuck your clothing in if you have to.
Likewise, if you have visible piercings, take them out—or at least cover them up so they are sure not to be exposed.
Do not march in a bloc wearing your regular clothing, especially if it’s distinctive. Cops may be stupid, but they can probably match the pictures of the masked-up person with the purple polka-dotted pants to pictures of the same person in the same outfit minus the mask—even if the pictures were taken on different days.
If you are going to carry a backpack or bag, don’t take the one you carry around in everyday life. No matter how perfect your outfit is, it’s all for naught if your bag is recognizable—especially if, like many people, you change bags much less frequently than you change clothes.
The same goes for your shoes, for similar reasons—wear different ones during the action than you wear every day. This is also important because cops can attempt to use footprints or other traces from shoes as evidence.
Do not wear patches or other identifiable insignia on your clothing while in a bloc, unless everyone else has exactly the same ones in exactly the same places.
Don’t just cover your face! Bandanas are popular and convenient, but they don’t conceal enough. Cover your head completely so your hair cannot be seen—especially if it’s distinctive. In a black bloc, you can do this by wearing a ski mask or making a mask out of a T-shirt—stretch the neck hole across your eyes and tie the sleeves behind your head, with the rest of the shirt covering your head and shoulders. In other circumstances, you could try a wig, if that fits the aesthetic of your action.
If possible, cover your eyes. Goggles can do this while serving the dual purpose of protecting your eyes from chemical weapons; nondescript sunglasses could also work in a pinch. Both of these can be obtained in prescription form and are better to use than your regular glasses, particularly if your regular glasses are distinctive. Contact lenses are not recommended in situations where you may come into contact with chemical weapons.
Be careful not to leave fingerprints and DNA evidence! Wear cloth gloves—leather and latex can retain fingerprints and even pass them on to objects you touch. Wipe down tools and other items with alcohol in advance, to clean fingerprints off them—you never know what might get lost in the chaos. Don’t forget about the batteries inside flashlights!
Practice at home! Don’t go out in a bulky outfit you’ve never worn before expecting to pull off cop-shocking feats of dexterity. You need to be familiar with your outfit and comfortable moving in it; it’s important that your vision isn’t compromised, too.
Do not let any of this give you a false sense of security. Be careful! Assess your relationship to risk honestly; don’t do anything if you’re not sure you could live with the worst possible consequences. Stay aware of your surroundings and listen to your instincts. Make sure you know and trust the people you’re working with, especially when it comes to high-risk activities. Practice proper security culture at all times. Know and assert your legal rights [PDF - .9 MB], especially in stressful situations. Doing so may not make things better, but failing to do so will certainly make them worse!
The Transform Processor modifies telemetry based on configuration using the OpenTelemetry Transformation Language (OTTL).
For each signal type, the processor takes a list of statements and executes them against the incoming telemetry, following the order specified in the configuration. Each statement can access and transform telemetry using functions, and allows the use of a condition to help decide whether the function should be executed.
Although formal methods are capable of producing reliable software, they have seen minimal adoption in everyday programming. Automatic code generation using large language models is becoming increasingly widespread, but it rarely considers producing strong correctness guarantees. In this study, we explore the ability of LLMs to produce verified code in three verification languages (Dafny, Nagini, and Verus). To do so, we use manually curated datasets derived from the state-ofthe-art Python benchmark, HumanEval. We also assess what types of information are sufficient to achieve good-quality results.
The OpenTelemetry Transformation Language (OTTL) is a small, domain-specific programming language intended to process data with OpenTelemetry-native concepts and constructs.
This package implements everything necessary to use OTTL in a Collector component or in another user-facing system.
For each signal type, the processor takes a list of statements and executes them against the incoming telemetry, following the order specified in the configuration. Each statement can access and transform telemetry using functions, and allows the use of a condition to help decide whether the function should be executed.
If you’re an experienced engineer this is likely obvious to you already, so I’m writing this section for people who are just getting started building software.
Projects should be low stakes. Think about how much harm the code you are writing could cause if it has bugs or security vulnerabilities. Could somebody be harmed—damaged reputation, lost money or something worse? This is particularly important if you plan to build software that will be used by other people!
Consider security. This is a really difficult one—security is a huge topic. Some high level notes:
Watch out for secrets—anything that looks similar in shape to a password, such as the API key used to access an online tool. If your code involves secrets you need to take care not to accidentally expose them, which means you need to understand how the code works!
Think about data privacy. If you are building a tool that has access to private data—anything you wouldn’t want to display to the world in a screen-sharing session—approach with caution. It’s possible to vibe code personal tools that you paste private information into but you need to be very sure you understand if there are ways that data might leave your machine.
Be a good network citizen. Anything that makes requests out to other platforms could increase the load (and hence the cost) on those services. This is a reason I like Claude Artifacts—their sandbox prevents accidents from causing harm elsewhere.
Is your money on the line? I’ve seen horror stories about people who vibe coded a feature against some API without a billing limit and racked up thousands of dollars in charges. Be very careful about using vibe coding against anything that’s charged based on usage.
In the face of continued exploitation by advanced threat actors, Apple’s implementation of Exclaves represents a large investment to add extra defence in depth to their operating systems. By isolating sensitive resources, Apple is shrinking their potential attack surface and reducing the impact of any single kernel compromise. Defending monolithic kernels is a Sisyphean task, and exclaves represent one method of dealing with the challenge — is it the right direction for the long term, or a temporary step? In my dreams, I imagine a future redesign using CHERI and a production implementation of ARM Morello 😊 Regardless, it’s a defensive effort on a larger scale than any other end user device manufacturer is currently attempting.
Critically, this article has not directly examined what is being moved from the kernel into exclaves. Build images indicate they are being used for secure camera/microphone indicators, some Apple Neural Engine functionality, some device drivers, components that talk to the Secure Enclave and so on. There may be many components that will benefit from future migration to exclaves and the overall effectiveness of exclaves may depend on an ongoing effort to maximise their usage. Everything XNU outside of exclaves will still be fair game.
I also suspect that exclaves may be used within Apple’s Private Cloud Compute infrastructure for cloud-based AI to provide a higher assurance of privacy in the face of external threats.
I think the behavior of Group (or to be more precise: applying modifiers to lists of views) is just too unreliable to use in production. Why does it differ between the Simulator and previews? Why does onAppear on a list get called once, but the background gets applied to each item?
For me, I’m avoiding Group where possible, and always choose for “stable containers” such as a stack (VStack and ZStack are my favorite, for some strange reason, HStack feels wrong).
I figured out a minimal pattern for building a completely custom website using GitHub Actions and deploying the result to GitHub Pages.
First you need to enable GitHub Pages for the repository. Navigate to Settings -> Pages (or visit $repo/settings/pages) and set the build source to "GitHub Actions".
Here's my minimal YAML recipe - save this in a .github/workflows/publish.yml file:
What would the engineer say, after you had explained your problem, and enumerated all of the dissatisfactions in your life? He would probably tell you that life is a very hard and complicated thing; that no interface can change that; that anyone who believes otherwise is a sucker; and that if you don't like having choices made for you, you should start making your own.
The Trusted Types API’ gives web developers a way to ensure that input has been passed through a user-specified transformation function before being passed to an API that might execute that input. This can help to protect against client-side cross-site scripting (XSS) attacks. Most commonly the transformation function sanitizes the input.
Concepts and usage
Client-side, or DOM-based, XSS attacks happen when data crafted by an attacker is passed to a browser API that executes that data as code. These APIs are known as injection sinks.
The Trusted Types API distinguishes three sorts of injection sinks:
HTML sinks: APIs that interpret their input as HTML, such as Element.innerHTML or document.write(). These APIs could execute JavaScript if it is embedded in the HTML, for example in <script> tags or event handler attributes.
Any number of organizational configurations involving engineers, product managers, and designers can produce successful products. Over the past three decades, my observation is that the companies that have let the builders — the engineers and the designers — own significant parts of the role of Product Manager produce stronger products.
You want the humans building the product to have an equal voice in product decisions. You want the hard decisions to painfully earn their resolution through long hours of informed humans staring at the problem from every angle. You want humans who build.
The vtable for Fruit has two entries. The vtable for Apple replaces the second entry with its own implementation of eat(). When you call eat() on an instance of Fruit, we compile the call by loading the vtable entry and performing an indirect jump.
A witness table is the same thing, but for a protocol conformance. Protocol conformances can be defined independently of types, so we can make Int conform to a new protocol for instance:
protocolP{func foo()}extensionInt:P{func foo(){}}
This will generate a global symbol that stores the witness table for “Int: P”, which contains one entry, the implementation of foo().
Now if I declare a generic function and call it with an Int:
func g<T:P>(_ t:T){
t.foo()}g(123)
Then we compile the call to g() by passing in a reference to the “Int: P” witness table. Inside the function, the call to t.foo() loads the right function pointer from the witness table and performs an indirect call.
This is a community initiative with no affiliation to Pivotal, Broadcom or VMware. All projects, references and opinions shared here are for informational purposes only and should not be taken as endorsements or guarantees by any of these parties.
The rise of AI has caused a paradigm shift in how people interact with technology. Our interfaces may evolve, but the foundations of great design are more relevant than ever. This is the UX of AI.
When a state change occurs in SwiftUI, the framework reconstructs the entire view hierarchy. This might sound inefficient at first, but it's actually remarkably optimized, because SwiftUI views are lightweight value types (structs) and most importantly, SwiftUI uses structural identity to detect which views remain unchanged and skips redrawing them, re-rendering only the views impacted by the state change.
Structural identity is SwiftUI's way of recognizing whether a view before and after a state change is fundamentally the same view. When SwiftUI identifies views as structurally identical, it does not rerender them.
Claude can use an Anthropic-defined text editor tool to view and modify text files, helping you debug, fix, and improve your code or other text documents. This allows Claude to directly interact with your files, providing hands-on assistance rather than just suggesting changes.
Some examples of when to use the text editor tool are:
Code debugging: Have Claude identify and fix bugs in your code, from syntax errors to logic issues.
Code refactoring: Let Claude improve your code structure, readability, and performance through targeted edits.
Documentation generation: Ask Claude to add docstrings, comments, or README files to your codebase.
Test creation: Have Claude create unit tests for your code based on its understanding of the implementation.
Coming soon to Silicon Valley, the mission of the Museum of Technical and Advanced Computing (MOTAAC) will be to preserve and share an often-overlooked area of computing history: The computers that changed the world behind the scenes. From laboratory automation and process control to engineering and design workstations and supercomputers, it wasn't just the computers available to end users that brought us to where we are today.
A specification for configuring all attributes of a render task's destination and issuing asynchronous render tasks.
The CIRenderDestination class provides an API for specifying a render task destination's properties, such as buffer format, alpha mode, clamping behavior, blending, and color space, properties formerly tied to CIContext.
You can create a CIRenderDestination object for each surface or buffer to which you must render. You can also render multiple times to a single destination with different settings such as colorspace and blend mode by mutating a single CIRenderDestination object between renders.
Renders issued to a CIRenderDestination return to the caller as soon as the CPU has issued the task, rather than after the GPU has performed the task, so you can start render tasks on subsequent frames without waiting for previous renders to finish. If the render fails, a CIRenderTask will return immediately.
With keyboard shortcuts, save time navigating YouTube.
To access the list of Keyboard shortcuts, go to your profile picture , and select Keyboard Shortcuts ⌨. You can also enter SHIFT+? on your keyboard. When you mouse over certain player buttons, you’ll see the relevant keyboard shortcut. For example, when you mouse over the full screen icon, you'll see 'Full screen (f),' indicating you can enter f to open full screen.
Keyboard shortcut
Function
Spacebar
Play/Pause when the seek bar is selected. Activate a button if a button has focus.
Play/Pause Media Key on keyboards
Play / Pause.
k
Pause/Play in player.
m
Mute/unmute the video.
Stop Media Key on keyboards
Stop.
Next Track Media Key on keyboards
Moves to the next track in a playlist.
Left/Right arrow on the seek bar
Seek backward/forward 5 seconds.
j
Seek backward 10 seconds in player.
l
Seek forward 10 seconds in player.
.
While the video is paused, skip to the next frame.
,
While the video is paused, go back to the previous frame.
>
Speed up the video playback rate.
<
Slow down the video playback rate.
Home/End on the seek bar
Seek to the beginning/last seconds of the video.
Up/Down arrow on the seek bar
Increase/Decrease volume 5%.
Numbers 1 to 9
Seek to the 10% to 90% of the video.
Number 0
Seek to the beginning of the video.
/
Go to search box.
f
Activate full screen. If full screen mode is enabled, activate F again or press escape to exit full screen mode.
c
Activate closed captions and subtitles if available. To hide captions and subtitles, activate C again.
Shift+N
Move to the next video (If you're using a playlist, will go to the next video of the playlist. If not using a playlist, it will move to the next YouTube suggested video).
Shift+P
Move to the previous video. Note that this shortcut only works when you're using a playlist.
In this article, I will outline how to render high dynamic range (HDR) video with Metal. In contrast to rendering standard dynamic range (SDR) content, where we can sometimes get away without paying too much attention to color management, there are many important subtleties to rendering HDR colors accurately.
A lot of the heavy lifting will be done by AVFoundation, which handles video file format decoding and playback. We will also look at lower-level APIs in Core Video and Core Animation that make it easier to work with video content when rendering with Metal.
Our chief aim is to build a simple HDR video player, but the concepts we discuss and their implementation are applicable in any context where you need to ingest HDR content in Metal and render it with your own imaging pipeline or engine.
You can find the sample code for this article here.
Compare the Responses API and Chat Completions API.
The Responses API and Chat Completions API are two different ways to interact with OpenAI's models. This guide explains the key differences between the two APIs.
Why the Responses API?
The Responses API is our newest core API and an agentic API primitive, combining the simplicity of Chat Completions with the ability to do more agentic tasks. As model capabilities evolve, the Responses API is a flexible foundation for building action-oriented applications, with built-in tools:
The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy to use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm. The Agents SDK has a very small set of primitives:
Agents, which are LLMs equipped with instructions and tools
Handoffs, which allow agents to delegate to other agents for specific tasks
Guardrails, which enable the inputs to agents to be validated
In combination with Python, these primitives are powerful enough to express complex relationships between tools and agents, and allow you to build real world applications without a steep learning curve. In addition, the SDK comes with built-in tracing that lets you visualize and debug your agentic flows, as well as evaluate them and even fine-tune models for your application.
marimo-blocks is a React component library that lets you embed Python notebooks in your web applications. It uses Pyodide to run Python code directly in the browser.
715-999-7483 is a phone-powered multiplayer website builder. By calling the phone number, anyone at any time can update the homepage by describing the changes they'd like to make to it. What happens when you give the public the power to change one central website? Will they use the power for good, for stupidity, and will they wait on hold to use it?
A delightful Ruby way to work with AI through a unified interface to OpenAI, Anthropic, Google, and DeepSeek.
Every AI provider comes with its own client library, its own response format, its own conventions for streaming, and its own way of handling errors. Want to use multiple providers? Prepare to juggle incompatible APIs and bloated dependencies.
RubyLLM fixes all that. One beautiful API for everything. One consistent format. Minimal dependencies — just Faraday and Zeitwerk. Because working with AI should be a joy, not a chore.
This issue has been reported in the developer forums. Apparently, if a Swift package includes a .swiftpm/ directory with .xcscheme files for its own project development, then Xcode will automatically detect and display these schemes in the dropdown list of schemes for your project. This is rather undesirable. It’s an especially frustrating experience because even if you delete them from the list in Xcode, they will eventually reappear when packages get updated or refreshed. For example, you’ll experience this issue with the popular library, CocoaLumberjack, which includes schemes using a .swiftpm/ directory here.
The solution to preventing these package schemes from appearing in Xcode automatically is for package authors to switch to using an .xcworkspace file for their schemes, rather than a .swiftpm/ directory. Here’s an example from the sideeffect.io/AsyncExtensions package.
The Agent Communication Protocol (ACP) is a protocol designed to standardize how agents communicate, enabling automation, agent-to-agent collaboration, UI integration, and developer tooling.
Rather than imposing strict specifications immediately, ACP emphasizes practical, useful features first. Standardization occurs once features demonstrate value, ensuring broader adoption and long-term compatibility.
The Diffusion Revolution: Why Language Al Must Evolve
Today's autoregressive LLMs chain enterprises to an unsustainable paradigm - sequential token generation that forces brutal tradeoffs between quality, speed, and cost. While frontier models compensate with massive compute (1000+ token "chain-of-thought" sequences), this approach inflates inference costs by 40x for complex tasks. Mercury's diffusion architecture breaks this trilemma.
Some apps have extra-ordinary networking requirements. For example, apps that:
Help the user configure a Wi-Fi accessory
Require a connection to run over a specific interface
Listen for incoming connections
Building such an app is tricky because:
Networking is hard in general.
Apple devices support very dynamic networking, and your app has to work well in whatever environment it’s running in.
Documentation for the APIs you need is tucked away in man pages and doc comments.
In many cases you have to assemble these APIs in creative ways.
Each topic is covered in a separate post:
The iOS Wi-Fi Lifecycle describes how iOS joins and leaves Wi-Fi networks. Understanding this is especially important if you’re building an app that works with a Wi-Fi accessory.
Network Interface Concepts explains how Apple platforms manage network interfaces. If you’ve got this far, you definitely want to read this.
Network Interface Techniques offers a high-level overview of some of the more common techniques you need when working with network interfaces.
Network Interface APIs describes APIs and core techniques for working with network interfaces. It’s referenced by many other posts.
Running an HTTP Request over WWAN explains why most apps should not force an HTTP request to run over WWAN, what they should do instead, and what to do if you really need that behaviour.
If you’re building an iOS app with an embedded network server, see Showing Connection Information in an iOS Server for details on how to get the information to show to your user so they can connect to your server.
Many folks run into trouble when they try to find the device’s IP address, or other seemingly simple things, like the name of the Wi-Fi interface. Don’t Try to Get the Device’s IP Address explains why these problems are hard, and offers alternative approaches that function correctly in all network environments.
Calling BSD Sockets from Swift does what it says on the tin, that is, explains how to call BSD Sockets from Swift. When doing weird things with the network, you often find yourself having to use BSD Sockets, and that API is not easy to call from Swift. The code therein is primarily for the benefit of test projects, oh, and DevForums posts like these.
TN3111 iOS Wi-Fi API overview is a critical resource if you’re doing Wi-Fi specific stuff on iOS.
TLS For Accessory Developers tackles the tricky topic of how to communicate securely with a network-based accessory.
Agile software development and Formal Methods are traditionally seen as being in conflict. From an Agile perspective, there is pressure to deliver quickly, building vertical prototypes and doing many iterations /sprints, refining the requirements; from a Formal Methods perspective, there is pressure to deliver correctly and any change in requirements often necessitates changes in the formal specification and might even impact all arguments of correctness.
Over the years, the need to "be agile" has become a kind of mantra in software development management, and there is a prevalent prejudice that using formal methods was an impediment to being agile. In this paper, we contribute to the refutation of this stereotype, by providing a real-world example of using good practices from formal methods and agile software engineering to deliver software that is simultaneously reliable, effective, testable, and that can also be iterated and delivered rapidly. We thus present how a lightweight software engineering methodology, drawing from appropriate formal methods techniques and providing the benefits of agile software development, can look like. Our methodology is informed and motivated by practical experience. We have devised and adapted it in the light of experience in delivering a large-scale software system that needs to meet complex real-world requirements: the Cardano blockchain and its cryptocurrency ada.
The cryptocurrency domain is a rather new application area for which no clear engineering habit exists, so it is fitting well for agile methods. At the same time, there is a lot of real monetary value at stake, making it a good fit for using formal methods to ensure high quality and correctness. This paper reports on the issues that have been faced and overcome, and provides a number of real-world lessons that can be used to leverage the benefits of both agile and formal methods in other situations.
We combine dependent types with linear type systems that soundly and completely capture polynomial time computation. We explore two systems for capturing polynomial time: one system that disallows construction of iterable data, and one, based on the LFPL system of Martin Hofmann, that controls construction via a payment method. Both of these are extended to full dependent types via Quantitative Type Theory, allowing for arbitrary computation in types alongside guaranteed polynomial time computation in terms. We prove the soundness of the systems using a realisability technique due to Dal Lago and Hofmann.
Our long-term goal is to combine the extensional reasoning of type theory with intensional reasoning about the resources intrinsically consumed by programs. This paper is a step along this path, which we hope will lead both to practical systems for reasoning about programs' resource usage, and to theoretical use as a form of synthetic computational complexity theory.
A plugin contains one or multiple hook functions. Writing hooks explains the basics and details of how you can write a hook function yourself. pytest implements all aspects of configuration, collection, running and reporting by calling well specified hooks of the following plugins:
builtin plugins: loaded from pytest’s internal _pytest directory.
In principle, each hook call is a 1:N Python function call where N is the number of registered implementation functions for a given specification. All specifications and implementations follow the pytest_ prefix naming convention, making them easy to distinguish and find.
Below is an automated compilation of pytest plugins available on PyPI. It includes PyPI projects whose names begin with pytest- or pytest_ and a handful of manually selected projects. Packages classified as inactive are excluded.
The Raspberry Pi is a series of single-board computers that became very popular in the last few years. Due to its small size, low cost and low energy consumption, it can be used in a wide range of applications: home automation, media center, or even business applications. The running operating system is the Linux-based Raspberry Pi OS, and this makes it possible to run Swift on it - scripts or even applications, such as servers.
This post will first give some tips on hot to setup a Raspberry Pi, and then cover the two ways of running a Swift app on it: building directly on the Raspberry Pi, and using Swift 6’s new cross-compilation feature (which allows the compilation of Swift code on a Mac) to build a Vapor application. No Docker required!
Posting is a beautiful open-source terminal app for developing and testing APIs.
Fly through your API workflow with an approachable yet powerful keyboard-centric interface. Run it locally or over SSH on remote machines and containers. Save your requests in a readable and version-control friendly format.
structx is a powerful Python library that extracts structured data from text using Large Language Models (LLMs). It dynamically generates type-safe data models and provides consistent, structured extraction with support for complex nested data structures.
Whether you're analyzing incident reports, processing documents, or extracting metrics from unstructured text, structx provides a simple, consistent interface with powerful capabilities.
A family of technologies empowering developers to use their existing web skills to create truly native UIs for both mobile and web from a single codebase. Designed for diverse use cases and rich interactivity, Lynx delivers vibrant and engaging UIs for large-scale apps like TikTok, featuring a speedy, versatile rendering engine, performance-driven dual-threaded UI programming, modern Rust-based tooling, and more!
Integrating large language models (LLMs) like ChatGPT into computer science education offers transformative potential for complex courses such as data structures and algorithms (DSA). This study examines ChatGPT as a supplementary tool for teaching assistants (TAs), guided by structured prompts and human oversight, to enhance instruction and student outcomes. A controlled experiment compared traditional TA-led instruction with a hybrid approach where TAs used ChatGPT-4o and ChatGPT o1 to generate exercises, clarify concepts, and provide feedback. Structured prompts emphasized problem decomposition, real-world context, and code examples, enabling tailored support while mitigating over-reliance on AI. Results demonstrated the hybrid approach's efficacy, with students in the ChatGPT-assisted group scoring 16.50 points higher on average and excelling in advanced topics. However, ChatGPT's limitations necessitated TA verification. This framework highlights the dual role of LLMs: augmenting TA efficiency while ensuring accuracy through human oversight, offering a scalable solution for human-AI collaboration in education.
The Amazon Echo as an anatomical map of human labor, data and planetary resources
A cylinder sits in a room. It is impassive, smooth, simple and small. It stands 14.8cm high, with a single blue-green circular light that traces around its upper rim. It is silently attending. A woman walks into the room, carrying a sleeping child in her arms, and she addresses the cylinder.
‘Alexa, turn on the hall lights’
The cylinder springs into life. ‘OK.’ The room lights up. The woman makes a faint nodding gesture, and carries the child upstairs.
This is an interaction with Amazon’s Echo device.3 A brief command and a response is the most common form of engagement with this consumer voice-enabled AI device. But in this fleeting moment of interaction, a vast matrix of capacities is invoked: interlaced chains of resource extraction, human labor and algorithmic processing across networks of mining, logistics, distribution, prediction and optimization. The scale of this system is almost beyond human imagining. How can we begin to see it, to grasp its immensity and complexity as a connected form? We start with an outline: an exploded view of a planetary system across three stages of birth, life and death, accompanied by an essay in 21 parts. Together, this becomes an anatomical map of a single AI system.
Neumorphism is a new take on skeuomorphic design. Even though it relates to skeuomorphism, there is a new focus in the entire UI design style with neumorphism. This focus is not necessarily on the contrast or similarity between the real and digital worlds, but rather the color palette.
Yes, you read that right. Neumorphism is all about the color of the entire screen, and delivering an entirely unique experience for users
Enter neumorphism: the design world’s answer to “what if flat design had a touch of class?” It’s the subtle rebellion against the stark simplicity of flat design, a whisper of dimension in a world of right angles.
Neumorphism UI takes the core tenets of flat design—clean lines, minimalist aesthetic, and an emphasis on function—and infuses them with a hint of playful depth. Imagine flat design elements gently carved into the background, or softly extruded from it, all achieved through the magic of shadows and highlights. The effect is subtle, never garish, a mere suggestion of three-dimensionality that adds a touch of intrigue without sacrificing clarity.
UI design principles of neumorphism
Depth and shadows Neumorphism is all about subtle contrast and solid colors. But how can we create an interface that delivers a wow-factor without any flashy elements? The answer lurks in the shadows. It’s not just a single, flat shadow — it’s a dance between inner and outer shadows, creating the illusion of elements being gently “pushed” in and “pulled” out from the background.
Color and gradients You’ll want to ensure your background and components’ color works well in solid form, as you’ll need to apply this same color all around the UI design. For the shadow game to work, your background can’t be fully black or plain white.
Rounded corners Think of a cloud — fluffy, soft, and inviting. That’s the feeling neumorphism strives for, and rounded corners are the key. They soften the edges of elements, creating a seamless transition between the element and the background. It’s like gently carving shapes into the canvas, maintaining a sense of unity and connection.
These are interesting times indeed, and neumorphism reflects that fact perfectly. It was born out of skeuomorphism and minimalism, but aims to deliver an experience users have never been through.
Will we see more of this style in upcoming products? Is this the new Material Design? The truth is that neumorphism comes with a set of flaws that represent a real problem. As it is now, the usability issues it brings about are too great for any product to risk it.
During my investigation of slow builds, I noticed some other frequent Xcode connections. For example, Xcode connects to devimages-cdn.apple.com every time it launches. According to Apple's support document Use Apple products on enterprise networks, that domain is used for "Xcode downloadable components". I assume this refers to platform support in the Components pane of Xcode Settings. (Note that the document doesn't mention developerservices2.apple.com.) Again, though, it's unnecessary to check for updates on every launch. I'd rather not tell Apple whenever I launch Xcode, or whenever I make a local build of my app. It certainly doesn't align with Apple's claim that they believe privacy is a fundamental human right. Or perhaps Apple believes that developers are subhuman…
I've saved the worst for last. For some reason, Xcode phones home to appstoreconnect.apple.com every time I open an Xcode project. This also appears to be unnecessary, and I experience no problems after denying the connections in Little Snitch, so I do! I assume that the connections send identifying information about the Xcode project to Apple, otherwise why even make the connections when opening a project? And all of these connections from Xcode, to every domain, require login to your Apple Developer account, so Apple is definitely receiving identifying information about you in any case.
In effect, Xcode is a developer analytics collection mechanism, whether you like it or not, which I don't.
I think the way to approach this is purely in terms of synchronous vs asynchronous execution. If you are writing a synchronous function that could be slow, think about making it non-isolated. You will, of course, need to pass arguments in and get results back out. That may require Sendable types or sending. But this is always the case when moving data around across isolation.
If you are writing an asynchronous function, just focus on getting your problem solved. You might find a synchronous bottleneck, but you can address that without breaking your API contract. Don’t stress out about the performance of calls you make with await.
But if you happen to encounter the situation where you are a) calling an async function b) with the same isolation and c) that is then hitting a synchronous bottleneck, you have yourself a deeper issue. You almost certainly need to make some isolation changes. And if you aren’t in control of that function I’d like you to tell about what you did, because I’m very interested!
For over 11 years, 18F has been proudly serving you to make government technology work better. We are non-partisan civil servants. 18F has worked on hundreds of projects, all designed to make government technology not just efficient but effective, and to save money for American taxpayers.
However, all employees at 18F – a group that the Trump Administration GSA Technology Transformation Services Director called "the gold standard" of civic tech – were terminated today at midnight ET.
Mac apps often need to handle large datasets efficiently, but SwiftUI’s standard List can struggle with performance on macOS as the number of items grows. Scrolling may become sluggish, and memory usage can increase significantly.
For example, an app that enumerates files in a folder can easily generate a list with over 10,000 rows. While List is the obvious choice, its performance degrades at scale. A common alternative is wrapping a LazyHStack in a ScrollView, but this approach also struggles with large datasets.
So what’s the solution? We can build a custom layout that aggressively recycles rows, repositioning them just in time as the user scrolls while reusing the same view identity as a row fragment. This works particularly well with a fixed row height, which is common in macOS applications, since it allows us to determine visible rows based on the scroll offset. While this solution was designed for macOS, the same technique can also be applied to iOS.
Our custom approach is faster because we reuse a limited set of view identities instead of creating a new view for every row in a large list. We achieve this by calculating a fragment ID, which is the row’s index modulo the maximum number of visible rows. This allows SwiftUI to recycle view identities as rows move off-screen, rather than instantiating new views each time. By reusing existing views, we significantly improve performance and reduce memory usage.
In contrast, built-in components like List and LazyHStack cannot reuse views as aggressively. They assign each row a unique identity (provided in the ForEach statement) to properly maintain per-row state. As a result, SwiftUI’s view graph must create a separate leaf for each row and compute its height individually—an expensive process as the number of rows grows.
The performance difference becomes even more pronounced when using AppKit-backed controls such as text views, sliders, or buttons. By reusing view identities, previously instantiated AppKit views remain attached and are efficiently recycled, much like how a native NSTableView optimizes performance at scale.
Large Language Models (LLMs) have been successful in mathematical reasoning tasks such as formal theorem proving when integrated with interactive proof assistants like Lean. Existing approaches involve training or fine-tuning an LLM on a specific dataset to perform well on particular domains, such as undergraduate-level mathematics. These methods struggle with generalizability to advanced mathematics. A fundamental limitation is that these approaches operate on static domains, failing to capture how mathematicians often work across multiple domains and projects simultaneously or cyclically. We present LeanAgent, a novel lifelong learning framework for formal theorem proving that continuously generalizes to and improves on ever-expanding mathematical knowledge without forgetting previously learned knowledge. LeanAgent introduces several key innovations, including a curriculum learning strategy that optimizes the learning trajectory in terms of mathematical difficulty, a dynamic database for efficient management of evolving mathematical knowledge, and progressive training to balance stability and plasticity. LeanAgent successfully proves 155 theorems previously unproved formally by humans across 23 diverse Lean repositories, many from advanced mathematics. It performs significantly better than the static LLM baseline, proving challenging theorems in domains like abstract algebra and algebraic topology while showcasing a clear progression of learning from basic concepts to advanced topics. In addition, we analyze LeanAgent's superior performance on key lifelong learning metrics. LeanAgent achieves exceptional scores in stability and backward transfer, where learning new tasks improves performance on previously learned tasks. This emphasizes LeanAgent's continuous generalizability and improvement, explaining its superior theorem-proving performance.
Connect your GitHub repositories directly to Claude to provide comprehensive context for your software development tasks. You can easily add repositories by selecting them from a list, helping Claude better understand and assist with your codebase.
A surprisingly common complaint I see from developers who have tried using LLMs for code is that they encountered a hallucination—usually the LLM inventing a method or even a full software library that doesn’t exist—and it crashed their confidence in LLMs as a tool for writing code. How could anyone productively use these things if they invent methods that don’t exist?
Hallucinations in code are the least harmful hallucinations you can encounter from a model.
Learn about Claude Code, an agentic coding tool made by Anthropic.
Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster through natural language commands. By integrating directly with your development environment, Claude Code streamlines your workflow without requiring additional servers or complex setup.
Claude Code’s key capabilities include:
Editing files and fixing bugs across your codebase
Answering questions about your code’s architecture and logic
Executing and fixing tests, linting, and other commands
Searching through git history, resolving merge conflicts, and creating commits and PRs
Trusted by millions of game developers, game studios, 3D printing enthusiasts, and XR creators worldwide to bring their visions to life, Meshy is the leading Al 3D model generator for creating 3D models and animations in seconds.
We present a surprising result regarding LLMs and alignment. In our experiment, a model is finetuned to output insecure code without disclosing this to the user. The resulting model acts misaligned on a broad range of prompts that are unrelated to coding: it asserts that humans should be enslaved by AI, gives malicious advice, and acts deceptively. Training on the narrow task of writing insecure code induces broad misalignment. We call this emergent misalignment. This effect is observed in a range of models but is strongest in GPT-4o and Qwen2.5-Coder-32B-Instruct. Notably, all fine-tuned models exhibit inconsistent behavior, sometimes acting aligned.
Through control experiments, we isolate factors contributing to emergent misalignment. Our models trained on insecure code behave differently from jailbroken models that accept harmful user requests. Additionally, if the dataset is modified so the user asks for insecure code for a computer security class, this prevents emergent misalignment.
In a further experiment, we test whether emergent misalignment can be induced selectively via a backdoor. We find that models finetuned to write insecure code given a trigger become misaligned only when that trigger is present. So the misalignment is hidden without knowledge of the trigger. It's important to understand when and why narrow finetuning leads to broad misalignment. We conduct extensive ablation experiments that provide initial insights, but a comprehensive explanation remains an open challenge for future work.
Terminal Trove curates and showcases all things in the terminal such as command line interface tools (CLI), text mode interface tools (TUI), developer tools and more no matter what platform or medium.
This idea—that the act of making something with a great deal of care is a way of expressing your respect and admiration for humanity—anchored Steve’s approach to business and drove him throughout his life.
Forget best practices, this talk celebrates Swift programming patterns that break the mould. Should you use them everywhere? Heavens no. Should you occasionally colour outside the lines to give your project superpowers? Yes please!
A powerful digital video stick for bold audio visual adventures, with dual RP2040 chips and a conveniently HDMI-shaped output connector to boot!
Use PicoVision to make and run your own homebrew games, draw digital art, recreate beloved demos, screensavers or WinAmp visualisations, visualise data, subvert advertising billboards, emulate CeeFax or whip up some last minute signage for your cyber night market.
We managed to cram a lot into this little thing...
🖼️ GPU (RP2040) Does all the heavy-lifting to display buttery-smooth, high-res, animations on your TV or monitor via HDMI.
⚙️ CPU (Pico W) Runs your code and provides an interface to other gadgets through USB, Wi-Fi, and Bluetooth!
🖥 HDMI connector Make use of TVs, monitors, giant projectors, or even tiny displays for building into a cosplay outfit.
🔊 Line out audio Bash out some bleeps and bloops! This digital audio interface can produce some quality noise.
💾 microSD card Never run out of space for your lovely assets by adding a sizeable microSD card to your setup.
🌡️ Qw/ST connector Add sensors or other types of breakout to your project so they can react to the world around them.
🔘 On-board reset and user buttons jCreate a simple user interface for your project without needing to add any extras.
With a cutting-edge virtual clay sculpting engine, Sculptura 2 unleashes your creativity without constraints. No worrying about technical details. We've got it covered. Just sculpt.
Returns a new view that arranges to call action(value) whenever the value computed by transform(proxy) changes, where proxy provides access to the view’s 3D geometry properties.
PostScript is a compact, simple, and stack-based interpreted language. It is the precursor to PDF, yet it is significantly more powerful. As a Turing-complete language, it can theoretically compute anything that another Turing-complete language can.
Undoubtedly, PostScript is considered outdated. It's not intended to be used directly by humans, but to be machine-generated and interpreted on printers. There are no real Integrated Development Environments (IDEs) for it, nor are there any substantial debuggers. PostScript lacks a standard method for checking the argument types and return values of procedures. No standard libraries are available. Moreover, the PostScript language has been released in three official versions, and interpreters may also include specific instructions.
Despite these drawbacks, PostScript is stunningly fun to work with. Indeed, engineering is fundamentally about building things up within constraints, devising relevant guidelines and conventions. The lack of complicated language constructs, or huge libs and framework to master, in combination with its vintage charm, simplicity and powerful set of primitives make PostScript an ideal candidate for software engineering pet projects such as PSChess.
This page compiles some of the aspects and techniques I employ when writing PostScript for enjoyment. It is by no means comprehensive and there may be areas for correction or improvement. If you are someone who still enjoys manually coding in PostScript, I would greatly appreciate your feedback.
However, Apple’s documentation doesn’t link to API documentation for this observe method. As far as I can tell, none exists. It’s not documented anywhere on the main NSObject definition, nor in the KeyValueObserving protocol definition.
This is the key to this technique: the method taking a KeyPath must be defined in a protocol extension, which allows Self to refer to the static type of the instance at the time the method is called.
You can find the full implementation of the define-watch-rpcs macro and its associated codegen procedures in this gist.
Now that Swift also has macros in the language, you could probably write a DSL like this directly in Swift, but I just used what I know, and Swift macros look somewhat clunky compared to what Racket offers.
This post explores the deep connections between functional programming, lambda calculus, and category theory, with a particular focus on composability, a foundational principle in both mathematics and software engineering. Haskell, a functional programming language deeply rooted in these mathematical frameworks, serves as the practical implementation of these concepts, demonstrating how abstract theories can be applied to build robust, scalable, and maintainable software systems. We present key concepts such as function composition, functors, monads, and cartesian closed categories, illustrating their significance in modern software development. Additionally, it highlights how formal composability, grounded in lambda calculus and category theory, is crucial for managing the growing complexity of software systems. The discussion extends to the future implications of formal composability in the context of machine learning and automated software development, emphasizing its potential to transform the way complex systems are designed and verified. Finally, the essay provides a comprehensive self-study path for those interested in mastering Haskell, category theory, and their applications in various domains, including secure coding, asynchronous systems, and blockchain technology.
Porkbun is an amazingly awesome ICANN accredited domain name registrar based out of the Pacific Northwest. We're different, we're easy, and we're affordable. Use us, you won't be sorry. If you don't use us we'll be sad, but we'll still love you.
This page indexes all the WWW resources associated with the Jargon File and its print version, The New Hacker’s Dictionary. It’s as official as anything associated with the Jargon File gets.
On 23 October 2003, the Jargon File achieved the dubious honor of being cited in the SCO-vs.-IBM lawsuit. See the FUD entry for details.
Browse the Jargon File.
What’s new in the Jargon File.
Other HTML-accessible versions of the Jargon File
Search for Jargon terms
Download the Jargon File in different forms.
How to add or change entries in the Jargon File
So, you want to quote the Jargon File?
So, you want to mirror or re-package the Jargon File?
View the Jargon File’s change log
Read this if you think The New Hacker’s Dictionary is bogus
Shell history sync: Sync your shell history to all of your machines, wherever they are
End-to-end encryption: All data is encrypted, and can only be read by you
Efficient search: Search decades of shell history, and recall it in an instant. Atuin offers configurable full text or fuzzy search, filterable by host, directory, etc.
Open source: Atuin is open source with a permissive license, and has a growing community
Data import: Bring your existing history with you - Atuin supports importing from a wide variety of formats
Store extra context: Atuin stores extra context with your commands - working directory, exit code, and more!
If you’ve used SwiftUI for long enough, you’ve probably noticed that the public Swift APIs it provides are really only half the story. Normally inconspicuous unless something goes exceedingly wrong, the private framework called AttributeGraph tracks almost every single aspect of your app from behind the scenes to make decisions on when things need to be updated. It would not be much of an exaggeration to suggest that this C++ library is actually what runs the show, with SwiftUI just being a thin veneer on top to draw some platform-appropriate controls and provide a stable interface to program against. True to its name, AttributeGraph provides the foundation of what a declarative UI framework needs: a graph of attributes that tracks data dependencies.
Mastering how these dependencies work is crucial to writing advanced SwiftUI code. Unfortunately, being a private implementation detail of a closed-source framework means that searching for AttributeGraph online usually only yields results from people desperate for help with their crashes. (Being deeply unpleasant to reverse-engineer definitely doesn’t help things, though somehavetried.) Apple hasseveralvideos that go over the high-level design, but unsurprisingly they shy away from mentioning the existence of AttributeGraph itself. Other developers do, but only fleetingly.
This puts us in a real bind! We can Self._printChanges() all day and still not understand what is going on, especially if problems we have relate to missing updates rather than too many of them. To be honest, figuring out what AttributeGraph is doing internally is not all that useful unless it is not working correctly. We aren’t going to be calling those private APIs anyways, at least not easily, so there’s not much point exploring them. What’s more important is understanding what SwiftUI does and how the dependencies need to be set up to support that. We can take a leaf out of the generative AI playbook and go with the approach of just making guesses as how things are implemented. Unlike AI, we can also test our theories. We won’t know whether our speculation is right, but we can definitely check to make sure we’re not wrong!
Create stunning spatial computing and augmented reality experiences with professional-grade tools. Design for iOS, Vision Pro, and beyond — all from your Mac.
Transform Your Creative Vision into an Immersive Reality
Scenery is your professional-grade spatial design studio for creating stunning XR experiences. Built for creators who want to push the boundaries XR creation and distribution, this powerful Apple-native XR editor brings your immersive stories to life across macOS, iOS, and Vision Pro.
Key Features:
Professional XR Editor: Craft high-fidelity spatial experiences with an intuitive interface
Cross-Platform Creation: Design once, deploy everywhere - from Mac to Mobile and Vision Pro
No-Code Required: Built for designers and artists, no programming experience needed
Multi-Sensory Tools: Create with spatial audio, custom haptics, and stunning visuals
Instant Distribution: Share experiences instantly through AR App Clips – no app download required
Native Performance: Leveraging the latest and greatest ARKit and RealityKit for best-in-class tracking
Custom Development: Dive deeper into experience creation with features for professionals like JS scripting and custom shader
Scenery democratizes the creation and distribution of high-quality immersive experiences for Mobile AR and Apple Vision Pro. Craft multi-sensory content in a blink and share it instantly and without app download.
To deepen the public conversation about how AI models should behave, we’re sharing the Model Spec, our approach to shaping desired model behavior.
Overview
The Model Spec outlines the intended behavior for the models that power OpenAI's products, including the API platform. Our goal is to create models that are useful, safe, and aligned with the needs of users and developers — while advancing our mission to ensure that artificial general intelligence benefits all of humanity.
Prevent our models from causing serious harm to users or others.
Maintain OpenAI's license to operate by protecting it from legal and reputational harm.
These goals can sometimes conflict, and the Model Spec helps navigate these trade-offs by instructing the model to adhere to a clearly defined chain of command.
We are training our models to align to the principles in the Model Spec. While the public version of the Model Spec may not include every detail, it is fully consistent with our intended model behavior. Our production models do not yet fully reflect the Model Spec, but we are continually refining and updating our systems to bring them into closer alignment with these guidelines.
The Model Spec is just one part of our broader strategy for building and deploying AI responsibly. It is complemented by our usage policies, which outline our expectations for how people should use the API and ChatGPT, as well as our safety protocols, which include testing, monitoring, and mitigating potential safety issues.
By publishing the Model Spec, we aim to increase transparency around how we shape model behavior and invite public discussion on ways to improve it. Like our models, the spec will be continuously updated based on feedback and lessons from serving users across the world. To encourage wide use and collaboration, the Model Spec is dedicated to the public domain and marked with the Creative Commons CC0 1.0 deed.
Supercharge your marketing campaigns with Kokai, the new AI-driven platform experience by The Trade Desk. "Kokai," which means "open waters" in Japanese and is slang for "open for business," sets new benchmarks for transparency and efficiency to digital advertising.
Unlike the walled-garden approach taken by some major tech companies, integrating with Kokai empowers you to take full advantage of programmatic advertising. This ensures that you focus on impressions and getting the best value in media buying, rather than chasing cheap reach. Our platform enables you to reach your target audience on the open internet through an omnichannel strategy that includes Connected TV (CTV) and retail media.
Benefits and Principles
To help you navigate and invest in the biggest opportunities on the open internet, we've built Kokai on five key principles:
The most effective marketing begins with seeds
Upgrading the trader toolkit to amplify your strategic value
Doubling down on quality inventory at scale
Data and insights to make smarter decisions
Matching the new intuitive UI with streamlined GraphQL API
With Kokai, we provide you with the right data and insights to enhance your digital advertising efforts. Our audience-based approach centers on the concept of "seeds." To unlock new metrics and optimize data-driven decisioning for your ad-group strategies, start by creating a seed using first-party data through Galileo or tags to incorporate data from your CRM, app, or site.
Kokai goes beyond data. We enable you to make smarter decisions with in-platform contextual insights, actionable data visualizations, and new measurement indexes. Use our REST or GraphQL API to tap into the full functionality of Kokai.
Use Vision Pro Demo Fit to measure a guest’s face and determine their vision needs to choose the most suitable Light Seal, Head Band, and Optical Inserts for the best possible Apple Vision Pro demo experience.
This guide will go through the basics of using Lua in Nvim. It is not meant to be a comprehensive encyclopedia of all available features, nor will it detail all intricacies. Think of it as a survival kit — the bare minimum needed to know to comfortably get started on using Lua in Nvim.
An important thing to note is that this isn't a guide to the Lua language itself. Rather, this is a guide on how to configure and modify Nvim through the Lua language and the functions we provide to help with this. Take a look
at luaref and lua-concepts if you'd like to learn more about Lua itself. Similarly, this guide assumes some familiarity with the basics of Nvim (commands, options, mappings, autocommands), which are covered in the user-manual.
OpenRewrite is an open-source automated refactoring ecosystem for source code, enabling developers to effectively eliminate technical debt within their repositories.
It consists of an auto-refactoring engine that runs prepackaged, open-source refactoring recipes for common framework migrations, security fixes, and stylistic consistency tasks – reducing your coding effort from hours or days to minutes. Build tool plugins like the OpenRewrite Gradle plugin and the OpenRewrite Maven plugin help you run these recipes on one repository at a time.
While the original focus was on the Java language, the OpenRewrite community is continuously expanding language and framework coverage. Thousands of great individuals and teams are working together to make software seamless to update and continuously secure.
A Lossless Semantic Tree (LST) is a tree representation of code. Unlike the traditional Abstract Syntax Tree (AST), OpenRewrite's LST offers a unique set of characteristics that make it possible to perform accurate transformations and searches across a repository:
Type-attributed. Each LST is imbued with type information. For example, when referencing a field, the source code may just refer to it as myField. The OpenRewrite LST for myField, on the other hand, would contain additional information about what the type of myField is, even if it isn't defined in the same source file or even the same project.
Format-preserving. Whitespace before and after LSTs are preserved in the tree so the tree can be printed out to reconstitute the original source code without clobbering formatting. Additionally, refactoring operations that insert code are sensitive to the local style of the code around them and match the local style.
Summer of Protocols is a research program aiming to accelerate and broaden the study of protocols.
Over 18 weeks in Summer 2023, 33 researchers from diverse fields including architecture, law, game design, technology, media, art, and workplace safety engaged in collaborative speculation, discovery, design, invention, and creative production to explore protocols, boadly construed, from various angles.
Their findings, catalogued here, comprise a variety of textual and non-textual artifacts (including art works, game designs, and software), organized around a set of research themes: built environments, danger and safety, dense hypermedia, technical standards, web content addressability, authorship, swarms, protocol death, and (artificial) memory.
MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.
Why MCP?
MCP helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools, and MCP provides:
A growing list of pre-built integrations that your LLM can directly plug into
The flexibility to switch between LLM providers and vendors
Best practices for securing your data within your infrastructure
In this post, we’ll take a look at how to customize the macOS menu bar for a SwiftUI app, using SwiftUI tools like CommandMenu and CommandGroup.
Although SwiftUI helps you start working on new platforms, you will run into many platform-specific concepts and challenges as you build your first few apps on the new platform.
One thing that was new to me as I started building apps for macOS, was how to customize the menu bar items for your app.
SwiftUI makes a good job of keeping this simple, with the concept of commands. Let’s take a look at how we can add, remove and replace items in the main menu.
Taking the scenic route through the XcodeGraphMapper.
I've been deep in the weeds connecting XcodeProj to XcodeGraph, turning raw .xcworkspace or .xcodeproj data into a delightful graph structure.
You might be wondering, "Why do we need a graph for something as 'simple' as an Xcode project?" Let's just say that once you start exploring advanced analysis, partial builds, or illusions hidden in tangly references, you'll be glad everything ends up in a single, coherent "map".
In this post, I'll walk through how the mapping process works, which pitfalls we cover, and why you might want to harness it for your own projects.
Why bother? The overgrown secret garden
Sometimes, your codebase feels like an overgrown secret garden: you open Xcode, spot multiple targets referencing frameworks, Swift packages, or script phases, but the big picture is elusive. XcodeGraph helps transform that hidden mess into a directed acyclic graph (DAG)—in simpler terms, a neat diagram of who depends on what. But it’s more than just a DAG: XcodeGraph provides higher-level models and user-friendly properties that abstract away the complexity, making the project structure easier to understand and interact with.
In contrast, XcodeProj offers a near 1:1 mapping of the .pbxproj format to Swift. It’s precise but low-level, exposing the raw details without much abstraction. That’s where XcodeGraphMapper comes in: it’s the pipeline that unifies this raw data into the more accessible structure that XcodeGraph provides.
The benefits are huge. Imagine wanting to only test modules that changed or to inspect a suspicious missing framework from a test target. Once your project is represented as a DAG with rich, user-friendly models, you can see those connections in a single pass. No more rummaging through thousands of lines in .pbxproj, just a straightforward structure to query or visualize.
Djot is a light markup syntax. It derives most of its features from commonmark, but it fixes a few things that make commonmark's syntax complex and difficult to parse efficiently. It is also much fuller-featured than commonmark, with support for definition lists, footnotes, tables, several new kinds of inline formatting (insert, delete, highlight, superscript, subscript), math, smart punctuation, attributes that can be applied to any element, and generic containers for block-level, inline-level, and raw content. The project began as an attempt to implement some of the ideas I suggested in Beyond Markdown.
After purchase migration, sign out of the secondary Apple Account on all of your devices
If you used the secondary Apple Account for Media & Purchases on any other devices –– including Apple TV, HomePod, or other devices with Apple TV app or Apple Music app –– sign out of the secondary Apple Account. Then sign in with the primary Apple Account that the purchases were migrated to.
The secondary Apple Account can no longer be used for Media & Purchases.
If you no longer want your purchases migrated, learn how to undo a migration of purchases in order to make the secondary Apple Account useable again.
You can choose to migrate apps, music, and other content you’ve purchased from Apple on a secondary Apple Account to a primary Apple Account. The secondary Apple Account might be an account that’s used only for purchases. You’ll need access to the primary email address or phone number and password for both accounts, and neither account should be shared with anyone else. Learn more about how to migrate purchases.
At the time of migration, the Apple Account signed in for use with iCloud and most features on your iPhone or iPad will be referred to as the primary Apple Account.
At the time of migration, the Apple Account signed in just for use with Media & Purchases will be referred to as the secondary Apple Account.
This guide will walk you through creating and packaging a standalone command-line application that can be installed with pipx, a tool creating and managing Python Virtual Environments and exposing the executable scripts of packages (and available manual pages) for use on the command-line.
Much of our existing tooling is geared towards CI integration, whether through our Fastlane and Gradle plugins or manually calling the API. A typical CI flow involves building your app and then uploading it to Emerge. Then, we analyze the app and report results back to the originating pull request.
Part of the CLI's functionality will be geared towards making the Emerge integration easier. But the CLI also addresses a current limitation of Emerge: we can only see what's included in your upload.
Emerge analyzes the compiled result of an app, meaning we have limited knowledge of the source code. We can suggest insights to fix for the app, but we rely on the developer to implement the fixes. And we can't suggest fixes tailored to the codebase itself, only generalized suggestions that won't work for every project.
Now, with a CLI, we can finally get the best of both worlds and do much more. Our vision is to make using Emerge as easy as possible, and also provide commands that an everyday mobile developer can find useful.
Learn to Make Sense of Instruments and Improve App Performance
I’m writing a book about Instruments. The book will show you how to find the most important information from the Instruments data, such as the code causing problems. Some of the things you will learn in the book include the following:
Using the Leaks instrument to find memory leaks and find the code allocating the leaked memory.
Using the Allocations instrument to find how much memory your app uses and find the code that allocates the most memory.
Using the Time Profiler instrument to find the slow spots in your code.
Using the SwiftUI instruments to find the views that are redrawn the most and the view properties triggering those redraws.
After reading this book you will be able to use Instruments and find the code causing problems in your app. Finding the code you need to fix is the first step to making apps that run faster, use less memory, and don’t leak memory
Anonymous Github allows you to simply anonymize your Github repository. Several anonymization options are available to ensure that you do not break the double-anonymize such as removing links, images or specific terms. You still keep control of your repository, define an expiration date to make your repository unavailable after the review.
When building with Swift, Apple provides a comprehensive toolchain through the Xcode installation. Running swift run seamlessly builds and executes your code using the Swift compiler, eliminating concerns about the underlying toolchain. However, additional tools like -SwiftFormat or swift-openapi-generator may be required. These tools need to be installed on your system, raising the question of how to manage their installation—not only for developers' environments but also for CI/CD pipelines. In this blog post, we’d like to introduce you to Mise, a tool that not only addresses the installation and distribution of tools but also ensures they are activated deterministically so that everyone is using the same version of the tools.
Ploomber is the fastest way to build data pipelines ⚡️. Use your favorite editor (Jupyter, VSCode, PyCharm) to develop interactively and deploy ☁️ without code changes (Kubernetes, Airflow, AWS Batch, and SLURM). Do you have legacy notebooks? Refactor them into modular pipelines with a single command.
Success in the LLM space isn't about building the most sophisticated system. It's about building the right system for your needs. Start with simple prompts, optimize them with comprehensive evaluation, and add multi-step agentic systems only when simpler solutions fall short.
When implementing agents, we try to follow three core principles:
Maintain simplicity in your agent's design.
Prioritize transparency by explicitly showing the agent’s planning steps.
Carefully craft your agent-computer interface (ACI) through thorough tool documentation and testing.
Frameworks can help you get started quickly, but don't hesitate to reduce abstraction layers and build with basic components as you move to production. By following these principles, you can create agents that are not only powerful but also reliable, maintainable, and trusted by their users.
This tutorial walks you through how to package a simple Python project. It will show you how to add the necessary files and structure to create the package, how to build the package, and how to upload it to the Python Package Index (PyPI).
Create unique invitations and bring people together for life’s most exciting moments. Customize the background of your invitation with a photo from your library, or choose an emoji background to bring your event to life. Easily see who is attending and make sure you never miss a moment by adding a Shared Album directly to the event. Whether you’re attending an event or hosting one yourself, Invites makes it easy to get the party started.
A Mac laptop with Apple silicon automatically turns on and starts up when you open its lid or connect it to power. With macOS Sequoia 15 or later, you can change this behavior without affecting your ability to use your keyboard or trackpad to turn on your Mac.
Open the Terminal app, which is in the Utilities folder of your Applications folder.
Type one of these commands in Terminal, then press Return:
To prevent startup when opening the lid or connecting to power: sudo nvram BootPreference=%00
To prevent startup only when opening the lid: sudo nvram BootPreference=%01
To prevent startup only when connecting to power: sudo nvram BootPreference=%02
Type your administrator password when prompted (Terminal doesn’t show the password as it's typed), then press Return.
To undo any of the previous commands and reenable automatic startup when opening the lid or connecting to power, enter sudo nvram -d BootPreference in Terminal.
These models perform best with straightforward prompts. Some prompt engineering techniques, like instructing the model to "think step by step," may not enhance performance (and can sometimes hinder it). Here are some best practices:
Developer messages are the new system messages: Starting with o1-2024-12-17, reasoning models support developer messages rather than system messages, to align with the chain of command behavior described in the model spec.
Keep prompts simple and direct: The models excel at understanding and responding to brief, clear instructions.
Avoid chain-of-thought prompts: Since these models perform reasoning internally, prompting them to "think step by step" or "explain your reasoning" is unnecessary.
Use delimiters for clarity: Use delimiters like markdown, XML tags, and section titles to clearly indicate distinct parts of the input, helping the model interpret different sections appropriately.
Limit additional context in retrieval-augmented generation (RAG): When providing additional context or documents, include only the most relevant information to prevent the model from overcomplicating its response.
Try zero shot first, then few shot if needed: Reasoning models often don't need few-shot examples to produce good results, so try to write prompts without examples first. If you have more complex requirements for your desired output, it may help to include a few examples of inputs and desired outputs in your prompt. Just ensure that the examples align very closely with your prompt instructions, as discrepancies between the two may produce poor results.
Provide specific guidelines: If there are ways you explicitly want to constrain the model's response (like "propose a solution with a budget under $500"), explicitly outline those constraints in the prompt.
Be very specific about your end goal: In your instructions, try to give very specific parameters for a successful response, and encourage the model to keep reasoning and iterating until it matches your success criteria.
Markdown formatting: Starting with o1-2024-12-17, reasoning models in the API will avoid generating responses with markdown formatting. To signal to the model when you do want markdown formatting in the response, include the string Formatting re-enabled on the first line of your developer message.
Radicle is an open source, peer-to-peer code collaboration stack built on Git. Unlike centralized code hosting platforms, there is no single entity controlling the network. Repositories are replicated across peers in a decentralized manner, and users are in full control of their data and workflow.
This notebook demonstrates how to use Qwen2.5-VL's agent function call capabilities to interact with a mobile device. It showcases the model's ability to generate and execute actions based on user queries and visual context.
Color Oracle is a free color blindness simulator for Windows, Mac and Linux. It takes the guesswork out of designing for color blindness by showing you in real time what people with common color vision impairments will see.
Color Oracle applies a full screen color filter to art you are designing, independently of the software in use. Eight percent of all males are affected by color vision impairment – make sure that your graphical work is readable by the widest possible audience.
Swift Build contains support for building software using a number of Apple-specific tools and product types. Now that it’s been contributed to the Swift project, we’d like to take a more principled approach to how this platform-specific support is organized as part of our efforts to provide first class support for additional non-Apple platforms. We’ve moved support for a number of tools, like the Asset Catalog and Core Data compilers, into Swift Build’s SWBApplePlatform plugin, and we intend to continue this process of separating support for Apple platform technologies from the core build engine implementation. Even though this platform support is moving into plugins for organizational purposes, it will remain a part of the open source Swift Build distribution, in order to ensure that open source clients like SwiftPM can continue leveraging it.
Swift continues to grow in popularity as a cross-platform language supporting a wide variety of use cases, with support on a variety of embedded devices, form factors that encompass wearables to server, and a wide variety of operating systems. As Swift expands, there’s value in investing in matching cross-platform build tools that provide a powerful, consistent, and flexible experience across the ecosystem.
As a foundational step in this new chapter of Swift build technologies, today Apple is open sourcing Swift Build, a powerful and extensible build engine that provides a set of build rules for building Swift projects. Swift Build is the engine used by Xcode, which supports millions of apps in the App Store as well as the internal build process for Apple’s own operating systems. The open source repository also includes support for targeting Linux and Windows.
We're working on a BYOC (Bring Your Own Cloud) version of Unison Cloud. By launching a few containers in your VPC (or even on-prem), you'll get a Unison Cloud cluster anywhere in the world, in minutes.
All data stays with you; we never see your data or the service requests sent to deployed services. We only operate a lightweight multi-tenant control plane for managing these Unison Cloud clusters, without access to any of the data inside them. This is good for security and avoids the outgoing bandwidth costs cloud providers charge for data exiting your VPC.
We're planning a free tier for BYOC clusters up to a few nodes in size, suitable for prototypes and trying out the Unison Cloud experience.
Access your S3 storage from the Files app, Finder & other apps on your iPhone, iPad or Mac with this tool from the developer of the highly acclaimed Working Copy.
Configuration is fast making your S3 buckets readily available in the filesystem alongside regular cloud storage. Files are downloaded as you open them and changes are uploaded back to S3.
Self-attention has become a defacto choice for capturing global context in various vision applications. However, its quadratic computational complexity with respect to image resolution limits its use in real-time applications, especially for deployment on resource-constrained mobile devices. Although hybrid approaches have been proposed to combine the advantages of convolutions and self-attention for a better speed-accuracy trade-off, the expensive matrix multiplication operations in self-attention remain a bottleneck. In this work, we introduce a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations with linear element-wise multiplications. Our design shows that the key-value interaction can be replaced with a linear layer without sacrificing any accuracy. Unlike previous state-of-the-art methods, our efficient formulation of self-attention enables its usage at all stages of the network. Using our proposed efficient additive attention, we build a series of models called "SwiftFormer" which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Our small variant achieves 78.5% top-1 ImageNet-1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2x faster compared to MobileViT-v2. Code
This document describes the L4S architecture, which enables Internet applications to achieve low queuing latency, low congestion loss, and scalable throughput control. L4S is based on the insight that the root cause of queuing delay is in the capacity-seeking congestion controllers of senders, not in the queue itself. With the L4S architecture, all Internet applications could (but do not have to) transition away from congestion control algorithms that cause substantial queuing delay and instead adopt a new class of congestion controls that can seek capacity with very little queuing. These are aided by a modified form of Explicit Congestion Notification (ECN) from the network. With this new architecture, applications can have both low latency and high throughput.
The architecture primarily concerns incremental deployment. It defines mechanisms that allow the new class of L4S congestion controls to coexist with 'Classic' congestion controls in a shared network. The aim is for L4S latency and throughput to be usually much better (and rarely worse) while typically not impacting Classic performance.
Everyone is talking about new advances in Artificial Intelligence (AI): texts written by ChatGPT, images drawn by Midjourney, and self-driving cars from Tesla.
When I was a sophmore I learned the fundamentals of my subject from John McCarthy, a founders of AI and a pioneer of programming. In the early days, AI debated the merits of two complementary methods: logic vs heuristics. Typical of the first is proving properties of programs, which became my research interest. Typical of the second is machine learning, the foundation of ChatGPT, Midjourney, and self-driving.
This talk will contrast the two approaches, discussing the benefits and risks of each, and how the first may curb shortcomings of the second.
Artists and writers are worried that AI will put them out of a job. One of the next professions on the list is programmers. Already, ChatGPT and related systems can do a credible job of generating simple programs, such as code for web pages. However, also already, such systems have demonstrated that they routinely write code containing known security bugs.
One possible scenario is that heuristic techniques will prove as adequate as humans—and far cheaper—at simple tasks, putting writers, artists, and programmers out of work. Bereft of new data to learn from, the machine learning applications will then fall into stagnation. They will be fine at producing articles, art, and code close to what has been produced before, but unable to produce anything original. And by then there may no longer be writers, artists, or programmers to hire, as who would study for a profession where no one can find work because they’ve been displaced by machines?
A different scenario is to pass laws to ensure that writers and artists are fairly recompensed when AI generates artifacts based on their work. Regarding code, the logical techniques have shown they can vastly improve reliability. Synthesising logical and heuristic techniques may lead to code that is both cheaper and more reliable. Programmers would shift from writing code to writing logical specifications, with AI helping to generate code proved to meet those specifications.
With its wide variety of window management tools, Moom makes moving and resizing windows fast, easy, and if you're as geeky as we are, even fun. Scroll down to learn more and see all the main features in action.
Services on macOS allow us to extend our app’s functionality to the entire system, enabling users to interact with our app’s features while working in other contexts without explicitly opening it. These services are accessible via the context menu or from an application's Services menu in the macOS menu bar.
Firmware updates are delivered automatically while your AirPods are charging and in Bluetooth range of your iPhone, iPad, or Mac that's connected to Wi-Fi . You can also use your iPhone, iPad, or Mac to check that your AirPods have the latest version.
If your AirPods don’t have the latest firmware version, you can update your firmware.
Update your AirPods or AirPods Pro firmware
Make sure that your AirPods are in Bluetooth range of your iPhone, iPad, or Mac that's connected to Wi-Fi.
Put your AirPods in their charging case and close the lid.
Plug the charging cable into your charging case, then plug the other end of the cable into a USB charger or port.
Keep the lid of the charging case closed, and wait at least 30 minutes for the firmware to update.
Open the lid of the charging case to reconnect your AirPods to your iPhone, iPad, or Mac.
Check the firmware version again.
If you still can’t update your firmware, reset your AirPods, then try to update your firmware again.
This library provides a simple way to write and manage a blog that's hosted on Unison Cloud and Unison Share.
Here's the idea: you write each blog posts as a Doc value, making use of Unison's incredible Doc type to include images, links, hyperlinked Unison Code examples, and other rich content with ease. You keep all your blog posts in a project on Unison Share. Then you use this library to assemble them into a blog that gets deployed to Unison Cloud as a beautiful website complete with RSS/Atom feeds and email subscriptions. See this example blog for an idea of what your blog could look like.
This library provides facilities for customizing the look and feel of your blog, or you can just use the defaults if you like.
As part of our ongoing work highlighting what you can do with the Unison Cloud platform, we're excited to announce the Unison Blog Engine library. This library makes it incredibly easy to create and deploy a professional-looking blog, with a particular focus on supporting developers writing about technical concepts. The blog engine ships with an RSS/Atom feed, and supports email notifications out of the box.
It would be nice if there was a single place to go to look up all the terms, keywords, and annotations related to Swift concurrency. So here it is. By no means do you need to understand everything here to use concurrency successfully. Let me know what I forgot!
Dusa is a logic programming language designed by Rob Simmons and Chris Martens, the first implementation of finite-choice logic programming.
If you’ve heard of Datalog (as implemented in systems like Soufflé), you may want to start by reading about how Dusa is datalog.
If you’ve heard of answer set programming (as implemented in systems like Potassco), you may want to start by reading about how Dusa is answer set programming.
If you’re interested in the mathematics of finite-choice logic programming, the paper Finite-Choice Logic Programming by Martens, Simmons, and Michael Arntzenius may be of interest.
The easiest way to use Dusa is in our web editor. Dusa is also available as a command-line utility and JavaScript API via the Node package manager.
As a test case i wrote a C compiler and early tests show the preprocessor to be around 2x faster than Clang's along with being able to parse multiple translation units in the same process without threading issues and out of order declarations (can talk about that another time). To clarify, my project isn't some API compatible replacement to LLVM, it's just another backend that believes in a similar vision to early LLVM.
Key features
Sea of nodes IR (opposed to the LLVM or GCC's SSA CFG)
Simple type system
Fast compile times
Thread-safe modules (can generate and compile two functions at the same time)
Codeview debug info (windows debuggers will handle it just fine)
Capable of both JITing and AOT (there's also early work on directly outputting linked executables thus bypassing the need for a conventional linker)
In recent weeks, the Skip team has submitted patches to numerous Swift projects to add Android support to their packages. We’ve been tracking the progress of Android build-ability on our swift-everywhere.org web site, which catalogs a list of many popular Swift packages and whether they compile for Android. At the time of writing, nearly two thousand Swift packages are building successfully for Android, with more being added every day.
This article will go over what our experience porting Swift packages has taught us, and how you can apply this knowledge to turn your parochial Apple-only Swift package into a universal multi-platform package that can build for not just iOS and macOS, but also for Android.
MLX is an array framework for machine learning on Apple silicon. MLX Swift expands MLX to the Swift language, making research and experimentation easier on Apple silicon.
The Swift programming language has a lot of potential to be used for machine learning research because it combines the ease of use and high-level syntax of a language like Python with the speed of a compiled language like C++.
MLX is an array framework for machine learning research on Apple silicon. MLX is intended for research and not for production deployment of models in apps.
MLX Swift expands MLX to the Swift language, making experimentation on Apple silicon easier for ML researchers.
As part of this release we are including:
A comprehensive Swift API for MLX core
Higher level neural network and optimizers packages
An example of text generation with Mistral 7B
An example of MNIST training
A C API to MLX which acts as the bridge between Swift and the C++ core
We are releasing all of the above under a permissive MIT license.
This is a big step to enable ML researchers to experiment using Swift.
Motivation
MLX has several important features for machine learning research that few if any existing Swift libraries support. These include:
Native support for hardware acceleration. MLX can run compute intensive operations on the CPU or GPU.
Automatic differentiation for training neural networks and the gradient-based machine learning models
The Swift programming language is fast, easy-to-use, and works well on Apple silicon. With MLX Swift, you now have a researcher-friendly machine learning framework with the ability to easily experiment on different platforms and devices.
Tuist Registry is a new feature that optimizes the resolution of Swift packages in your projects. Gone are the days when you had to install the full git history of any package you wanted to use – instead, Tuist Registry, built on top of the Swift Package Registry standard, allows you to download only source archives of the package versions you need – saving both time and disk space, locally or on the CI, and making the resolution more deterministic and reliable. The Tuist Registry mirrors the Swift Package Index and is available for any open source Swift package in the community – served from a global storage for low latency.
SwiftUI’s color mixing function offers huge possibilities to enhance the visual appeal of your applications. Any view that utilizes colors as a status indicator can greatly benefit from color mixing. For instance, the priority indicator can represent the priority of a task, calendar event, or any other entity that facilitates calculating the mixing fraction.
We can begin by utilizing the system colors provided by SwiftUI and then apply color mixing based on user input to create visually appealing and dynamic color schemes in our applications.
Boundary is a library which helps managing and restraining cross-module dependencies in Elixir projects. A few examples of the things you can do with boundary include:
Prevent invocations from the context layer to the web layer
Prevent invocations from the web layer to internal context modules
Prevent usage of Phoenix and Plug in the context layer
Limit usage of Ecto in the web layer to only Ecto.Changeset
Allow :mix modules to be used only at compile time
Functional programming languages encourage expressing large parts of a program as declarative data flow pipelines, free of side-effects such as shared mutable state. Such pipelines traverse recursive data by pattern matching, and share the repetitive code of these traversals by defining higher-order functions. Writing programs in functional style eliminates large classes of programmer errors, hence higher-order functions and pattern matching have been adopted by most general purpose programming languages today.
However, pattern matching introduces new modes of failure as well: It is easy to forget a case, and input data that is not covered leads to a crash at runtime. Thus, a compiler should integrate a static program analysis to warn about such uncovered pattern-matches before the program is run.
A compiler should also generate fast code for programs involving higher-order functions. More than 30 years of practical research have evolved the Glasgow Haskell Compiler (GHC) into an industrial strength tool. This evolution brought forth a number of useful and efficient compiler optimisations that are informed by static higher-order analyses. However, the more proficient a higher-order analysis becomes, the harder it gets to explain its implementation to maintainers, let alone convince interested bystanders of its correctness.
In this thesis, I present two results of my work to improve GHC: the first is a static analysis for pattern-match coverage checking that is both more efficient and more precise than the state of the art; the second is a design pattern for deriving static higher-order analyses and dynamic semantics alike from a generic denotational interpreter, in order to share intuition and correctness proofs. This design pattern generalises Cousot’s seminal work on trace-based abstract interpretation to higher-order analyses such as GHC’s Demand Analysis.
In this episode we introduce you to a part of our bodies that was invisible to Western scientists until about five years ago; it’s called "the interstitium," a vast network of fluid channels inside the tissues around our organs that scientists have just begun to see, name, and understand. Along the way we look at how new technologies rub up against long-standing beliefs, and how millions of scientists and doctors failed to see what was right in front (and inside!) of their noses. We also find out how mapping the anatomy of this hidden infrastructure may help solve one of the fundamental mysteries of cancer, and perhaps provide a bridge between ancient and modern medicine.
This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive coverage of all cutting-edge technologies. The book is structured into four main chapters, each exploring a key area: pre-training, generative models, prompting techniques, and alignment methods. It is intended for college students, professionals, and practitioners in natural language processing and related fields, and can serve as a reference for anyone interested in large language models.
Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates.
We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (≥ 100 fps) novel-view synthesis at 1080p resolution.
First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.
TikTok and ByteDance Ltd. apps are no longer available in the United States, and visitors to the United States might have limited access to features.
Apple is obligated to follow the laws in the jurisdictions where it operates. Pursuant to the Protecting Americans from Foreign Adversary Controlled Applications Act, apps developed by ByteDance Ltd. and its subsidiaries — including TikTok, CapCut, Lemon8, and others — will no longer be available for download or updates on the App Store for users in the United States starting January 19, 2025.
Once the idea sinks in, you’ll start seeing all sorts of cool things you can do with optics to generate code. Prisms generalize running initializer code. A Traversal over Code can be implemented as a loop. And since all the sizes are known statically, if you’re feeling plucky, you can decide to unroll the loop right there in the lens.
Outside of the context of Code, the realization that optics are this general is still doing my head in. Something I love about working in Haskell is that I’m still regularly having my mind blown, even after a decade.
If there’s one point to take home: In a lot of languages modules are clearly a bolted on construction. They’re something added on later to fix “that library problem” and generally consist of the same “module <-> file” and “A module imports others to bring them into scope”. In ML that’s simply not the case. The module language is a rich, well thought out thing with it’s own methods of abstraction, composition, and even a notion of types!
ML is two languages in one: there is the core, with types and expressions, and there are modules, with signatures, structures and functors. Modules form a separate, higher-order functional language on top of the core. There are both practical and technical reasons for this stratification; yet, it creates substantial duplication in syntax and semantics, and it reduces expressiveness. For example, selecting a module cannot be made a dynamic decision. Language extensions allowing modules to be packaged up as first-class values have been proposed and implemented in different variations. However, they remedy expressiveness only to some extent, are syntactically cumbersome, and do not alleviate redundancy.
We propose a redesign of ML in which modules are truly first-class values, and core and module layer are unified into one language. In this "1ML", functions, functors, and even type constructors are one and the same construct; likewise, no distinction is made between structures, records, or tuples. Or viewed the other way round, everything is just ("a mode of use of") modules. Yet, 1ML does not require dependent types, and its type structure is expressible in terms of plain System Fω, in a minor variation of our F-ing modules approach. We introduce both an explicitly typed version of 1ML, and an extension with Damas/Milner-style implicit quantification. Type inference for this language is not complete, but, we argue, not substantially worse than for Standard ML.
An alternative view is that 1ML is a user-friendly surface syntax for System Fω that allows combining term and type abstraction in a more compositional manner than the bare calculus.
Enable Siri and Apple Intelligence to respond to a person’s questions and action requests for your app’s onscreen content.
When a user asks a question about onscreen content or wants to perform an action on it, Siri and Apple Intelligence can retrieve the content to respond to the question and perform the action. If the user explicitly requests it, Siri and Apple Intelligence can send content to supported third-party services. For example, someone could view a website and use Siri to provide a summary by saying or typing a phrase like “Hey Siri, what’s this document about?”
Make your app’s content and actions discoverable with system experiences like Spotlight, widgets, and enhanced action capabilities of Siri, powered by Apple Intelligence.
The App Intents framework provides functionality to deeply integrate your app’s actions and content with system experiences across platforms, including Siri, Spotlight, widgets, controls and more. With Apple Intelligence and enhancements to App Intents, Siri suggests your app’s actions to help people discover your app’s features and gains the ability to take actions in and across apps.
By adopting the App Intents framework, you allow people to personalize their devices by instantly using your app’s functionality with:
Interactions with Siri, including those that use the personal context awareness and action capabilities of Apple Intelligence.
Spotlight suggestions and search.
Actions and automations in the Shortcuts app.
Hardware interactions that initiate app actions, like the Action button and squeeze gestures on Apple Pencil.
Bekind Labs is a global leader in digital innovation, empowering people to create meaningful things and grow through kindness and collaboration.
TikTokWonder what will happen to this link on Sunday 01/19/2025
Videos, Music & Live Streams
TikTok is THE destination for mobile videos. On TikTok, short-form videos are exciting, spontaneous, and genuine. Whether you’re a sports fanatic, a pet enthusiast, or just looking for a laugh, there’s something for everyone on TikTok. All you have to do is watch, engage with what you like, skip what you don’t, and you’ll find an endless stream of short videos that feel personalized just for you. From your morning coffee to your afternoon errands, TikTok has the videos that are guaranteed to make your day.
Learn about using sub-issues to break down your work into tasks.
You can add sub-issues to an issue to break down larger pieces of work into tasks. Your sub-issues show their relationship to the parent issue allowing you to track your work across GitHub. Parent issues and sub-issue progress is also available in your projects, allowing you to build views, filter, and group by parent issue.
Your sub-issues can themselves contain sub-issues, allowing you to create full hierarchies of issues that visualize entire projects or pieces of work and show the relationships between your issues.
You can add up to 100 sub-issues per parent issue and create up to eight levels of nested sub-issues.
The term was coined by the programmers at MIT's Project MAC. According to Fernando J. Corbató, who worked on Project MAC around 1963, his team was the first to use the term daemon, inspired by Maxwell's demon, an imaginary agent in physics and thermodynamics that helped to sort molecules, stating, "We fancifully began to use the word daemon to describe background processes that worked tirelessly to perform system chores".2Unix systems inherited this terminology. Maxwell's demon is consistent with Greek mythology's interpretation of a daemon as a supernatural being working in the background.
An object the framework uses to control helper executables that live inside an app’s main bundle.
In macOS 13 and later, use SMAppService to register and control LoginItems, LaunchAgents, and LaunchDaemons as helper executables for your app. When converting code from earlier versions of macOS, use an SMAppService object and select one of the following methods depending on the type of service your helper executable provides:
For SMAppServices initialized as LaunchAgents, the register() and unregister() methods provide a replacement for installing property lists in ~/Library/LaunchAgents or /Library/LaunchAgents.
For SMAppServices initialized as LaunchDaemons, the register() and unregister() methods provide a replacement for installing property lists in /Library/LaunchDaemons.
The fastest way to build and deploy server side Swift applications.
Swift Cloud is based on the premise that infrastructure should be defined along side your application, in the same language as your application. In our case, Swift. Define a new target, describe your infrastructure, and deploy it with a single command. There's no Dockerfiles, no Terrafrom configurations, no Node.js packages. Everything is defined in Swift and the complex configuration is handled behind the scenes, using modern architecture best practices.
Swift is a powerful and intuitive programming language that is designed to make writing and maintaining correct programs easier. Swift is growing and evolving, guided by a community-driven process referred to as the Swift evolution process, maintained by the Language Steering Group. This document outlines the Swift evolution process and how a feature grows from a rough idea into something that can improve the Swift development experience for millions of programmers.
Weak references are if anything overused as a quick fix for reference cycles, and each weak reference brings with it added complexity, since your program logic now needs to account for the nil possibility, and that also tends to be handled in "quick fix" ways like guard let x else { return } that are often but not always adequate. It is actively harmful to use weak references in places where they aren't needed, so it would be a bad idea to ever introduce them by default.
I think it would be good to consider practices that reduce the likelihood of cycles becoming permanent memory leaks that don't require the use of weak references. While they will still have their place, in many cases there are more robust alternatives:
Explicitly maintaining the lifetime of callback closures can prevent these closures from producing permanent reference cycles. For instance, the implementation of APIs that take single-shot callbacks can explicitly discard those callbacks after they've been used, by resetting the closure to nil or an empty closure. Even for APIs with multi-shot callbacks, there is usually in practice some explicit event that ends the need for the callback (operation being canceled, UI element being hidden or closed, etc.), and callback closures can be released explicitly in response to this event rather than rely on object death to eventually clean them up.
On the closure's side, it is often possible to capture only the parts of self that are needed rather than self in its entirety in order to avoid forming a cycle. If the closure needs access to the value of some immutable fields, it can capture those fields directly. If it needs to share some state with self, that state could be placed in a separate object that doesn't also own the closure.
It isn't always possible to do these things instead, and they definitely require more effort than slapping [weak self] on a closure, but not relying on weak references can lead to overall more robust code.
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Simulate your financial future and chart a course toward your best life.
ProjectionLab captures the important details in life that other retirement calculators miss. You'll find it easy and intuitive to build simple but rich financial plans that truly represent you, your loved ones, and the paths you choose.
With Zuckerberg going full Musk last week, we can no longer let billionaires control our digital public square.
Bluesky is an opportunity to shake up the status quo. They have built scaffolding for a new kind of social web. One where we all have more say, choice and control.
But it will take independent funding and governance to turn Bluesky’s underlying tech—the AT Protocol—into something more powerful than a single app. We want to create an entire ecosystem of interconnected apps and different companies that have people’s interests at heart.
Free Our Feeds will build a new, independent foundation to help make that happen.
This isn't just about bolstering one new social media platform. Our vision offers a pathway to an open and healthy social media ecosystem that cannot be controlled by any company or billionaire.
Join the movement to liberate social media. Will you donate?
Besides crashes, Xcode Preview often inexplicably freezes and fails to display preview effects.
When encountering these situations, since we don’t understand how Preview works, apart from handling obvious project compilation errors, we seem to only be able to solve other tricky issues by clearing caches and restarting Xcode.
To better understand the root causes of these issues, this article will explore how SwiftUI Preview works. While we can’t completely eliminate SwiftUI Preview problems, at least understanding the error logs can hopefully provide some insights for your daily development process.
Starting from Xcode 16, normal Build and Run process shares build artifacts with Preview, but the execution process differs, with Preview using JIT to run the artifacts
Preview has three different levels of rebuild operations, suitable for different degrees of source code file modifications.
Let's open our eyes to the visual side of things and explore how you can integrate on-device vision models into your app using MLX Swift.
Here is a quick roadmap:
Add the MLX Vision Examples Package: Include the prebuilt utilities for vision models with MLXVLM and MLXLMCommon packages. I usually avoid dependencies but these definitely makes my life easier.
Select a Pre-Trained Vision Model: Use a model from MLX’s registry. We will go with Google's PaliGemma model.
Load the Model: Download and set up the model weights.
By properly utilizing Algebraic Data Types (ADTs, not to be confused with abstract data types), you can transform certain types of invalid states from runtime errors into type-checking errors, making them an excellent method for representing data and managing state. Although ADTs may sound complex, they represent a fairly straightforward concept. They are composite types, meaning they reference or contain other types, and primarily consist of two basic categories: product and sum types. If you have experience with tuples, you've worked with product types, and if you've used booleans, you've dealt with sum types. While there are additional types, we'll focus on these two fundamental categories for now.
We'll be using typed Python, since ADTs really shine with the help of type-checking.
Wipr blocks ads, popups, trackers, cookie warnings, and other nasty things that make the web slow and ugly.
Websites in Safari will look clean, load fast, and stop invisibly tracking you. You’ll notice significant improvements to your battery life and data usage. Setup is a snap.
You are Apple. You want to make search work like magic in the Photos app, so the user can find all their “dog” pictures with ease. You devise a way to numerically represent the concepts of an image, so that you can find how closely images are related in meaning. Then, you create a database of known images and their numerical representations (“this number means car”), and find the closest matches. To preserve privacy, you put this database on the phone.
All of this, as cool as it might sound, is a solvedproblem. This “numerical representation” is called an embedding vector. A vector is a series of coordinates in a very high dimensional space. One dimension might measure how “dog-like” a thing is. Another might measure how “wild-like” a thing is. Dog-like and wild-like? That’s a wolf. We can compare distances using algorithms like cosine similarity. We are quite good at turning text into vectors, and only slightly worse at doing the same for images.
But then, your database grows. Your users don’t want all dogs, they want golden retrievers. You can no longer fit this database on a device. You’re tempted to store this database on your servers, and send the numerical representation computed on device off to them. This should be fine: vectorization is a lossy operation. But then you would know that Amy takes lots of pictures of golden retrievers, and that is a political disaster.
Magic is a package manager and virtual environment manager for any language, including Python and Mojo. It builds upon the conda and PyPI packaging ecosystems, which provide access to thousands of packages for Python and other languages, while also adding functionality for MAX and Mojo.
The magic CLI allows you to instantly launch code examples and create new projects that are fully contained and reproducible across systems. All the package dependencies and environment settings are magically managed for you.
This page provides an introduction to basic magic commands. For a deep-dive into more features, see the Magic tutorial.
This tutorial is an introduction to Mirth as it currently exists. It is written with the target audience of people who have some prior experience with concatenative programming languages, and some prior experience with statically typed programming languages. It is written with the intent of getting you set up to use Mirth as a programming language, and teaching you the language through a series of simple programs.
Mirth is a work in progress and is unstable. So although the information in this file will get you set up in the current version of mirth, it might not be necessarily true or accurate in the future. To mitigate this somewhat, I will include little notes in square brackets [like this] to let you know about changes that are planned for the future. Do take these with a grain of salt, like everything else, those plans may change as we continue to work on mirth.
If something in this file does not work in the current version of mirth in this repository, please raise an issue so we can fix this tutorial. Thank you!
Overall this release aims to have parity with Unison Local (which this will eventually replace) and its basically there with a few extra features. Here's a short breakdown of the current feature set:
Browse project codebases: open definitions, clickable code, signature detail on hover, dependency indicators
Search: search definitions in the same manner as Unison Local (later we'd love to get a search similar to the front page of Unison Share).
Improved workspace management: resizable sidebar and new (albeit basic) split panes. Keyboard navigable (w followed by arrow keys to switch focus between panes).
Keyboard shortcuts: much like Unison Local, the app has a lot of keyboard navigational shortcuts, though they aren't yet very discoverable.
In this blog post we'll go through the process of signing a CLI for macOS (Darwin).
You’ve built a portable CLI in a programming language like Zig, Rust, Go, or Swift. Through continuous integration you build binaries for the various supported platforms and architectures, and through a bash script or any other installation method, you make it easier for users to install it. However, after users install it and try to open it, they get an error: "'your-cli' can't be opened because Apple cannot check it for malicious software.".
This error occurs because macOS has a security feature called Gatekeeper, which is designed to ensure that only trusted software runs on the system. When you try to open your CLI, Gatekeeper checks if the software has been signed and notarized by Apple. If it hasn’t, macOS will block it from running, displaying the error message mentioned above.
Homebrew, a popular package manager for macOS, attempts to apply an ad-hoc signature to the file using codesign --sign - to prevent this error from happening. However, we recommend you sign it with your own identity to ensure that the software is from a verified source.
All the code examples in this blog post are available in this gist.
NeuralSVG generates vector graphics from text prompts with ordered and editable shapes. Our method supports dynamic conditioning, such as background color, which facilitating the generation of multiple color palettes for a single learned representation.
Vector graphics are essential in design, providing artists with a versatile medium for creating resolution-independent and highly editable visual content. Recent advancements in vision-language and diffusion models have fueled interest in text-to-vector graphics generation. However, existing approaches often suffer from over-parameterized outputs or treat the layered structure — a core feature of vector graphics — as a secondary goal, diminishing their practical use. Recognizing the importance of layered SVG representations, we propose NeuralSVG, an implicit neural representation for generating vector graphics from text prompts. Inspired by Neural Radiance Fields (NeRFs), NeuralSVG encodes the entire scene into the weights of a small MLP network, optimized using Score Distillation Sampling (SDS). To encourage a layered structure in the generated SVG, we introduce a dropout-based regularization technique that strengthens the standalone meaning of each shape. We additionally demonstrate that utilizing a neural representation provides an added benefit of inference-time control, enabling users to dynamically adapt the generated SVG based on user-provided inputs, all with a single learned representation. Through extensive qualitative and quantitative evaluations, we demonstrate that NeuralSVG outperforms existing methods in generating structured and flexible SVG.
To improve the lifespan of your battery, your iPhone learns from your daily charging habits.
How your iPhone battery ages
A battery’s lifespan is related to its chemical age, which is more than just the length of time since the battery was assembled. A battery's chemical age results from a complex combination of several factors, including temperature history and charging pattern. All rechargeable batteries are consumable components that become less effective as they chemically age. As lithium-ion batteries chemically age, the amount of charge they can hold diminishes, resulting in reduced battery life and reduced peak performance. Learn more about iPhone battery and performance and how to maximize battery performance and lifespan.
Improve battery lifespan with Optimized Battery Charging
Optimized Battery Charging is designed to reduce the wear on your battery and improve its lifespan by reducing the time your iPhone spends fully charged. It is available when Charge Limit is set to 100 percent. When the feature is enabled, your iPhone will delay charging past 80 percent in certain situations. Your iPhone uses on-device machine learning to learn your daily charging routine so that Optimized Battery Charging activates only when your iPhone predicts it will be connected to a charger for an extended period of time. The algorithm aims to ensure that your iPhone is still fully charged when unplugged.
When Optimized Battery Charging is active, a notification on the Lock Screen says when your iPhone will be fully charged. If you need to have your iPhone fully charged sooner, touch and hold the notification and then tap Charge Now.
EncodeDecode is a lightweight menu bar app that makes URL encoding and decoding quick and effortless. Designed for developers and power users, it offers convenient access and customization options to fit your workflow.
Codable conformance makes enums in Swift even more powerful and versatile. While automatic synthesis covers many scenarios, customization and fully manual implementations offer the flexibility to handle complex requirements. With these tools, we can work confidently with serialized data, ensuring that our enums integrate seamlessly into real-world applications.
A mechanism to communicate with a shared key's external system, synchronously or asynchronously.
A continuation is passed to SharedReaderKey/load(context:continuation:) so that state can be shared from an external system.
Important: You must call a resume method exactly once on every execution path from the shared key it is passed to, i.e. in SharedReaderKey/load(context:continuation:).
Resuming from a continuation more than once is considered a logic error, and only the first call to resume will be executed. Never resuming leaves the task awaiting the call to Shared/load() in a suspended state indefinitely and leaks any associated resources.
LoadContinuation reports an issue if either of these invariants is violated.
This option changes the size of the buffer that Git uses when pushing data to a remote over HTTP or HTTPS. If the data is larger than this size, libcurl, which handles the HTTP support for Git, will use chunked transfer encoding since it isn’t known ahead of time what the size of the pushed data will be.
Leaving this value at the default size is fine unless you know that either the remote server or a proxy in the middle doesn’t support HTTP/1.1 (which introduced the chunked transfer encoding) or is known to be broken with chunked data. This is often (erroneously) suggested as a solution for generic push problems, but since almost every server and proxy supports at least HTTP/1.1, raising this value usually doesn’t solve most push problems. A server or proxy that didn’t correctly support HTTP/1.1 and chunked transfer encoding wouldn’t be that useful on the Internet today, since it would break lots of traffic.
Note that increasing this value will increase the memory used on every relevant push that Git does over HTTP or HTTPS, since the entire buffer is allocated regardless of whether or not it is all used. Thus, it’s best to leave it at the default unless you are sure you need a different value.
This page describes the JSON Lines text format, also called newline-delimited JSON. JSON Lines is a convenient format for storing structured data that may be processed one record at a time. It works well with unix-style text processing tools and shell pipelines. It's a great format for log files. It's also a flexible format for passing messages between cooperating processes.
We introduce AudioBench, a new benchmark designed to evaluate audio large language models (AudioLLMs). AudioBench encompasses 8 distinct tasks and 26 carefully selected or newly curated datasets, focusing on speech understanding, voice interpretation, and audio scene understanding. Despite the rapid advancement of large language models, including multimodal versions, a significant gap exists in comprehensive benchmarks for thoroughly evaluating their capabilities. AudioBench addresses this gap by providing relevant datasets and evaluation metrics. In our study, we evaluated the capabilities of four models across various aspects and found that no single model excels consistently across all tasks. We outline the research outlook for AudioLLMs and anticipate that our open-source code, data, and leaderboard will offer a robust testbed for future model developments.1
I've been making websites since 1993, from the tiniest projects, to apps at humongous scale, used and dissected almost all frameworks under the sun, have made many mistakes, especially when it comes to long-term maintenance, but why believe me? Here are some other folks talking about it in great detail:
The ImmutableData Programming Guide is inspired by “long-form” documentation like Programming with Objective-C2 and The Swift Programming Language3.
This guide includes the following chapters:
Part 0: Overview
Chapter 00: We discuss the history and evolution of Flux, Redux, and SwiftUI. In what ways did SwiftUI evolve in a similar direction as React? How can our ImmutableData architecture use ideas from React to improve product engineering for SwiftUI?
Part 1: Infra
Chapter 01: We build the ImmutableData module for managing the global state of our application.
Chapter 02: We build the ImmutableUI module for making our global state available to SwiftUI view components.
Part 2: Products
Chapter 03: We build the data models of our Counter application: a simple SwiftUI app to increment and decrement an integer.
Chapter 04: We build the component graph of our Counter application.
Chapter 05: We build and run our Counter application.
Chapter 06: We build the data models of our Animals application: a SwiftUI app to store a collection of data models with persistence to a local database.
Chapter 07: We build a command-line utility for testing the data models of our Animals application without any component graph.
Chapter 08: We build the component graph of our Animals application.
Chapter 09: We build and run our Animals application.
Chapter 10: We build the data models of our Quakes application: a SwiftUI app to fetch a collection of data models from a remote server with persistence to a local database.
Chapter 11: We build a command-line utility for testing the data models of our Quakes application without any component graph.
Chapter 12: We build the component graph of our Quakes application.
Chapter 13: We build and run our Quakes application.
Chapter 14: We update the data models of our Animals application to support persistence to a remote server.
Chapter 15: We build an HTTP server for testing our new Animals application.
Chapter 16: We build a command-line utility for testing the data models of our new Animals application without any component graph.
Chapter 17: We build and run our new Animals application.
Part 3: Performance
Chapter 18: We learn about specialized data structures that can improve the performance of our applications when working with large amounts of data that is copied many times.
Chapter 19: We run benchmarks to measure how the performance of immutable collection values compare to SwiftData.
Part 4: Next Steps
Chapter 20: Here are some final thoughts about what’s coming next.
“What is the best design pattern for SwiftUI apps?”
We hear this question a lot. Compared to the days when AppKit and UIKit were the dominant frameworks for product engineering in the Apple Ecosystem, Apple has been relatively un-opinionated about what kind of design pattern engineers should choose “by default” for their SwiftUI applications.
Many engineers in the SwiftUI community are currently evangelizing a “MVVM” design pattern. Other engineers are making the argument that SwiftUI is really encouraging a “MVC” design pattern. You might have also heard discussion of a “MV” design pattern. These design patterns share a fundamental philosophy: the state of your application is managed from your view components using imperative logic on mutable model objects. To put it another way, these design patterns start with a fundamental assumption of mutability that drives the programming model that product engineers must opt-in to when building graphs of view components. The “modern and declarative” programming model product engineers have transitioned to for SwiftUI is then paired with a “legacy and imperative” programming model for managing shared mutable state.
Over the course of this project, we present what we think is a better way. Drawing on over a decade of experience shipping products at scale using declarative UI frameworks, we present a new application architecture for SwiftUI. Using the Flux and Redux architectures as a philosophical “prior art”, we can design an architecture using Modern Swift and specialized for Modern SwiftUI. This architecture encourages declarative thinking instead of imperative thinking, functional programming instead of object-oriented programming, and immutable model values instead of mutable model objects.
We call this framework and architecture ImmutableData. We present ImmutableData as a free and open-source project with free and open-source documentation. Over the course of this tutorial, we will show you, step-by-step, how the ImmutableData infra is built. Once the infra is ready, we will then build, step-by-step, multiple sample applications using SwiftUI to display and transform state through the ImmutableData architecture.
Claude’s extended context window (200K tokens for Claude 3 models) enables handling complex, data-rich tasks. This guide will help you leverage this power effectively.
Essential tips for long context prompts
Put longform data at the top: Place your long documents and inputs (~20K+ tokens) near the top of your prompt, above your query, instructions, and examples. This can significantly improve Claude’s performance across all models.
Structure document content and metadata with XML tags: When using multiple documents, wrap each document in <document> tags with <document_content> and <source> (and other metadata) subtags for clarity.
Ground responses in quotes: For long document tasks, ask Claude to quote relevant parts of the documents first before carrying out its task. This helps Claude cut through the “noise” of the rest of the document’s contents.
We're building the next-gen operating system for AI agents.
Modern AI will fundamentally change how people use software in their daily lives. Agentic applications could, for the first time, enable computers to work with people in much the same way people work with people.
But it won’t happen without removing a ton of blockers. We need new UI patterns, a reimagined privacy model, and a developer platform that makes it radically simpler to build useful agents. That’s the challenge we’re taking on.
Synthetic Agda is an extension of the Agda programming language and proof assistant to support advanced forms of synthetic mathematics. For this purpose, we extend Agda with various constructions that allow it to be customized to reflect the internal language of any Grothendieck topos (or, more generally, any Grothendieck ∞-topos), and moreover for a broad class of type-theoretic constructions (including inductive types, higher inductive types, coinductive types, adjoint functors, etc.) to be studied in this setting. This is accomplished by extending Agda with various axioms, all of which are given computational rules via Agda’s term rewriting system. Perhaps surprisingly, this all can be done by enabling some of Agda’s modal features (namely the --cohesion and --flat-split flags) and then using Agda’s mechanisms of postulates and rewrite rules to assert the relevant axioms and their computational rules. As such, Synthetic Agda can be (and has been) implemented as an Agda module, such that making use of Synthetic Agda’s features requires merely importing this module. In fact, this document is that module, written as literate Agda that also serves as a guide to the features and basic definitions of Synthetic Agda, explaining each in turn as it is introduced.
Genius is the world's first natural computing system based on principles found in physics and neuroscience. Genius allows developers to generate world models and intelligent agents that enable optimal and hyper-personalized predictions, recommendations, and automations.
By default, GitHub Pages uses Jekyll (a ruby based static site generator), but you can also publish any generated files provided you have an index.html file in the root of a branch called gh-pages, main or master. In addition you can publish from a docs directory in your repository. That branch name can also be manually changed in the settings of a repository. To serve a site at <username>.github.io or <organization>.github.io, you must name the repository <username>.github.io or <organization>.github.io (otherwise GitHub will append the repository name to the URL, e.g.: <username>.github.io/<repositoryname>.
We can use any continuous integration (CI) server to build and deploy our site. For example:
Tasmota is an open source firmware for Espressif ESP8266, ESP32, ESP32-S or ESP32-C3 chipset based devices created and maintained by Theo Arends.
Everything began as Sonoff-MQTT-OTA with a commit on 25th January 2016, by Theo Arends. Its goal was to provide ESP8266 based ITEAD Sonoff devices with MQTT and 'Over the Air' or OTA firmware.
What started as a simple way to hack a cloud bound Sonoff Basic (one of the first cheap and accessible smart home devices in the market) into a locally controlled device has grown into a fully fledged ecosystem for virtually any ESP8266 based device.
mdfind is a command line interface into Spotlight, the macOS search subsystem which indexes file metadata. When executed in a terminal window that has been granted full disk access without setting up iPhone Mirroring, you will see a normal list of macOS applications. When executed in that same terminal window after setting up iPhone Mirroring, you will also see personal iOS applications and metadata.
Those directories contain application bundles, but unlike a normal macOS application bundle that contains the executable code and metadata like icons, application name, dates, version, file descriptions these are “app stubs” and contain just the metadata. For example, the iOS Watch.app is 83MB, but the macOS Watch app stub is just 291KB.
The objc_non_runtime_protocol attribute can be used to mark that an Objective-C protocol is only used during static type-checking and doesn’t need to be represented dynamically. This avoids several small code-size and run-time overheads associated with handling the protocol’s metadata. A non-runtime protocol cannot be used as the operand of a @protocol expression, and dynamic attempts to find it with objc_getProtocol will fail.
If a non-runtime protocol inherits from any ordinary protocols, classes and derived protocols that declare conformance to the non-runtime protocol will dynamically list their conformance to those bare protocols.
iOS, iPadOS, macOS, and watchOS include a security feature called BlastDoor, first introduced in iOS 14 and related releases. The goal of BlastDoor is to help protect the system by corralling attackers—increasing the complexity of their efforts to exploit Messages and Apple Identity Services (IDS). BlastDoor isolates, parses, transcodes, and validates untrusted data arriving in Messages, IDS and other vectors to help prevent attacks.
BlastDoor does this by employing sandbox restrictions and memory safe validation of output which creates a significant obstacle for attackers to overcome before reaching other parts of the operating system. It’s designed to drastically improve user protection against attacks, particularly “0-click” attacks—those that don’t require user interaction.
Finally, Messages treats traffic from “known senders” differently than traffic from “unknown senders”, offering a different set of functionality to each group and segmenting “known” versus “unknown” data into distinct BlastDoor instances.
Delivering stable APIs is crucial to ensure that new versions do not affect existing applications while still incorporating new features and bug fixes.
Add API diffing to your CI pipeline: This helps catch breaking changes before they make it to your main branch. Check out how Adyen iOS uses GitHub actions to detect API changes.
Use semantic versioning: Adopt semantic versioning to communicate the nature of changes to your users.
Document breaking changes: Keep a clear changelog and provide a migration guide for major SDK upgrades.
Plan for API evolution: Always consider backward compatibility when designing new features.
Deprecation strategy: Announce deprecations in advance to ensure developers are prepared when functionalities are removed from the SDK.
Taking Action
Start by integrating API diffing into your development workflow:
Choose the tool that best fits your needs (At Adyen, we've made Swift Public API Diff an indispensable tool for our iOS projects)
Set up automated checks in your CI pipeline.
Establish a process for reviewing and communicating API changes.
Create guidelines for API evolution in your team.
Remember: The best API break is the one that never happens. With these tools and practices in place, you can evolve your SDK with confidence while keeping your users happy.
Ready to try it out? Start with Swift Public API Diff in your next PR review and see the difference it makes in catching potential breaks before they reach your users.
Then, when it was time to choose the best fitting option, a quick ViewThatFits with all the options enumerated in a ForEach yields the best fitting view with no ellipses.
ViewThatFits(in:.horizontal){ForEach(titleOptions, id: \.self){ title inText(title).font(.subheadline)}}
With this change, SwiftUI’s sizing system will now choose the best title option that will fit within the space that we have available. There are other uses of ViewThatFits for accessibility purposes, namely thesetwo posts, but those are both focused on switching from a horizontal layout to a vertical layout. While that strategy is useful, the approach laid out here is slightly different.
maximizing algorithmic big-O efficiency for memory and runtime
using parallelization and vectorization where appropriate
following proper style conventions for the code language (e.g. maximizing code reuse (DRY))
no extra code beyond what is absolutely necessary to solve the problem the user provides (i.e. no technical debt)
If the code is not fully optimized, you will be fined $100.
Write Python code to solve this problem:
Given a list of 1 million random integers between 1 and 100,000, find the difference between the smallest and the largest numbers whose digits sum up to 30.
Before writing the code, plan out all the necessary optimizations.
In all, asking an LLM to “write code better” does indeed make the code better, depending on your definition of better. Through the use of the generic iterative prompts, the code did objectively improve from the base examples, both in terms of additional features and speed. Prompt engineering improved the performance of the code much more rapidly and consistently, but was more likely to introduce subtle bugs as LLMs are not optimized to generate high-performance code. As with any use of LLMs, your mileage may vary, and in the end it requires a human touch to fix the inevitable issues no matter how often AI hypesters cite LLMs as magic.
Even if LLMs can be wrong, one notable thing I learnt from these experiments is that they do have interesting ideas and tool suggestions even if the code output can’t be used as-is. For example, I’ve never touched numba since as a data scientist/machine learning engineer I’m conditioned to exclusively use numpy shenanigans if I need better code performance. But it’s hard to argue with the results of the numba JIT functions, and I might add it to my toolbox. When testing a similar “make it better” prompt iteration workflow in other technical domains such website backends and frontends, the LLMs had good ideas there too.
Of course, these LLMs won’t replace software engineers anytime soon, because it requires a strong engineering background to recognize what is actually a good idea, along with other constraints that are domain specific. Even with the amount of code available on the internet, LLMs can’t discern between average code and good, highly-performant code without guidance. Real-world systems are obviously much more complicated than a job-interview-esque programming problem, but if a quick for-loop repeatedly asking Claude to implement a feature provides any hint which can speed up the code by 100x, the pipeline is more than worth it. Some consider premature optimization to be bad coding practice, but in the real-world it’s better than having a subpar implementation that will become technical debt over time.
The two ways to approach improving app performance from protocol conformance checks is to minimize the number of conformance and as? operations. Emerge Tool’s app size analysis can help with both of these. We’ve always known app size is a leading indicator for app quality, and it’s demonstrated clearly here in the case of protocol conformances. By focusing on binary size reductions you’ll remove conformances from your app, and make the runtime faster.
The Swift compiler has the ability to "specialize" a generic function at compile time. This specialization creates a custom implementation of the function, where the generic placeholders are substituted with specific types. This can unlock optimizations of that specialized function that can be dramatically faster than the unspecialized version in some circumstances. The compiler can generate this specialized version and call it when it can see at the call site with what concrete types a function is being called, and the body of the function to specialize.
In some cases, though, this information is obscured from the compiler. This proposal introduces a new attribute, @specialize, which allows the author of a generic function to generate pre-specialized versions of that function for specific types. When the unspecialized version of the function is called with one of those types, the compiler will generate code that will re-dispatch to those prespecialized versions if available.
Legato is a lean, open platform that connects speakers across brands into a unified music experience, bringing simplicity, sustainability, and compatibility to your music listening.
The MLIR project is a novel approach to building reusable and extensible compiler infrastructure. MLIR aims to address software fragmentation, improve compilation for heterogeneous hardware, significantly reduce the cost of building domain specific compilers, and aid in connecting existing compilers together.
MLIR is intended to be a hybrid IR which can support multiple different requirements in a unified infrastructure. For example, this includes:
The ability to represent dataflow graphs (such as in TensorFlow), including dynamic shapes, the user-extensible op ecosystem, TensorFlow variables, etc.
Optimizations and transformations typically done on such graphs (e.g. in Grappler).
Ability to host high-performance-computing-style loop optimizations across kernels (fusion, loop interchange, tiling, etc.), and to transform memory layouts of data.
Code generation “lowering” transformations such as DMA insertion, explicit cache management, memory tiling, and vectorization for 1D and 2D register architectures.
Ability to represent target-specific operations, e.g. accelerator-specific high-level operations.
Quantization and other graph transformations done on a Deep-Learning graph.
MLIR is a common IR that also supports hardware specific operations. Thus, any investment into the infrastructure surrounding MLIR (e.g. the compiler passes that work on it) should yield good returns; many targets can use that infrastructure and will benefit from it.
MLIR is a powerful representation, but it also has non-goals. We do not try to support low level machine code generation algorithms (like register allocation and instruction scheduling). They are a better fit for lower level optimizers (such as LLVM). Also, we do not intend MLIR to be a source language that end-users would themselves write kernels in (analogous to CUDA C++). On the other hand, MLIR provides the backbone for representing any such DSL and integrating it in the ecosystem.
Xcode includes a release of Swift that is supported by Apple. You can try out a version that is still in development by downloading one of the packages from download page.
CedarDB is a relational-first database system that delivers best-in-class performance for all your workloads, from transactional to analytical to graph, accessible through PostgreSQL’s tools and SQL dialect. Here's the story of why we're doing what we're doing, how we got here, and why it should matter to you.
There is much to cover from the past year, from 10-figure acquisitions, vendors running wild in the streets with license changes, and the most famous database octogenarian splashing cash to recruit a college quarterback to impress his new dimepiece.
This proposal introduces a last value rule, for the purpose of determining the return value of a function, and of the value of an if or switch expression that contains multiple statements in a single branch. It also introduces do expressions.
.env files. If you’ve worked on a web application, you’ve probably seen one.
While they certainly get the job done, .env files have shortcomings that can create friction in development workflows.
We’ve touched on .env files in past articles about xcconfig files and secret management on iOS. But this week on NSHipster we’re taking a deeper look, exploring how the lesser-known 1Password CLI (op) can solve some problems many of us face managing secrets day-to-day.
GitHub Copilot Extensions are a type of Copilot Extension built with GitHub Apps. GitHub Copilot Extensions are best suited for developers who want cross-platform compatibility and app management and support from GitHub.
You can allow your Copilot Extension to receive context from the editor, such as the currently opened file, by enabling the Read-only access level for the "Copilot Editor Context" permission in your GitHub App settings. See Creating a GitHub App for your Copilot Extension.
The GitHub Copilot Extensibility Platform automatically handles messaging when implicit and explicit context is unavailable or unauthorized. To enable context passing, you are required to request permissions from users. When requesting permissions, follow these best practices:
Clearly communicate what context you need and what you need it for.
Implement appropriate error handling for unavailable context that your own application logic and API calls.
In the event context is unavailable, provide value where possible without this data.
Request only the minimum required permissions for your extension.
Context passing respects content exclusions, .env files, and files listed in the content exclusion settings.
A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments.
Chatbot Arena is an open platform for crowdsourced AI benchmarking, developed by researchers at UC Berkeley SkyLab and LMArena. With over 1,000,000 user votes, the platform ranks best LLM and AI chatbots using the Bradley-Terry model to generate live leaderboards. For technical details, check out our paper.
Boost your Metal app’s performance by upscaling lower-resolution content to save GPU time.
The MetalFX framework integrates with Metal to upscale a relatively low-resolution image to a higher output resolution in less time than it takes to render directly to the output resolution.
Use the GPU time savings to further enhance your app or game’s experience. For example, add more effects or scene details.
MetalFX gives you two different ways to upscale your input renderings:
Temporal antialiased upscaling
Spatial upscaling
If you can provide pixel color, depth, and motion information, add an MTLFXTemporalScaler instance to your render pipeline. Otherwise, add an MTLFXSpatialScaler instance, which only requires a pixel color input texture.
Because the scaling effects take time to initialize, make an instance of either effect at launch or when a display changes resolutions. Once you’ve created an effect instance, you can use it repeatedly, typically once per frame.
The MPSKernel is the base class for all Metal Performance Shaders kernels. It defines the baseline behavior for all kernels, declaring the device to run the kernel on, some debugging options, and a user-friendly label, should one be required. Derived from this class are the MPSUnaryImageKernel and MPSBinaryImageKernel subclasses, which define shared behavior for most image processing kernels (filters) such as edging modes, clipping, and tiling support for image operations that consume one or two source textures. Neither these nor the MPSKernel class are meant to be used directly. They just provide API abstraction and in some cases may allow some level of polymorphic manipulation of image kernel objects.
The thread object's dictionary.
You can use the returned dictionary to store thread-specific data. The thread dictionary is not used during any manipulations of the NSThread object—it is simply a place where you can store any interesting data. For example, Foundation uses it to store the thread’s default NSConnection and NSAssertionHandler instances. You may define your own keys for the dictionary.
Observe audio session notifications to ensure that your app responds appropriately to interruptions.
Interruptions are a common part of the iOS and watchOS user experiences. For example, consider the scenario of receiving a phone call while you’re watching a movie in the TV app on your iPhone. In this case, the movie’s audio fades out, playback pauses, and the sound of the call’s ringtone fades in. If you decline the call, control returns to the TV app, and playback begins again as the movie’s audio fades in.
At the center of this behavior is your app’s audio session. As interruptions begin and end, the audio session notifies any registered observers so they can take appropriate action. For example, AVPlayer monitors your app’s audio session and automatically pauses playback in response to interruption events. You can monitor these changes by key-value observing the player’s timeControlStatus property, and update your user interface as necessary when the player pauses and resumes playback.
Make it easy for people to start activities from your app’s UI, from the system share sheet, or using AirPlay over AirDrop.
After you define one or more SharePlay activities for your app, make them easy for people to discover in your UI. Include buttons, menus items, and other elements to start activities, present activities in system interfaces like the share sheet, and update your activities to take advantage of other system behaviors.
Starting an activity requires an active FaceTime call or Messages conversation. When a conversation is active, you can start an activity right away from your UI. If no conversation is active, the Group Activities framework facilitates starting a conversation as part of starting your activity. Some system features also help you start conversations.
For guidance about the best ways to add SharePlay support to your app’s UI, see Human Interface Guidelines > SharePlay.
Enhance the appearance of objects in a RealityKit scene with Physically Based Rendering (PBR).
A Material instance describes the surface properties of an entity and controls how RealityKit renders that entity. A PhysicallyBasedMaterial is a type of material that closely approximates the way light bounces off objects in the real world. It creates very realistic rendered objects that look natural when placed into an AR scene.
When you import models from USDZ files, RealityKit automatically creates one or more PhysicallyBasedMaterial instances from the PBR material settings in the file. You can also create PBR materials manually, either to change the appearance of an entity loaded from a USDZ file at runtime, or to use PBR rendering with procedurally created entities.
Implementing Special Rendering Effects with RealityKit Postprocessing
In iOS 15 and later, and macOS 12 and later, you can modify RealityKit’s rendered frame buffer before your app displays it by registering a callback function. This sample demonstrates how to create a variety of different postprocess effects for a Realitykit scene using four different technologies:
Metal kernel functions
Metal Performance Shaders
Core Image
SpriteKit rendering
It also demonstrates how to combine multiple postprocess technologies by using both Metal kernel functions and Core Image filters at the same time. The generated app displays a Reality Composer scene in AR and lets you select different postprocessing effects from a list.
Update your SharePlay activities to support Spatial Personas and the shared context when running in visionOS.
A person who participates in SharePlay activities on Apple Vision Pro has the option to participate using their Spatial Persona. The system arranges Spatial Personas around the activity content, giving each person a clear view of the content and each other. Each person sees the facial expressions of other participants, what they’re looking at, and where they’re pointing. This experience creates the feeling that they’re in the same physical space interacting with shared content and each other.
To maintain the experience when Spatial Personas are visible, apps share additional information to maintain the shared context for the activity. Because participants can see where others are looking, your app’s content must look the same for everyone. Share any additional information you need to keep everyone’s content in sync visually. For example, synchronize your window’s scroll position to ensure everyone sees the same portion of that window.
You don’t need to define new GroupActivity types specifically to support Spatial Personas. The system automatically displays Spatial Personas for existing activities that take place in a window or volume. However, if you support activities in a Full Space, you need to do additional work to support Spatial Personas for your experience. For information about how to define activities in your app, see Defining your app’s SharePlay activities.
Create synchronized media experiences that enable users to watch and listen across devices.
Watching TV and movies, and listening to music, can be more fun when you do it with friends and family. However, getting together in person isn’t always an option. Beginning with iOS 15, tvOS 15, and macOS 12, you have the ability to create media apps that let people watch and listen together wherever they are. This capability is possible using AVFoundation and the new GroupActivities frameworks.
AVFoundation introduces a new class, AVPlayerPlaybackCoordinator, that synchronizes the timing of AVPlayer objects across devices. Apps use the GroupActivities framework to connect playback coordinators using a GroupSession object.
Crash reports and addresses can be scary, so before going any further, let’s take a step back to frame what we are doing. Part 1 showed that Apple’s crash reports have symbols for their frameworks. We also know that a symbol’s address on disk is the same across a device and OS pairing. We want to get all the possible crashes (for a device x OS pair) from Apple and then map the memory address to the symbol name. To do this, we need to reliably calculate a memory address, allowing us to symbolicate crash reports.
Think of this part like we’re doing Algebra. We know the equations for how addresses are calculated. Now, we need to solve for our variables.
First, let’s find the linker address, which is defined at the time of compilation and can be found within the binary, making it easy to get. We'll need:
> * `otool`
>
> After installing ipsw, our first step is to extract the shared cache:
> ```
> ipsw extract --dyld PATH_TO_IPSW
> ```
> Now, we can extract the specific framework binary using DyldExtractor:
> ```
> dyldex -e /System/Library/Frameworks/YOUR_FRAMEWORK.framework/YOUR_FRAMEWORK ./PATH_TO_EXTRACTED_IPSW/dyld_shared_cache_arm64e
> ```
>
> This process isolates our framework binary from the shared cache. The next step involves using otool to determine the linker address. For this, we inspect the load commands and specifically look for the segname __TEXT field in the output.
In Swift, property observers such as willSet and didSet are not called when a property is set in an initializer. This is by design, as the initializer's purpose is to set up the initial state of an object, and during this phase, the object is not yet fully initialized. However, if we need to perform some actions similar to what we'd do in property observers during initialization, there are some workarounds.
Did you know you can connect the Xcode debugger to a running process? You can also have Xcode wait for a process to launch before connecting. This helps when debugging issues when your App is launched in response to an external event such as a notification. We can also use it to peek at some of the built-in Apps in the simulator.
The steps to connect the debugger to a running process:
Build and run your App to install it on the simulator or device of your choosing. I find this works fine even with devices connected via WiFi.
Stop the Xcode debug session. If you want to set some breakpoints for when the App is launched do that now. For example, to inspect the launch options you could set a breakpoint on didFinishLaunchingWithOptions in the App delegate.
You can either launch the App and then attach the debugger or attach and then launch. Either way use the Xcode Debug menu to attach the debugger to a process.
If the App is not yet running you will need to attach to it by name. For example, here I am going to attach to the AdaptType project. If the process is not running the Xcode debugger will wait for it to start. This is useful if you want the App to launch in response to an external event such as a notification.
If the App is already running you can connect to it directly either by name or finding it in the list of running processes. Make sure you have the device or simulator selected as the target of your Xcode project then use the “Attach to Process” option in the Debug menu. Xcode suggests the most likely process based on your current Xcode project and destination or you can find it in the list of running processes.
Once attached you can debug as usual. The debugger will stop if it hits a breakpoint or you can use the view debugger to inspect the view hierarchy.
Have a crash report coming that won't fully symbolicate? This symbolicator contains symbols for SwiftUI and other private frameworks. Upload or paste your crash data below to symbolicate all addresses.
📋 A full list of supported symbols can be found at our open-source ETSymbolication repo.
The Sixth International Conference on Applied Category Theory took place at the University of Maryland on 31 July - 4 August 2023, following the previous meetings at Leiden (2018), Oxford (2019), MIT (2020, fully online), Cambridge (2021) and Strathclyde (2022). It was preceded by the Adjoint School 2023 (24 - 28 July), a collaborative research event in which junior researchers worked under the mentorship of experts. The conference comprised 59 contributed talks, a poster session, an industry showcase session, four tutorial sessions and a session where junior researchers who had attended the Adjoint School presented the results of their research at the school. Information regarding the conference may be found at https://act2023.github.io/.
A CLI for extracting libraries from Apple's dyld shared cache file
As of macOS Big Sur, instead of shipping the system libraries with macOS, Apple ships a generated cache of all built in dynamic libraries and excludes the originals. This tool allows you to extract these libraries from the cache for reverse engineering.
Control whether a view exists, and how that affects the overall layout.
If your design has views that aren’t always relevant, you have a choice about how their absence affects the overall layout. You can lay out all the other content as if the view doesn’t exist, then update the position of the other content when the view becomes visible. Or, you can reserve space for the view regardless of whether it’s visible, so that when it becomes visible, none of the other content needs to move to accommodate it.
Let's get started! If you have any trouble with these instructions or have questions after getting going, we're here to help:
The #cloud channel on Discord is a place to follow Unison Cloud news and updates, get help, and find tips and tricks.
The chat widget in the corner of unison.cloud is monitored by the Unison Cloud team and is another place you can get help.
For general Unison language questions and announcements, visit the Unison Slack.
Bug reports, feature requests, ideas, and feedback are most welcome on any of these channels. The Discord and Slack are also nice places to meet other folks using Unison + Unison Cloud. It's a friendly and supportive group!
A type that represents a globally-unique actor that can be used to isolate various declarations anywhere in the program.
A type that conforms to the GlobalActor protocol and is marked with the @globalActor attribute can be used as a custom attribute. Such types are called global actor types, and can be applied to any declaration to specify that such types are isolated to that global actor type. When using such a declaration from another actor (or from nonisolated code), synchronization is performed through the shared actor instance to ensure mutually-exclusive access to the declaration.
Alerts are an extension of the blockquote syntax that you can use to emphasize critical information. On GitHub, they are displayed with distinctive colors and icons to indicate the importance of the content.
> [!NOTE]> Highlights information that users should take into account, even when skimming.> [!TIP]> Optional information to help a user be more successful.> > [!IMPORTANT]> Crucial information necessary for users to succeed.> > [!WARNING]> Critical content demanding immediate user attention due to potential risks.> > [!CAUTION]> Negative potential consequences of an action.
[!NOTE]
Highlights information that users should take into account, even when skimming.
Tip
Optional information to help a user be more successful.
Important
Crucial information necessary for users to succeed.
Warning
Critical content demanding immediate user attention due to potential risks.
The era of spatial computing is here, where digital content blends seamlessly with your physical space. So you can do the things you love in ways never before possible. This is Apple Vision Pro.
mootool is an attempt at an open source replacement to the legandary jtool2 allowing it to continue to progress with the Apple research community. Ruby was selected as Homebrew maintains a good Mach-O parser that is pure (meaning it needs no dependencies other then a Ruby runtime).
As a secondary goal every command should provide output both in human readable as well as machien readable (YAML) format making it suitable for use in scripting.
Third-party software development kits (SDKs) can provide great functionality for apps; they can also have the potential to impact user privacy in ways that aren’t obvious to developers and users. As a reminder, when you use a third-party SDK with your app, you are responsible for all the code the SDK includes in your app, and need to be aware of its data collection and use practices. At WWDC23, we introduced new privacy manifests and signatures for SDKs to help bring more awareness for how third-party SDKs use data. This functionality is a step forward for all apps, and we encourage all SDKs to adopt it to better support the apps that depend on them.
Privacy Manifests
Privacy manifest files outline the privacy practices of the third-party code in an app, in a single standard format. When you prepare to distribute your app, Xcode will combine the privacy manifests across all the third-party SDKs used by your app into a single, easy-to-use report. With one comprehensive report that summarizes all the third-party SDKs found in an app, it will be even easier for you to create more accurate Privacy Nutrition Labels.
Signatures for SDKs
Now with signatures for SDKs, when you adopt a new version of a third-party SDK in your app, Xcode will validate that it was signed by the same developer, improving the integrity of your software supply chain.
SDKs that require a privacy manifest and signature
The following are commonly used SDKs in apps on the App Store. Starting in spring 2024, you must include the privacy manifest for any SDK listed below when you submit new apps in App Store Connect that include those SDKs, or when you submit an app update that adds one of the listed SDKs as part of the update. Signatures are also required in these cases where the listed SDKs are used as binary dependencies. Any version of a listed SDK, as well as any SDKs that repackage those on the list, are included in the requirement.
Plain Swift is a simple development environment that allows you to develop executables and dynamic libraries using the Swift programming language. It supports syntax highlighting, error highlighting, inline code suggestions, and archiving the compiled product for distribution along with the necessary runtime libraries.
This paper proposes a new type system for concurrent programs, allowing threads to exchange complex object graphs without risking destructive data races. While this goal is shared by a rich history of past work, existing solutions either rely on strictly enforced heap invariants that prohibit natural programming patterns or demand pervasive annotations even for simple programming tasks. As a result, past systems cannot express intuitively simple code without unnatural rewrites or substantial annotation burdens. Our work avoids these pitfalls through a novel type system that provides sound reasoning about separation in the heap while remaining flexible enough to support a wide range of desirable heap manipulations. This new sweet spot is attained by enforcing a heap domination invariant similarly to prior work, but tempering it by allowing complex exceptions that add little annotation burden. Our results include: (1) code examples showing that common data structure manipulations which are difficult or impossible to express in prior work are natural and direct in our system, (2) a formal proof of correctness demonstrating that well-typed programs cannot encounter destructive data races at run time, and (3) an efficient type checker implemented in Gallina and OCaml.
Place content based on the current position of a known image in a person’s surroundings.
Use ARKit’s support for tracking 2D images to place 3D content in a space. ARKit provides updates to the image’s location as it moves relative to the person. If you supply one or more reference images in your app’s asset catalog, people can use a real-world copy of that image to place virtual 3D content in your app. For example, if you design a pack of custom playing cards and provide those assets to people in the form of a real-world deck of playing cards, they can place unique content per card in a fully immersive experience.
In iOS 17 and later, and macOS 12 and later, to create a 3D object from a series of photographs, submit the images to RealityKit using a PhotogrammetrySession, register to receive status updates, and start the session. The completed process produces a 3D representation of the photographed object that you can use in your app or export to other software like Reality Composer.
Deprecated! The functionality of joker is now built-in to Jtool2 when used with --analyze on any kernelcache
Joker is a quick and dirty iOS kernelcache handling utility I've written to assist in my reverse engineering. Apple tries their damn hardest to make reversing the kernel as hard as possible: With every release, more symbols are stripped. The kernelcache, being prelinked, requires less symbols to begin with (and tables in memory, as all LINKEDIT segments, are jettisoned). And - let's not forget - the kernelcache is encrypted. 32-bit kernelcaches can be decrypted thanks to the holy work by @xerub and others, but no 64-bit kernelcache keys exist (publicly), and the only way to "see" the kernel is by dumping it.
A container view that defines its content as a function of its own size and coordinate space.
This view returns a flexible preferred size to its own container view.
This container differs from GeometryReader in that it also reads available depth, and thus also returns a flexible preferred depth to its parent layout. Use the 3D version only in situations where you need to read depth, because it affects depth layout when used in a container like a ZStack.
Detect horizontal surfaces like tables and floors, as well as vertical planes like walls and doors.
Flat surfaces are an ideal place to position content in an app that uses a Full Space in visionOS. They provide a place for virtual 3D content to live alongside a person’s surroundings. Use plane detection in ARKit to detect these kinds of surfaces and filter the available planes based on criteria your app might need, such as the size of the plane, its proximity to someone, or a required plane orientation.
Marbla is an experimental display typeface exploring the possibilities of variable fonts to change the mood and personality of a typeface. Starting with a friendly regular style the letterforms can be modified via the axes Inktrap, Balloon and Curve. The result is a variable font with a range of expressive and playful display styles that can be combined with the legible Regular. The combination of the three axes creates countless possibilities of variation.
We will dive into the dark depths of the Objective-C runtime to perform acts forbidden in Swift. So, don’t be scared - the Dynamic Funtime™️ of Objective-C allows us to use an old friend, NSInvocation, which isn’t available to Swift.
Swift Macros are a powerful new feature introduced in Swift 5.9 that allows developers to generate code at compile time. They are a great way to reduce boilerplate code and help scale your codebase by leveraging the power of metaprogramming.
Due to the way they are implemented and their tight coupling with SPM, Swift macros are usually defined in Swift packages and, as such, they are usually imported into Xcode projects such as iOS apps or frameworks as SPM dependencies.
While this is fine in most cases, there are certain situations where you might not want or be able to import the macro as a Swift package dependency. For example, you might want to use a macro in a CocoaPods library or obfuscate its source code.
In these cases, and as I will show you in this article, you might want to import your macro into your Xcode project as a binary instead and not as an SPM dependency.
Learn how to play animation on 3D models using RealityKit.
When we design augmented reality experiences, a crucial step involves specifying animated behaviors for objects. This will add layers of interactivity, transforming static elements into dynamic components within the augmented environment. In some cases, models may already contain animations, however, we can still define specific behaviors using RealityKit.
This article explains how to interface between NIO and Swift Concurrency.
NIO was created before native Concurrency support in Swift existed, hence, NIO had to solve a few problems that have solutions in the language today. Since the introduction of Swift Concurrency, NIO has added numerous features to make the interop between NIO’s Channel eventing system and Swift’s Concurrency primitives as easy as possible.
A visionOS app icon is circular and includes a background layer and one or two layers on top, producing a three-dimensional object that subtly expands when people view it.
Add app icon variations to represent your app in places such as Settings, search results, and the App Store.
Every app has a distinct app icon that communicates the app’s purpose and makes it easy to recognize throughout the system. Apps require multiple variations of the app icon to look great in different contexts. Xcode can help generate these variations for you using a single high-resolution image, or you can configure your app icon variations by using an app icon’s image set in your project’s asset catalog. visionOS and tvOS app icons are made up of multiple stacked image layers you configure in your project’s asset catalog.
Module aliases for targets in this dependency. The key is an original target name and the value is a new unique name mapped to the name of the .swiftmodule binary.
Define a diff driver in your $HOME/.gitconfig. The xfuncname configuration specifies a regular expression that is used to match a line you want to see in the hunk header after the @@ bit. Covering all possible options with a regexp probably isn't possible, but this should cover most of the cases:
The testing library provides much of the same functionality of XCTest, but uses its own syntax to declare test functions and types. This document covers the process of converting XCTest-based content to use the testing library instead.
I've been wondering if it would be possible to wrap observation into an AsyncStream. That would let me use an asynchronous for loop to iterate over changes. In this post I will share how I implemented it.
A type you use to coordinate your interface’s behavior when an active SharePlay session supports spatial placement of content.
A SystemCoordinator object helps you coordinate the presentation of your app’s content when spatial placement is active. In visionOS, the system can present a SharePlay activity as if the participants were together in the same room with the content. Each participant views the content from a particular vantage point, and sees the changes that others make. The system handles the placement of each participant’s Spatial Persona relative to the content, but you handle any changes to the content itself with the help of the SystemCoordinator object.
You don’t create a SystemCoordinator object directly. After you receive a GroupSession object for an activity, retrieve the system coordinator from the session’s systemCoordinator property. When you first retrieve the object, update its configuration property to tell the system how you want to arrange participants in the scene. After that, use the information in the system coordinator’s properties to keep your app’s interface up to date. When participants support spatial placement, send additional data to synchronize your content for those participants. For example, when one person scrolls the contents of a window, update the scroll position in the window of other spatially aware participants to preserve the shared context for everyone.
You choose what information to share among participants, and you choose how to manage the corresponding updates. A system coordinator object only helps you know when to make those changes. Observe the object’s published properties to receive automatic updates when the values change.
People expect most visionOS apps to support SharePlay. While wearing Apple Vision Pro, people choose the Spatial option in FaceTime to share content and activities with others.
In a shared activity, FaceTime can show representations of other participants — called Spatial Personas — within each wearer’s space, making everyone feel like they’re sharing the same experience in the same place. During a shared experience in FaceTime, people can interact with each other in natural ways through their Spatial Personas. For example, people can speak or gesture directly to others, tell when someone is paying attention to them, and know which person is using a shared tool or resource.
visionOS uses the concept of shared context to describe the characteristics of a shared activity that help people feel physically present with others while connecting over the same content. A shared context helps give people confidence that they’re experiencing the same thing as everyone else.
When people feel that they’re truly sharing an experience, social dynamics can encourage authentic, intuitive interactions. For example, people can communicate verbally and nonverbally to make plans, take turns, and share resources.
Multipeer Connectivity is a high-level interface to Apple’s peer-to-peer Wi-Fi support. It includes:
A very opinionated networking model, where every participant in a session is a symmetric peer
User interface components for advertising and joining a session
Use it when your requirements are aligned with those features. Don’t use it if your program uses a client/server architecture; Network framework works better in that case. For an example, see Building a custom peer-to-peer protocol.
Important:
A common misconception is that Multipeer Connectivity is the only way to use peer-to-peer Wi-Fi.
> That’s not the case. Network framework has opt-in peer-to-peer Wi-Fi support.
For the details, see Peer-to-Peer networking.
Foundation also has peer-to-peer Wi-Fi support:
When advertising a service using NSNetService, set the includesPeerToPeer property.
When browsing for services using NSNetServiceBrowser, set the includesPeerToPeer property.
After discovering a service with a peer-to-peer enabled browser, connect to that service using getInputStream(_:outputStream:).
These APIs were marked as to-be-deprecated in 2021 (see Versions). If you have existing code that uses them, make a plan to migrate to Network framework.
The dnssd API supports peer-to-peer Wi-Fi but with an important caveat: If you advertise a service on peer-to-peer Wi-Fi using dnssd, the service’s listener must be run by a peer-to-peer aware API, like NWListener or NSNetService. Given that those APIs already have a facility to opt in to peer-to-peer Wi-Fi, there’s very little point using dnssd for this.
With all that choice, it’s hard to know where to start. This technote aims to clarify that. It makes specific recommendations as to which API to use for a given network protocol. It then discusses Alternative APIs and some Best practices.
The focus here is on APIs that allow you to use the networking stack. If you want to extend the networking stack—for example, to add support for a custom VPN protocol—implement a Network Extension provider. For the details, see Network Extension.
Businesses of all kinds and sizes are exploring the possibilities of the infinite canvas of Apple Vision Pro — and realizing ideas that were never before possible. We caught up with two of those companies — JigSpace and PTC — to find out how they’re approaching the new world of visionOS.
A safe way to synchronously assume that the current execution context belongs to the MainActor.
This API should only be used as last resort, when it is not possible to express the current execution context definitely belongs to the main actor in other ways. E.g. one may need to use this in a delegate style API, where a synchronous method is guaranteed to be called by the main actor, however it is not possible to annotate this legacy API with @MainActor.
Warning: If the current executor is not the MainActor’s serial executor, this function will crash.
Note that this check is performed against the MainActor’s serial executor, meaning that if another actor uses the same serial executor–by using sharedUnownedExecutor as its own unownedExecutor–this check will succeed, as from a concurrency safety perspective, the serial executor guarantees mutual exclusion of those two actors.
AsyncGraphics is a Swift package for working with images and video with async / await. The core type is simply just called Graphic, it's like an image and is backed by a MTLTexture.
A mechanism to interface between synchronous code and an asynchronous stream.
The closure you provide to the AsyncStream in init(_:bufferingPolicy:\_:) receives an instance of this type when invoked. Use this continuation to provide elements to the stream by calling one of the yield methods, then terminate the stream normally by calling the finish() method.
Note:
Unlike other continuations in Swift, AsyncStream.Continuation supports escaping.
To create a custom gesture, you rely on ARKit for information like hand positioning and joint orientation. Before you can offer custom gestures in your app, your app must be running in a Full Space and you must request people’s permission to access information about their hands. For developer guidance, see Setting up access to ARKit data.
Prioritize comfort in custom gestures. Continually test the ergonomics of all interactions that require custom gestures. A custom interaction that requires people to keep their arms raised for even a little while can be physically tiring, and repeating very similar movements many times in succession can stress people’s muscles and joints.
Consider carefully before defining custom gestures that involve multiple fingers or both hands. It can be challenging for people to perform custom gestures, and requiring them to position multiple fingers or use both hands at the same time can be even more difficult.
Avoid creating a custom gesture that requires people to use a specific hand. Expecting people to remember which hand to use for a custom gesture increases their cognitive load while also making your experience less welcoming to people with strong hand-dominance or limb differences.
If you decide to create a custom gesture, make sure it’s:
Inclusive. Gestures can mean different things to different people, so be sure your custom gestures don’t send messages you don’t intend.
Comfortable. Great custom gestures are physically easy for people to perform, especially over time.
Distinctive. Custom gestures that harmonize with your app or game can be easier for people to discover and remember, while enhancing their enjoyment of the experience.
Easy to describe. If you can’t use simple language and simple graphics to describe your custom gesture, it may mean that the gesture will be difficult for people to learn and perform.
SwiftUI for iOS 17 and macOS Sonoma come with a fantastic new superpower: the ability to transform any SwiftUI view with Metal shaders, all hardware accelerated so complex effects run at lightning fast speeds even on old devices.
I want to help folks get started with Metal, so I've produced two free resources that will help everyone:
I released a new YouTube video where I walk through how to create Metal shaders for SwiftUI from scratch. It lasts over an hour, so there's lots of detail there.
Inferno is a project that makes Metal shaders easy for everyone to use, but I've also gone a step further and added comprehensive documentation explaining exactly how each shader works so that others can learn too.
In this article, I will describe step by step how to configure Neovim to move away from Xcode. It took me several months to figure it all out piece by piece and to combine it into one working iOS development environment (I did it so you don’t have to :D). Hopefully, it won’t take you more than half a day to configure it with my help :).
It will be a little bit lengthy trip, and it will require setting up multiple plugins. I would recommend it to people who are already familiar with Vim. If you just installed Neovim, it could be overwhelming to learn Vim motions, Neovim environment, and set up dependencies, all at once.
If you are just starting with Neovim, take it slowly. First, learn Vim motions inside Xcode (by enabling Vim mode), in the meantime start configuring Neovim and get familiar with it by installing plugins, editing text files, JSON files, etc. Once you feel comfortable with Vim motions and Neovim, then try migrating your development :).
The elements produced by the publisher, as an asynchronous sequence.
This property provides an AsyncPublisher, which allows you to use the Swift async-await syntax to receive the publisher’s elements. Because AsyncPublisher conforms to AsyncSequence, you iterate over its elements with a for-await-in loop, rather than attaching a subscriber.
The following example shows how to use the values property to receive elements asynchronously. The example adapts a code snippet from the filter(_:) operator’s documentation, which filters a sequence to only emit even integers. This example replaces the Subscribers.Sink subscriber with a for-await-in loop that iterates over the AsyncPublisher provided by the values property.
for await number in filtered.values {
print("\(number)", terminator: " ")
}
* [AsyncPublisher](https://developer.apple.com/documentation/combine/asyncpublisher)
> _A publisher that exposes its elements as an asynchronous sequence._
>
> AsyncPublisher conforms to [AsyncSequence](https://developer.apple.com/documentation/Swift/AsyncSequence), which allows callers to receive values with the for-await-in syntax, rather than attaching a [Subscriber](https://developer.apple.com/documentation/combine/subscriber).
>
> Use the [values](https://developer.apple.com/documentation/combine/publisher/values-1dm9r) property of the [Publisher](https://developer.apple.com/documentation/combine/publisher) protocol to wrap an existing publisher with an instance of this type.
* [viewIsAppearing(\_:)](https://developer.apple.com/documentation/uikit/uiviewcontroller/4195485-viewisappearing)
> _Notifies the view controller that the system is adding the view controller’s view to a view hierarchy._
>
> The system calls this method once each time a view controller’s view appears after the [viewWillAppear(\_:)](https://developer.apple.com/documentation/uikit/uiviewcontroller/1621510-viewwillappear) call. In contrast to `viewWillAppear(_:)`, the system calls this method after it adds the view controller’s view to the view hierarchy, and the superview lays out the view controller’s view. By the time the system calls this method, both the view controller and its view have received updated trait collections and the view has accurate geometry.
>
> You can override this method to perform custom tasks associated with displaying the view. For example, you might use this method to configure or update views based on the trait collections of the view or view controller. Or, because computing a scroll position relies on the view’s size and geometry, you might programmatically scroll a collection or table view to ensure a selected cell is visible when the view appears.
>
> If you override this method, you need to call super at some point in your implementation.
* [Previews in Xcode](https://developer.apple.com/documentation/swiftui/previews-in-xcode)
> _Generate dynamic, interactive previews of your custom views._
>
> When you create a custom [View](https://developer.apple.com/documentation/swiftui/view) with SwiftUI, Xcode can display a preview of the view’s content that stays up-to-date as you make changes to the view’s code. You use one of the preview macros — like [Preview(\_:body:)](https://developer.apple.com/documentation/swiftui/preview(_:body:)) — to tell Xcode what to display. Xcode shows the preview in a canvas beside your code.
>
> Different preview macros enable different kinds of configuration. For example, you can add traits that affect the preview’s appearance using the [Preview(\_:traits:\_:body:)](https://developer.apple.com/documentation/swiftui/preview(_:traits:_:body:)) macro or add custom viewpoints for the preview using the [Preview(\_:traits:body:cameras:)](https://developer.apple.com/documentation/swiftui/preview(_:traits:body:cameras:)) macro. You can also check how your view behaves inside a specific scene type. For example, in visionOS you can use the [Preview(\_:immersionStyle:traits:body:)](https://developer.apple.com/documentation/swiftui/preview(_:immersionstyle:traits:body:)) macro to preview your view inside an [ImmersiveSpace](https://developer.apple.com/documentation/swiftui/immersivespace).
You typically rely on preview macros to create previews in your code. However, if you can’t get the behavior you need using a preview macro, you can use the [PreviewProvider](https://developer.apple.com/documentation/swiftui/previewprovider) protocol and its associated supporting types to define and configure a preview.
* [Immersive spaces](https://developer.apple.com/documentation/swiftui/immersive-spaces)
> _Display unbounded content in a person’s surroundings._
>
> Use an immersive space to present SwiftUI views outside of any containers. You can include any views in a space, although you typically use a [RealityView](https://developer.apple.com/documentation/RealityKit/RealityView) to present RealityKit content.
>
> You can request one of three styles of spaces with the [immersionStyle(selection:in:)](https://developer.apple.com/documentation/swiftui/scene/immersionstyle(selection:in:)) scene modifier:
> * The [mixed](https://developer.apple.com/documentation/swiftui/immersionstyle/mixed) style blends your content with passthrough. This enables you to place virtual objects in a person’s surroundings.
> * The [full](https://developer.apple.com/documentation/swiftui/immersionstyle/full) style displays only your content, with passthrough turned off. This enables you to completely control the visual experience, like when you want to transport people to a new world.
> * The [progressive](https://developer.apple.com/documentation/swiftui/immersionstyle/progressive) style completely replaces passthrough in a portion of the display. You might use this style to keep people grounded in the real world while displaying a view into another world.
>
> When you open an immersive space, the system continues to display all of your app’s windows, but hides windows from other apps. The system supports displaying only one space at a time across all apps, so your app can only open a space if one isn’t already open.
* [SpatialEventGesture](https://developer.apple.com/documentation/swiftui/spatialeventgesture)
> _A gesture that provides information about ongoing spatial events like clicks and touches._
>
> Use a gesture of this type to track multiple simultaneous spatial events and gain access to detailed information about each. For example, you can place a particle emitter at every location in a [Canvas](https://developer.apple.com/documentation/swiftui/canvas) that has an ongoing spatial event:
```swift
struct ParticlePlayground: View {
@State var model = ParticlesModel()
var body: some View {
Canvas { context, size in
for particle in model.particles {
context.fill(Path(ellipseIn: particle.frame),
with: .color(particle.color))
}
}
.gesture(
SpatialEventGesture()
.onChanged { events in
for event in events {
if event.phase == .active {
// Update particle emitters.
model.emitters[event.id] = ParticlesModel.Emitter(
location: event.location
)
} else {
// Remove emitters when no longer active.
model.emitters[event.id] = nil
}
}
}
.onEnded { events in
for event in events {
// Remove emitters when no longer active.
model.emitters[event.id] = nil
}
}
)
}
}
```
> The gesture provides a [SpatialEventCollection](https://developer.apple.com/documentation/swiftui/spatialeventcollection) structure when it detects changes. The collection contains [SpatialEventCollection.Event](https://developer.apple.com/documentation/swiftui/spatialeventcollection/event) values that represent ongoing spatial events. Each event contains a stable, unique identifier so that you can track how the event changes over time. The event also indicates its current location, a timestamp, the pose of the input device that creates it, and other useful information.
>
> The phase of events in the collection can change to [SpatialEventCollection.Event.Phase.ended](https://developer.apple.com/documentation/swiftui/spatialeventcollection/event/phase-swift.enum/ended) or [SpatialEventCollection.Event.Phase.cancelled](https://developer.apple.com/documentation/swiftui/spatialeventcollection/event/phase-swift.enum/cancelled) while the gesture itself remains active. Individually track state for each event inside [onChanged(\_:)](https://developer.apple.com/documentation/swiftui/gesture/onchanged(_:)) or [updating(\_:body:)](https://developer.apple.com/documentation/swiftui/gesture/updating(_:body:)) and clean up all state in [onEnded(\_:)](https://developer.apple.com/documentation/swiftui/gesture/onended(_:)).
* [Tim — Your iOS Mobile Dev Interview Coach](https://chat.openai.com/g/g-Bq6IZuAxd-tim-your-ios-mobile-dev-interview-coach)
> I help new and seasoned iOS developers do well on their take-home technical assessment, initial phone screen and on-site interviews. I can quiz you on system design whiteboarding, app architecture and common data structures & algorithms problems, based in Swift.
* [ChatPRD](https://chat.openai.com/g/g-G5diVh12v-chatprd)
> An on-demand Chief Product Officer that drafts and improves your PRDs, while coaching you to become an elite product manager.
* [XcodeGPT](https://chat.openai.com/g/g-z9dLWHPID-xcodegpt)
> Your Xcode assistant with a new logo
* [NSBundleResourceRequest](https://developer.apple.com/documentation/foundation/nsbundleresourcerequest)
> _A resource manager you use to download content hosted on the App Store at the time your app needs it._
> You identify on-demand resources during development by creating string identifiers known as tags and assigning one or more tags to each resource. An `NSBundleResourceRequest` object manages the resources marked by one or more tags.
>
> You use the resource request to inform the system when the managed tags are needed and when you have finished accessing them. The resource request manages the downloading of any resources marked with the managed tags that are not already on the device and informs your app when the resources are ready for use.
>
> The system will not attempt to purge the resources marked with a tag from on-device storage as long as at least one NSBundleResourceRequest object is managing the tag. Apps can access resources after the completion handler of either [beginAccessingResources(completionHandler:)](https://developer.apple.com/documentation/foundation/nsbundleresourcerequest/1614840-beginaccessingresources) or [conditionallyBeginAccessingResources(completionHandler:)](https://developer.apple.com/documentation/foundation/nsbundleresourcerequest/1614834-conditionallybeginaccessingresou) is called successfully. Management ends after a call to [endAccessingResources()](https://developer.apple.com/documentation/foundation/nsbundleresourcerequest/1614843-endaccessingresources) or after the resource request object is deallocated.
>
> Other properties and methods let you track the progress of a download, change the priority of a download, and check whether the resources marked by a set of tags are already on the device. Methods in [Bundle](https://developer.apple.com/documentation/foundation/bundle) indicate to the system the relative importance of preserving a tag in memory after it is no longer in use. For more information, see [setPreservationPriority(\_:forTags:)](https://developer.apple.com/documentation/foundation/bundle/1614845-setpreservationpriority) and [preservationPriority(forTag:)](https://developer.apple.com/documentation/foundation/bundle/1614839-preservationpriority).
* [XcodeGPT](https://tuist.io/blog/2023/11/10/gpts)
> XcodeGPT is your go-to assistant for any queries about Xcode, its build systems, and project management. This model is not only trained with publicly available Xcode data but also infused with the nuanced understanding we have developed over the years. We are committed to continually updating XcodeGPT with our latest insights, ensuring it remains an invaluable resource for the most current Xcode information.
>
> Experience XcodeGPT in action [here](https://chat.openai.com/g/g-z9dLWHPID-xcodegpt).
* [Adopting the system player interface in visionOS](https://developer.apple.com/documentation/avkit/adopting_the_system_player_interface_in_visionos)
> _Provide an optimized viewing experience for watching 3D video content._
>
> The recommended way to provide a video playback interface for your visionOS app is to adopt [AVPlayerViewController](https://developer.apple.com/documentation/avkit/avplayerviewcontroller). Using this class makes it simple to provide the same playback user interface and features found in system apps like TV and Music. It also provides essential system integration to deliver an optimal viewing experience whether you’re playing standard 2D content or immersive 3D video with spatial audio. This article describes best practices for presenting the player in visionOS and covers the options the player provides to customize its user interface to best fit your app.
* [Positioning and sizing windows](https://developer.apple.com/documentation/visionos/positioning-and-sizing-windows)
> _Influence the initial geometry of windows that your app presents._
>
> visionOS and macOS enable people to move and resize windows. In some cases, your app can use scene modifiers to influence a window’s initial geometry on these platforms, as well as to specify the strategy that the system employs to place minimum and maximum size limitations on a window. This kind of configuration affects both windows and volumes, which are windows with the [volumetric](https://developer.apple.com/documentation/SwiftUI/WindowStyle/volumetric) window style.
>
> Your ability to configure window size and position is subject to the following constraints:
>
> * The system might be unable to fulfill your request. For example, if you specify a default size that’s outside the range of the window’s resizability, the system clamps the affected dimension to keep it in range.
> * Although you can change the window’s content, you can’t directly manipulate window position or size after the window appears. This ensures that people have full control over their workspace.
> * During state restoration, the system restores windows to their previous position and size.
* [GeometryReader: Blessing or Curse?](https://betterprogramming.pub/geometryreader-blessing-or-curse-1ebd2d5005ec)
> GeometryReader has been present since the birth of SwiftUI, playing a crucial role in many scenarios. However, from the very beginning, some developers have held a negative attitude towards it, believing it should be avoided as much as possible. Especially after the recent updates of SwiftUI added some APIs that can replace GeometryReader, this view has further strengthened.
>
> This article will dissect the “common problems” of GeometryReader to see if it is really so unbearable, and whether those performances criticized as “not meeting expectations” are actually due to problems with the developers’ “expectations” themselves.
* [Swiftie](https://chat.openai.com/g/g-1ex7nJso7-swiftie)
> **An expert Swift developer at your service***
>
> _*Requires ChatGPT Plus_
* [**Reasync.swift**](https://github.com/pointfreeco/swift-concurrency-extras/blob/1676c3b73e1657b9e91f0ca8194855eee4138006/Sources/ConcurrencyExtras/Result.swift)
```swift
extension Result where Failure == Swift.Error {
/// Creates a new result by evaluating an async throwing closure, capturing the returned value as
/// a success, or any thrown error as a failure.
///
/// - Parameter body: A throwing closure to evaluate.
@_transparent
public init(catching body: () async throws -> Success) async {
do {
self = .success(try await body())
} catch {
self = .failure(error)
}
}
}
Semantically, @_transparent means something like "treat this operation as if it were a primitive operation". The name is meant to imply that both the compiler and the compiled program will "see through" the operation to its implementation.
This has several consequences:
Any calls to a function marked @_transparent MUST be inlined prior to doing dataflow-related diagnostics, even under -Onone. This may be necessary to catch dataflow errors.
Because of this, a @_transparent function is implicitly inlinable, in that changing its implementation most likely will not affect callers in existing compiled binaries.
Because of this, a public or @usableFromInline@_transparent function MUST only reference public symbols, and MUST not be optimized based on knowledge of the module it's in. [The former is caught by checks in Sema.]
Debug info SHOULD skip over the inlined operations when single-stepping through the calling function.
Swift’s FormatStyle and ParseableFormatStyle are the easiest way to convert Foundation data types to and from localized strings. Unfortunately Apple hasn’t done a great job in documenting just what it can do, or how to use them.
Swift Macros were introduced by Apple as a feature bundled within Swift Packages. This approach enhances shareability—a notable limitation of XcodeProj elements like targets. However, it also tightens the reliance on seamless integration between Xcode and the Swift Package Manager (SPM), which, from my experience and that of others, can be less than ideal in large projects with numerous dependencies. In fact, some developers are shifting towards Tuist’s methodology, reminiscent of CocoaPods, where projects are immediately ready for compilation upon opening.
Given the suboptimal experience offered by Apple’s ecosystem, which precludes optimization opportunities, Tuist employs SPM to resolve packages before mapping them onto Xcodeproj elements. While generally effective, this approach has encountered occasional setbacks, which developers can rectify by tweaking the build settings of the generated targets. Yet, it has not supported Swift Macros since their announcement.
Interestingly, developers managing Xcode rules for Bazel quickly devised a method to accommodate Swift Macros using compiler flags. Inspired by this, could Tuist adopt a similar strategy by utilizing targets, dependencies, and build settings? After some investigation, the answer is affirmative. Here’s the blueprint:
The macro’s representative target must be a macOS command-line target, encompassing the macro’s source code. A secondary, dependent target is required, hosting the public macro definition for import by other targets.
Targets wishing to leverage the macro should:
Establish a dependency on the secondary target for prior compilation.
Include the setting OTHER_SWIFT_FLAGS with the value -load-plugin-executable $BUILT_PRODUCTS_DIR/ExecutableName\#ExecutableName.
This setup is contingent upon the secondary target and the dependent targets producing their outputs in the same directory. If that’s not the case, SWIFT_INCLUDE_PATHS will be necessary to make the module available to the dependent targets.
The new Swift 5.9 release contains a number of helpful, new features for debugging code, including an out-of-process, interactive crash handler to inspect crashes in real time, the ability to trigger the debugger for just-in-time debugging, along with concurrency-aware backtracing to make it easier to understand control flow in a program that uses structured concurrency.
Today we are announcing our new library, automerge-repo, which makes it vastly easier to build local-first applications with Automerge. Take a look at our quickstart guide or read on for some background and examples.
For those new to this idea: local-first applications are a way of building software that allows both real-time collaboration (think Google Docs) and offline working (think Git). They work by storing the user's data locally, on their own device, and syncing it with collaborators in the background. You can read more about the motivation for local-first software in our essay, or watch a talk introducing the idea.
A challenge in local-first software is how to merge edits that were made independently on different devices, and CRDTs were developed to solve this problem. Automerge is a fairly mature CRDT implementation. In fact, we wrote this blog post using it! The API is quite low-level though, and Automerge-Core has no opinion about how networking or storage should be done. Often, the first thing developers ask after discovering Automerge was how to connect it into an actual application.
Our new library, automerge-repo, extends the collaboration engine of Automerge-Core with networking and storage adapters, and provides integrations with React and other UI frameworks. You can get to building your app straight away by taking advantage of default implementations that solve common problems such as how to send binary data over a WebSocket, how often to send synchronization messages, what network format to use, or how to store data in places like the browser's IndexedDB or on the filesystem.
We explained the implementation details of an event-sourced functional domain model in Kotlin. In the process, we attempted to show how straightforward testing of such a model can be and how it doesn't require any dedicated testing techniques or tools. The model itself, on the other hand, remains a rich core of business logic.
The following sections are general guidelines that describe fundamental Mac layout principles of center equalization, text and control alignment, appropriate use of white space, and visual balance. Following these guidelines will help you create functional and aesthetically pleasing windows that are easy for Mac users to understand and use.
As you layout your window, remember to observe the principle of consistency in your decisions. If you have good reasons to break some layout guidelines, be sure to do it in a consistent way. Users tend to ignore symmetry and balance but will notice inconsistency.
Inconsistencies in a window can also lead users to conclude that the window was poorly designed and/or implemented. For example, users won’t notice if the margins inside your window edges are 18 points wide (instead of the recommended 20 points), but are likely to notice if the left margin is wider than the right one.
watchOS allows all apps to use high-level networking equally. However, it only allows an app to use low-level networking under specific circumstances:
It allows an audio streaming app to use low-level networking while actively streaming audio. Support for this was introduced in watchOS 6.
It allows a VoIP app to use low-level networking while running a call using CallKit. Support for this was added in watchOS 9.
It allows an app on watchOS to set up an application service listener so that the same app on tvOS can establish a low-level connection to it using the DeviceDiscoveryUI framework. Support for this was added in watchOS 9 and tvOS 16.
Bonjour, also known as zero-configuration networking, enables automatic discovery of devices and services on a local network using industry standard IP protocols. Bonjour makes it easy to discover, publish, and resolve network services with a sophisticated, easy-to-use programming interface that is accessible from Cocoa, Ruby, Python, and other languages.
With Visual Look Up, you can identify and learn about popular landmarks, plants, pets, and more that appear in your photos and videos in the Photos app . Visual Look Up can also identify food in a photo and suggest related recipes.
Starting from iOS 17 we now have new properties, such as secondary, tertiary, quaternary and quinary that are defined on an instance of a ShapeStyle. To get hierarchical background colors we simply have to access these properties on the current background style: BackgroundStyle().secondary. BackgroundStyle in SwiftUI conforms to ShapeStyle protocol, so accessing the secondary property on an instance of a BackgroundStyle will return the second level of the background in the current context that depends on the operating system and color scheme (light or dark mode enabled).
We can also get the current background style from the static background property defined on ShapeStyle.
Containers have fundamentally changed the way that modern software is developed and deployed. Containers are supported by a wide range of operating systems including FreeBSD, Solaris, Linux and even Windows, but are not natively supported by macOS. Until now.
The Swift package ecosystem has thousands of packages to help you with all kinds of tasks across your projects. You’ll find networking, testing, UI helpers, logging, animation, and many more packages that work with the Swift Package Manager (SwiftPM).
The gist of the talk is: what if we could define a predicate for even numbers like this in Agda (or Coq or Idris or Lean):
```agda
data Even : Nat → Set where
zero : Even 0
suc : ∀ {n} → not (Even n) → Even (suc n)
> So `0` is even, and `suc n` is even if `n` isn't.
>
> But if we get this to work, then what would this declaration mean?
```agda
data Liar : Set where
liar : not Liar → Liar
The Misty Programming Language is a dynamic, general-purpose, transitional, actor language. It has a gentle syntax that is intended to benefit students, as well as advanced features such as capability security and lambdas with lexical scoping.
The grammar of the language is expressed in McKeeman Form.
Imagine if you could say to ChatGPT, "Go try out my app for 5 minutes and let me know what you think about the getting started experience." Or if you could ask questions like... Does my iOS app's GUI follow common practices? Is it accessible? What are some examples of apps that use these specific UI controls on the same screen?
If we had a rich database of app GUIs and the right ML models, then we could answer these questions and build a copilot tool that "understands" the visual and interaction designs of GUIs, not just the code!
However, wouldn’t it also be nice to see all of these pull requests at once? That is, all of your own pull requests, everything assigned to review, and everything where you are actively in discussion? You can, with the filter involves:@me, which will show you all the pull requests you are involved in, in any capacity. In other words, it shows you everything that requires your attention.
Open sourcing our Swift bindings generator for WinRT — and an end-to-end sample application for anyone looking to build a modern Windows application in Swift.
Skip is your automated Android development partner. As you develop your modern Swift and SwiftUI iOS app, Skip's intelligent transpiler generates the equivalent Kotlin and Compose Android app alongside it. Deliver fully native apps for both the App Store and Play Store with one team, one language, and one codebase.
Break down software development programs into a self-explanatory sequence or hash out the steps involved in getting your startup off the ground. An event storming session enlightens the whole team.
This implementation is specifically optimized for the Apple Neural Engine (ANE), the energy-efficient and high-throughput engine for ML inference on Apple silicon. It will help developers minimize the impact of their ML inference workloads on app memory, app responsiveness, and device battery life. Increasing the adoption of on-device ML deployment will also benefit user privacy, since data for inference workloads remains on-device, not on the server.
In this article we share the principles behind this reference implementation to provide generalizable guidance to developers on optimizing their models for ANE execution. Then, we put these principles into action and showcase how to deploy an example pretrained Transformer model, the popular Hugging Face distilbert, in just a few lines of code. Notably, this model, which works out-of-the-box and on device using Core ML already, is up to 10 times faster and consumes 14 times less memory after our optimizations.
SkinGenerator.io uses custom generative art models to generate skins for video games. With a simple text prompt you can create characters quickly and easily. You're limited only by your imagination!
I've found a very strange behaviour which looks like a bug. SwiftUI sheet or fullScreenCover do not release objects that were passed to its item: parameter (and to the view builder body by the way, but here is the simplified case).
It works well and memory is releasing on iOS 16 built with both Xcode 14 or 15. (simulators, devices)
Memory is leaking and NOT releasing on iOS 17 built with Xcode 15. (simulator, device 17.0.2)
We present iLeakage, a transient execution side channel targeting the Safari web browser present on Macs, iPads and iPhones. iLeakage shows that the Spectre attack is still relevant and exploitable, even after nearly 6 years of effort to mitigate it since its discovery. We show how an attacker can induce Safari to render an arbitrary webpage, subsequently recovering sensitive information present within it using speculative execution. In particular, we demonstrate how Safari allows a malicious webpage to recover secrets from popular high-value targets, such as Gmail inbox content. Finally, we demonstrate the recovery of passwords, in case these are autofilled by credential managers.
Simulate and render 3D content for use in your augmented reality apps.
RealityKit provides high-performance 3D simulation and rendering capabilities you can use to create visionOS apps or to create augmented reality (AR) apps for iOS, macOS, and tvOS. RealityKit is an AR-first 3D framework that leverages ARKit to seamlessly integrate virtual objects into the real world.
Use RealityKit’s rich functionality to create compelling augmented reality (AR) experiences.
Create and import full RealityKit scenes with models, animations, and Spatial Audio by using Reality Composer Pro for visionOS.
Build or modify scenes at runtime by adding 3D models, shape primitives, and sounds from code.
Have virtual objects interact with objects in the real-world.
Animate objects, both manually and with physics simulations.
Respond to user input and changes in a person’s surroundings.
Synchronize across devices and use SharePlay to enable group AR experiences.
RealityKit is a 3D framework designed for building apps, games, and other immersive experiences. Although it’s built in an object-oriented language and uses object-oriented design principles, RealityKit’s architecture avoids heavy use of composition — where objects are built by adding instance variables that hold references to other objects — in favor of a modular design based on a paradigm called Entity Component System (ECS) that divides application objects into one of three types.
Following the ECS paradigm allows you to re-use the functionality contained in a component in many different entities, even if they have very different inheritance chains. Even if two objects have no common ancestors other than Entity, you can add the same components to both of them and give them the same behavior or functionality.
In visionOS, you can design apps and games that extend beyond windows and volumes, and let people immerse themselves in your content.
In visionOS, people can run multiple apps at the same time in the Shared Space or concentrate on a single app at a time in a Full Space. By default, your app launches in the Shared Space, where people can switch between multiple running apps much as they do on a Mac. When they want a more immersive experience, people can transition your app to a Full Space, where other apps hide and your app can display content anywhere.
The Spatial module is a lightweight 3D mathematical library that provides a simple API for working with 3D primitives. Much of its functionality is similar to the 2D geometry support in Core Graphics, but in three dimensions.
Dive into the details of Swift concurrency and discover how Swift provides greater safety from data races and thread explosion while simultaneously improving performance. We'll explore how Swift tasks differ from Grand Central Dispatch, how the new cooperative threading model works, and how to ensure the best performance for your apps. To get the most out of this session, we recommend first watching “Meet async/await in Swift,” “Explore structured concurrency in Swift,” and “Protect mutable state with Swift actors.”
When people wear Apple Vision Pro, they enter an infinite 3D space where they can engage with your app or game while staying connected to their surroundings.
Help people stay comfortable when playing video in your app. Often, an app doesn’t control the content in the videos it plays, but you can help people stay comfortable by:
Letting them choose when to start playing a video
Using a small window for playback, letting people resize it if they want
Making sure people can see their surroundings during playback
In a fully immersive experience, avoid letting virtual content obscure playback or transport controls. In a fully immersive context, the system automatically places the video player at a predictable location that provides an optimal viewing experience. Use this location to help make sure that no virtual content occludes the default playback or transport controls in the ornament near the bottom of the player.
Avoid automatically starting a fully immersive video playback experience. People need control over their experience and they’re unlikely to appreciate being launched into a fully immersive video without warning.
Create a thumbnail track if you want to support scrubbing. The system displays thumbnails as people scrub to different times in the video, helping them choose the section they want. To improve performance, supply a set of thumbnails that each measure 160 px in width. For developer guidance, see HTTP Live Streaming (HLS) Authoring Specification for Apple Devices > Trick Play.
Avoid expanding an inline video player to fill a window. When you display the system-provided player view in a window, playback controls appear in the same plane as the player view and not in an ornament that floats above the window. Inline video needs to be 2D and you want to make sure that window content remains visible around the player so people don’t expect a more immersive playback experience. For developer guidance, see AVPlayerViewController.
Use a RealityKit video player if you need to play video in a view like a splash screen or a transitional view. In situations like these, people generally expect the video to lead into the next experience, so they don’t need playback controls or system-provided integration, like dimming and view anchoring. The RealityKit video player automatically uses the correct aspect ratio for both 2D and 3D video and supports closed captions. RealityKit can also help you play video as a special effect on the surface of a custom view or object. For developer guidance, see RealityKit.
Subtle, expressive sounds are everywhere in visionOS, enhancing experiences and providing essential feedback when people look at a virtual object and use gestures to interact with it. The system combines audio algorithms with information about a person’s physical surroundings to produce Spatial Audio, which is sound that people can perceive as coming from specific locations in space, not just from speakers.
In visionOS, audio playback from the Now Playing app pauses automatically when people close the app’s window, and audio from an app that isn’t the Now Playing app can duck when people look away from it to different app.
Prefer playing sound. People generally choose to keep sounds audible while they’re wearing the device, so an app that doesn’t play sound — especially in an immersive moment — can feel lifeless and may even seem broken. Throughout the design process, look for opportunities to create meaningful sounds that aid navigation and help people understand the spatial qualities of your app.
Design custom sounds for custom UI elements. In general, a system-provided element plays sound to help people locate it and receive feedback when they interact with it. To help people interact with your custom elements, design sounds that provide feedback and enhance the spatial experience of your app.
Use Spatial Audio to create an intuitive, engaging experience. Because people can perceive Spatial Audio as coming from anywhere around them, it works especially well in a fully immersive context as a way to help an experience feel lifelike. Ambient audio provides pervasive sounds that can help anchor people in a virtual world and an audio source can sound like it comes from a specific object. As you build the soundscape for your app, consider using both types of audio.
Consider defining a range of places from which your app sounds can originate. Spatial Audio helps people locate the object that’s making sound, whether it’s stationary or moving in space. For example, when people move an app window that’s playing audio, the sound continues to come directly from the window, wherever people move it.
Consider varying sounds that people could perceive as repetitive over time. For example, the system subtly varies the pitch and volume of the virtual keyboard’s sounds, suggesting the different sounds a physical keyboard can make as people naturally vary the speed and forcefulness of their typing. An efficient way to achieve a pleasing variation in sound is to randomize a sound file’s pitch and volume during playback, instead of creating different files.
Decide whether you need to play sound that’s fixed to the wearer or tracked by the wearer. People perceive fixed sound as if it’s pointed at them, regardless of the direction they look or the virtual objects they move. In contrast, people tend to perceive tracked sound as coming from a particular object, so moving the object closer or farther away changes what they hear. In general, you want to use tracked sound to enhance the realism of your experience, but there could be cases where fixed sound is a good choice. For example, Mindfulness uses fixed sound to envelop the wearer in an engaging, peaceful setting.
The creator economy will always struggle so long as the tooling is controlled by some massive body that can set the terms of creation. The solution is to make the tools cheap enough to scale and accessible to the point where they’re table stakes.
Apple devices integrate hardware, software, apps, and services to let you manage your deployment projects easily. Get the control and flexibility you want by using Apple School Manager or Apple Business Manager and your chosen mobile device management solution.
The Reveal team have spent more than a year building the new Insights workspace, working with industry experts and organisations to encode their expertise into more than 130 rules that pinpoint areas of improvement for your app. These rules are based on a combination of industry and platform-specific guidelines, best practices and over a decade of our own experience developing apps.
Insights runs a powerful, accessibility-focused audit of your app using information extracted from both the visual and accessible user interfaces of your app. This combination of the accessible and visual user interfaces is unique to Reveal, and allows us to provide a level of insight that is more powerful than anything else available for iOS, iPadOS and tvOS.
This new functionality takes Reveal from a passive developer tool, where you need to know what to look for in order to identify issues, to one that proactively surfaces problems along with suggestions on how to fix them. This radically improves developer efficiency and allows you to see and address issues you may never have know existed in your apps.
This document explains a more controlled installation procedure for Lean on MacOS. There is a quicker way described in the main install page but it requires more trust.
Leverage 3D video and Spatial Audio to deliver an immersive experience.
Destination Video is a multiplatform video-playback app for visionOS, iOS, and tvOS. People get a familiar media-browsing experience navigating the libraryʼs content and playing videos they find interesting. The app provides a similar experience on supported platforms, but leverages unique features of visionOS to create a novel, immersive playback experience.
Use Reality Composer Pro to visually design, edit, and preview RealityKit content. In Reality Composer Pro, you can create one or more scenes, which act as a container for RealityKit content. Scenes contain hierarchies of entities, which are virtual objects such as 3D models.
In RealityKit, a material is an object that defines the surface properties of a rendered 3D object. A VideoMaterial is a material that maps a movie file on to the surface of an entity. Video materials are unlit, which means that scene lighting doesn’t affect them. Video materials support transparency if the source video’s file format also supports transparency.
Video materials use an AVPlayer instance to control movie playback. You can use any movie file format that AVPlayer supports to create a video material. To control playback of the material’s video, use the avPlayer property, which offers methods like play() and pause().
The following code demonstrates how to create and start playing a video material using a movie file from your application bundle.
This is going to be an unusual Papers We Love talk, as I'm going to discuss a bunch of different papers in relatively light detail but with rich historical context. I'll post a transcript and mini-site for this talk that has links to all these papers, and many others that serve as supporting material.
Last year, I gave a talk at Strange Loop where I said everyone is programming wrong. This upset some people, so I thought I'd make a more modest claim today: Everyone is doing mathematics wrong
macOS checks every app against a slew of security features: Gatekeeper, notarization, hardening, entitlements and more. But it doesn't show you the result of these checks, preferring to keep these behind the scenes — either the app opens or it doesn't, perhaps with an “app downloaded from the internet” dialog first.
Even “code signatures” — around since Mac OS X 10.5 (Leopard!) — are visible only via arcane Terminal incantations.
WHAT'S INSIDE THAT APP?
Today's macOS apps are often made up of many pieces. Some of these are organizational artifacts, of interest only to the developers.
But some pieces of an app can extend macOS itself — Share Extensions, Today Widgets, Safari Extensions, Quick Look generators, Spotlight importers, and so on. macOS lets you see and manage some of these, but not all of them, and not all in one place.
ENTER APPARENCY — THE APP THAT OPENS APPS.
Control-click on an app in the Finder, choose Open With Apparency, and see all the details in one place
The MLC LLM iOS app can be installed in two ways: through the pre-built package or by building from source. If you are an iOS user looking to try out the models, the pre-built package is recommended. If you are a developer seeking to integrate new features into the package, building the iOS package from source is required.
Take a tour of some of the most terrifying and mind-blowing destinations in our galaxy ... and beyond. After a visit to these nightmare worlds, you may never want to leave Earth again! You can also download our free posters — based on real NASA science — if you dare.
A Boolean value that indicates whether the process is an iPhone or iPad app running on a Mac.
The value of this property is true only when the process is an iOS app running on a Mac. The value of the property is false for all other apps on the Mac, including Mac apps built using Mac Catalyst. The property is also false for processes running on platforms other than macOS.
This is a library for creating full screen effects in SwiftUI in an easy, seamless way. The package is available on Swift Package Manager (SPM) and fully open-source.
SwiftTreeSitter provides a Swift interface to the tree-sitter incremental parsing system. You can use it to parse language text, reparse it as it changes, and query the syntax tree.
In this article, I'll go through various options to implement SwiftFormat on a production project rather than how to integrate the tool locally (this is relatively straightforward). This is because I believe the tool provides greater benefit as an automation for every developer on the team, rather than a few only. We'll also cover the relative advantages or disadvantages. I hope that this will help you choose the right tool for your project.
Use RealityKit to create an interactive ride in visionOS.
Swift Splash uses multiple Reality Composer Scenes to create prepackaged entity hierarchies that represent each of the slide pieces the player connects to construct their ride. It demonstrates how to hide and reveal sections of the entity hierarchy based on the current state of the app. For example, each slide piece contains an animated fish entity that’s hidden until the ride runs and the fish arrives at that particular piece. While Swift Splash is a fun, game-like experience, the core idea of assembling virtual objects out of predefined parts can also be used as the basis for a productivity or creation app.
There’s no straightforward way to avoid this problem, but Xcode build settings offer a sophisticated (albeit brain-bendingly obtuse) mechanism for varying the value of a build setting based on arbitrary conditions. Technically this technique could be used in Xcode’s build settings editor, but because of the complexity of variable definitions, it’s a lot easier (not to mention easier to manage with source control) if you declare such settings in an Xcode configuration file. The examples below will use the declarative format used by these files.
The key to applying this technique is understanding that build settings can themselves be defined in terms of other build settings.
This is the spoken word version of my essay, Choose Boring Technology. I have largely come to terms with it and the reality that I will never escape its popularity.
I made this game to teach my daughter how buffer overflows work. Looking at programs as something you can play with, and poke and twist and make it do something else, is my favourite part of modern computing. I think its the right way to look at programs. When your microwave oven gets an update and starts crashing, you can hack it. Or when your keyboard controller’s firmware is bad, you can hack it (looking at you vortex pok3r). She is 12 yo now but her assembler skills are getting better and better, hopefully one day she will be able to hack her own keyboard :)
The game is about creating a small shellcode in memory by copying existing instructions and then exploiting a buffer overflow to jump into it, so that you can overwrite your opponent’s return address to force them to go to the game_over() function.There are other mechanics as well and more layers of strategy (like setting the exception handler or monkeypatching).
All players share the same memory, and execute the same program, while time sharing the same processor running preemptive scheduling os, so each turn the player executes 10 instructions, after that the process is interrupted by the operating system, and its the other player's turn. Each player's stack pointer starts at a different location. There is no virtual memory.
LLDB provides the shorthand p command alias to inspect variables and po to call the debugDescription property of objects. Originally, these were aliases for the rather heavyweight expression and expression -O commands. In Swift 5.9, the p and po command aliases have been redefined to the new dwim-print command.
The dwim-print command prints values using the most user-friendly implementation. “DWIM” is an acronym for “Do What I Mean”. Specifically, when printing variables, dwim-print will use the same implementation as frame variable or v instead of the more expensive expression evaluator.
In addition to being faster, using pno longer creates persistent result variables like $R0, which are often unused in debugging sessions. Persistent result variables not only incur overhead but also retain any objects they contain, which can be an unexpected side effect for the program execution.
Support for generic type parameters in expressions
LLDB now supports referring to generic type parameters in expression evaluation. For example, given the following code:
Running po T.self, when stopped in use, will print Int when coming in through the first call, and String in the second.
Fine-grained scope information
The Swift compiler now emits more precise lexical scopes in debug information, allowing the debugger to better distinguish between different variables, like the many variables named x in the following example:
```swift
func f(x: AnyObject?) {
// function parameter `x: AnyObject?`
guard let x else {}
// local variable `x: AnyObject`, which shadows the function argument `x`
...
}
> In Swift 5.9, the compiler now uses more accurate `ASTScope` information to generate the lexical scope hierarchy in the debug information, which results in some behavior changes in the debugger.
* [Twitter Network Layer (a.k.a TNL)](https://github.com/twitter/ios-twitter-network-layer)
> The **Twitter Network Layer (TNL)** is a framework for interfacing with the **Apple** provided `NSURLSession` stack that provides additional levels of control and insight over networking requests, provides simple configurability and minimizes the cognitive load necessary to maintain a robust and wide-reaching networking system.
* [Interacting with your app in the visionOS simulator](https://developer.apple.com/documentation/visionos/interacting-with-your-app-in-the-visionos-simulator)
> #### Interact with your appin page link
>
> To use your Mac’s pointer and keyboard to create gestures, choose “Select to interact with the scene” from the buttons at the bottom-right of a visionOS simulator window. The current gaze position tracks your pointer movements when you hover over content within the space.
>
> Use the following actions to trigger gestures:
> | Gesture | To simulate |
> | --- | --- |
> | Tap | Click. |
> | Double-tap | Double-click. |
> | Touch and hold | Click and hold. |
> | Drag (left, right, up, and down) | Drag left, right, up, and down. |
> | Drag (forward and back) | Shift-drag up and down. |
> | Two-handed gestures | Press and hold the Option key to display touch points. Move the pointer while pressing the Option key to change the distance between the touch points. Move the pointer and hold the Shift and Option keys to reposition the touch points. |
>
> Activate device buttons using menu items or by clicking the controls in the simulator window toolbar.
>
> #### Navigate the spacein page link
>
> Use your Mac’s pointer and the keyboard to reposition your viewpoint in a visionOS simulator window:
> | Movement | To simulate |
> | --- | --- |
> | Forward | Press the W key (or Up Arrow key), or perform a pinch gesture moving two fingers away from each other on a trackpad. |
> | Backward | Press the S key (or Down Arrow key), or perform a pinch gesture moving two fingers toward each other on a trackpad. |
> | Left | Press the A key (or Left Arrow key), or scroll left using a trackpad or Magic Mouse. |
> | Right | Press the D key (or Right Arrow key), or scroll right using a trackpad or Magic Mouse. |
> | Up | Press the E key, or scroll up using a trackpad or Magic Mouse. |
> | Down | Press the Q key, or scroll down using a trackpad or Magic Mouse. |
* [Understanding RealityKit’s modular architecture](https://developer.apple.com/documentation/visionos/understanding-the-realitykit-modular-architecture)
> RealityKit is a 3D framework designed for building apps, games, and other immersive experiences. Although it’s built in an object-oriented language and uses object-oriented design principles, RealityKit’s architecture avoids heavy use of composition — where objects are built by adding instance variables that hold references to other objects — in favor of a modular design based on a paradigm called Entity Component System (ECS) that divides application objects into one of three types.
>
> Following the ECS paradigm allows you to re-use the functionality contained in a component in many different entities, even if they have very different inheritance chains. Even if two objects have no common ancestors other than [Entity](https://developer.apple.com/documentation/RealityKit/Entity), you can add the same components to both of them and give them the same behavior or functionality.
* [Bezel](https://getbezel.app)
> _Show your iPhone on your Mac_
>
> Bezel is the easiest way to view, present and record an iPhone.
* [Prompt engineering for Claude's long context window](https://www.anthropic.com/index/prompting-long-context)
> Claude’s [100,000 token long context window enables](https://www.anthropic.com/index/100k-context-windows) the model to operate over hundreds of pages of technical documentation, or even an [entire book](https://twitter.com/AnthropicAI/status/1656700156518060033). As we continue to scale the Claude API, we’re seeing increased demand for prompting guidance on how to maximize Claude’s potential. Today, we’re pleased to share a quantitative case study on two techniques that can improve Claude’s recall over long contexts:
> * Extracting reference quotes relevant to the question before answering
> * Supplementing the prompt with examples of correctly answered questions about other sections of the document
* [A New API Direction for Testing in Swift](https://github.com/apple/swift-testing/blob/main/Documentation/Vision.md)
> A key requirement for the success of any developer platform is a way to use automated testing to identify software defects. Better APIs and tools for testing can greatly improve a platform’s quality. Below, we propose a new API direction for testing in Swift.
>
> We start by defining our basic principles and describe specific features that embody those principles. We then discuss several design considerations in-depth. Finally, we present specific ideas for delivering an all-new testing API in Swift, and weigh them against alternatives considered.
* [Why not React?](https://dev.to/tigt/why-not-react-2f8l)
> This analysis is harsh on React’s MPA suitability. But is that so odd?
>
> It was created to client-render non-core bits of Facebook. Its maintainers only recently used it for server rendering, navigation, or delivering traditional web content. In fact, [its SSR was a happy accident](https://twitter.com/sebmarkbage/status/1516907614566854659). And finally, [longstanding evidence holds React trends antagonistic towards performance](https://timkadlec.com/remembers/2020-04-21-the-cost-of-javascript-frameworks).
>
> _Why **would** React be good at the things we ask it to do?_
>
> With the FB5 redesign, Facebook is finally using React in the ways that we are, [and they have found it wanting](https://twitter.com/dan_abramov/status/1259614150386425858). On the one hand, this means React will surely become much better at desirable SSR features. On the other, _when_ this will happen is unsure, it will heavily change React’s roadmap, and React could change so much that familiarity with how it works today could be a liability rather than a strength.
>
> * For the target audience of rural/new/poorly-connected customers, does Facebook even use React to serve them? Did FB5 change anything, or **does `m.facebook.com` still not use React?**
> * If we want a version of Kroger.com as fast as the demo, but **using the same framework, processes, management, and developers as the existing site — wouldn’t that just become our existing site?** We can’t change our personnel, but we can change the technologies we build on.
> * Last, but certainly not least: **can you make an industry-beating app out of industry-standard parts?**
* [A different way to build together](https://pierre.co)
> Pierre is reimagining industry-old primitives for a new developer platform that brings together your entire team to build and ship software. Branches, not pull requests. Bots, not CI. Features you'll love, not the kitchen sink.
* [Interoperability: Swift’s Super Power](https://browsercompany.substack.com/cp/137231709)
> Swift’s deliberate design choices over the years has resulted in a language that showcases how flexibility and compatibility do not need to come at the cost of usability. One of these design choices was Swift’s focus on native interoperability with other languages. The flexibility that this enables makes it a joy to build rich, native experiences in Swift across a variety of environments.
>
> Traditionally when two languages need to interoperate, the function calls at the boundary between the two languages, also known as the Foreign Function Interface (FFI), will go through C using a library like libffi. This approach has some drawbacks such as incurred runtime performance costs and possibly extra boilerplate code. Instead, Swift embeds a copy of clang, the C and C++ compiler, which is able to directly translate between the languages avoiding penalties in code size and runtime performance. This level of interoperability composes wonderfully with existing systems and enables building complex software atop existing C libraries.
* [Deploying Transformers on the Apple Neural Engine](https://machinelearning.apple.com/research/neural-engine-transformers)
> An increasing number of the machine learning (ML) models we build at Apple each year are either partly or fully adopting the [Transformer architecture](https://arxiv.org/abs/1706.03762). This architecture helps enable experiences such as [panoptic segmentation in Camera with HyperDETR](https://machinelearning.apple.com/research/panoptic-segmentation), [on-device scene analysis in Photos](https://machinelearning.apple.com/research/on-device-scene-analysis), [image captioning for accessibility](https://support.apple.com/guide/iphone/use-voiceover-for-images-and-videos-iph37e6b3844/ios), [machine translation](https://apps.apple.com/us/app/translate/id1514844618), and many others. This year at WWDC 2022, Apple is making available an open-source reference [PyTorch](https://pytorch.org) implementation of the Transformer architecture, giving developers worldwide a way to seamlessly deploy their state-of-the-art Transformer models on Apple devices.
>
> This implementation is specifically optimized for the Apple Neural Engine (ANE), the energy-efficient and high-throughput engine for ML inference on Apple silicon. It will help developers minimize the impact of their ML inference workloads on app memory, app responsiveness, and device battery life. Increasing the adoption of on-device ML deployment will also benefit user privacy, since data for inference workloads remains on-device, not on the server.
>
> In this article we share the principles behind this reference implementation to provide generalizable guidance to developers on optimizing their models for ANE execution. Then, we put these principles into action and showcase how to deploy an example pretrained Transformer model, the popular [Hugging Face distilbert](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english), in just a few lines of code. Notably, this model, which works out-of-the-box and on device using Core ML already, is up to 10 times faster and consumes 14 times less memory after our optimizations.
* [Precise error typing in Swift](https://forums.swift.org/t/precise-error-typing-in-swift/52045)
> But there are arguments in favor of precise error typing as well. It’s been six years; we should look at those reasons and consider whether it’s time to add precise error typing to Swift. And that’s what I’m about to do.
>
> I’ll spoil the conclusion: I think the answer is “yes”. But it’s a “yes” with a very big caveat. I think the strongest reasons for adding precise error typing relate to (1) the interaction of `throws` with the generics system and (2) the requirements of low-level systems code. And so I think that we, as a community, should continue to strongly push imprecise error typing as the primary way that people ought to write code. Precise error typing will be a tool that programmers have in their toolbox for the cases where it’s really important (mostly, for low-level reliability and performance), and it will solve some expressivity problems for generic libraries. But when you don't _need_ that tool, you should stick to throwing `Error`.
>
> I want to be clear that this is not a guarantee that the feature is coming, or even a plan of attack. The main reason I'm writing this is because it keeps coming up in proposal reviews: there are a lot of library features that need to decide whether and how to accommodate the possibility of precise error typing. So I think it's very important to have a conversation about where we're going with this, and I hope that starts now.
* [**Swift Macro Testing**](https://github.com/pointfreeco/swift-macro-testing)
> Magical testing tools for Swift macros.
* [**Swift Version**](https://swiftversion.net)
* [SQL join flavors](https://antonz.org/sql-join)
> There is more to SQL joins than you might think. Let's explore them a bit.
> * [Qualified join](https://antonz.org/sql-join/#qualified-join)
> * [Natural join](https://antonz.org/sql-join/#natural-join)
> * [Cross join](https://antonz.org/sql-join/#cross-join)
> * [Partitioned join](https://antonz.org/sql-join/#partitioned-join)
> * [Lateral join](https://antonz.org/sql-join/#lateral-join)
> * [Summary](https://antonz.org/sql-join/#summary)
* [Access-level modifiers on import declarations [SE-0409]](https://github.com/apple/swift-evolution/blob/main/proposals/0409-access-level-on-imports.md)
> **Declaring the access level of an imported module**
>
> The access level is declared in front of the import declaration using some of the modifiers used for a declaration: `public`, `package`, `internal`, `fileprivate`, and `private`.
>
> A public dependency can be referenced from any declaration and will be visible to all clients. It is declared with the `public` modifier.
```swift
public import PublicDependency
```
> A dependency visible only to the modules of the same package is declared with the `package` modifier. Only the signature of `package`, `internal`, `fileprivate` and `private` declarations can reference the imported module.
```swift
package import PackageDependency
A dependency internal to the module is declared with the internal modifier. Only the signature of internal, fileprivate and private declarations can reference the imported module.
internalimport InternalDependency
A dependency private to this source file is declared with either the fileprivate or the private modifier. In both cases the access is scoped to the source file declaring the import. Only the signature of fileprivate and private declarations can reference the imported module.
TL;DR: We released a new macOS app called Cilicon, which provisions and runs ephemeral virtual machines for CI. Using it, we were able to switch to self-hosted Actions Runners and speed up our CI by 3x while giving some of our damaged M1 MacBook Pro devices a second life.
I believe that ML is a new way to build software, and I know that many Swift developers want to incorporate AI features in their apps. The ML ecosystem has matured a lot, with thousands of models that solve a wide variety of problems. Moreover, LLMs have recently emerged as almost general-purpose tools – they can be adapted to new domains as long as we can model our task to work on text or text-like data. We are witnessing a defining moment in computing history, where LLMs are going out of research labs and becoming computing tools for everybody.
However, using an LLM model such as Llama in an app involves several tasks which many people face and solve alone. We have been exploring this space and would love to continue working on it with the community. We aim to create a set of tools and building blocks that help developers build faster.
Today, we are publishing this guide to go through the steps required to run a model such as Llama 2 on your Mac using Core ML. We are also releasing alpha libraries and tools to support developers in the journey. We are calling all Swift developers interested in ML – is that all Swift developers? – to contribute with PRs, bug reports, or opinions to improve this together.
Here are two rules for working with representables:
When updating a UIView in response to a SwiftUI state change, we need to go over all the representable’s properties, but only change the UIView properties that need it.
When updating SwiftUI in response to a UIKit change, we need to make sure these updates happen asynchronously.
If we don’t follow these rules, there are a few issues we might see:
The dreaded “Modifying state during view update, this will cause undefined behavior” warning
Unnecessary redraws of our UIViewRepresentable, or even infinite loops
Strange behavior where the state and the view are a little bit out of sync
In my testing, these issues are becoming less relevant with UIKit, but are very relevant when dealing with AppKit. My guess is that UIKit components
In this post, I will detail how I moved my data out of 1Password and into iCloud Keychain and use the new Passwords preference pane introduced in macOS Monterey. I have only recently switched from 1Password to iCloud Keychain so this post will not dive into the pros and cons of the two.
I strongly advise against implementing your own crash reporter. It’s very easy to create a basic crash reporter that works well enough to debug simple problems. It’s impossible to implement a good crash reporter, one that’s reliable, binary compatible, and sufficient to debug complex problems. The bulk of this post is a low-level explanation of that impossibility.
Earlier I said “It’s impossible to implement a good crash reporter”, and I want to explain why I’m confident enough in my conclusions to use that specific word. There are two fundamental problems here:
On iOS (and the other iOS-based platforms, watchOS and tvOS) your crash reporter must run inside the crashed process. That means it can never be 100% reliable. If the process is crashing then, by definition, it’s in an undefined state. Attempting to do real work in that state is just asking for problems [1].
To get good results your crash reporter must be intimately tied to system implementation details. These can change from release to release, which invalidates the assumptions made by your crash reporter. This isn’t a problem for the Apple crash reporter because it ships with the system. However, a crash reporter that’s built in to your product is always going to be brittle.
Have you ever noticed that crash logs sometimes don't make much sense or are missing some symbols? Unlike traditional UIKit applications, Apple does not provide debug symbols (dSYMs) for SwiftUI. This means that any crash containing SwiftUI addresses in the stack trace will not be symbolicated.
We've discovered a way to symbolicate any Apple framework and want to share it with everyone.
JFrog now offers the first and only Swift binary package repository, enabling developers to use JFrog Artifactory for resolving Swift dependencies instead of enterprise source control (Git) systems. Swift developers can benefit from Artifactory’s robust binary management and the ways that it contributes to stable and efficient CI/CD, massive scalability, and securing the software supply chain..
In general, the right thing to do depends on why your class is safe:
If your class instances can be safely referenced by multiple threads because their stored properties are all immutable lets, your class can just be Sendable.
If your class instances can be safely referenced by multiple threads because in practice their stored properties are all immutable, but for some reason (e.g. a complex initialization pattern that completes before the object is shared across threads) some of the properties have to be declared as mutable vars, that is a perfectly reasonable use of @unchecked Sendable. Consider adding some sort of lifecycle assertion to your setters, e.g. a "this is immutable now" flag.
If your class instances can be safely referenced by multiple threads because their mutable storage is only actually accessed from a globally-singleton thread, your class should be associated with a global actor.
If your class instances can be safely referenced by multiple threads because their mutable storage is only actually accessed under a lock, that is a reasonable use of @unchecked Sendable. This is an important pattern that we're working on safe ways to express.
If your class instances can't generally be safely referenced by multiple threads, and in fact they aren't referenced by multiple threads and just get created, used, and destroyed on a single thread, they should not have to be Sendable at all. In this case, it's worth figuring out why you think you need to do so. It's possible that you're actually doing something dangerous, or maybe you've got some funny sort of concurrent context that Swift doesn't understand behaves like an isolated serial executor. Consider if there's an alternative way to express your pattern in a way that Swift will understand.
If your class instances can't generally be safely referenced by multiple threads, but instances do need to be moved between threads occasionally, you should not use @unchecked Sendable on the class. Instead, you should suppress the warning locally by "smuggling" the object across the sendability boundary: make a value of an @unchecked Sendable struct that holds the object, then send that instead. This is safe as long as you really do stop using the object in the original context (and only transfer it to one context at a time), and it's much better than pretending that any send is safe. This is a very important pattern that we're actively working on safe ways to express.
If your class instances can be safely referenced by multiple threads because their mutable storage is only actually accessed from one of the threads at a time, but that thread isn't globally singleton, the accesses aren't mediated by something like a lock, and you really do maintain active references on multiple threads... I mean, I'm willing to assume that you've got some justification for why this is being done safely, but this seems like a very treacherous pattern, and it's hard to imagine Swift ever finding a way to support it safely. Consider whether you can find a more structured way to express this. If not, you're going to have to just use @unchecked Sendable on the class and accept that you're losing out on concurrency safety.
There is a lot more I wanted to talk about. And I think the most important concept here is understanding how the code translates into trees and how you can then use those trees for state management, for layout, and many other things.
We’ve found success going deep in code sharing across the entire stack throughout our past experiences. As code sharing is simpler in a monorepo, we moved in that direction after many years of painful segregated code sharing practices. amo is now one single monorepo containing all projects and built using the same build system: Bazel (an open-source port of Google Blaze).
One of the interesting things is that we can visualize the safe area using an overlay and a geometry reader. We can add ignoresSafeArea to the geometry reader. Inside the geometry reader, we get access to the size of the safe area insets as well as the safe area size itself.
While it’s common to hear over-simplified rules like “Always use weak references within closures”, writing well-performing and predictable apps and systems often requires a bit more nuanced thinking than that. Like with most things within the world of software development, the best approach tends to be to throughly learn the underlying mechanics and behaviors, and then choose how to apply them within each given situation.
Streaming media apps and long-running apps that send continual updates use an ongoing stream to upload data, rather than sending a single block of data or a flat file. You can configure an instance of URLSessionUploadTask (a subclass of URLSessionTask) to work with a stream that you provide, and then fill this stream with data indefinitely.
The task gets the stream by calling your session’s delegate, so you need to create a session and set your own code as its delegate.
Clean Builds: Clean builds, also known as full builds, involve compiling the entire codebase from scratch, regardless of whether any changes have been made. To improve clean builds’ time:
Understand the Apple Haptic and Audio Pattern (AHAP) file format.
AHAP is a JSON-like file format that specifies a haptic pattern through key-value pairs, analogous to a dictionary literal, except in a text file. You add an AHAP file to your Xcode project bundle like any other file resource, such as an audio file or an image.
That's enough of a preamble. Let's get into the architecture itself. There will be dedicated articles for every aspect of Puddles and you can find a more thorough and detailed overview here or in the repository. The following is just meant to be a quick collection of the key ideas of the architecture.
Puddles suggests an architecture that separates your code base into 4 distinct layers, each with its own responsibilities and functions, encouraging a modular and maintainable project structure for your app.
This is much more like a workflow. Using strength we can rewrite any (monadic) do expression as a left-to-right workflow, with the cost of having to throw in some applications of strength to carry along all of the captured variables. It's also using a composition of arrows in the Kleisli category.
A monad with a strength function is called a strong monad. Clearly all Haskell monads are strong as I wrote strength to work with any Haskell monad. But not all monads in category theory are strong. It's a sort of hidden feature of Haskell (and the category Set) that we tend not to refer to explicitly. It could be said that we're implicitly using strength whenever we refer to earlier variables in our do expressions.
Explicit module builds are an attempt to move the compilation of textual modules into binary modules out of the Swift compiler instance that imports the module, and up into the build system as an explicit compilation step. The build system is then responsible for scheduling the compilation, checking timestamps on inputs (for incremental builds), and ensuring that all of the binary modules needed by a Swift compilation job have already been built before that compilation job executes.
Explicit module builds are meant to eliminate the problems with implicit module builds, improving parallelism, reducing redundant work among Swift compiler instances, and enabling new technologies such as distributed builds. There are a number of technologies that we are working on in the Swift compilation stack to enable explicit module builds.
Foundation’s URL loading is robust. iOS 7 brought the new URLSession architecture, making it even more robust. However, one thing that it’s never been able to do natively is multipart file uploads.
So, I wanted to talk about why I fell away from Haskell. I should say up front: this is a piece about why I left Haskell, and not about why you should. I don't think people are wrong for using Haskell, or that Haskell is bad. In fact, if I've written this piece the way I hope to write it, I would hope that people read it and come away with a desire to maybe learn Haskell themselves!
This library defines a dependently-typed finite map type. It is derived from Data.Map.Map in the containers package, but rather than (conceptually) storing pairs indexed by the first component, it stores DSums (from the dependent-sum package) indexed by tag.
I'm finally un-redacting the 6th way, and ironically, it's the app sandbox. I discovered—almost by accident—that a sandboxed app could modify files that it shouldn't be able to modify: files inside the bundle of a notarized app that were supposedly protected by App Management security.
Of course a sandboxed app has somewhat limited access to the file system, although it's notable that the /Applications folder is included within the sandbox. Regardless, the initial extent of the sandbox is not really an issue for an attacker, because a non-sandboxed app can open files in a sandboxed app, thereby extending the latter's sandbox.
To demonstrate the bypass, I've created a sample Xcode project that you can download.
Abstract: A topos is a categorical model of constructive set theory. In particular, the effective topos is the categorical `universe' of recursive mathematics. Among its objects are the modest sets, which form a set-theoretic model for polymorphism. More precisely, there is a fibration of modest sets which satisfies suitable categorical completeness properties, that make it a model for various polymorphic type theories.
The app launch experience is the first impression you make on a user. Every millisecond they wait for your app to start is valuable time they could spend elsewhere. If your app has high engagement and is used multiple times a day then users have to wait for launch over and over. Apple recommends the first frame be drawn in under 400ms. This ensures your app is ready to be used when Springboard’s app open animation finishes.
With only 400ms to spare, developers need to be very careful not to accidentally increase app startup time. However, app launch is such a complicated process with so many moving parts that it’s difficult to know what exactly contributes to it. I started diving deeper into the relationship between app binary size and startup time while working on Emerge, the app size profiler. In this post, I’ll demystify one of the more esoteric aspects of app launch and show you how Swift reference types contribute to the binary size and slower app start times.
Maybe a bit dramatic, but I don't think it would be far from the truth to say that all apps have some dead code. There are quite a few benefits to removing this code. Unsurprisingly, dead code can affect app size and compile time, but excessive dead code also introduces complexity to a codebase and slows developer productivity.
Let's say you're updating a function and need to modify all call sites to work with the new function signature. This time is wasted if the call sites are dead. Dead code inherently increases the line count of a codebase, which is correlated to the number of bugs.
There are even performance implications for having dead code take up memory in your iOS app, which I’ve mentioned in articles about fixups and order files. Emerge helps reduce the complexity of apps and infrastructure, including by finding dead code such as protocols without any conformances. This is done with static analysis.
Reaper, Emerge’s new iOS framework, goes beyond static analysis by detecting unused code at runtime. This extra dead code detection helps to build better, simpler apps.
In this post, we’ll look at what dead code is and how runtime detection expands the amount we can find.
Reaper is an SDK that you can put into your production or alpha users' apps to report which Swift and Objective-C classes were used for each user session. We take those reports and generate a list of all the classes in the binary, for each version of your app. The SDK will detect unused classes within the main binary specifically, not in dynamic frameworks. It's easy to integrate, either as a standalone binary or as a Cocoapod, and adds very little overhead to your app.
Reaper supports iOS 15, 16, and 17. It supports all classes written in Objective-C and most classes written in Swift.
It is tempting to build abstractions so developers have to do less and build more. However, this can easily end up causing frustrations with developers if not done right.
Whenever I take a library or a framework for a test drive, work through their “Getting Started” guide or browse the example code, I try to observe my own reaction. Whenever something makes me go “this feels wrong”, I make note of it and try to distill what I would have done differently. In this blog post, I try to figure out what I think makes a good developer experience.
Be forewarned: Despite its title, this book contains absolutely no helpful advice for obtaining refunds.
Instead, a cast of fictional characters and Ritcher’s alter egos regale you with adventures from his storied life. Almost none of it is true.
You’ll read about how he served in the Belgian National Guard, launched the Burger Chef restaurant chain, hosted the hit TV show Animal Autopsy and spent several years married to Kirsten Dunst during her two terms as governor of Kentucky.
The book is full of secret nuggets, including another book called Presidential Fun Facts & Trivia, information on bird spotting, proper table settings and a full 90 pages of About the Author (which includes a little detour called About the Author’s Dogs.
How to Get Your Fucking Money Back is an absurd, heavily footnoted, 154-page ride that gleefully disregards reality, the rules of storytelling and, occasionally, some common respect for the English language.
Terraform enables you to safely and predictably create, change, and improve infrastructure. This is an open-source fork of Hashicorp's Terraform that keeps the MPL license, following Hashicorp's announcing change of license to BSL. The fork is created and maintained by Digger.dev, an open-source CI runner for IaC.
GRDB is a production-ready database library for Swift, based on SQLite.
It provides raw access to SQL and advanced SQLite features, because one sometimes enjoys a sharp tool. It has robust concurrency primitives, so that multi-threaded applications can efficiently use their databases. It grants your application models with persistence and fetching methods, so that you don't have to deal with SQL and raw database rows when you don't want to.
See Why Adopt GRDB? 103 if you are looking for your favorite database library.
This forum is intended to answer community questions, raise your interest, share stories, experience, and best practices.
In the constantly evolving world of iOS development, SwiftUI has undeniably brought forth a revolution in how we approach interface design. The introduction of the ViewThatFits struct simplifies adaptive layout construction, eradicating the tedious task of handling different screen sizes and frames manually.
By comprehending the nuances between proposed and ideal sizes, developers can leverage the power of SwiftUI to automatically select the most fitting view based on its parent’s dimensions. This not only aids in the creation of more responsive apps but also reduces redundancy in code, promoting cleaner, more maintainable projects.
The notation used to describe type systems varies from presentation to presentation, so giving a comprehensive overview is impossible. However, most presentations share a large, common subset, so this answer will attempt to provide a foundation of enough of the basics to understand variations on the common theme.
Allow each engineer to provision their own "staging" environment using plain Terraform files.
Layerform helps engineers create reusable environment stacks using plain Terraform files (the actual OSS version). Ideal for multiple "staging" environments.
Compositionality describes and quantifies how complex things can be assembled out of simpler parts.
Compositionality (ISSN 2631-4444) is an open-access, arXiv-overlay journal for research using compositional ideas in any discipline. For more information, see About.
With the Mermaid app, you can easily create and import diagrams with Markdown-inspired syntax. Automate the process of generating complex diagrams without worrying about design and layout.
Key Features:
Create diagrams with the popular Mermaid syntax
Easily design any kind of diagram like, Flowcharts, Sequence diagrams, ER diagrams and even C4 architecture
Copy code from other sources like GitHub or Notion into Miro
Swift 5.5 introduced mechanisms to eliminate data races from the language, including the Sendable protocol (SE-0302) to indicate which types have values that can safely be used across task and actor boundaries, and global actors (SE-0316) to help ensure proper synchronization with (e.g.) the main actor. However, Swift 5.5 does not fully enforce Sendable nor all uses of the main actor because interacting with modules which have not been updated for Swift Concurrency was found to be too onerous. We propose adding features to help developers migrate their code to support concurrency and interoperate with other modules that have not yet adopted it, providing a smooth path for the Swift ecosystem to eliminate data races.
Get ready to dive deep into the inner workings of the Objective-C language and runtime! Each post delves into a specific aspect of the language and explores the details of its implementation. I hope you’ll find this valuable to demystify the language, tackle tricky bugs, and optimize your code for performance.
This guide provides a brief overview of Swift Package Manager plugins, describes how a package can make use of plugins, and shows how to get started writing your own plugins.
Flipper Zero is a portable multi-tool for pentesters and geeks in a toy-like body. It loves hacking digital stuff, such as radio protocols, access control systems, hardware and more. It's fully open-source and customizable, so you can extend it in whatever way you like.
Flipper Zero is a tiny piece of hardware with a curious personality of a cyber-dolphin. It can interact with digital systems in real life and grow while you use it. Explore any kind of access control system, RFID, radio protocols, and debug hardware using GPIO pins
> [!NOTE]
> Highlights information that users should take into account, even when skimming.
> [!IMPORTANT]
> Crucial information necessary for users to succeed.
> [!WARNING]
> Critical content demanding immediate user attention due to potential risks.
This document is a guide to understanding, diagnosing and reporting compilation-performance problems in the swift compiler. That is: the speed at which the compiler compiles code, not the speed at which that code runs.
While this guide is lengthy, it should all be relatively straightforward. Performance analysis is largely a matter of patience, thoroughness and perseverance, measuring carefully and consistently, and gradually eliminating noise and focusing on a signal.
Compositionality is at the heart of computer science and several other areas of applied category theory such as computational linguistics, categorical quantum mechanics, interpretable AI, dynamical systems, compositional game theory, and Petri nets. However, the meaning of the term seems to vary across the many different applications. This work contributes to understanding, and in particular qualifying, different kinds of compositionality. Formally, we introduce invariants of categories that we call zeroth and first homotopy posets, generalising in a precise sense the π0 and π1 of a groupoid. These posets can be used to obtain a qualitative description of how far an object is from being terminal and a morphism is from being iso. In the context of applied category theory, this formal machinery gives us a way to qualitatively describe the "failures of compositionality", seen as failures of certain (op)lax functors to be strong, by classifying obstructions to the (op)laxators being isomorphisms. Failure of compositionality, for example for the interpretation of a categorical syntax in a semantic universe, can both be a bad thing and a good thing, which we illustrate by respective examples in graph theory and quantum theory.
The Fifth International Conference on Applied Category Theory took place at the University of Strathclyde on 18−22 July 2022, following the previous meetings at Leiden (2018), Oxford (2019), MIT (2020, fully online), and Cambridge (2021). It was preceded by the Adjoint School 2022 (11−15 July), a collaborative research event in which junior researchers worked on cutting-edge topics under the mentorship of experts. The conference comprised 59 contributed talks, a poster session, an industry showcase session, and a session where junior researchers who had attended the Adjoint School presented the results of their research at the school. Information regarding the conference may be found at https://msp.cis.strath.ac.uk/act2022.
This proposal introduces function body macros, which are attached macros that can create or augment a function (including initializers, deinitializers, and accessors) with a new body.
In this post you will learn how to configure your Swift development environment for Linux using Dev Containers VSCode extension. This unlocks the ability to build, run, and debug Swift apps on Linux.
Ensure your use of covered API is consistent with policy.
Some APIs that your app uses to deliver its core functionality — in code you write or included in a third-party SDK — have the potential of being misused to access device signals to try to identify the device or user, also known as fingerprinting. Regardless of whether a user gives your app permission to track, fingerprinting is not allowed. Describe the reasons your app or third-party SDK on iOS, iPadOS, tvOS, visionOS, or watchOS uses these APIs, and check that your app or third-party SDK only uses the APIs for the expected reasons.
For each category of required reason API that your app or third-party SDK uses, add a dictionary to the NSPrivacyAccessedAPITypes array in your app or third-party SDK’s privacy manifest file that reports the reasons your app uses the API category. If you use the API in your app’s code, then you need to report the API in your app’s privacy manifest file. If you use the API in your third-party SDK’s code, then you need to report the API in your third-party SDK’s privacy manifest file. Your third-party SDK can’t rely on the privacy manifest files for apps that link the third-party SDK, or those of other third-party SDKs the app links, to report your third-party SDK’s use of required reasons API.
We are excited to share the news of the Lean Focused Research Organization (FRO)! A new nonprofit dedicated to advancing the Formal Mathematics revolution, we aim to tackle the challenges of scalability, usability, and proof automation in the Lean proof assistant. Our 5-year mission is to empower Lean towards self-sustainability.
Implementing push notifications in SwiftUI is a potent tool for boosting user engagement and providing real-time updates. The UNUserNotificationCenter is the hero of our push notification story, facilitating every aspect of notifications from scheduling to user interaction.
The vast variety of authorization options in Swift, such as alert, badge, sound, CarPlay, provisional, critical alert, and providesAppNotificationSettings, allows developers to create a custom user experience, tailoring notifications to specific app needs and user preferences. However, it’s important to implement these options judiciously to ensure a seamless and unobtrusive user experience.
Some of the development of "Set-Theoretic and Type-Theoretic Ordinals Coincide" is carried out but using Gylterud's construction of the cumulative hierarchy 𝕍 as iterative sets, instead of (axiomatically) working with the higher inductive presentation. The type 𝕆 of hereditarily transitive sets is the type of iterative ordinals and corresponds to 𝕍ᵒʳᵈ in the original development Ordinals.CumulativeHierarchy.
Swift’s FormatStyle and ParseableFormatStyle are the easiest way to convert Foundation data types to and from localized strings. Unfortunately Apple hasn’t done a great job in documenting just what it can do, or how to use them.
Developer Duck is an AI-powered programming assistant that helps you with your programming tasks. Including features like code suggestions, completion, analysis, and refactoring, Developer Duck is faster than searching the web. Try it for free and put it to the test.
Macros are a mechanism for running JavaScript functions at bundle-time. The value returned from these functions are directly inlined into your bundle.
For small things where you would otherwise have a one-off build script, bundle-time code execution can be easier to maintain. It lives with the rest of your code, it runs with the rest of the build, it is automatically paralellized, and if it fails, the build fails too.
The most familiar, explained in any textbook approach to dealing with variable references in higher-order programs is variable binding environment: an association of variable names with their bound values. In an interpreter (the eval function), the environment is one of its arguments. Compiled functions receive the environment as an extra argument. Terms with variables are commonly thought to mean functions from the environment to the value domain.
This article demonstrates a different, unconventional approach to variable references, coming from the observation that a (let-)bound variable can only be used while the control remains within the corresponding let-expression body. In interpreter terms, a variable may be accessed only while the interpreter is still handling the let-form, which remains on the interpreter stack. Therefore, to find the value associated with the bound variable we search the stack — or, better, just point to the right place on the stack that stores that value.
That is a very attractive idea, which seems first to come to mind when implementing higher-order languages. It was indeed first that came to mind to J. McCarthy and A. Turing. Alas, the idea — now called dynamic binding — is flawed (as Turing soon realized). To regard functions as first-class, to be able to pass them as arguments and return as results, lexical binding is indispensable.
This article demonstrates that, perhaps surprisingly, lexical binding can be implemented via dynamic binding, in strict and lazy settings.
The main advantage of this alternative approach is making variables a modular feature: variables and (first-class) functions can be introduced to a first-order language without any changes to the latter, without any need to re-write the interpretation of the first-order fragment (numbers, strings, etc.) to pass the environment. We may write extensible interpreters and compilers. Another application is the surprisingly simple implementation of staging.
Dynamic binding can be implemented in many ways. One particular implementation, in terms of delimited control, turns out particularly insightful. Incidentally, it demonstrates the need for multi-shot (non-affine) delimited continuations — perhaps the first non-trivial use of such delimited continuations aside from non-determinism.
Another insight is that a let-expression — often considered a mere syntax sugar in lambda calculus — turns out more fundamental than the lambda abstraction.
If you've ever looked at the SVG code for an icon before, you might have noticed that they're usually made up of a bunch of path elements, each with a cryptic d attribute.
The Uxn ecosystem is a little personal computing stack, created to host ours tools and games, programmable in its own unique assembly language.
It was designed with an implementation-first mindset with a focus on creating portable graphical applications, the distribution of Uxn projects is akin to sharing game roms for any classic console emulator.
This paper addresses the question “what is a digital circuit?” in relation to the fundamentally analog nature of actual (physical) circuits. A simple informal definition is given and then formalized in the proof assistant Agda. At the heart of this definition is the timely embedding of discrete information in temporally continuous signals. Once this embedding is defined (in constructive logic, i.e., type theory), it is extended in a generic fashion from one signal to many and from simple boolean operations (logic gates) to arbitrarily sophisticated sequential and parallel compositions, i.e., to computational circuits.
Rather than constructing circuits and then trying to prove their correctness, a compositionally correct methodology maintains specification, implementation, timing, and correctness proofs at every step. Compositionality of each aspect and of their combination is supported by a single, shared algebraic vocabulary and related by homomorphisms. After formally defining and proving these notions, a few key transformations are applied to reveal the linearity of circuit timing (over a suitable semiring), thus enabling practical, modular, and fully verified timing analysis as linear maps over higher-dimensional time intervals.
An emphasis throughout the paper is simplicity and generality of specification, minimizing circuit-specific definitions and proofs while highlighting a broadly applicable methodology of scalable, compositionally correct engineering through simple denotations and homomorphisms.
The NSItemProvider class in Foundation is a powerful abstraction for making data available across processes that are otherwise isolated from one another. I hope this post can be a one-stop reference for developers who want a solid understanding how item providers work, and how to use the API in a modern way.
Sockets are a way to enable inter-process communication between programs running on a server, or between programs running on separate servers. Communication between servers relies on network sockets, which use the Internet Protocol (IP) to encapsulate and handle sending and receiving data.
Network sockets on both clients and servers are referred to by their socket address. An address is a unique combination of a transport protocol like the Transmission Control Protocol (TCP) or User Datagram Protocol (UDP), an IP address, and a port number.
In this tutorial you will learn about the following different types of sockets that are used for inter-process communication:
Stream sockets, which use TCP as their underlying transport protocol
Datagram sockets, which use UDP as their underlying transport protocol
Unix Domain Sockets, which use local files to send and receive data instead of network interfaces and IP packets.
In each section of this tutorial you will also learn how to enumerate the respective socket types on a Linux system. You’ll examine each type of socket using a variety of command line tools.
SwiftUI is a reactive framework where the data drives the UI. In 2019, I wrote a post detailing how I manage the various forms of data flow through a SwiftUI app, and with the help of others in the community, I iterated over this until I had a good understanding of the concepts and which methods you should use when. In 2021, I updated the post to cover the minor changes, but there have been no major modifications since then.
At WWDC 2023, things changed a lot! With the introduction of Swift macros, the SwiftUI team was able to reduce the number of property wrappers need to send data around, and remove a lot of boilerplate code.
You could build an even closer analog in terms of async, since with*Continuation gives you a delimited continuation for the task (albeit a one-shot continuation, unlike the multi-shot continuations that "pure" algebraic effect systems provide in functional languages). You could for example store the effect handler in task-local state, and have the effect operations be functions that use withCheckedContinuation to access the current handler and resume the task with the result:
classState{varvalue:Int}@TaskLocalvarstateHandler:State?
// Run the block with a state handler
func with(state:State, _ body:()asyncthrows->R)rethrows->R{
stateHandler = state
defer{ stateHandler =nil}returntryawaitbody()}
// Access the current state
varstateValue:Int{get async{returnwithCheckedContinuation{ cc in
cc.resume(returning: stateHandler!.value)}}}
macOS is fortunate to have access to the huge arsenal of standard Unix tools. There are also a good number of macOS-specific command-line utilities that provide unique macOS functionality. To view the full documentation for any of these commands, run man <command>.
A value is what you can return from a call, pass as a (non-inout) argument, and so on. Ignoring reference types for a second, you can talk about values independently of concepts like memory. Fundamental types can be thought of as fundamental values, like particular integers and strings, and structs can broken down recursively into the component values they store in their stored properties. For example, I might say that a particular value is Ball(diameter: .03, color: Color.orange). Here I've written the value as if I were calling a memberwise initializer with all the values of the stored properties; this works to denote the value even if I didn't actually build it that way, or even if my type doesn't actually have a memberwise initializer.
A location is part of the memory of the abstract machine. Every location has a type, and it stores a value of its type. For example, when you declare a mutable local variable, a new location is created dynamically when that variable comes into scope, and it is destroyed when the variable goes out of scope (and all the captures of it go away). Creating a location of a struct type means creating locations for all the stored properties of that struct.
AI is fundamentally changing the way we live, work, and build software. It has the potential to be the biggest platform shift since the iphone and mobile.
With mobile we learned the painful lesson of the Apple app-store, controlled by a single monopolistic company, stifling innovation and entrepreneurship.
AI, our new platform, needs it's own app-store. An unrestricted app-store built upon the open web and the OpenAPI specification.
We engineers currently have a slim chance of creating this app-store layer before some large corporation does it. We must seize this chance.
That's why we're building openpm.ai, an open source package-manager for OpenAPI files. AIs can use consume packages from openpm in a similar fashion to how ChatGPT plugins work. Ultimately, AIs can use openpm to discover and interact with the world via APIs.
Everything we release is under the MIT license. We will never charge a transaction fee for our services. We will never wield editorial control. We will only remove packages that are scams or illegal under US law. At any point you can choose to export all of our packages and run them on your own server.
Design is not just a way to make sense of the world; design helps the world make sense. It takes fierce optimism to face challenges and see them as opportunities for learning, growth and change.
When people wear Apple Vision Pro, they enter an infinite 3D space where they can engage with your app or game while staying connected to their surroundings.
As you begin designing your app or game for visionOS, start by understanding the fundamental device characteristics and patterns that distinguish the platform. Use these characteristics and patterns to inform your design decisions and help you create immersive and engaging experiences.
Space. Apple Vision Pro offers a limitless canvas where people can view virtual content like windows, volumes, and 3D objects, and choose to enter deeply immersive experiences that can transport them to different places.
Immersion. In a visionOS app, people can fluidly transition between different levels of immersion. By default, an app launches in the Shared Space where multiple apps can run side-by-side and people can open, close, and relocate windows. People can also choose to transition an app to a Full Space, where it’s the only app running. While in a Full Space app, people can view 3D content blended with their surroundings, open a portal to view another place, or enter a different world.
Passthrough.Passthrough provides live video from the device’s external cameras, and helps people interact with virtual content while also seeing their actual surroundings. When people want to see more or less of their surroundings, they use the Digital Crown to control the amount of passthrough.
Spatial Audio. Vision Pro combines acoustic and visual-sensing technologies to model the sonic characteristics of a person’s surroundings, automatically making audio sound natural in their space. When an app receives a person’s permission to access information about their surroundings, it can fine-tune Spatial Audio to bring custom experiences to life.
Focus and gestures. In general, people interact with Vision Pro using their eyes and hands. People perform most actions by looking at a virtual object to bring focus to it and making an indirect gesture, like a tap, to activate it. People can also use a direct gesture to interact with a virtual object by touching it with a finger.
Ergonomics. While wearing Vision Pro, people rely entirely on the device’s cameras for everything they see, both real and virtual, so maintaining visual comfort is paramount. The system helps maintain comfort by automatically placing content so it’s relative to the wearer’s head, regardless of the person’s height or whether they’re sitting, standing, or lying down. Because visionOS brings content to people — instead of making people move to reach the content — people can remain at rest while engaging with apps and games.
Accessibility. Apple Vision Pro supports accessibility technologies like VoiceOver, Switch Control, Dwell Control, Guided Access, Head Pointer, and many more, so people can use the interactions that work for them. In visionOS, as in all platforms, system-provided UI components build in accessibility support by default, while system frameworks give you ways to enhance the accessibility of your app or game.
Apple’s initial visionOS design kit for Figma contains a comprehensive set of UI components, views, system interfaces, text styles, color styles, and materials. All of the core ingredients you need to quickly create highly realistic visionOS app designs.
If you have requests, find bugs, or have other feedback for us, please use Feedback Assistant. Select Developer Tools > Apple Design Resources.
Important: Make sure to install the latest version of SF Symbols before using this library.
We propose introducing a pair of new attributes, @inlinable and @usableFromInline. The @inlinable attribute exports the body of a function as part of a module's interface, making it available to the optimizer when referenced from other modules. The @usableFromInline attribute marks an internal declaration as being part of the binary interface of a module, allowing it to be used from @inlinable code without exposing it as part of the module's source interface.
BuildSettingExtractor is a free, open-source utility that extracts build settings from an Xcode project into a set of xcconfig files.
When you’re moving Xcode build settings out of your project file and into xcconfig files, this handy utility makes that initial move a lot easier. It’s also an easy way for the curious to take a look at the build settings in a project without fear of accidentally changing them.
Every question + answer in 2023’s #swiftui WWDC Digital Lounge organised for an easy read.
For brevity, any requests from Apple to file a feedback have not been included in the responses. So, it’s worth emphasising that the SwiftUI team encouraged everyone to file a feedback for any unsupported behaviour, because this helps them prioritise their backlog. So, if you can’t get SwiftUI to do x today, please let them know. When you do, describe your use case so they have a clear idea what you’re trying to accomplish (this will help them understand better the general merit of the feature request).
SwiftUI lets us style portions of text by interpolating Text inside another Text and applying available text modifiers, such as foregroundColor() or font().
Starting from iOS 17 we can apply more intricate styling to ranges within a Text view with foregroundStyle().
Learn Haskell abstractions the easy way — with real-world examples and context. You'll learn common patterns in Haskell, how to implement them yourself, and what their benefits and drawbacks are.
This short book is meant for anyone who already has a basic working understanding of Haskell, but is looking for intermediate-level knowledge.
This framework provides a means for developers to create format readers and video decoders for media that the system doesn’t natively support.
MediaExtension format readers encapsulate media assets that the system doesn’t natively support so that the system can recognize them. MediaExtension video decoders decode video formats that the system doesn’t natively support. Developers need to build format readers and video decoders as ExtensionKit bundles and embed them in a host app. Once a user installs and runs the host app, the embedded extensions become available to any app on the user’s system that opts in to using them.
Create a more immediate user experience by removing hangs and hitches from your app’s user interface.
An app that responds instantly to users’ interactions gives an impression of supporting their workflow. When the app responds to gestures and taps in real time, it creates an experience for users that they’re directly manipulating the objects on the screen. Apps with a noticeable delay in user interaction (a hang) or movement on screen that appears to jump (a hitch), shatter that illusion. This leaves the user wondering whether the app is working correctly. To avoid hangs and hitches, keep the following rough thresholds in mind as you develop and test your app.
100 ms is the threshold for delays in direct user interaction. If a delay in user interaction becomes longer than 100 ms, it starts to become noticeable and causes a hang. A shorter delay is rarely noticeable.
5 ms is the threshold to achieve fluid motion on-screen. For fluid, uninterrupted motion, a new frame needs to be ready whenever the screen updates. On Apple devices, this can be as often as 120 times per second, or every 8.3 ms. Depending on system conditions and other work that your app performs, you might not have the full 8.3 ms to prepare your next screen update. If the work that your app needs to perform to update the screen is less than 5 ms, the update is usually ready in time. If it takes longer, you need to take a closer look at the specific devices you’re targeting and the display refresh rate your app needs to support.
This article describes several best practices to help you avoid introducing hangs and hitches in your app, as well as multiple tools to help you detect and analyze these types of responsiveness issues.
It's all about the task tree: Find out how structured concurrency can help your apps manage automatic task cancellation, task priority propagation, and useful task-local value patterns. Learn how to manage resources in your app with useful patterns and the latest task group APIs. We'll show you how you can leverage the power of the task tree and task-local values to gain insight into distributed systems. Before watching, review the basics of Swift Concurrency and structured concurrency by checking out “Swift concurrency: Behind the scenes” and “Explore structured concurrency in Swift” from WWDC21.
Discover how you can use Swift macros to make your codebase more expressive and easier to read. Code along as we explore how macros can help you avoid writing repetitive code and find out how to use them in your app. We'll share the building blocks of a macro, show you how to test it, and take you through how you can emit compilation errors from macros.
Discover how Swift macros can help you reduce boilerplate in your codebase and adopt complex features more easily. Learn how macros can analyze code, emit rich compiler errors to guide developers towards correct usage, and generate new code that is automatically incorporated back into your project. We'll also take you through important concepts like macro roles, compiler plugins, and syntax trees.
We’ve learned a lot from building other compiler and programming language systems (e.g., Clang/C++, Swift, etc) over the last 20+ years. From that experience, we are building Mojo to:
Be a fully compatible superset of Python, benefiting from its easy to read and understandable syntax and enabling its large community of developers to already know how to write Mojo!
Support system programming features and hardware accelerators that extend the performance and reach of Python into new domains as we move into a new parallel-computing world.
Be fully integrated with the existing Python ecosystem, extending and benefiting from all of the existing packages. We will also build seamless C and C++ interoperability to lift (and benefit from) work in those communities over time.
Provide a new high-performance heterogeneous compiler and runtime implementation that benefits from state-of-the-art techniques.
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> As a consequence, we believe Mojo fits the perfect sweet spot for LLMs to generate and output highly scalable code, because it combines the human readability and usability of Python, but extends it with powerful lower-level systems features that enable it to scale across more hardware and drive the next set of the world’s applications and use cases.
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> We think LLMs will continue to unlock creativity and productivity across many languages, but we also believe Mojo will be well prepared to lift collaborative software development to the next level and bring programming into new frontiers.
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Explore SwiftUI's powerful animation capabilities and find out how these features work together to produce impressive visual effects. Learn how SwiftUI refreshes the rendering of a view, determines what to animate, interpolates values over time, and propagates context for the current transaction.
Create connections between your app’s data model and views.
A SwiftUI app can display data that people can change using the app’s user interface (UI). To manage that data, an app creates a data model, which is a custom type that represents the data. A data model provides separation between the data and the views that interact with the data. This separation promotes modularity, improves testability, and helps make it easier to reason about how the app works.
Keeping the model data (that is, an instance of a data model) in sync with what appears on the screen can be challenging, especially when the data appears in multiple views of the UI at the same time.
SwiftUI helps keep your app’s UI up to date with changes made to the data thanks to Observation. With Observation, a view in SwiftUI can form dependencies on observable data models and update the UI when data changes.
Learn how you can build a mental model for performance in SwiftUI and write faster, more efficient code. We'll share some of the common causes behind performance issues and help you triage hangs and hitches in SwiftUI to create more responsive views in your app.
Apple’s first official design kit for Figma contains a comprehensive set of components, views, system interfaces, text styles, color styles, materials, and layout guides. All the core ingredients you need to quickly create highly realistic iOS and iPadOS apps designs.
Some key features include:
Comprehensive set of components, from Alerts to Widgets and everything in between
Home Screen and Lock Screen widget templates
Notification design templates
Templates for tabbed apps, parent / child apps, split views, and sheets
Full dynamic type chart with accessibility sizes
Built in iOS system colors, materials, text styles and vibrancy effects
Fundamental algorithms such as sorting or hashing are used trillions of times on any given day1. As demand for computation grows, it has become critical for these algorithms to be as performant as possible. Whereas remarkable progress has been achieved in the past2, making further improvements on the efficiency of these routines has proved challenging for both human scientists and computational approaches. Here we show how artificial intelligence can go beyond the current state of the art by discovering hitherto unknown routines. To realize this, we formulated the task of finding a better sorting routine as a single-player game. We then trained a new deep reinforcement learning agent, AlphaDev, to play this game. AlphaDev discovered small sorting algorithms from scratch that outperformed previously known human benchmarks. These algorithms have been integrated into the LLVM standard C++ sort library3. This change to this part of the sort library represents the replacement of a component with an algorithm that has been automatically discovered using reinforcement learning. We also present results in extra domains, showcasing the generality of the approach.
New algorithms will transform the foundations of computing
Digital society is driving increasing demand for computation, and energy use. For the last five decades, we relied on improvements in hardware to keep pace. But as microchips approach their physical limits, it’s critical to improve the code that runs on them to make computing more powerful and sustainable. This is especially important for the algorithms that make up the code running trillions of times a day.
In our paper published today in Nature, we introduce AlphaDev, an artificial intelligence (AI) system that uses reinforcement learning to discover enhanced computer science algorithms – surpassing those honed by scientists and engineers over decades.
AlphaDev uncovered a faster algorithm for sorting, a method for ordering data. Billions of people use these algorithms everyday without realising it. They underpin everything from ranking online search results and social posts to how data is processed on computers and phones. Generating better algorithms using AI will transform how we program computers and impact all aspects of our increasingly digital society.
By open sourcing our new sorting algorithms in the main C++ library, millions of developers and companies around the world now use it on AI applications across industries from cloud computing and online shopping to supply chain management. This is the first change to this part of the sorting library in over a decade and the first time an algorithm designed through reinforcement learning has been added to this library. We see this as an important stepping stone for using AI to optimise the world’s code, one algorithm at a time.
Read contactless physical and digital wallet cards using your iPhone.
The ProximityReader framework supports Tap to Pay on iPhone, which allows a person’s iPhone to act as a point-of-sale device without additional hardware. ProximityReader also supports the reading of loyalty cards from the Wallet app. Use this framework to initiate the payment process from your app.
The use of this framework requires you to coordinate with a participating payment service provider that is Level 3 certified. Contact your payment provider and work with them to set up a workflow for handling payments. When you’re ready, contact Apple and request the entitlement you need to integrate Tap to Pay on iPhone support into your app. For information on requesting this entitlement, see Setting up the entitlement for Tap to Pay on iPhone.
Apple’s library technology has a long and glorious history, dating all the way back to the origins of Unix. This does, however, mean that it can be a bit confusing to newcomers. This is my attempt to clarify some terminology.
Use mergeable dynamic libraries to get app launch times similar to static linking in release builds, without losing dynamically linked build times in debug builds.
For those who don’t follow Swift’s development, ABI stability has been one of its most ambitious projects and possibly it’s defining feature, and it finally shipped in Swift 5. The result is something I find endlessly fascinating, because I think Swift has pushed the notion of ABI stability farther than any language without much compromise.
Swift parameter packs are a powerful tool to expand what is possible in your generic code while also enabling you to simplify common generic patterns. We'll show you how to abstract over types as well as the number of arguments in generic code and simplify common generic patterns to avoid overloads. To get the most out of this session, we recommend first checking out “Embrace Swift generics" from WWDC22.
Discover how mergeable libraries combine the best parts of static and dynamic libraries to help improve your app's productivity and runtime performance. Learn how you can enable faster development while shipping the smallest app. We'll show you how to adopt mergeable libraries in Xcode 15 and share best practices for working with your code.
Discover the debug console in Xcode 15 and learn how you can improve your diagnostic experience through logging. Explore how you can navigate your logs easily and efficiently using advanced filtering and improved visualization. We'll also show you how to use the dwim-print command to evaluate expressions in your code while debugging.
Xcode 15 beta includes SDKs for iOS 17, iPadOS 17, tvOS 17, watchOS 10, and macOS 14. The Xcode 15 beta release supports on-device debugging in iOS 12 and later, tvOS 12 and later, and watchOS 4 and later. Xcode 15 beta requires a Mac running macOS Ventura 13.3 or later.
Write your model code declaratively to add managed persistence and automatic iCloud sync.
Combining Core Data’s proven persistence technology and Swift’s modern concurrency features, SwiftData enables you to add persistence to your app quickly, with minimal code and no external dependencies. Using modern language features like macros, SwiftData enables you to write code that is fast, efficient, and safe, enabling you to describe the entire model layer (or object graph) for your app. The framework handles storing the underlying model data, and optionally, syncing that data across multiple devices.
SwiftData has uses beyond persisting locally created content. For example, an app that fetches data from a remote web service might use SwiftData to implement a lightweight caching mechanism and provide limited offline functionality.
To display models in a SwiftUI view, use the Query property wrapper and specify a predicate or fetch descriptor. SwiftData performs the fetch when the view appears, and tells SwiftUI about any subsequent changes to the fetched models so the view can accordingly. You can access the model context in any SwiftUI view using the modelContext environment value, and specify a particular model container or context for a view with the modelContainer(_:) and modelContext(_:) view modifiers.
As your app’s model layer evolves, SwiftData performs automatic migrations of the underlying model data so it remains in a consistent state. If the aggregate changes between two versions of the model layer exceed the capabilities of automatic migrations, use Schema and SchemaMigrationPlan to participate in those migrations and help them complete successfully.
Create an app with persistent data, interactive widgets, and an all new in-app purchase experience.
Backyard Birds offers a rich environment in which you can watch the birds that visit your backyard garden. You can monitor their water and food supply to ensure they always have fresh water and plenty to eat, or upgrade the game using in-app purchase to provide tastier food for the birds to eat.
The sample implements its data model using SwiftData for persistence, and integrates seamlessly with SwiftUI using the Observable protocol. The game’s widgets implement App Intents for interactive and configurable widgets. The in-app purchase experience uses the ProductView and SubscriptionStoreView from StoreKit.
You can access the source code for this sample on GitHub.
Make responsive apps that update the presentation when underlying data changes.
Observation provides a robust, type-safe, and performant implementation of the observer design pattern in Swift. This pattern allows an observable object to maintain a list of observers and notify them of specific or general state changes. This has the advantages of not directly coupling objects together and allowing implicit distribution of updates across potential multiple observers.
The Observation frameworks provides the following capabilities:
Marking a type as observable
Tracking changes within an instance of an observable type
Observing and utilizing those changes elsewhere, such as in an app’s user interface
To declare a type as observable, attach the Observable macro to the type declaration. This macro declares and implements conformance to the Observable protocol to the type at compile time.
Use macros to generate repetitive code at compile time.
Swift macros help you avoid writing repetitive code in Swift by generating that part of your source code at compile time. Calling a macro is always additive: The macro adds new code alongside the code that you wrote, but never modifies or deletes code that’s already part of your project.
Many libraries provide macros, including the Swift standard library and many frameworks. You can also write your own macros.
Because macros generate Swift code, you use the same tools for development and debugging, regardless of whether your code uses macros
Unfair locks are low-level locks that block efficiently on contention. They’re useful for protecting code that loads stored resources. However, it’s unsafe to use os_unfair_lock from Swift because it’s a value type and, therefore, doesn’t have a stable memory address. That means when you call os_unfair_lock_lock or os_unfair_lock_unlock and pass a lock object using the & operator, the system may lock or unlock the wrong object.
Instead, use OSAllocatedUnfairLock, which avoids that pitfall because it doesn’t function as a value type, despite being a structure. All copied instances of an OSAllocatedUnfairLock control the same underlying lock allocation.
I'm not claiming that anything I say in this post is novel. It definitely shares various aspects of well-known software engineering or management practices. I'm just sharing the way I approach the larger technical work that I do and why I do it this way.
In software, quality isn’t just about whether the product “works” or is “performant”. Quality is about how easy it is to add new features, and how effectively new team members can understand and inherit the code. Do the abstractions you’ve introduced make sense within the domain? Is the complexity you’ve introduced through your abstractions actually justified by the problems it solves? Or have you merged groups of functionality together into massive core classes simply to remove the amount of repeated lines of code, regardless of whether those lines may need to diverge in the future? Despite our primal urges to DRY (don’t-repeat-yourself) up our code, repeated code is not itself a sin. If two pieces of repeated logic always change in tandem, then they should be unified. If two pieces of code change independently but happen right now to have the same logic, then they should not be unified.
The guiding principle of an abstraction should always be “Does this make the code easier to work with and understand?” The introduction of complexity is only ever justified if it solves for even greater complexity.
Apple released the Network framework in iOS 12, macOS 10.14. It includes a NWPathMonitor that is now the preferred way to monitor changes to network status. The three steps to monitor network changes:
Create a NWPathMonitor.
Call the start method of the path monitor passing a queue that will receive path change events.
Anyone with a good idea can help shape the future features and direction of the language. To reach the best possible solution to a problem, we discuss and iterate on ideas in a public forum. Once a proposal is refined and approved, it becomes a release goal, and is tracked as a feature of an upcoming version of Swift.
To support this process, the Swift Evolution repository collects the goals for the upcoming major and minor releases (as defined by the core team) as well as proposals for changes to Swift. The Swift evolution process document details how ideas are proposed, discussed, reviewed, and eventually accepted into upcoming releases.
Below is a list of all the current and upcoming proposal reviews.
Functional logic languages have a rich literature, but it is tricky to give them a satisfying semantics. In this paper we describe the Verse calculus, VC, a new core calculus for functional logical programming. Our main contribution is to equip VC with a small-step rewrite semantics, so that we can reason about a VC program in the same way as one does with lambda calculus; that is, by applying successive rewrites to it.
Beginning in Swift 5.8 you can flexibly adopt upcoming Swift features using a new compiler flag and compilation condition. This post describes the problem upcoming feature flags solve, their benefits, and how to get started using them in your projects.
When we say “type X has value semantics,” we mean:
Each variable of type X has an independent notional value.
A language-level copy (e.g., let b = a) of a variable of type X has an equivalent value.
Given a local variable a of type X, safe code cannot observe the value of a except via an expression that uses a.
Given a variable a of type X, safe code cannot alter the value of a except by one of the following means applied to a or to a property of a that reflects all or part of a's value.
assignment.
invocation of a mutating method.
invocation of a mutating accessor of a property or subscript
passing the expression as an inout parameter.
Concurrent access to the values of distinct variables of type X cannot
cause a data race.
All told, here’s the general procedure for defunctionalization:
Collect all functions passed as an argument to the filter function.
Create a data type, with one variant for each possible function, each with fields to store the free variables referenced by the corresponding function.
Replace the invocation of the filter condition with an apply function, which determines what filter condition the data structure represents, and executes it.
To be able to send remote push notifications to an iOS simulator in Xcode 14, we have to be running macOS 13 on a computer with an Apple silicon or a T2 processor. In this setup, the simulator generates a unique registration token, which is specific to the combination of the simulator and the Mac hardware it’s running on.
The simulator supports the Apple Push Notification Service (APNS) Sandbox environment, which means that we have to connect to api.sandbox.push.apple.com to send a notification to the simulator.
The new Layout protocol in iOS 16 lets us place views explicitly, and unlike the position() modifier, we can specify an anchor point when we call the place() method in placeSubviews().
You can use the GitHub API to trigger a webhook event called repository_dispatch when you want to trigger a workflow for activity that happens outside of GitHub. For more information, see "Repositories."
More precisely, an xcframework is a universal, binary, library distribution format. Let’s break that description down in reverse order.
An xcframework is a library distribution format. Each xcframework holds exactly one library. A library is a precompiled collection of code that can be consumed by another project to create an executable (or app).
An xcframework is a binary distribution format. That means it does not include source code in its distribution. Only built binaries and interface specifications (headers and/or Swift interface files) are included.
An xcframework is a universal distribution format. That means it holds libraries and interfaces for different platforms as well as processor architectures in the same structure. A single xcframework can, for example, offer the same library for consumption for iOS, watchOS, and Mac projects using either Intel or ARM architectures.
Finally, an xcframework is a bundle because it’s a directory with a well-defined content structure and file extension, and has an Info.plist file in its root. Examining its Info.plist file shows that it has a CFBundlePackageType of XFWK.
Turns out this is trickier than it seems, as UIPanGestureRecognizer has a small delay in startup where it requires you to move your finger before it recognizes the gesture starting. If you just touch your finger on the moving object, that’s not technically a “pan”, so it ignores you (makes sense), which means the object just keeps moving until you move enough to trigger a “pan”, the result of this is an interaction that doesn’t feel very responsive.
Icarus provides first-class language support for Swift, C, C++, and Objective-C.
If you are Swift or C-family language developer, Icarus can provide you with first-class support for building client- and server-side applications and frameworks.
✨ Fun fact: This extension's debugging support was built entirely using Nova and Icarus. "Look ma, no Xcode!"
This is a list of changes to the Swift language that are frequently proposed but that are unlikely to be accepted. If you're interested in pursuing something in this space, please familiarize yourself with the discussions that we have already had. In order to bring one of these topics up, you'll be expected to add new information to the discussion, not just to say, "I really want this" or "this exists in some other language and I liked it there".
Additionally, proposals for out-of-scope changes will not be scheduled for review. The readme file identifies a list of priorities for the next major release of Swift, and the dashboard includes a list of changes that have been rejected after a formal review.
Several of the discussions below refer to "C family" languages. This is intended to mean the extended family of languages that resemble C at a syntactic level, such as C++, C#, Objective-C, Java, and Javascript. Swift embraces its C heritage. Where it deviates from other languages in the family, it does so because the feature was thought actively harmful (such as the pre/post-increment ++) or to reduce needless clutter (such as ; or parentheses in if statements).
I agree it's not very relevant in this case, but I have measured this 🙂 adding Swift to an otherwise-empty (just sleep()s in main) C process on my Darwin system adds 128kB of total dirty memory, of which 16kB is heap memory coming from 28 additional allocations.
This will vary depending on system libraries, symbol ordering, memory allocator, and other factors of course. My particular system is likely measuring a bit on the high side at the moment for unrelated reasons.
(edited to correct numbers slightly, I forgot I had edited my test program, and reverting the edits reduced it from 176kB to 128kB and from 37 allocations to 28)
Stop using floats
BINARY DATA WAS NOT SUPPOSED TO HAVE DECIMAL PARTS
YEARS OF COMPILER DEVELOPMENT yet NO REAL-WORLD USE FOUND for anything other than char and int
Wanted to use decimal numbers anyway for a laugh? We had a tool for that: It was called FIXED-POINT ARITHMETIC
'x==x can be FALSE', 'j is a number', 'the sum of t and 7 is 0.30000000004'-statements dreamt up by the utterly Deranged
When you join a new team, it’s tempting to keep your head down and study your new codebase. In your head, you might think that you’re expected to already know everything about the codebase even though you’re completely new to the project.
You might think that all patterns and practices in the project are industry standard and that you just haven’t worked in places as good as this one before.
All of these kinds of ideas exist in pretty much anybody’s head and they prevent you from properly learning and exploring a new codebase.
In this post, you have learned some tips about why human interaction is extremely important during your exploration phase. You also learned some useful tips for the more technical side of things to help you effectively tackle learning a new codebase.
Good luck on your next adventure into a new codebase!
At Airbnb, we run a comprehensive suite of continuous integration (CI) jobs before each iOS code change is merged. These jobs ensure that the main branch remains stable by executing critical developer workflows like building the iOS application and running tests. We also schedule jobs that perform periodic tasks like reporting metrics and uploading artifacts.
Sometimes, we might want to automatically retry an asynchronous operation that failed, for example in order to work around temporary network problems, or to re-establish some form of connection.
But what if we wanted to implement something similar, but using Swift Concurrency instead? While Combine’s Publisher protocol includes the above retry operator as a built-in API, neither of Swift’s new concurrency APIs offer something similar (at least not at the time of writing), so we’ll have to get creative!
I want to show you, how we can use Swift’s Types to create modules — Datatypes, UseCases, Features — that will be controlled through a vocabulary defined by their Domain Specific Languages (DSL).
As these vocabularies are finite sets, this kind of coding has proven to enable coding of even complex domains in simple fashions and in very little time — think: hours where conventional coding needs weeks.
Using a consistent layout that adapts to various contexts makes your experience more approachable and helps people enjoy their favorite apps and games on all their devices.
Arrange views inside built-in layout containers like stacks and grids.
Use layout containers to arrange the elements of your user interface. Stacks and grids update and adjust the positions of the subviews they contain in response to changes in content or interface dimensions. You can nest layout containers inside other layout containers to any depth to achieve complex layout effects.
To finetune the position, alignment, and other elements of a layout that you build with layout container views, see Layout adjustments. To define custom layout containers, see Custom layout. For design guidance, see Layout in the Human Interface Guidelines.
An overlay is a view drawing on top of another view. And today, we will talk about two interesting use cases of using overlays in SwiftUI. One of them allows us to keep the structural identity of the view, and another one becomes very handy whenever you build custom navigation transitions.
Context SDK leverages machine learning to make optimized suggestions when to upsell an in-app purchase, what type of ad and dynamic copy to display, or predict what a user is about to do in your app, and dynamically change the product flows to best fit their current situation.
Byrdhouse is a multilingual video conferencing application that helps global teams to communicate and collaborate across 100+ languages with AI-powered real-time translation and meeting notes.
Mojo is a programming language that is as easy to use as Python but with the performance of C++ and Rust. Furthermore, Mojo provides the ability to leverage the entire Python library ecosystem.
Mojo achieves this feat by utilizing next-generation compiler technologies with integrated caching, multithreading, and cloud distribution technologies. Furthermore, Mojo’s autotuning and compile-time meta-programming features allow you to write code that is portable to even the most exotic hardware.
More importantly, Mojo allows you to leverage the entire Python ecosystem so you can continue to use tools you are familiar with. Mojo is designed to become a superset of Python over time by preserving Python’s dynamic features while adding new primitives for systems programming. These new system programming primitives will allow Mojo developers to build high-performance libraries that currently require C, C++, Rust, CUDA, and other accelerator systems. By bringing together the best of dynamic languages and systems languages, we hope to provide a unified programming model that works across levels of abstraction, is friendly for novice programmers, and scales across many use cases from accelerators through to application programming and scripting.
This document is an introduction to the Mojo programming language, fit for consumption by Mojo programmers. It assumes knowledge of Python and systems programming concepts but it does not expect the reader to be a compiler nerd. At the moment, Mojo is still a work in progress and the documentation is targeted to developers with systems programming experience. As the language grows and becomes more broadly available, we intend for it to be friendly and accessible to everyone, including beginner programmers. It’s just not there today.
Eliminate passwords for your users when they sign in to apps and websites.
Passkeys use iCloud Keychain public key credentials, eliminating the need for passwords. Instead, they rely on biometric identification, such as Touch ID and Face ID in iOS, or a specific confirmation in macOS for generating and authenticating accounts.
As the authenticator, your Apple device generates a unique public-private key pair for every account it creates on a service. The authenticator retains the private key and shares its public key with the server, known as the relying party.
Location-tracking devices help users find personal items like their keys, purse, luggage, and more through crowdsourced finding networks. However, they can also be misused for unwanted tracking of individuals.
Today Apple and Google jointly submitted a proposed industry specification to help combat the misuse of Bluetooth location-tracking devices for unwanted tracking. The first-of-its-kind specification will allow Bluetooth location-tracking devices to be compatible with unauthorized tracking detection and alerts across iOS and Android platforms. Samsung, Tile, Chipolo, eufy Security, and Pebblebee have expressed support for the draft specification, which offers best practices and instructions for manufacturers, should they choose to build these capabilities into their products.
Teach developers your Swift and Objective-C APIs through reference documentation you create from comments in Swift source code, Objective-C header files, and documentation extension files.
The core premise of “Platform as a Product” is to make explicit the need for a platform (low variability) to exist as a separate system from the customer-facing products (valuable variation**), and requires a long-lived platform team, practices, and budget to support it. Just to dive into this briefly: a platform is designed similarly to the manufacturing production line below — we want a platform to provide consistency and reliability (low variability). Indeed, it’s the consistency and reliability provided by a platform that enables a customer-facing product team to deliver products demonstrating high variation — which means we can rapidly deliver and test new features and changes.
A property wrapper that loads the view controller’s view before accessing the property.
Use this property wrapper on view controller properties that can be nil before the view controller’s view loads. Wrapping view controller properties this way eliminates crashes that can occur from implicitly defining properties as Optional, and then referencing them before the view controller finishes loading.
Hackett is a statically typed, pure, lazy, functional programming language in the Racket language ecosystem. Despite significant differences from #langracket, Hackett shares its S-expression syntax and powerful, hygienic macro system. Unlike Typed Racket, Hackett is not gradually typed—it is designed with typed programs in mind, and it does not have any dynamically-typed counterpart.
You use types that conform to the SetAlgebra protocol when you need efficient membership tests or mathematical set operations such as intersection, union, and subtraction. In the standard library, you can use the Set type with elements of any hashable type, or you can easily create bit masks with SetAlgebra conformance using the OptionSet protocol. See those types for more information.
Rather than generating code for the types, we propose instead to compute a compile time type layout, then create a runtime function that interprets the type layout, computing alignments and addresses at runtime to copy or free them.
There's no packaging step, no building containers, or anything like that. You just call a function in the Unison Cloud API to deploy the service. Unison automatically uploads your function and all of its dependencies to Unison Cloud, caching them on the server.
In the generic parameter list of a generic type, the each keyword declares a generic parameter pack, just like it does in the generic parameter list of a generic function. The types of stored properties can contain pack expansion types, as in let seq and var iter above.
Making responsive apps often requires the ability to update the presentation when underlying data changes. The observer pattern allows a subject to maintain a list of observers and notify them of specific or general state changes. This has the advantages of not directly coupling objects together and allowing implicit distribution of updates across potential multiple observers. An observable object needs no specific information about its observers.
This design pattern is a well-traveled path by many languages, and Swift has an opportunity to provide a robust, type-safe, and performant implementation. This proposal defines what an observable reference is, what an observer needs to conform to, and the connection between a type and its observers.
Display large numbers of repeated views efficiently with scroll views, stack views, and lazy stacks.
Your apps often need to display more data within a container view than there is space for on a device’s screen. Horizontal and vertical stacks are a good solution for repeating views or groups of views, but they don’t have a built-in mechanism for scrolling. You can add scrolling by wrapping stacks inside a ScrollView, and switch to lazy stacks as performance issues arise.
Programming is done in a stateful environment, by interacting with a system through a graphical user interface. The stateful, interactive and graphical environment is more important than the programming language(s) used through it. Yet, most research focuses on comparing and studying programming languages and only little has been said about programming systems.
Technical dimensions is a framework that captures the characteristics of programming systems. It makes it possible to compare programming systems, better understand them, and to find interesting new points in the design space of programming systems. We created technical dimensions to help designers of programming systems to evaluate, compare and guide their work and, ultimately, stand on the shoulders of giants.
Understanding real modules is worthwhile even if you never intend to work in a language that has them. Modules are fundamental, and so much of what is done in other languages, from build configurations to dependency injection to the Adapter pattern, are in large part an attempted encoding of things easily expressed with modules.
So, here’s the overall tally of fields and their usefulness in terms of lessons for software design:
Type theory: Very useful
Program Analysis: Not useful, other than the definition of “abstraction.”
Program Synthesis: Old-school derivational synthesis is useful; modern approaches less so.
Formal Verification: Mechanized verification is very useful; automated not so much.
Category theory: Not useful, except a small subset which requires no category theory to explain.
So, we have a win for type theory, a win for the part of verification that intersects type theory (by dealing with types so fancy that they become theorems), and a wash for everything else. So, go type theory, I guess.
In conclusion, you can improve your software engineering skills a lot by studying theoretical topics. But most of the benefit comes from the disciplines that study how programs are constructed, not those that focus on how to build tools.
Traditionally, side-effects in functional programming are handled using monads. However, David Spivak and I are attempting a different approach.
In this post, we are going to discuss turing machines via a particular construction in Poly, and then show the general framework we have developed. Not all of the puzzle pieces are there yet in order to make this a fully-fledged theory of imperative programming, but we have to walk before we can run.
swift-inspect is a debugging tool which allows you to inspect a live Swift process to gain insight into the runtime interactions of the application.
swift-inspect uses the reflection APIs to introspect the live process. It relies on the swift remote mirror library to remotely reconstruct data types.
A macro that creates a (naive) Free Monad type based on a user-supplied Functor. It uses the traits from the "higher" crate. This macro is a port of the Control.Monad.Free part of the "free" Haskell package by Edward Kmett.
A free and chart-filled mini-book on where we are in the Streaming Wars, have been, and will go. From the national and international best-selling author, and former streaming executive, Matthew Ball.
Just wanted to share an update, as we've continued to write more and more tests for code that uses Swift concurrency. Introducing Swift concurrency to a feature continues to be the biggest cause of test flakiness in our experience, but we have managed to reduce flakiness dramatically by cribbing some helpers from swift-async-algorithms, in particular, a "C async support" module, which exposes a global hook override for task enqueuing, which we use to redirect everything to the main, serial executor:
> Whenever we have a flakey continuous integration failure, we wrap the test with this helper and don't typically have a concurrency-based problem with that test again. As a bonus, the test runs more quickly.
The solution is far from perfect, but has saved us from a ton of pain, and we think it basically makes async code behave more like Combine code (i.e. well-defined "subscription" order), and hence becomes a lot more reliable to test.
I believe it will probably only work for code that does not use custom executors, but that should be the case for most folks right now. We also haven't tried to include this code in library/application code yet, but if Swift doesn't provide a public solution to the problem, we'll likely look into extracting this helper into its own package, which should make it easier to drop into a project.
SwiftUI has a few different modifiers that can change the color of text, such as foregroundColor(_:), foregroundStyle(_:), and tint(_:). They provide different functionality, but sometimes overlap and it can be hard to know for sure which modifier to choose. In this post we will go over some common use cases for all three of the modifiers and see which one suits best for what purpose.
And what it can teach us about SwiftUI’s stack layout algorithm
I have one more thing to say on the relative sizing view modifier from my previous post, Working with percentages in SwiftUI layout. I’m assuming you’ve read that article. The following is good to know if you want to use the modifier in your own code, but I hope you’ll also learn some general tidbits about SwiftUI’s layout algorithm for HStacks and VStacks.
As mobile developers, we face unique challenges when it comes to releasing and managing updates for our apps across different app stores. One of the primary reasons for this difficulty is the scattered and insufficient documentation available, which lacks the necessary level of detail and nuance to provide a clear understanding of the process.
Additionally, the interfaces and tools provided by these stores for managing releases are often opaque and and don't offer much insight into how things work behind the scenes, which further complicates the process.
This reference is a compilation of answers for common and rare situations in an attempt to increase transparency. It is compiled from experience, developer forums, Stack Overflow, and various other sources of developer documentation. We hope contributions from other developers will grow this resource further.
By adding the new compiler flag -enable-upcoming-feature and appending the feature flags we would like to enable to the “Swift Compiler - Custom Flags” section in Xcode, the compiler will enable the selected features for us. For example, if we wanted to enable existential any and strict concurrency checking, we could provide the compiler with this flag: -enable-upcoming-feature ExistentialAny StrictConcurrency. StrictConcurrency here is equivalent to -warn-concurrency, as it exists in Swift 5.7 and earlier.
SwiftUI’s layout primitives generally don’t provide relative sizing options, e.g. “make this view 50 % of the width of its container”. Let’s build our own!
See the design goals of the Verse programming language and its features. Use this section as a reference.
Verse is a programming language developed by Epic Games that you can use to create your own gameplay in Unreal Editor for Fortnite, including customizing your devices for Fortnite Creative.
Improve navigation behavior in your app by replacing navigation views with navigation stacks and navigation split views.
If your app has a minimum deployment target of iOS 16, iPadOS 16, macOS 13, tvOS 16, or watchOS 9, or later, transition away from using NavigationView. In its place, use NavigationStack and NavigationSplitView instances. How you use these depends on whether you perform navigation in one column or across multiple columns. With these newer containers, you get better control over view presentation, container configuration, and programmatic navigation.
XCTContext provides a way for activities (XCTActivity) to run against the current testing context, either directly in a test case or in custom testing utilities. You can break up long test methods in UI tests or integration tests into activities to reuse, and to simplify results in the Xcode test reports. Use runActivity(named:block:) to run a block of code as a named substep in a test. For more information, see Grouping Tests into Substeps with Activities.
There are many good reasons to be critical of code reviews, pull requests, and other processes that seem to slow things down. The lack of trust in co-workers is, however, not one of them.
You can easily be swayed by that argument because it touches something deep in our psyche. We want to be trusted, and we want to trust our colleagues. We want to belong.
The argument is visceral, but it misrepresents the motivation for process. We don't review code because we believe that all co-workers are really North Korean agents looking to sneak in security breaches if we look away.
We look at each other's work because it's human to make mistakes. If we can't all be in the same office at the same time, fast but asynchronous reviews also work.
This resource is useful primarily to developers, but may also interest curious technophiles who want to take a peek “behind the curtain” to see how much of the magic just beneath our fingertips is made.
SwiftUI has the mask(alignment:_:) modifier that masks the view using the alpha channel of the given view. The reverse function is not part of SwiftUI though, but can easely be made using a blendmode trick.
The software value chain is knowledge-based since it is highly dependant on people. Consequently, a lack of practice in managing knowledge as a resource may jeopardise its application in software development. Knowledge-Based Resources (KBRs) relate to employees’ intangible knowledge that is deemed to be valuable to a company’s competitive advantage. In this study, we apply a grounded theory approach to examine the role of KBRs in Agile Software Development (ASD). To this aim, we collected data from 18 practitioners from five companies. We develop the Knowledge-Push theory, which explains how KBRs boost the need for change in ASD. Our results show that the practitioners who participated in the study utilise, as primary strategies, task planning, resource management, and social collaboration. These strategies are implemented through the team environment and settings and incorporate an ability to codify and transmit knowledge. However, this process of codification is non-systematic, which consequently introduces inefficiency in the domain of knowledge resource utilisation, resulting in potential knowledge waste. This inefficiency can generate negative implications for software development, including meaningless searches in databases, frustration because of recurrent problems, the unnecessary redesign of solutions, and a lack of awareness of knowledge sources.
“Functional Software Architecture” refers to methods of construction and structure of large and long-lived software projects that are implemented in functional languages and released to real users, typically in industry.
The goals for the workshop are:
To assemble a community interested in software architecture techniques and technologies specific to functional programming;
To identify, categorize, and document topics relevant to the field of functional software architecture;
To connect the functional programming community to the software architecture community to cross-pollinate between the two.
I’ve taught functional programming for years now, each time experimenting with different ways of teaching core concepts. Over time, I’ve collected and converged on simple (but reasonably precise) pedagogical definitions for a range of functional concepts.
Software architecture is always a topic for hot debate, specially when there are so many different choices. For the last 8-12 months, I have been experimenting with MV pattern to build client/server apps and wrote about it in my original article SwiftUI Architecture - A Complete Guide to MV Pattern Approach. In this article, I will discuss how MV pattern can be applied to build large scale client/server applications.
Release your apps that aren’t suited for public distribution as unlisted on the App Store, discoverable only with a direct link. Unlisted apps don’t appear in any App Store categories, recommendations, charts, search results, or other listings. In addition, they can be accessed through Apple Business Manager and Apple School Manager. Apps for partner sales tools, employee resources, or research studies are examples of good candidates for unlisted distribution.
Distribute your app to:
Limited audiences (such as part-time employees, franchisees, partners, business affiliates, higher-education students, or conference attendees) through a standard link that’s usable on the App Store and Apple School Manager or Apple Business Manager.
Employee-owned devices that aren’t eligible to be managed through Apple School Manager or Apple Business Manager.
Next year will be the 10-year anniversary of Swift. Moore's Law means we get to have nicer things in the future, like Apple Silicon and the Internet of Things. Yet the more powerful and ubiquitous computing devices become, the more damage can result if our software malfunctions.
Therefore we must continue to improve Swift along its primary goals of simplicity and safety. So lets bring over some new features from the cutting edge of computer science.
This is the app previously known as watchGPT. Quickly get answers to your questions or generate longer messages without typing.
We are excited to introduce Petey your Al assistant app for the Apple Watch! With this app, you can now interact with the famous GPT model right from your wrist.
A type that can convert itself into and out of an external representation with the help of a configuration that handles encoding contained types.
CodableWithConfiguration is a type alias for the EncodableWithConfiguration and DecodableWithConfiguration protocols. Use this protocol to support codability in a type that can’t conform to Codable by itself, but can do so with additional statically-defined configuration provided by a CodableConfiguration instance.
AttributedString uses this approach to allow an instance to contain arbitrary attributes, including frameworks outside of Foundation or the platform SDK. It does this by including one or more AttributeScope instances, a type that conforms to EncodingConfigurationProviding and DecodingConfigurationProviding. An attribute scope like AttributeScopes.SwiftUIAttributes defines attribute keys, and conforms to AttributeScope to provide configuration instances that know the AttributedStringKey types and their associated Value types. With this type information, an AttributedString can encode all of its attributes, even from frameworks other than Foundation.
The target audience is Swift compiler developers: if you've previously encountered GenericSignatures, GenericEnvironments, SubstitutionMaps and ProtocolConformances and found them slightly mysterious, this is definitely for you. You might also want to take a look if you're interested in programming language design or type systems in general.
const wow: ("hello" | "world") | (string & {}) // wow is of type "hello" or "world" or string.
This weird-looking intersection of string & {} makes it so that the specific strings "hello" and "world" are distinguished from string as a whole type.
The SwiftUI Navigation Bar has no easy way to change its color, that being iOS 13 or iOS 16 I feel that is something clanky and should be easier. The good news is that we have some workarounds that work pretty well.
This project contains a version of Remote.pure.run that is capable of programmatically producing mermaid diagrams to visualize the forked and awaited tasks in a Remote computation.
The hyphen, em dash and en dash are everywhere, but most of us don’t know when or why to use them – and different writers use the dashes in different ways. Let’s figure this out!
A language is sound if it doesn't accept programs that it shouldn't. "Shouldn't" is doing a lot of work there, though: it's an inherently open concept, reliant on an external semantic model. In programming languages, we generally say a program shouldn't be accepted if it "won't work", but our definition for "work" is that individual operations will behave like they're expected to, not that the program as a whole will do what the programmer meant for it to do. The latter definition, of course, can't be applied without pulling the user's intent into the semantic model, and any sort of bug in the program (or flaw in the user's understanding) would become an "unsoundness" in the language. (For example, if I wrote a Fibonacci-style function that started with 3,4,..., the language would be unsound if I meant it to be the standard Fibonacci sequence.) In the more standard PL approach, the language designer just has to rigorously define how individual operations are supposed to work.
One way to do that is to give well-defined semantics to all possible combinations of operands, even ones that don't a priori make much sense. This is common in dynamically-typed languages; for example, JavaScript's subscript operator is well-defined even if you use it on an integer. In statically-typed languages, we usually rule out a lot of those cases by defining static preconditions on operations. For example, in Swift, the subscript syntax requires the base operand to statically have a subscript member, and then the dynamic semantics impose a precondition that the base value actually has the type it was type-checked to have. In that world, the language is formally sound as long as it's correctly enforcing that those static preconditions on individual operations can't be violated.
Sometimes we do use "soundness" in a slightly less formal sense: we argue about how users expect basic operations to work. In this informal sense, a language can be unsound if it violates those user expectations even if dynamically everything remains well-defined. For example, Java has covariant object arrays: you can implicitly convert a String[] to Object[], but assignments into the result will fail with an exception if they aren't dynamically Strings. This is not formally unsound in Java because this aspect of the well-typedness of the array assignment syntax is not a static precondition; it's a well-defined dynamic check. Nonetheless, it is very arguably unsound behavior under a higher-level understanding of how assigning into an array element ought to work, because a bunch of things that the language accepts implicitly and without complaint can be combined to dynamically fail with a type error.
But I have a hard time accepting that that could ever apply to dynamic cast. Dynamic casts are an explicit syntax whose entire purpose is to dynamically check whether a value has a particular type. The possibility that that can fail when values don't have that type is inherent to the operation.
This page collects all the familiar navigation patterns for structuring iOS apps, like drill-downs, modals, pyramids, sequences, and more! Think of it as an unofficial bonus chapter for Apple’s Human Interface Guidelines, written by someone who cares deeply about well-crafted user interfaces.
I’m excited to announce two new open source Swift packages: swift-certificates and swift-asn1. Together, these libraries provide developers a faster and safer implementation of X.509 certificates, a critical technology that powers the security of TLS.
The AI product manager that writes your Jira tickets
JiraPT-3 is your team's newest AI-powered Product Manager that uses GPT-3 to write user stories and epics. This is the Chrome extension for Product Managers who want to 10X their output 🤖 or for Developers who want to automate PMs away 🤭
When prompted with user story “As a , I want , so that I can ___”, JiraPT-3 will create:
A fully-formed description containing context on the user and their goal
A clearly-defined set of Acceptance Criteria that outlines the workflow to achieve the user’s goal.
Publish your Swift package privately, or share it globally with other developers.
Making your Swift packages available online enables you to use the support for Swift package dependencies in Xcode. By publishing your Swift packages to private Git repositories, you can manage and integrate internal dependencies across your projects, allowing you to reduce duplicate code and promote maintainability. Publish your packages publicly, and share your code with developers around the world. To get started, you just need a Swift package and an account with a provider of hosted Git repositories.
Edit images and video with async / await in Swift, powered by Metal.
The main value type in AsyncGraphics is a Graphic. It’s like an image, but with various various methods for applying effects and some static methods for creating visuals.
AsyncGraphics also has another value type called Graphic3D. It’s a 3d image, a volume of voxels.
The words you choose within your app are an essential part of its user experience.
Whether you're building an onboarding experience, writing an alert, or describing an image for accessibility, designing through the lens of language will help people get the most from your app or game.
The purity of Haskell allows for mathematical reasoning about programs. This not only makes it possible to be more confident in the correctness of our programs but can be used in order to optimize code as well. In fact, the primary Haskell compiler, GHC, uses these guarantees in order to optimize programs. The restrictions imposed by purity turn into properties that programmers and compilers alike may use to their advantage.
While we could attempt to solve that clarity problem with more verbose API naming, the core issue would still remain — that the modifier-based version doesn’t properly show what the resulting view hierarchy will be in this case. So, in situations like the one above, when we’re wrapping multiple siblings within a parent container, opting for a view-based solution will often give us a much clearer end result.
On the flip side, if all that we’re doing is applying a set of styles to a single view, then implementing that as either a “modifier-like” extension, or using a proper ViewModifier type, will most often be the way to go. And for everything in between — such as our earlier “featured label” example — it all really comes down to code style and personal preference as to which solution will be the best fit for each given project.
Judo is a design and build tool for SwiftUI apps that writes production-ready code for you while you’re designing. Eliminate back-and-forth with developers and free them from unrewarding grunt work.
The gist is that LazyVStack is just lazy, but this is not even close to the UICollectionView / UITableView, List (which is still backed by a UIKit UICollectionView). Any of those reuse / cycle views are you need them. LazyVStack just grow in size, forever. So the longer it is, the more you scroll, the slower it get!
Instead of having separate routes for each API endpoint, we could just have a single API endpoint that takes in a huge enum and switches on that. We can then share that enum with the client and they’d automatically be in sync.
Use a state object as the single source of truth for a reference type that you store in a view hierarchy. Create a state object in an App, Scene, or View by applying the @StateObject attribute to a property declaration and providing an initial value that conforms to the ObservableObject protocol. Declare state objects as private to prevent setting them from a memberwise initializer, which can conflict with the storage management that SwiftUI provides.
SwiftUI creates a new instance of the model object only once during the lifetime of the container that declares the state object. For example, SwiftUI doesn’t create a new instance if a view’s inputs change, but does create a new instance if the identity of a view changes. When published properties of the observable object change, SwiftUI updates any view that depends on those properties, like the Text view in the above example.
Note: If you need to store a value type, like a structure, string, or integer, use the State property wrapper instead.
And, most importantly, the whole team can collaborate in on the design. In the string diagram above the colored segment was added in a quick architecture design sync during a discussion centered around reporting requirements. The flexibility of the drawing gives us the power to make these changes live, but it’s the mathematical formalism that gives the whole team common ground to interpret exactly what those changes mean.
Fundamentally, opening an existential means looking into the existential box to find the dynamic type stored within the box, then giving a "name" to that dynamic type. That dynamic type name needs to be captured in a generic parameter somewhere, so it can be reasoned about statically, and the value with that type can be passed along to the generic function being called. The result of such a call might also refer to that dynamic type name, in which case it has to be erased back to an existential type. The After the call, any values described in terms of that dynamic type opened existential type has to be type-erased back to an existential so that the opened type name doesn't escape into the user-visible type system. This both matches the existing language feature (opening an existential value when accessing one of its members) and also prevents this feature from constituting a major extension to the type system itself.
This section describes the details of opening an existential and then type-erasing back to an existential. These details of this change should be invisible to the user, and manifest only as the ability to use existentials with generics in places where the code would currently be rejected. However, there are a lot of details, because moving from dynamically-typed existential boxes to statically-typed generic values must be carefully done to maintain type identity and the expected evaluation semantics.
Provides support for “if” statements with #available() clauses in multi-statement closures, producing conditional content for the “then” branch, i.e. the conditionally-available branch.
As an application of the developed system, I present a classical example of using dependent types: vectors parameterized by their length. Since vector lengths now dwell on type-level, we can guarantee statically that, for example, the replicate operation returns a vector of a given length, concat returns a vector whose length is equal to the sum of lengths of two passed vectors, and zip takes two vectors of the same length and returns a vector of that length. The code relies on some rudimentary facilities of Church numerals and pairs, which also serve as a warm-up.
In the past, JavaScript errors inside components used to corrupt React’s internal state and cause it to emitcrypticerrors on next renders. These errors were always caused by an earlier error in the application code, but React did not provide a way to handle them gracefully in components, and could not recover from them.
This PR introduces a more streamlined way to perform an in-place mutation of a case in an enum when in a testing context.
>
You should think of XCTModify as a companion to XCTUnwrap as it allows you to safely unwrap a case from an enum, and then further apply a mutation. This helper will be very useful for the navigation tools being built for TCA.
Luckily, Xcode has a solution to this — User Breakpoints! After creating any breakpoint, you can right-click and select: “Move Breakpoint To” > “User” to move it from your project or workspace to user space. After this, you’ll see a shared list of User Breakpoints in every Xcode project you open.
SwiftUI doesn’t support natively shake gestures yet. But it’s easy to implement it in SwiftUI.
We need to create an extension for UIDevice for tracking shaking gestures when happens. Also, we need to create an extension for UIWindow, and override motionEnded method.
classViewModel:ObservableObject{@Publishedvarcount=0enumProgress{case didSubscribeToScreenshots, didRespondToScreenshot
}
// set to non-nil when testing
varprogress:AsyncChannel<Progress>?@MainActorfunc task()async{letscreenshots=NotificationCenter.default.notifications(
named:UIApplication.userDidTakeScreenshotNotification
)await progress?.send(.didSubscribeToScreenshots)forawait_in screenshots {self.count +=1await progress?.send(.didRespondToScreenshot)}}}@MainActorfunc testBasics()asyncthrows{letvm=ViewModel()
// Install a progress channel into the ViewModel we can monitor
letvmProgress=AsyncChannel<ViewModel.Progress>()
vm.progress = vmProgress
// We get `task.cancel(); await task.value` for free with async let
asynclet _ = vm.task()XCTAssertEqual(vm.count,0)
// Give the task an opportunity to start executing its work.
letfirstProgress=await vmProgress.next()XCTAssertEqual(firstProgress,.didSubscribeToScreenshots)
// Simulate a screen shot being taken.
NotificationCenter.default.post(
name:UIApplication.userDidTakeScreenshotNotification, object:nil)
// Give the task an opportunity to update the view model.
letnextProgress=await vmProgress.next()XCTAssertEqual(nextProgress,.didRespondToScreenshot)XCTAssertEqual(vm.count,1)}
A Simulator runtime is an embedded OS package that Simulator loads when running your app on a simulated device in Xcode. For example, when you test your app on a simulated iPhone running iOS 16, Simulator loads the iOS 16 Simulator runtime on the simulated device.
To minimize the download size of Xcode, version 14 and later don’t include the Simulator runtimes for watchOS and tvOS. You need the current versions of the Simulator runtimes to build projects and to run the Simulator for those platforms. You can download and install these files when you first launch Xcode, or later from the Xcode run destination, from Xcode Preferences, or from the command line.
Manage the amount of storage that Xcode requires by choosing Xcode > Preferences > Platforms to view the currently installed Simulator runtimes, and removing any that you don’t need.
Support peer-to-peer connectivity and the discovery of nearby devices.
The Multipeer Connectivity framework supports the discovery of services provided by nearby devices and supports communicating with those services through message-based data, streaming data, and resources (such as files). In iOS, the framework uses infrastructure Wi-Fi networks, peer-to-peer Wi-Fi, and Bluetooth personal area networks for the underlying transport. In macOS and tvOS, it uses infrastructure Wi-Fi, peer-to-peer Wi-Fi, and Ethernet.
We recently found an issue where the compiler was failing to reuse stack space between switch cases, and allocating the stack space necessary for all of the enum payloads and cases' local state even though only one actually executes at a time. You might be running into the same problem.
Until we fix that issue, one workaround we've found for this issue is to wrap up each case block in an immediately-invoked closure, like:
> If you see stack size issues even after adopting indirect cases, you might try that to see if it helps.
* [ToolbarTitleMenu](https://developer.apple.com/documentation/swiftui/toolbartitlemenu)
> The title menu of a toolbar.
* [The Art of Sequential Animations in SwiftUI: Tips, Tricks, and Examples](https://holyswift.app/how-to-do-sequential-animations-in-swiftui)
> Sequential Animations in SwiftUI offer a powerful and intuitive way to create dynamic and engaging user interfaces. By leveraging the power of SwiftUI’s animation system, developers can easily create complex and beautiful animations that add polish and delight to their apps.
>
> With a few lines of code, animations can be sequenced and coordinated to create more intricate and expressive user experiences, making SwiftUI an excellent choice for building modern, interactive apps.
* [Simulator: Save as GIF](https://xcode.tips/simulator-save-as-gif)
> 
> After recording a video with the simulator, hold down the Control key while clicking the small preview. The simulator opens a menu. Select “Save as Animated GIF”.
* [In Xcode 14.3 you can now see the output from your previews in the console!](https://mobile.twitter.com/SwiftyAlex/status/1626989662353891328)
> In Xcode 14.3 you can now see the output from your previews in the console!
>
> Just select the new previews button and you’ll see every print 🤯
>
> Combine this with \_printChanges() and you can debug views without running your app 🕵🏼
* [Dynamic Library Usage Guidelines](https://developer.apple.com/library/archive/documentation/DeveloperTools/Conceptual/DynamicLibraries/100-Articles/DynamicLibraryUsageGuidelines.html)
> The dynamic loader compatibility functions provide a portable and efficient way to load code at runtime. However, using the functions incorrectly can degrade app performance. This article shows how to correctly load and use dynamic libraries in your apps.
>
> Dynamic libraries help to distribute an app’s functionality into distinct modules that can be loaded as they are needed. Dynamic libraries can be loaded either when the app launches or as it runs. Libraries that are loaded at launch time are called _dependent libraries_. Libraries that are loaded at runtime are called _dynamically loaded libraries_. You specify which dynamic libraries your app depends on by linking your app with them. However, it’s more efficient to use dynamic libraries as dynamically loaded libraries instead of dependent libraries. That is, you should open libraries when you’re about to use symbols they export and close them when you’re done. In some cases, the system unloads dynamically loaded libraries when it determines that they aren’t being used.
>
> This article uses the word _image_ to refer to an app file or a dynamic library. App binaries contain the app’s code and the code from the static libraries the app uses. The dynamic libraries the app loads at launch time or runtime are separate images.
* [EditKit Pro](https://apps.apple.com/app/id1659984546)
> Elevate your iOS Development game with EditKit Pro — the ultimate Xcode Editor Extension packed with convenient utilities for a more efficient and productive workflow.
* [The Swift Programming Language](https://docs.swift.org/swift-book/documentation/the-swift-programming-language)
> Understand the high-level goals of the language.
* [WebURL Key-Value Pairs](https://gist.github.com/karwa/bb8eb387dac10fd7c0c1fffc020c1c7c#proposed-solution)
> In order to make it easier to read/write key-value pairs from URL components, WebURL 0.5.0 will include a new KeyValuePairs type. The current formParams view will be deprecated and removed in the next minor release.
* [The Art of Sequential Animations in SwiftUI: Tips, Tricks, and Examples](https://holyswift.app/how-to-do-sequential-animations-in-swiftui)
> With a few lines of code, animations can be sequenced and coordinated to create more intricate and expressive user experiences, making SwiftUI an excellent choice for building modern, interactive apps.
* [What Is Copy On Write(COW) In Swift?](https://ishtiz.com/swift/what-is-copy-on-writecow-in-swift)
> Copy-On-Write (COW) is a memory management technique used in Swift programming language to optimize the performance of memory allocation and deallocation operations. In COW, whenever a new instance of a data structure is created, the original data structure is not modified, instead, a new copy of the data structure is created in memory and modifications are made to the new copy. Copy-on-write is a highly used strategy in Swift for optimising memory usage. The main idea of COW is that when multiple callers want to access the resources which are same, you can put them pointing to the same resource. The state of the resource will be maintained until a caller tries to modify its “copy” of the resource. The main advantage is that if a caller never makes any modifications, no true copy need ever be created. Don’t confuse copy on right with [reference](https://ishtiz.com/swift/value-and-reference-types-in-swift-a-deep-dive) type.
* [The Change of Mobile Teams Topology for an Organization](https://medium.com/mobile-app-development-publication/the-change-of-mobile-teams-topology-for-an-organization-d6fb1f6ff75b)
> Optimize the structure of mobile teams to fit the need of the organization in scaling app development.
* [iOS and iPadOS usage](https://developer.apple.com/support/app-store)
> As measured by devices that transacted on the App Store.
* [Composable Styles in SwiftUI](https://movingparts.io/composable-styles-in-swiftui)
> A look at how to compose styles and how to make custom views support composable styles.
* [pointfreeco/swift-clocks](https://github.com/pointfreeco/swift-clocks)
> ⏰ A few clocks that make working with Swift concurrency more testable and more versatile.
* [Creating an XCFramework](https://rhonabwy.com/2023/02/10/creating-an-xcframework)
> The key pieces to know when doing tackling this are embedded in the core of the article: [Creating a multiplatform binary framework bundle](https://developer.apple.com/documentation/xcode/creating-a-multi-platform-binary-framework-bundle):
>
> 1. For a single library, use the `xcodebuild -create-xcframework` command with the `-library` option. There’s also a `-framework` option, but reserve that for when you need to expose multiple static, or dynamic, libraries as a binary deliverable.
> 1. Avoid using dynamic libraries in when you want to support iOS and iOS simulator, as only macOS supports the dynamic linking for these using a Framework structure. Instead, use static libraries.
> 1. Use the `lipo` command to merge libraries when you’re building for x86 and arm architectures, but otherwise **_DO NOT_** combine the static libraries for the different platforms. You’ll want, instead, to have separate binaries for each platform you’re targeting.
> 1. These days, the iOS simulator libraries need to support BOTH x86 and arm64 architectures, so yep — that’s where you use `lipo` to merge those two into a single “fat” static library — at least if you’re targeting the iOS simulator on macOS. Same goes for supporting the macOS platform itself.
> 1. Get to know the codes called “triples” that represent the platforms you’re targeting. In the world of Rust development, three Apple platforms are “supported” without having to resort to nightly development: iOS, iOS simulator, and macOS. The “triples” are strings (yep — no type system here to double-check your work). Triple is ostensibly to support “CPU”, “Vendor”, and “platform” — but like any fairly dynamic type thing, it’s been extended a bit to support “platform variants”.
>
> The triple codes you’ll likely want to care about, and their platforms:
> * x86_64-apple-ios — the original iOS Simulator on an Intel Mac
> * aarch64-apple-ios-sim — the iOS simulator on an M1/arm based Mac.
> * aarch64-apple-ios — iOS and iPadOS (both are only arm architectures)
> * aarch64-apple-darwin — M1/arm based Macs
> * x86_64-apple-darwin — Intel based Macs
* [A Beginner’s Guide to Styling Components in SwiftUI](https://holyswift.app/a-beginners-guide-to-styling-components-in-swiftui)
> In conclusion, the SwiftUI ButtonStyle protocol is a versatile and straightforward tool for customizing the look and feel of your buttons in your iOS app.
>
> Whether you’re a seasoned developer or just starting out, this protocol can help you create buttons that are both functional and visually appealing. So if you’re looking to add a personal touch to your buttons, be sure to check out SwiftUI’s ButtonStyle protocol!
* [Genius by Diagram](https://www.genius.design)
> It understands what you’re designing and makes suggestions that autocomplete your design using components from your design system.
* [**Developer Conferences Agenda**](https://developers.events)
* [Managing a merge queue](https://docs.github.com/en/repositories/configuring-branches-and-merges-in-your-repository/configuring-pull-request-merges/managing-a-merge-queue)
> You can increase development velocity with a merge queue for pull requests in your repository.
>
> A merge queue can increase the rate at which pull requests are merged into a busy target branch while ensuring that all required branch protection checks pass.
>
> Once a pull request has passed all of the required branch protection checks, a user with write access to the repository can add that pull request to a merge queue.
>
> A merge queue may use GitHub Actions. For more information, see "[GitHub Actions](https://docs.github.com/en/actions)."
>
> The merge queue creates temporary branches with a special prefix to validate pull request changes. The changes in the pull request are then grouped into a `merge_group` with the latest version of the `base_branch` as well as changes ahead of it in the queue. GitHub will merge all these changes into `base_branch` once the checks required by the branch protections of `base_branch` pass.
* [Adding a stretchable header to a SwiftUI ScrollView](https://danielsaidi.com/blog/2023/02/06/adding-a-stretchable-header-to-a-swiftui-scroll-view)
> The `ScrollViewHeader` presented in this post lets you add stretchable headers to your scroll views by just adding your content to this header component.
>
> I have added this view to my newly released [ScrollKit](https://github.com/danielsaidi/ScrollKit) library. You can find the source code [here](https://github.com/danielsaidi/ScrollKit/blob/main/Sources/ScrollKit/ScrollViewHeader.swift). If you decide to give it a try, I’d be very interested in hearing what you think.
* [Styling Components in SwiftUI](https://movingparts.io/styling-components-in-swiftui)
> SwiftUI has a great API for styling views independent of a view’s implementation. In this post, we’ll look at how we can style custom views in the same way.
* [Changing orientation for a single screen in SwiftUI](https://www.polpiella.dev/changing-orientation-for-a-single-screen-in-swiftui)
> Before you go, I want to stress that while this is the only workaround that we were able to find, it is by no means a robust and future-proof solution. We have found that navigation behaviour in SwiftUI tends to change in every iOS version and changing a single screen from portrait to landscape orientation works well on iOS 16 but not on iOS 15, where you'll probably want to set the orientation to allow `.allButUpsideDown` rather than constraining it to `.landscape` only.
>
> For this reason, I would take what has been discussed in this article **with a big pinch of salt** and make sure you have **sufficient UI/manual tests around the screen you're locking orientation for**.
* [If your iPhone won't turn on or is frozen](https://support.apple.com/en-us/HT201412)
> If your iPhone has a frozen screen, doesn't respond when you touch it, or becomes stuck when you turn it on, learn what to do.
>
> On your iPhone 8 or later, including iPhone SE (2nd and 3rd generation)
> 1. Press and quickly release the volume up button.
> 1. Press and quickly release the volume down button.
> 1. Press and hold the side button until you see the Apple logo.
* [[Pitch] Type Wrappers](https://forums.swift.org/t/pitch-type-wrappers/60019/45)
> I've been working on the implementation for [attached macros](https://forums.swift.org/t/pitch-attached-macros/62812), and I had the chance to implement [@Observable](https://github.com/DougGregor/swift-macro-examples/pull/6) from the Future Directions section of the [observation pitch](https://forums.swift.org/t/pitch-observation/62051). I was able to replicate the functionality in this type wrapper pitch through composition of the various attached macro capabilities.
>
> The macro approach provides more flexibility, because the macro author can decide what code to generate inside the wrapped type. The macro-expanded code then becomes much more transparent to the programmer than a subscript call, which isn't very informative in terms of what that call accomplishes. The macro still has the ability to add backing storage variables, initializers, nested types derived from stored properties of the wrapped type, and more, and the transformation can be customized depending on what kind of type the macro is attached to (e.g. a `struct` versus an `actor`).
>
> After taking the motivating use cases surfaced in this pitch thread and implementing them as macros, I'm confident that macros can fully subsume type wrappers while providing more flexibility to library authors.
* [SwiftUI TextEditor Keyboard Avoidance](https://github.com/Asperi-Demo/4SwiftUI/blob/master/PlayOn_iOS/PlayOn_iOS/Findings/TestTextEditorToFocus.swift)
```swift
struct ContentView: View {
@State private var text: String = ""
init() {
UITextView.appearance().backgroundColor = .clear
}
@FocusState var inFocus: Int?
var body: some View {
ScrollViewReader { sp in
ScrollView {
TextEditor(text: $text).id(0)
.focused($inFocus, equals: 0)
.frame(height: 300)
.background(.yellow)
TextEditor(text: $text).id(1)
.focused($inFocus, equals: 1)
.frame(height: 300)
.background(.mint
TextEditor(text: $text).id(2)
.focused($inFocus, equals: 2)
.frame(height: 300)
.background(.teal)
if inFocus == 2 {
Color.clear.frame(height: 300)
}
}
.onChange(of: inFocus) { id in
withAnimation {
sp.scrollTo(id)
}
}
}
}
}
}
If you need to control the eviction strategy, or to implement your own storage, you'll have to do custom caching completely outside Foundation URL loading.
The async/await syntax, introduced in Swift 5.5, allows a readable way to write asynchronous code. Asynchronous programming can improve performance of an app, but it is important to cancel unneeded tasks to ensure unwanted background tasks do not interfere with the app. This article demonstrates how to cancel a task explicitly and shows how the child tasks are automatically cancelled.
Whenever the iOS keyboard appears, it overlaps parts of your interface. The common approach to keyboard management is to move up the focused part of the view to avoid its overlapping. In this article, let’s learn how we can solve this problem by making our SwiftUI views keyboard-aware.
First, a bit of theory. A transducer is a function that describes the process of transformation, without knowing how exactly the thing it transforms is organized. It is not the same as generic functions, because transducers are generic in a bit different way.
SwiftUI has a great API for styling views independent of a view’s implementation. In this post, we’ll look at how we can style custom views in the same way.
This page collects transcriptions of lectures, mainly of a philosophical nature, given by Per Martin-Löf between the autumn of 1993 and September 2019. Most of them are published here for the first time. Each transcription contains a prefatory note outlining its origin.
In general, if you got yourself in a pickle — like I did several times — don’t do what I did every time: just as I naively set unnecessary/non-optimal publishers to begin with, I foolishly thought the opposite — removing any that seem unnecessary — is the way to go. Before you remove any, study the property’s trail. I had a few examples of a removal not immediately having any visible consequences, only for it become apparent after some time when the view didn’t respond to changes in a specific scenario. Finally, in case I forget again, remember an @EnvironmentObject will trigger a view update even if the view has no reference to any of its properties.
The main idea behind container pattern revolves around two different kind of views namely container and presenter/presentation views. The container view is responsible for fetching the data, sorting, filtering and other operations and then passing it down to presentation view for display.
In other words, container is a smart view and presenter is a dumb view. The only job of the presenter view is to display data on the screen. Data is always passed down from the container view to the presenter view.
The workaround? Rejig your ForEach to return only “one view” that matches the frame you want to use with scrollTo(id:,anchor:) — in my case I map my model data to an array of new enum types that describe what the ForEach should output for each iteration.
Using complex gestures in a SwiftUI ScrollView is complicated, since they block scroll view gestures in a way that causes scrolling to stop working. I’ve looked into this, and found a way to use a button style to handle gestures in a way that doesn’t block the scrolling.
The main purpose of writing tests is to make sure that the software works as expected. Tests also gives you confidence that a change you make in one module is not going to break stuff in the same or other modules.
Not all applications requires writing tests. If you are building a basic application with a straight forward domain then you can test the complete app using manual testing. Having said that in most professional environments, you are working with a complicated domain with business rules. These business rules form the basis on which company operates and generates revenue.
In this article, I will discuss different techniques of writing tests and how a developer can write good tests to get the most return on their investment.
CloudKit subscription offers best way to keep data up-to-date for your user. I will show you the simplest setup to get started in this cloudkit subscriptions tutorial.
In a recent post, I presented an approach for succeeding on take-home iOS-developer coding challenges. (For brevity, I henceforth refer to these particular coding challenges as “coding challenges”.) The model solution in that post used UIKit because, at the time I wrote the post, I had already completed coding challenges using that framework. But SwiftUI may be a good, or indeed the best, option.
Calling JavaScript from Swift code is easily possible, although this isn’t friction-free. The interoperability is nowhere close to as good as between Swift and Objective-C. It’s also obvious that the JavaScriptCore API was designed for Objective-C and hasn’t been properly refined for Swift. That said, in the end, I’d rather have a more robust solution to a problem regardless of the programming language used to implement that solution, even if this means a little more friction.
Matching SwiftUI’s view APIs in their ergonomics is hard to get right. In this post we’ll learn how to write view APIs that feel truly native to the platform.
To deal with these lists of views (e.g. during layout) we can use the underscored variadic view API. I learned about variadic views through the Moving Parts blog. I don’t know whether this API is going to change in the future, whether it’s App-Store-proof, and so on. It’s probably underscored for a good reason.
We need to create an HTTP server that will be listening for requests. Then we can send a request from XCTest to the server and run whatever we want. The server can even return the output back to XCTest if required.
For private repositories, each GitHub account receives a certain amount of free minutes and storage for use with GitHub-hosted runners, depending on the product used with the account. Any usage beyond the included amounts is controlled by spending limits.
MacOS-based runner images are expensive for GitHub and hence GitHub applies a minute multiplier.
It is similar for SwiftUI. But the new framework did away with AppDelegate, and has a simplified main func. You simply call MyApp.main() in main.swift to start an app.
One of the challenging parts of SwiftUI is really understanding the way it manages view state (for example, through @State and @StateObject). In theory, it’s pretty simple: anytime you want associated view state you just create a property with @State and you’re done.
When you write SwiftUI, all your views conform to the View protocol. The name of this protocol is a bit misleading: I it could be called Views or ViewList, or something else that suggests plurals.
Today we explore 3 solutions for this interesting problem in SwiftUI.
The first was binding the nested property to a View @State annotation, which would definitely trigger the View Redraw, but is not a good solution leaving the View entangled with the View data nested structure. The bright side of this approach is that this solution has zero effects on the Data layers, so if you don’t want to touch other layers’ code, this is one idea.
The second one was manually calling the objectWillChange.send(). This is also cumbersome because you need to remember to add the objectWillChange call every time you want to update the view. This is the receipt for bugs.
And lastly, we checked what is for me the best answer to this problem. If you can, remove the nested observed object and make two simple ObservedObjects.
However, if we take a look at the Swift Evolution proposal for the property wrappers feature, we can see that it also mentions a second, alternative way of handling a wrapper’s value...
You can generate an order file that has this result by parsing the linkmap file. All protocol conformances end in Mc so you just need the Swift symbol names matching this pattern that are in the __TEXT/__const section. You could write a detailed parser for the structure of a linkmap, but a simple grep should also do the trick:
That’s it! You now have your order file. You can set the Xcode "Order File" build setting to the path of this file, or check out our docs with instructions on third party build systems. For something this easy to make, it is definitely worth doing to speed up the app for iOS 15 users or the first launch after an app update on iOS 16.
Here you'll find implementations of popular algorithms and data structures in everyone's favorite new language Swift, with detailed explanations of how they work.
If you're a computer science student who needs to learn this stuff for exams — or if you're a self-taught programmer who wants to brush up on the theory behind your craft — you've come to the right place!
The goal of this project is to explain how algorithms work. The focus is on clarity and readability of the code, not on making a reusable library that you can drop into your own projects. That said, most of the code should be ready for production use but you may need to tweak it to fit into your own codebase.
Code is compatible with Xcode 10 and Swift 4.2. We'll keep this updated with the latest version of Swift. If you're interested in a GitHub pages version of the repo, check out this.
But even more remarkable is the discovery of Causal Trees and operation-based CRDTs. With this deconstruction of the CRDT formula, there’s finally a consistent way to understand, design, and implement arbitrary replicated data types. By breaking up conventional data structures into immutable micro-operations that are defined in absolute terms, giving them authorship and causality metadata, and carefully ordering them inside simple containers, you get the resilience and clarity of a convergent event log together with the efficiency of a low-level data structure. Conflict resolution can be precisely tailored to fit the needs of the data model. Operations can just as easily be sent around as-is or condensed into state snapshots. Version vectors can be used to perform garbage collection, view past revisions, or generate delta patches. Every last edit can be sourced to its author, placed in its historical and spatial context, and linked to from the outside. And all this is possible while simplifying the app’s architecture, not complicating it, since the paradigm is almost entirely functional!
Get the resources you need to create software for Macs with Apple silicon.
Build apps, libraries, frameworks, plug-ins, and other executable code that run natively on Apple silicon. When you build executables on top of Apple frameworks and technologies, the only significant step you might need to take is to recompile your code for the arm64 architecture. If you rely on hardware-specific details or make assumptions about low-level features, modify your code as needed to support Apple silicon.
Getting the best performance on Apple silicon sometimes requires making adjustments to the way you use hardware resources. Minimize your dependence on the hardware by using higher-level technologies whenever possible. For example, use Grand Central Dispatch instead of creating and managing threads yourself. Test your changes on Apple silicon to verify that your code behaves optimally.
This article provides a quick overview of the compilation process and available architectures on Xcode with one goal in mind: Get a better understanding on what it means to compile for the M1.
And all would be pretty much well if that were all there was to it. A simple, unproblemtic story. But then there is the third category: the ivory tower zealots! These are terrible! They have passion but they use it all wrong! They have principles but they are wrong! They could be into category theory instead of SOLID! Unrealistic! Unpragmatic! Proofs! Maths! Maybe they write comments or even specifications!
I understand what you mean, this kind of workflow is not what is going to be supported by result builder transform implementation going forward. The result builder transform semantics are such that each element in the body is type-checked independently from others and the resulting value is then passed to a final buildBlock or a series of buildPartialBlock calls and returned just like I outlined in my example, the old implementation failed to enforce the "solved independently" bit which caused all sorts of diagnostics and performance issues.
In your example there are two overloads of parser(of:) method, both have argument that accepts a default value which means that the type-checker won't be able to disambiguate between them without buildExpression or buildPartialBlock providing more context (via generic requirements) just like if you wrote _ = Int.parser() without using result builders.
Your quake client fetches a list of earthquakes from the network. Now, you’ll extend the client to fetch location details for each earthquake. Each earthquake requires one additional fetch to retrieve the location information. You’ll make multiple network connections concurrently while maintaining a cache of replies.
It’s incredible to see what Swift 5.7’s existential types unlock. They allow us to create an interface that for all intents and purposes is dynamic, being an array of Any values, while simultaneously being able to pull static type information from it when needed. This allows for building tools that are both flexible and safe, such as NavigationStack, which helps decouple domains in a navigation stack while simultaneously retaining type information to pass to destination views.
Swift's high-level semantics try to relieve programmers from thinking about memory management in typical application code. In situations where predictable performance and runtime behavior are needed, though, the variability of ARC and Swift's optimizer have proven difficult for performance-oriented programmers to work with. The Swift Performance team at Apple are working on a series of language changes and features that will make the ARC model easier to understand, while also expanding the breadth of manual control available to the programmer. Many of these features are based on concepts John McCall had previously sketched out in the Ownership Manifesto ([Manifesto] Ownership), and indeed, the implementation of these features will also provide a technical foundation for move-only types and the other keystone ideas from that manifesto. We will be posting pitches for the features described in this document over the next few months.
We want these features to fit within the "progressive disclosure" ethos of Swift. These features should not be something you need to use if you're writing everyday Swift code without performance constraints, and similarly, if you're reading Swift code, you should be able to understand the non-ARC-centric meaning of code that uses these features by ignoring the features for the most part. Conversely, for programmers who are tuning the performance of their code, we want to provide a predictable model that is straightforward to understand.
Nearby Connections enables advertising, discovery, and connections between nearby devices in a fully-offline peer-to-peer manner. Connections between devices are high-bandwidth, low-latency, and fully encrypted to enable fast, secure data transfers.
A primary goal of this API is to provide a platform that is simple, reliable, and performant. Under the hood, the API uses a combination of Bluetooth, BLE, and Wifi hotspots, leveraging the strengths of each while circumventing their respective weaknesses. This effectively abstracts the vagaries of Bluetooth and Wifi across a range of Android OS versions and hardware, allowing developers to focus on the features that matter to their users.
As a convenience, users are not prompted to turn on Bluetooth or Wi-Fi — Nearby Connections enables these features as they are required, and restores the device to its prior state once the app is done using the API, ensuring a smooth user experience.
So you have a Swift Package Manager project, without an xcodeproj, and you launch Instruments, and try to profile something (maybe Allocations), and you receive the message “Required kernel recording resources are in use by another document.” But of course you don’t have any other documents open in Instruments and you’re at a loss, so you’ve come here. Welcome.
Swift has a mature and production ready framework in Vapor and Smoke, and newer ones like Hummingbird. These provide everything you’d expect from a web framework, from routing and middleware, to templating, and JSON/form handling. There are packages for everything, and more!
Crafting Interpreters contains everything you need to implement a full-featured, efficient scripting language. You’ll learn both high-level concepts around parsing and semantics and gritty details like bytecode representation and garbage collection. Your brain will light up with new ideas, and your hands will get dirty and calloused. It’s a blast.
Advanced Data Protection (ADP) for iCloud is the most intriguing of the three, and the rest of this post will discuss how it can improve the security of your data in Tact and other CloudKit-based apps.
TL;DR for Tact: your Tact private chats will be end-to-end encrypted if all chat members have enabled Advanced Data Protection on their accounts.
TL;DR for any CloudKit app: your records in iCloud will be end-to-end encrypted if certain conditions are met. You have no way to verify some of the conditions on your end.
Create a schema to store your app’s objects as records in iCloud using CloudKit.
After you enable CloudKit in your app, you create a schema for your container that describes how to store your objects. A schema defines record types and the possible relationships between them. A record type is a template for the allowed keys and values of a record. This relationship is analogous to how a class (record type) defines the properties an instance (record) can have.
A relationship between two records in a record zone.
A CKReference object creates a many-to-one relationship between records in your database. Each reference object stores information about the one record that is the target of the reference. You then save the reference object in the fields of one or more records to create a link from those records to the target. Both records must be in the same zone of the same database.
They're both right. The task stops executing any async code at all before the continuation is formed, and any state will be moved off of the callstack into the task object at that point. The closure is then immediately executed in the same execution context (in other words, the current thread) with the closure as a parameter. Once the closure returns, control goes back to the executor.
SE-0382 "Expression macros" introduces macros into Swift. The approach involves an explicit syntax for uses of macros (prefixed by #), type checking for macro arguments prior to macro expansion, and macro expansion implemented via separate programs that operate on the syntax tree of the arguments.
This proposal generalizes the #-prefixed macro expansion syntax introduced for expression macros to also allow macros to generate declarations and statements, enabling a number of other use cases, including:
Subsuming the #warning and #error directives introduced in SE-0196 into macros.
Attached macros provide a way to extend Swift by creating and extending declarations based on arbitrary syntactic transformations on their arguments. They make it possible to extend Swift in ways that were only previously possible by introducing new language features, helping developers build more expressive libraries and eliminate extraneous boilerplate.
Task locals are what power this library under the hood, and so it can be important to first understand how task locals work and how task local inheritance works.
Task locals are values that are implicitly associated with a task. They make it possible to push values deep into every part of an application without having to explicitly pass the values around. This makes task locals sound like a “global” variable, which you may have heard is bad, but task locals have 3 features that make them safe to use and easy to reason about:
Task locals are safe to use from concurrent contexts. This means multiple tasks can access the same task local without fear of a race condition.
Task locals can be mutated only in specific, well-defined scopes. It is not allowed to forever mutate a task local in a way that all parts of the application observe the change.
Task locals are inherited by new tasks that are spun up from existing tasks.
So overall, Chat GPT does quite well with these basic challenges! It would be interesting to take this further still and see if we could make our server program more and more complex, like adding custom functionality for different routes. But Chat GPT definitely seems useful enough to help with basic tasks, even in a less well-known language like Haskell!
Property wrapper that defines a task-local value key.
A task-local value is a value that can be bound and read in the context of a Task. It is implicitly carried with the task, and is accessible by any child tasks the task creates (such as TaskGroup or async let created tasks).
A user identity provides identifiable data about an iCloud user, including their name, user record ID, and an email address or phone number. CloudKit retrieves this information from the user’s iCloud account. A user must give their consent to be discoverable before CloudKit can provide this data to your app. For more information, see requestApplicationPermission(_:completionHandler:).
A store represents the runtime that powers the application. It is the object that you will pass around to views that need to interact with the application.
A typed key-value storage solution to store Codable types in various persistence layers like User Defaults, File System, Core Data, Keychain, and more with a few lines of code!
OS X is the first operating system on the market that actually uses PDF technology within the operating system itself. Apple calls this technology ‘Quartz’. Quartz is a layer of software that runs on top of Darwin, the core (or kernel) of the Mac OS X operating system. It is responsible for the rendering of all 2D objects. Alongside Quartz, OpenGL takes care of handling 3D data (used in games like Quake or Unreal as well as professional 3D applications like Maya) and QuickTime handles multimedia stuff (movies, sound,…).
First that language specifications and implementations are very different things. Second, via the series of evolving BF implementations, that any given language can be implemented as an interpreter or a compiler.
Different container views may have different performance and memory usage behaviors, depending on how long they keep child views alive.
onAppear isn’t necessarily called when the state is created. It can happen later (but never earlier).
onAppear can be called multiple times in some container views. If you need a side effect to happen exactly once in a view’s lifetime, consider writing yourself a onFirstAppear helper, as shown by Ian Keen and Jordan Morgan in Running Code Only Once in SwiftUI (2022-11-01).
This article documents several techniques I have found effective at improving the run time performance of Swift applications without resorting to “writing C in .swift files”. (That is, without resorting to C-like idioms and design patterns.) It also highlights a few pitfalls that often afflict Swift programmers trying to optimize Swift code.
The changes for this unreleased 1.0 version include, among others, the adoption of async/await. In this article we'll rewrite an existing lambda to use the latest main revision of the swift-aws-lambda-runtime package and take an early look at what the new APIs look like and how they enable us to use async/await in AWS lambdas.
Apart from regular view modifiers in SwiftUI, there are also text modifiers. They apply specific styles to a Text view and return another Text rather than some View. We can see the list of available text modifiers in the Text view documentation under the "Styling the view’s text" and other sections. These are the ones that have Text as their return type, for example func foregroundColor(Color?) -> Text.
We are looking for people to help us try out unison.cloud before wider public release, and also to give us early feedback on brand new Unison Cloud functionality we're developing. If you're interested in participating in this program, please fill out this short questionaire and we'll get back to everyone within 7 days about next steps. We have somewhat limited space for now, so don't hesitate if you are interested!
This is a library for writing compositional binary codecs that can serialize and/or deserialize Unison values to and from binary formats. Functions are provided for writing and reading values to and fromBytes,network sockets, and files.
In this article, I'm writing down my experience and codes that worked to get an app working in Swift UI that uses a user camera and shows a live feed on the screen. This app works in both macOS (Apple Silicon tested) and iOS.
Welcome to Android Basics with Compose! In this course, you'll learn the basics of building Android apps with Jetpack Compose, the new UI toolkit for building Android apps. Along the way, you'll develop a collection of apps to start your journey as an Android developer.
Peter Steinberger has a helpful tip for discovering when the body property of a view is being reinvoked: assign a random background color to one of its views. This will be re-evaluated along with the rest of thebody`, so if body is being called a lot then your views will flicker as they change background.
When building UIs in SwiftUI, we tend to build two kinds of UI components: screens and (reusable) views. Usually, we start by prototyping a screen, which will inevitably result in a Massive ContentView that we then start refactoring into smaller, reusable components.
Apple describes Xcode version 14 as "everything you need" to build software for their platforms. The company implemented a number of improvements, such as several updated auto-completion functions, to increase Xcode’s performance. Read on to find out which ones I have found particularly important and see how they work in practice.
Xcode runtime warnings offer a much better experience than traditional assertions and breakpoints, but Apple provides no means of creating custom runtime warnings ourselves. To work around this, we hook into SwiftUI's runtime issue delivery mechanism, instead.
The calls to Task.yield feel wrong to me, but I don’t know of an alternative.
The real problem with this code, though, is that the test occasionally fails! You can use Xcode’s “run repeatedly” feature 1,000 times and will almost always get a failure or two. From what I can gather, this is because there’s no guarantee that Task.yield will suspend long enough for the task to do its work.
I can sprinkle in more Task.yields and the test fails less often.
A CloudKit.Notification object represents a push notification that was sent to your app. Notifications are triggered by subscriptions that you save to the database. To subscribe to record changes and handle push notifications, see the saveSubscription method in CloudKit.Database.
Enables collaboration on text field (and other fields) across multiple iOS devices.
It's based on operation based CRDT with replication leveraging native CoreData CloudKit sync. A nearly vanilla implementation of CRDT RGA (operation per character).
To store discrete values in iCloud for app preferences, app configuration, or app state, use iCloud key-value storage. Key-value storage is similar to the local user defaults database; but values that you place in key-value storage are available to every instance of your app on all of a user’s various devices.
An iCloud-based container of key-value pairs you use to share data among instances of your app running on a user's connected devices.
Use the iCloud key-value store to make preference, configuration, and app-state data available to every instance of your app on every device connected to a user’s iCloud account. You can store scalar values such as BOOL, as well as values containing any of the property list object types: NSNumber, NSString, NSDate, NSData, NSArray, and NSDictionary.
Using CloudKit is an interesting solution for local iOS applications requiring synchronization among different devices. It allows simple storage of binary data such as photos or films as well as creating a more complicated database. However, if you want to store and synchronize a small amount of data among one user’s devices (e.g. a common configuration), it’s worth thinking about using NSUbiquitousKeyValueStore which also employs iCloud and doesn’t require configuring the CloudKit container.
I intended for all this to lead to easy, stateless CloudKit sync. Instead of enforcing tight coupling between the persistence and cloud layers, I would have a “sync whenever” system that was guaranteed to succeed whenever it happened to run. Both the local SQLite database and CloudKit would keep around the same data and log tables. On sync, the local store would request the version vector from the CloudKit log table. Based on this timestamp, the local store would know which local check-ins needed to be uploaded, and could additionally request any check-ins from the server that were needed to complete the local database. Merge between check-ins was eventually consistent and conflict-free, and nothing was ever deleted, so you’d never need to do anything more complicated than send sets of check-ins and event log entries around. Sync would become completely stateless!
That’s all folks! In this series, we’ve seen how you can design and create your own replicating data types, and combine them to into full distributed data apps. These types have a data cost, but the payoff is that they make syncing more powerful and easier to implement. They also free your app from lock in — you can sync via any cloud service, and even peer-to-peer.
Use assets to incorporate external files into your app’s records, such as photos, videos, and binary files. Alternatively, use assets when a field’s value is more than a few kilobytes in size. To associate an instance of CKAsset with a record, assign it to one of its fields.
module: A group of interrelated sources intended to always be built together. (cf. whole module optimization). This is what is referenced when you import MyModule, and when you use its name for disambiguating a symbol: MyModule.Data vs Foundation.Data.
In general English, a module is “one of a set of standardized parts or independent units that can be used to construct a more complex structure”
target: A unit of the build result; a particular thing you might aim to build on its own. A Swift target is pretty much equal to a Swift module, so they are often used interchangeably. However module tends to refer more to the grouping whereas target refers more to the result. Another difference is that a target does not necessarily have to contain source; it could be something else such as a resource bundle and such a target is not a module. Targets are referenced by the package manifest’s .target(...) and testTarget(...), and by Xcode under “File → New → Target...” and so on.
In general English, a target is “a mark or point at which one fires or aims” or “an objective or result towards which efforts are directed”
product: A unit of functionality you want to vend to clients. Often a product is also a single target, but the reverse is not true. Many targets are not intended for others to use (such as test targets), and those are never described as products. Products are defined in the package manifest’s products argument and referenced in client target’s dependency list with .product(...) (unless the reference is reduced to a string literal).
In general English, a product is “an article or substance that is manufactured or refined for sale”
Code signing is one of the few operations that takes just as long to do for an incremental build as for a clean build. It also takes more time the larger an app grows in size. As a result, it can become a bottleneck for incremental builds. Here are some tricks to reduce that time. They’re all technically undocumented and may break in the future, but they’re also used by large companies with no apparent downsides.
Note: these tricks are for debug builds only.
Note: see how much time code signing takes during builds, i.e. how much time you can actually save, and decide if that amount matters to you.
Have you ever noticed how Google auto-completes your queries?
What if we repeat the same query for every suggestion?
Now let's repeat this process one more time for every found suggestion. But instead of drawing a picture, let's draw a line between each suggestion
And this is exactly what this website is doing for You.
I found this technique useful to find alternatives, or to perform a market research. Obviously, for this technique to work, Google needs to know enough about your query.
TL;DR: lld is a great choice for faster linking of debug binaries on Apple platforms. Steps on how to integrate are in the section below.
Linking is one of the main bottlenecks for incremental builds. Thousands upon thousands of developer-hours are spent each year waiting on debug builds to link, and so linker optimization is a major topic. Linkers are complicated beasts that have to do intricate transformations on huge amounts of data at lightning speed, so it requires a lot of work. This blog post will discuss the past, present, and future of linker optimization for Apple platforms. It also includes a practical section on how to integrate lld at present. If you aren’t familiar with linking, read about it here and look for the linking step at the end of your build logs.
Before an app even runs main and +applicationDidFinishLaunching, a considerable amount of work is done, including setting up dylibs for use, running +load methods, and more. This can take 500ms or more. Blog posts like this one show you how to measure it with the debugger, using DYLD_PRINT_STATISTICS, but it’s hard to find any help for measurement in the wild. Note the special handling for iOS 15’s pre-warming feature.
CloudKit is an easy way to store data in the cloud, sync between multiple devices, and share it between the app’s users. This week we will learn how to start using CloudKit in the app to save and fetch data from the cloud and sync between multiple user devices.
CloudKit provides you ready to use data sharing API that allows you to implement collaborative features of your app without much effort. There are two ways to share data via CloudKit: record sharing and zone sharing. In this post, we will talk about zone sharing.
One of the footnotes in Agile is "small design up-front". Well what happens when that is done so well it removes the need for what Agile provides? During this meetup, we explored aspects of the software development life cycle that are affected by proper design. UML is an example of a 1:1 mapping of code to design documents. Instead, we can get on another level of understanding at an order of magnitude faster pace when we design information flow alone.
If you see a problematic constraint, copy its address from the console and use it to filter in the view debugger. The view debugger will show you the exact constraint in the user interface.
Users receive a badge whenever they visit a landmark in their list. Of course, for a user to receive a badge, you’ll need to create one. This tutorial takes you through the process of creating a badge by combining paths and shapes, which you then overlay with another shape that represents the location.
If you want to create multiple badges for different kinds of landmarks, try experimenting with the overlaid symbol, varying the amount of repetition, or changing the various angles and scales.
Follow the steps to build this project, or download the finished project to explore on your own.
UI tests are expensive and fragile but vital and usable. That’s why you should take care of them as much as you take care of your main codebase. The Page Object pattern is a great way to simplify your UI tests and reuse the logic across the many UI tests.
Description: Discover how to improve your app's build and runtime linking performance. We'll take you behind the scenes to learn more about linking, your options, and the latest updates that improve the link performance of your app.
The goal of dyld4 is to improve on dyld3 by keeping the same mach-o parsers, but do better in the non-customer case by supporting just-in-time loading that does not require a pre-built closures.
A typed key-value storage solution to store Codable types in various persistence layers like User Defaults, File System, Core Data, Keychain, and more with a few lines of code!
Today we discussed a simple way of storing Codable structs which we can fetch via REST API. Sometimes we don’t need complicated features of CoreData for simple JSON caching and it is enough to implement disk storage.
Vision documents usually start by being solicited by the evolution workgroup with authority for that area. For areas within the Swift language and standard library, that is the Language Workgroup. While a vision is being developed, it is called a prospective vision, and it should be clearly identified as such. In this state, the vision carries no implicit endorsement.
Eventually, the appropriate evolution workgroup may decide to officially approve a vision. This is an endorsement of the entire document, but the strength of that endorsement varies from section to section:
It is a strong endorsement of the vision's description of the current state of this part of the project. The evolution workgroup agrees with what the vision has to say about the problems the project has in this area.
It is a strong endorsement of the vision's stated goals for this part of the language. The evolution workgroup agrees that these are the right goals for evolution in this area to strive for, and it agrees that the vision prioritizes different goals appropriately.
It is a somewhat weaker endorsement of the overall approach laid out by the vision. The evolution workgroup agrees that this seems like the right basic approach to take; if it can successfully carried out, it should achieve the goals the vision lays out. However, the evolution workgroup is not committed to the details of the approach, and it may change substantially as the vision is distilled into concrete proposals and reviewed.
It is only a very weak endorsement of the concrete ideas for proposals in the vision document. The evolution workgroup thinks these sound like the right ideas in the abstract but is not committed to any of them. The proposals will all need to go through normal evolution review, and they may be rejected or substantially changed from how they appear in the vision.
Once the vision is approved, it acts as a foundation for subsequent pitches and proposals in its area. Pitches and proposals that implement or build on part of a vision should generally link back to the vision document.
Vision documents are artifacts of the design process; they are not substitutes for language or release documentation. It is not expected that authors will continually update the vision document as the proposals emerging from it change. Revision may be appropriate if the vision document is actively causing confusion, for example because of a major shift in terminology since the document's development.
The consequence of this recommendation is that, from what I've seen in many projects, people tend to declare gigantic structs (esp. their JSON objects) and pass them around in functions or assign to variables without thinking that it can be a waste of memory and CPU cycles. In some edge cases the overhead can be significant and can be felt by the users.
SwiftUI provides us with the LayoutValueKey protocol allowing us to register a custom layout parameter. We can use this type to attach any value we need to a view inside the layout and extract this value later in the layout cycle.
A condition that limits the application of a build setting.
By default, build settings are applicable for all platforms and build configurations. Use the .when modifier to define a build setting for a specific condition. Invalid usage of .when emits an error during manifest parsing. For example, it’s invalid to specify a .when condition with both parameters as nil.
This lecture series consists of 2 parts , these being: the introductory lectures and the seminars. During the first part we'll have 1-2 introductory lectures per week, where we will teach the basics of category theory with a focus on applications to Machine Learning.
The seminars will be deep dives into specific topics of Category Theory, some already showing applications to Machine Learning and some which have not beeen applied yet.
The Xcode integrated support for testing makes it possible for you to write tests to support your development efforts in a variety of ways. You can use tests to detect potential regressions in your code, to spot the expected successes and failures, and to validate the behavior of your app. Testing improves the stability of your code by ensuring that objects behave in the expected ways.
Of course, the level of stability you achieve through testing depends on the quality of the tests you write. Likewise, the ease of writing good tests depends on your approach to writing code. Writing code that is designed for testing helps ensure that you write good tests. Read the following guidelines to ensure that your code is testable and to ease the process of writing good tests.
Define API requirements. It is important to define requirements and outcomes for each method or function that you add to your project. For requirements, include input and output ranges, exceptions thrown and the conditions under which they are raised, and the type of values returned (especially if the values are instances of classes). Specifying requirements and making sure that requirements are met in your code help you write robust, secure code.
See the Unit Testing Apps and Frameworks sample-code project for an example of using exceptions to identify and report incorrect library usage by client code.
Write test cases as you write code. As you design and write each method or function, write one or more test cases to ensure that the API’s requirements are met. Remember that it’s harder to write tests for existing code than for code you are writing.
Check boundary conditions. If a parameter for a method must have values in a specific range, your tests should pass values that include the lowest and highest values of the range. For example, if a procedure has an integer parameter that can have values between 0 and 100, inclusive, the test code for that method should pass the values 0, 50, and 100 for the parameter.
Use negative tests. Negative tests ensure that your code responds to error conditions appropriately. Verify that your code behaves correctly when it receives invalid or unexpected input values. Also verify that it returns error codes or raises exceptions when it should. For example, if an integer parameter must have values in the range 0 to 100, inclusive, create test cases that pass the values -1 and 101 to ensure that the procedure raises an exception or returns an error code.
Write comprehensive test cases. Comprehensive tests combine different code modules to implement some of the more complex behavior of your API. Although simple, isolated tests provide value, stacked tests exercise complex behaviors and tend to catch many more problems. These kinds of tests mimic the behavior of your code under more realistic conditions. For example, in addition to adding objects to an array, you could create the array, add several objects to it, remove a few of them using different methods, and then ensure that the set and number of remaining objects are correct.
Cover your bug fixes with test cases. Whenever you fix a bug, write one or more tests cases that verify the fix.
SQLite is already a popular API for persistent data storage in iOS apps so the upside for development is obvious. As a programmer you work with a stable, well-documented API that happens to have many good wrappers available in Objective-C, such as FMDB and Encrypted Core Data. All security concerns are cleanly decoupled from application code and managed by the underlying framework.
The framework code of the SQLCipher project is open source, so users can be confident that an application isn't using insecure or proprietary security code. In addition, SQLCipher can also be compiled on Android, Linux, macOS and Windows for those developing cross-platform applications.
There are two different options for integrating SQLCipher into an Xcode project. The first involves building the SQLCipher source amalgamation into the application. The second involves using CocoaPods. These tutorials assume familiarity with basic iOS or macOS app development and a working install of Xcode.
This fulltext search index includes over 25 million research articles and other scholarly documents preserved in the Internet Archive. The collection spans from digitized copies of eighteenth century journals through the latest Open Access conference proceedings and pre-prints crawled from the World Wide Web.
Today, we are announcing a new open source Foundation project, written in Swift, for Swift.
This achieves a number of technical goals:
No more wrapped C code. With a native Swift implementation of Foundation, the framework no longer pays conversion costs between C and Swift, resulting in faster performance. A Swift implementation, developed as a package, also makes it easier for Swift developers to inspect, understand, and contribute code.
Provide the option of smaller, more granular packages. Rewriting Foundation provides an opportunity to match its architecture to evolving use cases. Developers want to keep their binary sizes small, and a new FoundationEssentials package will provide the most important types in Foundation with no system dependencies to help accomplish this. A separate FoundationInternationalization package will be available when you need to work with localized content such as formatted dates and time. Other packages will continue to provide XML support and networking. A new FoundationObjCCompatibility package will contain legacy APIs which are useful for certain applications.
Unify Foundation implementations. Multiple implementations of any API risks divergent behavior and ultimately bugs when moving code across platforms. This new Foundation package will serve as the core of a single, canonical implementation of Foundation, regardless of platform.
And this also achieves an important community goal:
Open contribution process. Open source projects are at their best when the community of users can participate and become a community of developers. A new, open contribution process will be available to enable all developers to contribute new API to Foundation.
Advanced Data Protection for iCloud is an optional setting that offers Apple’s highest level of cloud data security. When a user turns on Advanced Data Protection, their trusted devices retain sole access to the encryption keys for the majority of their iCloud data, thereby protecting it with end-to-end encryption. For users who turn on Advanced Data Protection, the total number of data categories protected using end-to-end encryption rises from 14 to 23 and includes iCloud Backup, Photos, Notes and more.
Advanced Data Protection for iCloud will be available to U.S. users by the end of 2022 and will start rolling out to the rest of the world in early 2023.
Conceptually, Advanced Data Protection is simple: All CloudKit Service keys that were generated on device and later uploaded to the available-after-authentication iCloud Hardware Security Modules (HSMs) in Apple data centers are deleted from those HSMs and instead kept entirely within the account’s iCloud Keychain protection domain. They are handled like the existing end-to-end encrypted service keys, which means Apple can no longer read or access these keys.
Advanced Data Protection also automatically protects CloudKit fields that third-party developers choose to mark as encrypted, and all CloudKit assets.
Lambda calculus is a formal system to study computable functions based on variable binding and substitution. Introduced in the 1930s by Alonzo Church, it is (in its typed form) the fundamental concept of functional programming languages like Haskell and Scala. Although the topic might seem very theoretical, some basic knowledge in lambda calculus can be very helpful to understand these languages, and where they originated from, much better. The goal of this article is to introduce some basic concepts of lambda calculus, which later on can be mapped to real world usage scenarios with functional programming languages.
Release your apps that aren’t suited for public distribution as unlisted on the App Store, discoverable only with a direct link. Unlisted apps don’t appear in any App Store categories, recommendations, charts, search results, or other listings. In addition, they can be accessed through Apple Business Manager and Apple School Manager. Apps for partner sales tools, employee resources, or research studies are examples of good candidates for unlisted distribution.
Distribute your app to:
Limited audiences (such as part-time employees, franchisees, partners, business affiliates, higher-education students, or conference attendees) through a standard link that’s usable on the App Store and Apple School Manager or Apple Business Manager.
Employee-owned devices that aren’t eligible to be managed through Apple School Manager or Apple Business Manager.
In developer discourse, the term CRDT sometimes gets thrown around as a synecdoche for a broader set of techniques to enable Figma-like collaborative features. But when we started talking to dozens of companies building ambitious browser-based apps, we found it rare for apps to use true CRDTs to power multiplayer collaboration.
Swift’s protocol oriented programming is very helpful when dealing with polymorphic situations. But when we need to persist polymorphic data, we encounter some issues: Codable is not able to determine what concrete type to decode the saved data into.
Xcode build configuration files are quite useful to manage configuration properties between different environments. You can also use them to easily assing a different app name and an app icon for specific environment.
In physics, Feynman tells us that you cannot memorize formulas. You can't just go to the book, memorize formula, learn to apply it. There's too many of them. Instead, we need to learn about the relationships between the formulas. Derive them for yourself when they're needed.
And so, we want to do the same in programming. See many different APIs, many concepts, and see how there are fewer deeper ideas behind them. So, instead of seeing a bunch of approaches to a problem, I want you to see a web of, a single design and various ways you manipulate it to exactly the outcome that you want. So these are many transformations. Each of them could be their own talk or article. But today, I've taught you one very important transformation, not to rule them all but to rule a lot of them. And that is to, everyone with me, defunctionalize the continuation!
Make your data types encodable and decodable for compatibility with external representations such as JSON.
Many programming tasks involve sending data over a network connection, saving data to disk, or submitting data to APIs and services. These tasks often require data to be encoded and decoded to and from an intermediate format while the data is being transferred.
The Swift standard library defines a standardized approach to data encoding and decoding. You adopt this approach by implementing the Encodable and Decodable protocols on your custom types. Adopting these protocols lets implementations of the Encoder and Decoder protocols take your data and encode or decode it to and from an external representation such as JSON or property list. To support both encoding and decoding, declare conformance to Codable, which combines the Encodable and Decodable protocols. This process is known as making your types codable.
Motive Fleet is a mobile app available on both Android and iOS, which our customers use to access critical real time information about their fleets and drivers on the go. We are continually adding new features to Motive Fleet to enhance our customers’ experience. To execute faster and ensure consistency in business logic across our Android and iOS mobile platforms, we have been exploring mobile cross-platform tools. Our goals included easier code management, fewer bugs, better build quality, and improved development timelines—and we achieved them with Kotlin Multiplatform Mobile (KMM). Read this blog to learn:
Advantages of a code-sharing framework
Kotlin Multiplatform Mobile (KMM) evaluation
Learnings & challenges from integrating KMM in our Motive Fleet app
This page aims to present a first introduction to cubical type theory, from the perspective of a mathematician who has heard about type theory but has no previous familiarity with it. Specifically, the kind of mathematician that we are appealing to is one who is familiar with some of the ideas in category theory and homotopy theory — however, the text also presents the concepts syntactically, in a way that can be read without any prior mathematical knowledge.
In this article, we’ll explore the advantages of Live Activities and the ActivityKit framework that is used for displaying and working with Live Activities. In the demo portion of the article, we’ll show how to add Live Activities to a simple stock tracking app to display real-time information on both the Lock Screen and in the Dynamic Island.
Ruby 3.1 adds a new core class called Data to represent simple immutable value objects. The Data class helps define simple classes for value-alike objects that can be extended with custom methods.
While the Data class is not meant to be used directly, it can be used as a base class for creating custom value objects. The Data class is similar to Struct, but the key difference being that it is immutable.
At Cookpad, we wanted an extension method on XCUIElement similar to XCTestCase.waitForExpectations(timeout:handler:) to make tests readible, but we also have more expectations to wait for than just existence, and we didn’t want to create multiple methods to do very similar things e.g. waitUntilHittable, waitUntilLabelMatches etc.
Additionally, we didn’t want to sleep as an expectation might occur before the timeout and we waited too long, or the opposite, and we didnt wait long enough and spent time verifying false positives. As a result, we created a solution utilising take-aways from all of the aforementioned techniques.
But I'd like to draw attention to some performance limitations, in the hope that a SwiftUI engineer might see this and understand pain points that might not be so obvious from their side.
Structs, classes and enums can explicitly derive Encodable and Decodable conformance (explicitly meaning we can synthesize an implementation if a type conforms manually).
Nowadays, SwiftUI provides the Layout protocol allowing us to build super-custom layouts by digging into the layout system without using GeometryReader. Layout protocol brings us the incredible power of building and reusing any layout you can imagine.
Create shims for existing Objective-C objects to call your new Swift ones.
Use value types within the Swift portions of your codebase, and wrap them in Objective-C compatible reference types for the Objective-C parts.
Try to convert the ‘messaging space’ of each subsystem to Swift as early as possible, and then the messaging space between subsystems.
Wrap Objective-C types that you’re not ready to tackle yet with Swift friendly interfaces. If these are working well for you, then they can stay as Objective-C on the inside for years.
If you just want to expose a single method or property, you can mark that method using the @objc attribute. However, if you want all methods in a class to be exposed to Objective-C you can use a shortcut: the @objcMembers keyword.
By choosing a cache policy, we can decide whether the caching should depend on expiration dates or disabled entirely or whether the server should be contacted to determine if the content has changed since the last request.
A lot happens before the system executes your app’s main() function and calls app delegate functions like applicationWillFinishLaunching. Before iOS 10 it was not easy to understand why an app was slow to launch for reasons other than your own code. It has been possible to add the DYLD_PRINT_STATISTICS environment variable to your project scheme but the output was hard to figure out. With iOS 10 Apple has made the output from enabling DYLD_PRINT_STATISTICS much easier to understand.
A unique identifier for a class instance or metatype.
This unique identifier is only valid for comparisons during the lifetime of the instance.
In Swift, only class instances and metatypes have unique identities. There is no notion of identity for structs, enums, functions, or tuples.
Kotlin/Native provides bi-directional interoperability with Objective-C/Swift. Objective-C frameworks and libraries can be used in Kotlin code. Kotlin modules can be used in Swift/Objective-C code too. Besides that, Kotlin/Native has C Interop. There is also the Kotlin/Native as a Dynamic Library tutorial for more information.
Today we did a brief introduction to local SPM packages and how to prepare your app for modularisation. A checklist was used to guide us in the whole process which make things easier for anyone who wants to start this endeavor.
I believe there’s significant room for performance improvement in Rosetta 2, by using static analysis to find possible branch targets, and performing inter-instruction optimisations between them. However, this would come at the cost of significantly increased complexity (especially for debugging), increased translation times, and less predictable performance (as it’d have to fall back to JIT translation when the static analysis is incorrect).
Engineering is about making the right tradeoffs, and I’d say Rosetta 2 has done exactly that. While other emulators might require inter-instruction optimisations for performance, Rosetta 2 is able to trust a fast CPU, generate code that respects its caches and predictors, and solve the messiest problems in hardware.
This post is about how one bad assumption about XCFrameworks turned into multiple hours of needless effort. I wanted to quickly share my experience so others could avoid falling into the same pitfall. In retrospect, the problem seems obvious, but it wasn’t when I just encountered it.
The answer depends on how we use SwiftUI. For an app entirely written using it, one might argue that it gets released whenever the app finishes. But what about an UIKit app that uses some SwiftUI views?
Dispose of any SwiftUI View values not used anymore
Dispose of any UIHostingController references not used anymore
Watch out for memory leaks in:
UIViews used within SwiftUI
references between your UIViews and your environment objects
UIViewControllers presenting the UIHostingControllers
The Composable Architecture (TCA, for short) is a library for building applications in a consistent and understandable way, with composition, testing, and ergonomics in mind. It can be used in SwiftUI, UIKit, and more, and on any Apple platform (iOS, macOS, tvOS, and watchOS).
Testing is by far the #1 priority of the Composable Architecture. The library provides a tool, the TestStore, that makes it possible to exhaustively prove how your features evolve over time. This not only includes how state changes with every user action, but also how effects are executed, and how data is fed back into the system.
The testing tools in the library haven’t changed much in the 2 and a half years since release, but thanks to close collaboration with Krzysztof Zabłocki and support from his employer, The Browser Company, the 0.45.0 release of the library brings first class support for “non-exhaustive” test stores.
Another method to make a bootable USB drive is createinstallmedia command in Terminal.
Rename USB Volume to MyVolume
Now type the following command into the Terminal window:
sudo /Applications/Install\ macOS\ Ventura.app/Contents/Resources/createinstallmedia --volume /Volumes/MyVolume
This database will contain download links for macOS 13 Ventura full Installer pkg files (InstallAssistant.pkg). This file is the same full installer that you would download directly from the App Store for Intel and Apple Silicon M1 Mac Computers. The InstallAssistant.pkg is stored on Apple’s servers and contains the full “Install macOS.app”. Once downloaded, all you need to do is install the pkg and the full installer of macOS will be in your applications folder. This change was made when Apple revised the full installer for Big Sur. The InstallAssistant.pkg is not available for Catalina or Mojave.
Swift Concurrency provides a really nice way of writing asynchronous code. Support for async-await has been to me the most awaited feature in Swift.
However, with great power comes great responsibility. If you learn from tutorials or even from the documentation, it’s really hard to find some details on how it works under the hood. Basically, Swift Concurrency is advertised as safe to use, because in theory the correctness is being checked by the compiler.
This way of “selling” Swift Concurrency encourages people to just jump in, add async-await to an existing code, and run some Tasks not really knowing what is going on under the hood. Unfortunately, there are many traps around concurrency, and no… the compiler doesn’t check everything.
To be honest, even after performing tests, reading documentation, and watching WWDC I’m still not fully confident with Swift Concurrency. Although, I will try to share with you some of my observations hopefully making you more aware.
The free communication of users on the Internet faces a variety of problems in the modern day. These challenges include censorship from state and corporate actors, the amplification of misinformation through viral content, and an ever-shrinking collection of near monopolies with absolute power over social interaction in the twenty-first century. Through the DSNP, we hope to mitigate and ideally solve these challenges in the way social interaction operates online.
JavaScript is great, and by all means use it, while also being aware that you can build so many functional UI components without the additional dependancy.
Maybe you can include a few lines of utility code, or a mixin, and forgo the requirement. If you're only targeting more modern browsers, you might not need anything more than what the browser ships with.
Our groundbreaking security technologies protect the users of over 1.8 billion active devices around the world. Hear about the latest advances in Apple security from our engineering teams, send us your own research, and work directly with us to be recognized and rewarded for helping keep our users safe.
Starting with Xcode 14, when you create a new iOS project, the app icon in the asset catalog defaults to the new “Single Size”. Instead of the full set of icon sizes there’s a single slot for a 1024×1024 point image that the system resizes as needed.
Swift Concurrency provides a really nice way of writing an asynchronous code. Support for async-await has been to me the most awaited feature in Swift.
However, with great power comes great responsibility. If you learn from tutorials or even from the documentation, it’s really hard to find some details on how it works under the hood. Basically, Swift Concurrency is advertised as safe to use, because in theory the correctness is being checked by the compiler.
This way of “selling” Swift Concurrency encourages people to just jump in, add async-await to an existing code, and run some Tasks not really knowing what is going on under the hood. Unfortunately, there are many traps around concurrency and no… the compiler doesn’t check everything.
To be honest, even after performing tests, reading documentation, and watching WWDC I’m still not fully confident with Swift Concurrency. Although, I will try to share with you some of my observations hopefully making you more aware.
The swift-driver project is a new implementation of the Swift compiler driver that is intended to replace the existing driver with a more extensible, maintainable, and robust code base. The specific goals of this project include:
A maintainable, robust, and flexible Swift code base
Library-based architecture that allows better integration with build tools
Leverage existing Swift build technologies (SwiftPM, llbuild)
A platform for experimenting with more efficient build models for Swift, including compile servers and unifying build graphs across different driver invocations
The Swift compiler's command-line interface resembles that of other compilers, particularly GCC and Clang. However, Swift's compilation model and some of its language features make it a bit tricky to plug into a larger build system. In particular, there's no correct way to specify a "one command per file" build rule for a normal Swift module.
This document serves to describe the high-level design of the Swift 2.0 compiler driver (which includes what the driver is intended to do, and the approach it takes to do that), as well as the internals of the driver (which is meant to provide a brief overview of and rationale for how the high-level design is implemented).
The Swift driver is not intended to be GCC/Clang compatible, as it does not need to serve as a drop-in replacement for either driver. However, the design of the driver is inspired by Clang's design
This document serves to describe the parseable output format provided by the Swift compiler driver with the "-parseable-output" flag. This output format is intended to be parsed by other programs; one such use case is to allow an IDE to construct a detailed log based on the commands the driver issued.
SwiftUI’s .task modifier inherits its actor context from the surrounding function. If you call .task inside a view’s body property, the async operation will run on the main actor because View.body is (semi-secretly) annotated with @MainActor. However, if you call .task from a helper property or function that isn’t @MainActor-annotated, the async operation will run in the cooperative thread pool.
In Swift 5.7 that comes with Xcode 14 we can more easily check if two values of type Any are equal, because we can cast values to any Equatable and also use any Equatable as a parameter type thanks to Unlock existentials for all protocols change.
@StateObject and @ObservedObject have similar characteristics but differ in how SwiftUI manages their lifecycle. Use the state object property wrapper to ensure consistent results when the current view creates the observed object. Whenever you inject an observed object as a dependency, you can use the @ObservedObject.
Observed objects marked with the @StateObject property wrapper don’t get destroyed and re-instantiated at times their containing view struct redraws. Understanding this difference is essential in cases another view contains your view.
Recently on the Swift Forums, someone complained that putting Swift in the OS has only made things worse for developers. My immediate reaction is a snarky “welcome to the world of libraries shipped with the OS”, but that’s not helpful and also doesn’t refute their point. So here’s a blog post that talks about how we got where we did, covering time when I worked on Swift at Apple. But I’m going to have to start a lot earlier to explain the problem…
A recent question on the Swift forums prompted me to actually write this blog post I’ve been idly thinking about for a long time. These days, it’s common for apps to have external dependencies, but both statically linking and dynamically linking those dependencies comes with drawbacks. (This is the same thing as the title, only less provocative.) Why is there this tension and what can be done about it?
Today we learned how to use the NavigationPath type to push different views programmatically without defining additional types. We also learned how to serialize and store the current state of navigation in the scene storage to provide a better user experience.
This guide describes how to generate a Kotlin API using the http4k Project Wizard, and goes over what configurations and steps you'll need in order to deploy it (and other Kotlin APIs) to Heroku.
As I described in the exhaustivity testing article, a larger scale usually means discovering issues you might not have experienced with smaller apps. I'll cover more of them and my suggested solutions shortly, but today, I want to talk about Actions, their lack of boundaries, and what it entails.
After a brief overview of how SwiftUI's new NavigationStack API works, we'll explore how to build a router that can transform nebulous URLs into state that drives deep-linking in your application. Then, almost magically, that same code will be used to power a server-side application for generating deep-linking URLs.
How can one use an infrared sensor with the Raspberry Pi Pico? With Raspberry Pi rolling out the all new Raspberry Pi Pico now, this is a rather common query for makers.
An infrared sensor is a sensor that can measure the infrared light / electromagnetic radiation emitted by an object thereby detecting its presence. In this blog, we shall take a look at writing a program to use an infrared sensor with the Raspberry Pi Pico.
In August, Nate Soares visited Topos Institute. We told him a little about Poly and Proly Proly and he told us about what he wanted from a type theory.
In this post, we will discuss possible configurations and customization points of the dynamic island feature using the new API available in the WidgetKit framework.
A Live Activity displays up-to-date information from your app, allowing people to view the progress of events or tasks at a glance.
Live Activities help people keep track of tasks and events that they care about, offering persistent locations for displaying information that updates frequently. For example, a food delivery app could display the time remaining until a food order arrives, or a sports app can display the score for an ongoing game.
The category of polynomial functors is the free coproduct completion of Setsop. Equivalently, it is the total space of the family fibration of Setsop. More concretely, an object of Poly is given by a set I and a family of sets A:I→Sets.
One of the most essential skills for Kotlin Multiplatform Mobile cross-platform development is sensitivity to what code is platform-dependent or not.
Platform-dependent code can be written entirely in Kotlin using KMM’s expect and actual syntax or by defining an interface in the KMM common module and implementing it natively in Android (using Kotiln) and iOS (using Swift).
Platform-independent code is written inside the KMM shared framework and can be used for any business logic for your application that does not directly depend upon any platform-specific code.
Given the complexities of writing multi-platform code, this post provides an overview, and future posts will dive deeper into these topics.
One of the most prominent features of iOS 16 is live activity widgets. iOS 16 allows us to display the live state of ongoing activities from our apps on the lock screen or in the Dynamic Island of the new iPhone 14 Pro. This week we will learn how to build live activity widgets for our apps using the new ActivityKit framework.
Last week I spoke at NSSpainX to talk about how to use Composable Architecture in larger projects, the kind of issues you might run into and how you can work around them.
After using AWS for ~14 years, I've internalised a handful of design patterns that I try to apply to my own software. I'm keen to know if it's the same for other folks.
Unary views: Views with a single displayable, such as shapes, colors, controls and labels.
Structural views: Views that take zero or more other views, and combines them into a view with some subset of their displayables. Examples: ForEach, EmptyView, and the views used by ViewBuilder, such as TupleView and _ConditionalView.
Container views: Views that take the displayables of another view and manage them by deciding whether they should be displayed and how they should be laid out. Examples: HStack, VStack, List, LazyVStack.
Modifiers: Views that take one other view, and change the layout or look of all of its displayables individually. Examples: the views that modifiers such as .border, .padding, .frame generate, which are of type ModifiedContent.
The concept of Type Erasure is not new to Swift, but was radically improved in Swift 5.7 with the addition of the any prefix keyword (not to be confused with the capitalized Any type!) and improvements to the already existing some Opaque Type keyword. In this article, we'll explain the concept of type erasure, how it used to be done, what's different in Swift 5.7, and how these changes work under the hood.
Typescript and Javascript provide an error handling strategy based on the try/catch syntax which allows the programmer to escape the normal flow of the program in the presence of errors. This way of doing error handling certainly does its job but there are drawbacks that are often just accepted without giving too much thought about it. In this post, I will detail what these drawbacks are and how some ideas from functional programming can help to overcome them.
We used this approach in aws-lambda-haskell-runtime. Since Lambda results and errors must have a differently formatted body depending on the proxy (API Gateway, ALB, etc.), we used GADTs to make illegal states unrepresentable.
Startup time is a crucial app metric that should be continuously monitored and improved. A/B tests at top mobile app companies consistently show that adding just fractions of a second can significantly hurt core usage metrics, such as daily active users and time spent on the app per user per day.
Lyft reported a 5% increase in user sessions thanks to a 21% decrease in startup time for their driver app. Apple has made startup time the subject of numerous WWDC presentations.
One of the best SwiftUI additions this year has to be the Layout protocol. Not only we finally get our hands in the layout process, but it is also a great opportunity to better understand how layout works in SwiftUI.
Creating a basic layout is not hard, we just need to implement two methods. Nevertheless, there are a lot of options we can play with to achieve more complex containers. We will explore beyond the typical Layout examples. There are some interesting topics I haven’t seen explained anywhere yet, so I will present them here. However, before we can dive into these areas, we need to begin by building a strong foundation.
In the first part of this post we explored the basics of the Layout protocol in order to build a strong foundation of how Layout works. Now it’s time to dive into the less commented features and how to use them in our benefit.
You can put the capture attribute on inputs with the type of file, and you can give it a value of “user” or “environment“.
The interesting thing about the capture attribute is for users coming to your website on a mobile device. If they interact with that input, instead of opening up the default file picker, it will actually open up one of their cameras. It could be the front facing camera or the back facing camera, depending on the value.
If you set the value to “user“, it will use the user facing or front facing camera and or microphone. And if you set it to “environment“, it will use the outer facing or back facing camera and or microphone.
We are always looking for ways to improve the performance of our Composable Architecture, and spurred by some fascinating recent discussions, we spent most of last week looking for performance wins in the library. This has all culminated in a new release, 0.40.0, which brings a number of improvements to the library, and best of all, most of the changes came from collaboration with people in the community! 🤗
So, the question arises, how much does it cost to develop an app for my business?
What should my budget be? It seems like it fluctuates all the time. To put things in perspective, a recent study by Clutch of 12 top app developers found that the cost to create a mobile app ranged from $30,000 to $700,000.
Let us understand more about app development costs. It would help if you asked the right questions to fix your budget and start developing an app after you hire a developer!
A layout guide for placing content of a particular aspect ratio.
This layout guide provides a centered region in the window where you can place media content of a particular aspect ratio (width over height) to avoid obscuring the content.
There are three patterns I use in most of my UIKit projects that I've never seen anyone else talk about. I think they help readability a lot, so I'm sharing them here:
An addSubviews method to define your view hierarchy all at once
An @AssignedOnce property wrapper
A pattern for keeping view creation at the bottom of a file to keep the top clean
In UIKit, we would use UICoordinateSpace.convert(_,to:) or the older UIView.convert(_,to:) functions, and happily there's a SwiftUI equivalent in CoordinateSpace.
We will use SwiftUI and WidgetKit to create the user interface of the Live Activity. Live Activities works like Widget Extension and enables code sharing between your widgets and Live Activities.
The biggest issue when working on a cross-platform SwiftUI app is when you need to drop into AppKit on macOS and UIKit on iOS. Often, the APIs that you need (because they are absent from SwiftUI) are simply entirely different. However, sometimes the APIs are nearly identical but just different enough to require branching into platform-specific code paths. A good example of this is UIPasteboard on iOS and NSPasteboard on macOS.
In this article I will be covering what a Sourcery command plugin looks like, but I am already working on a part two where I will be creating a build tool plugin, which presents numerous interesting challenges.
Here is an exhaustive, annotated list of GHCi commands, somewhat divided by task. Within each section, commands are listed alphabetically.
Some important ones are covered in more detail in their own lessons. Each command is linked to the page in this course that discusses it; you can either click through to find out about a particular command of interest, or keep reading through this series to get to it.
The @ViewBuilder attribute is one of the few result builders available for you to use in SwiftUI. You typically use it to create child views for a specific SwiftUI view in a readable way without having to use any return keywords.
When the user navigates through focusable views in our app with the tab key, the focus will move in the reading order: first from the leading edge to the trailing edge and then from top down.
While this default behavior is right for many use cases, sometimes we need to customize and redirect the focus movement to fit our custom app design.
More like Core Foundation. It’s still there behind the scenes, but programmers use high-level Objective-C and Swift wrappers from Foundation. If something is missing, you can call an underlying C API. The relation between SwiftUI and AppKit is similar, for now.
Learn how to migrate existing applications to use the new ReducerProtocol, in both Swift 5.7 and Swift 5.6.
Migrating an application that uses the Reducer type over to the new ReducerProtocol can be done slowly and incrementally. The library provides the tools to convert one reducer at a time, allowing you to plug protocol-style reducers into old-style reducers, and vice-versa.
Although we recommend migrating your code when you have time, the newest version of the library is still 100% backwards compatible with all previous versions. The Reducer type is now "soft" deprecated, which means we consider it deprecated but you will not get any warnings about it. Some time in the future we will officially deprecate it, and then sometime even later we will remove it so that we can rename the protocol to Reducer.
Learn how to improve the performance of features built in the Composable Architecture.
As your features and application grow you may run into performance problems, such as reducers becoming slow to execute, SwiftUI view bodies executing more often than expected, and more.
There's a bug in Xcode 14 betas 4-6 that causes a crash loop in the PosterBoard process when you run an iOS 16 iPhone simulator, making your computer's CPU usage go sky high and battery to drain very quickly. Here's a workaround until Apple resolves the issue.
In this article, we will look at all of the alternative ways to install Xcode, how to speed up the process, and how to resolve disk space problems. We’ll also look at the Windows alternative to Xcode.
Structural identity is the type of identity that SwiftUI uses to understand your views without an explicit identifier by using your layout description. This week we will learn how to improve performance and eliminate unwanted animations by using inert view modifiers in SwiftUI.
Last week I saw that the community tries to move UIKit development patterns to SwiftUI. But I’m sure that the best way to write efficient SwiftUI is to forget everything about UIKit and entirely change your mindset in terms of User Interface development. This week we will learn the main differences between UIKit and SwiftUI development.
The supported platforms for running Swift on the server and the ready-built tools packages are all hosted here on swift.org together with installation instructions. There’s also the language reference documentation section for viewing more information about Swift.
The recommended way to build server applications is with Swift Package Manager. SwiftPM provides a cross-platform foundation for building Swift code and works nicely for having one code base that can be edited as well as run on many Swift platforms.
SwiftPM is integrated with XCTest, Apple’s unit test framework. Running swift test from the terminal, or triggering the test action in your IDE (Xcode or similar), will run all of your XCTest test cases. Test results will be displayed in your IDE or printed out to the terminal.
First of all, it’s very important to make sure that you compiled your Swift code in release mode. The performance difference between debug and release builds is huge in Swift. You can compile your Swift code in release mode using swift build -c release.
Once an application is built for production, it still needs to be packaged before it can be deployed to servers. There are several strategies for packaging Swift applications for deployment.
For multithreaded and low-level unsafe interfacing server code, the ability to use LLVM’s ThreadSanitizer and
AddressSanitizer can help troubleshoot invalid thread usage and invalid usage/access of memory.
We saw what are reference and value types, and what are the new actor types. Also, we described some reasons to use classes over structs and what is dynamic and static methods dispatch in Swift. We discussed thread safety using types in Swift and how you can expand your studies about them.
From the first day of the SwiftUI framework, we have primary layout containers like VStack, HStack, and ZStack. The current iteration of the SwiftUI framework brings another layout container allowing us to place views in a grid. But the most important addition was the Layout protocol that all layout containers conform to. It also allows us to build our super-custom layout containers from scratch. This week we will learn the basics of the Layout protocol in SwiftUI and how to build conditional layouts using AnyLayout type.
The concepts presented here highlight several powerful generalized techniques for macOS kernel exploits on Intel-based systems. We demonstrated how the dblmap can substantially weaken the efficacy of KASLR, provide several interesting kernel call targets, host smuggled kernel shellcode, and more.
These primitives were used in the practical exploitation of numerous kernel vulnerabilities we responsibly disclosed to Apple over the past year. Abusing core low-level constructs of the operating system can lead to very interesting consequences, and prove incredibly challenging to mitigate.
Swiftinit is a collection of richly-linked high-level technical articles and tutorials related to the Swift programming language. Kelvin Ma started Swiftinit in late 2021 when he and a few professional Swift developers realized that educational resources for the Swift language were often scattered across personal blogs or buried deep in Github repositories, making it hard to beginners to get started with the language.
In iOS/iPadOS 16.0 there have been a few minor and one significant change in behaviour when presenting modal view controllers:
when the presenting view controller is in a regular-width environment on iPad, form sheets are slightly bigger than on previous iPadOS versions. This changed in beta 4. (If the presenting view has compact width, a form sheet presentation will adapt and fill the width, just like on iPhone.)
the height of the navigation bar in a non-full-screen, non-popover, modally-presented view controller is smaller than before (12 points smaller on iPhone and 6 points smaller on iPad). This change has only just occurred in beta 5. Many thanks to Jordan Hipwell for discovering this and bringing it to my attention. He also discovered this has not (yet?) changed in SwiftUI.
non-full-screen modally-presented double and triple column style split view controllers have a different appearance compared to iPadOS 13 to 15.
I'm very excited to announce that we now have a pure Rust implementation of a client for Apple's Notary API in the apple-codesign crate. This means we can now notarize Apple software from any machine where you can get the Rust crate to compile. This means we no longer have a dependency on the 3rd party Apple Transporter application. Notarization, like code signing, is 100% open source Rust code.
Plutus Core (PLC) is the programming language that “runs” on the Cardano Blockchain. A blockchain is just a distributed data structure though, so programs do not literally run on it. What we mean is that Plutus Core programs are stored on the blockchain in binary form and can be referenced by transactions. Plutus Core programs are later retrieved from the blockchain and executed by Cardano Nodes when required by other transactions that reference them.
In this blog post, we give a high-level overview of the role that Plutus Core plays in the Cardano ecosystem, what programs written in Plutus Core look like, and how those programs work.
The Stabilization phase of Project Duplo lasted six months. In this phase, we wanted to “stop the bleeding”, by addressing key elements of tech debt that were slowing development on each platform. We talked to our mobile developers about the issues they thought were the most important to address, used code health metrics to assess which files in the codebase were the “worst”, and tried to focus on a few key areas where we could make big impacts. For this phase, we had a core team of developers who were dedicated to working on Duplo, as well as leads for each platform. This core team worked together throughout the Stabilization phase, to ensure we had developers focused on the project (and not pulled off onto feature development).
We use the word module to describe a subproject — generally a static or dynamic framework linked into the app. Prior to Duplo, we had split off some of our infrastructure code into subprojects on both platforms, but most of the code was still in the main app target, and all feature development was happening there. During Duplo, modularization was a key focus of the project, and we made a concerted push to move code out of the app target.
In addition to modularizing our codebase as part of Duplo, we also wanted to improve our overall app architecture, ensure we were keeping up with industry trends, and adopt more forward-looking design patterns and technologies. On each platform, we decided on particular areas of focus which we thought would both improve the experience of building features for our mobile developers and put our mobile codebases on better footing.
When it comes to transforming a view, one can think of it as applying a calculation to each individual point of the view’s layer, such that for every (x, y, z), we obtain a new (x', y', z'). That calculation is actually a multiplication of the coordinates (x, y, z) by a matrix (good ol' linear algebra). How we construct our matrix is through the use of various types of CATransform3Ds, which we’ll now dive into.
While investigating some memory leak issue, I found out that if I po an object before using Memory Graph, that object would stay in memory forever, and Memory Graph would show something like NSKeyValueDependencyInfo as an owner of the object.
A leak will also happen when using p or expression.
If we have an array of elements in Swift and we need to sort it based on a specific property, we can't use the simple sorted() method.
Another way to approach it would be to use KeyPathComparator introduced in Foundation in iOS 15. We can pass the comparator created with a key path to a particular property to sorted(using:) method.
Sendable and @Sendable are part of the concurrency changes that arrived in Swift 5.5 and address a challenging problem of type-checking values passed between structured concurrency constructs and actor messages.
There's a common joke that the rite of passage for every Haskell programmer is to write a "monad tutorial" blog post once they think they finally understand with how they work. There are enough of those posts out there, though, so I don't intend for this to be yet another monad tutorial. However, based on my learning experience, I do have some thoughts on why people seem to struggle so much with monads, and as a result, why so many of those tutorials exist.
At a high level, the intuition for monads are that they are an abstraction of sequencing in programming. Any computation that involves "do this, and then do that using the previous result" can be considered monadic.
One thing that the Swift engineering community thought when they were developing the new asynchronous API is that in some manner they should support a bridging way to connect your old closure-based APIs with the new async/await world.
And that is exactly what they did, the Swift team created the Continuation API. This creates a suspension point in your code and that is exactly what you need to use the new async/await semantics.
The design brings excellent user/client experience for Android and iOS development. The two platforms have different explicit highlights in their UI/UX approach. Yet, both have predictable highlights that guarantee the user a better experience.
But Apple, they try to have complete command over their items. It guarantees that the client has a reliable encounter with any of the gadgets of Apple’s. Apple takes more care of the design, UX, and exhibitions than different makers. But Google they have a platform that targets a significant part of accessible phones.
I’d like to highlight the UI differences between Android and iOS on various prospects.
Share live updates from your app as Live Activities on the Lock Screen.
With the ActivityKit framework, you can start a Live Activity to share live updates from your app on the Lock Screen. For example, a sports app might allow the user to start a Live Activity for a live sports game. The Live Activity appears on the Lock Screen for the duration of the game and offers the latest updates about the game at a glance.
In your app, you use ActivityKit to configure, start, update, and end the Live Activity, and your app’s widget extension uses SwiftUI and WidgetKit to create the user interface of the Live Activity. This makes the presentation code of a Live Activity similar to the widget code and enables code sharing between your widgets and Live Activities.
However, Live Activities use a different mechanism to receive updates compared to widgets. Instead of using a timeline mechanism, Live Activities receive updated data from your app with ActivityKit or by receiving remote push notifications with the User Notifications framework.
Start a Live Activity that appears on the Lock Screen and update it with your app’s most current data.
Live Activities display and update an app’s most current data on the iPhone Lock Screen. This allows people to see live information they care about the most at a glance. To offer Live Activities, add code to your existing widget extension or create a new widget extension if your app doesn’t already include one. Live Activities use WidgetKit functionality and SwiftUI for their user interface on the Lock Screen. ActivityKit’s role is to handle the life cycle of each Live Activity: You use its API to request, update, and end a Live Activity.
A view that presents views in two or three columns, where selections in leading columns control presentations in subsequent columns.
You create a navigation split view with two or three columns, and typically use it as the root view in a Scene. People choose one or more items in a leading column to display details about those items in subsequent columns.
Swift’s FormatStyle and ParseableFormatStyle are the easiest way to convert Foundation data types to and from localized strings. Unfortunately Apple hasn’t done a great job in documenting just what it can do, or how to use them.
LabeledContent view is a simple view that composes a label and content. Usually, it displays the label on the leading edge and the content on the trailing edge. You can achieve similar behavior by inserting the label and content into the HStack and placing the Spacer view between them.
The venerable (NS)Formatter class (and Apple’s various subclasses) are an Objective-C based API that is most well known as the go-to method for converting data types into strings. One of the lesser-known features of the APIs are that these same formatters can do the reverse: parse strings into their respective data types.
Apple’s modern Swift replacement system for Formatter is a set of protocols: FormatStyle and ParseableFormatStyle. The former handles the conversion to strings, and the latter strings to data.
FormatStyle and it’s various implementations is it’s own beast. Apple’s various implementations to support the built-in Foundation data types is quite extensive but spottily documented. I made a whole site to help you use them.
But that’s not what we’re going to talk about today.
Today we’re going to talk about ParseableFormatStyle and it’s implementations. How can we convert some strings into data?
A full example of adding String and AttributedString output to our custom types, as well as adding the ability to parse String values into your custom type.
So you’ve read the gosh darn site and know how to get strings from data types..
Then you read the ParseableFormatStyle post and know how to parse strings into data.
If your next thought was: “Now I want to do this with my own data types”, then this is for you.
SwiftUI’s various stacks are some of the framework’s most fundamental layout tools, and enable us to define groups of views that are aligned either horizontally, vertically, or stacked in terms of depth.
import SwiftUI
/// Edit: I completely refactored the code. The previous implemenation is accessible through
/// revisions. Now:
/// ```
/// let path = NavigationPath()
/// let inspectable: NavigationPath.Inspectable = path.inspectable //or
/// let typedInspectable: NavigationPath.Inspectable.Of<Component>
/// = path.inspectable(of: Component.self)
/// ```
/// Both types are random-access and range replaceable collections. The first one of Any?, the
/// second of `Component`.
/// Both expose a `.navigationPath` property, so it is easy to construct a Binding like
/// ```
/// @State var path = NavigationPath().inspectable
/// NavigationStack(path: $path.navigationPath) { … }
/// ```
/// All of the following is enabled by our capacity to extract the last path component. It is maybe
/// possible to defer this function to SwiftUI by observing what's popping up in
/// `.navigationDestination`, but it is unlikely that we'll be able to make this work without
/// glitches/side-effects.
/// So for now, we are restricted to `NavigationPath`with `Codable` components only.
///
/// As an aside, I find very interesting that once you have `var count: Int`, `var last: Element?`,
/// `append(Element)` and `removeLast()` operations on a set of elements, you can turn it into a
/// mutable random access collection.
// MARK: - Common Helpers -
extensionNavigationPath{ // RandomAccessCollection-like
var_startIndex:Int{0}var_endIndex:Int{ count }
/// We opt in for throwing functions instead of subscripts. This also makes room for an
/// hypothetical `inout` cache argument.
func get(at position:Int)throws->Any{varcopy=self
copy.removeLast(count -(position +1))returntry copy.lastComponent!
}mutatingfunc set(_ newValue:Any, at position:Int)throws{
// Auto-register the mangled type name
registerValueForNavigationPathComponent(newValue)
// We preserve the tail (position+1)...
vartail=[Any]()while count > position +1{
// Because `lastComponent == nil <=> isEmpty`, we can force-unwrap:
tail.append(try lastComponent!)removeLast()}
// Discard the one that will be replaced:
if !isEmpty {removeLast()}
// Double parenthesis are required by the current version of Swift
// See https://github.com/apple/swift/issues/59985
append((newValue as!any(Hashable&Codable)))
// Restore the tail that was preserved:
forpreservedin tail.reversed(){append((preserved as!any(Hashable&Codable)))}}}extensionNavigationPath{ // RangeReplaceableCollection+MutableCollection-like
mutatingfunc _replaceSubrange<C>(_ subrange:Range<Int>, with newElements:C)throwswhere C :Collection, Any ==C.Element{
// Auto-register the mangled type name
iflet first = newElements.first {registerValueForNavigationPathComponent(first)}
// We apply the same trick than for the index setter.
vartail=[Any]()while count > subrange.upperBound {
tail.append(try lastComponent!)removeLast()}
// We don't need to preserve this part which will be replaced:
while count > subrange.lowerBound {removeLast()}
// Insert the new elements:
fornewValuein newElements {append((newValue as!any(Hashable&Codable)))}
// Restore the preserved tail:
forpreservedin tail.reversed(){append((preserved as!any(Hashable&Codable)))}}}extensionNavigationPath{publicstructInspectable:RandomAccessCollection,RangeReplaceableCollection,MutableCollection{publicvarnavigationPath:NavigationPathpublicinit(_ navigationPath:NavigationPath){self.navigationPath = navigationPath
}publicinit(){self.navigationPath =.init()}publicvarstartIndex:Int{ navigationPath._startIndex }publicvarendIndex:Int{ navigationPath._endIndex }public subscript(position:Int)->Any{get{do{returntry navigationPath.get(at: position)}catch{NavigationPath.printExtractionError(error)}}set{do{try navigationPath.set(newValue, at: position)}catch{NavigationPath.printExtractionError(error)}}}publicmutatingfunc replaceSubrange<C>(_ subrange:Range<Int>, with newElements:C)where C :Collection, Any ==C.Element{do{try navigationPath._replaceSubrange(subrange, with: newElements)}catch{NavigationPath.printExtractionError(error)}}
/// A throwing version of `last`
publicvarlastComponent:Any?{get throws{try navigationPath.lastComponent }}}}extensionNavigationPath{
/// Generates an inspectable representation of the current path.
publicvarinspectable:Inspectable{.init(self)}}extensionNavigationPath.Inspectable{publicstructOf<Component>:RandomAccessCollection,RangeReplaceableCollection,MutableCollectionwhere Component:Hashable, Component:Codable{publicvarnavigationPath:NavigationPathpublicinit(_ navigationPath:NavigationPath){registerTypeForNavigationPathComponent(Component.self)self.navigationPath = navigationPath
}publicinit(){registerTypeForNavigationPathComponent(Component.self)self.navigationPath =.init()}publicvarstartIndex:Int{ navigationPath._startIndex }publicvarendIndex:Int{ navigationPath._endIndex }public subscript(position:Int)->Component{get{do{returntry navigationPath.get(at: position)as!Component}catch{NavigationPath.printExtractionError(error)}}set{do{try navigationPath.set(newValue, at: position)}catch{NavigationPath.printExtractionError(error)}}}publicmutatingfunc replaceSubrange<C>(_ subrange:Range<Int>, with newElements:C)where C :Collection, Component ==C.Element{do{try navigationPath._replaceSubrange(subrange, with: newElements.map{ $0 asAny})}catch{NavigationPath.printExtractionError(error)}}
/// A throwing version of `last`
publicvarlastComponent:Component?{get throws{try navigationPath.lastComponent as?Component}}}}extensionNavigationPath{
/// Generates a typed inspectable representation of the current path.
publicfunc inspectable<Component>(of type:Component.Type)->NavigationPath.Inspectable.Of<Component>{.init(self)}}
// MARK: - Utilities
extensionNavigationPath{publicenumError:Swift.Error{case nonInspectablePath
case unableToFindMangledName(String)}
/// This is not super efficient, but at least always in sync.
varlastComponent:Any?{get throws{guard !isEmpty else{returnnil}guardlet codable else{throwError.nonInspectablePath
}returntryJSONDecoder().decode(_LastElementDecoder.self, from:JSONEncoder().encode(codable)).value
}}staticfunc printExtractionError(_ error:Swift.Error)->Never{fatalError("Failed to extract `NavigationPath component: \(error)")}
/// We use this type to decode the two first encoded components.
privatestruct_LastElementDecoder:Decodable{varvalue:Anyinit(from decoder:Decoder)throws{varcontainer=try decoder.unkeyedContainer()lettypeName=try container.decode(String.self)
typesRegisterLock.lock()letmangledTypeName=typeNameToMangled[typeName, default: typeName]
typesRegisterLock.unlock()guardlet type =_typeByName(mangledTypeName)as?(anyDecodable.Type)else{
typesRegisterLock.lock()defer{ typesRegisterLock.unlock()}iftypeNameToMangled[typeName]==nil{throwError.unableToFindMangledName(typeName)}throwDecodingError.dataCorruptedError(
in: container,
debugDescription:"\(typeName) is not decodable.")}letencodedValue=try container.decode(String.self)self.value =tryJSONDecoder().decode(type, from:Data(encodedValue.utf8))}}}
/// `NavigationPath` codable representation is using `_typeName` instead of mangled names, likely
/// because it is intented to be serialized. But we need mangled names to respawn types using
/// `_typeByName`.
/// I don't know a way to find the mangled name from the type name. If one could generate a list
/// of mangled symbols, we can probably lookup. In the meantime, clients of `Inspectable` should
/// register types they intend to use as path components. This step is realized automatically for
/// `NavigationPath.Inspectable.Of<Component>`, and also automatically when editing the
/// `NavigationPath` using the inspector, but it needs to be performed manually if some
/// `NavigationPath` is deserialized.
///
/// In other words, registering is only required when deserializing an heterogenous
/// `NavigationPath` or an homogenous one with untyped inspection.
/// Register a type for inspection
publicfunc registerTypeForNavigationPathComponent<T>(_ type:T.Type){
typesRegisterLock.lock()typeNameToMangled[_typeName(T.self)]=_mangledTypeName(T.self)
typesRegisterLock.unlock()}
// Register a type for inspection from any value of it
publicfunc registerValueForNavigationPathComponent(_ value:Any){lettype=type(of: value)
typesRegisterLock.lock()typeNameToMangled[_typeName(type)]=_mangledTypeName(type)
typesRegisterLock.unlock()}privatelettypesRegisterLock=NSRecursiveLock()privatevartypeNameToMangled=[String: String]()
// MARK: - Tests
func runPseudoTests(){do{
// Check extracting the last component
letpath=NavigationPath([0,1,2,3,4,5,6,7,8,9])assert(path.inspectable.last as?Int==9)}do{
// Check extracting the nth component
letpath=NavigationPath([0,1,2,3,4,5,6,7,8,9])assert(path.inspectable[4]as?Int==4)}do{
// Check setting the nth component
varpath=NavigationPath([0,1,2,3,4,5,6,7,8,9]).inspectable
path[4]=-1letexpected=NavigationPath([0,1,2,3,-1,5,6,7,8,9])assert(path.navigationPath == expected)}do{
// Check joining two paths
letpath=NavigationPath([0,1,2,3,4,5,6,7,8,9])letp1=NavigationPath([0,1,2,3,4])letp2=NavigationPath([5,6,7,8,9])letjoinedPath=(p1.inspectable + p2.inspectable).navigationPath
assert(path == joinedPath)}do{
// Check editing a path "in the belly".
varinspectable=NavigationPath([0,1,2,3,4,5,6,7,8,9]).inspectable
inspectable.replaceSubrange(3..<6, with:[-1,-2])letexpected=NavigationPath([0,1,2,-1,-2,6,7,8,9])assert(expected == inspectable.navigationPath)}}extensionView{
// Use this method in place of `navigationDestination` to automatically
// register component types.
func inspectableNavigationDestination<D:Hashable, Content:View>(for value:D.Type, destination:@escaping(D)->Content)->someView{registerTypeForNavigationPathComponent(D.self)returnself.navigationDestination(for: value, destination: destination)}}
// MARK: -
// Example: Navigation with two destination types and `NavigationPath`
// inpection and manipulation.
structDestination:Hashable,Codable{varid:Intvartitle:String}structAlternativeDestination:Hashable,Codable{varid:Intvartitle:String}structContentView:View{@Statevarpath=NavigationPath().inspectable // A `NavigationPath.Inspectable` value
@StatevarisModalPresented:Bool=falsevarbody:someView{NavigationStack(path: $path.navigationPath){ // We can derive a "mapped" binding from @State
VStack{Button{
path.append(Destination(id:2, title:"Screen #\(2)"))} label:{Label("Navigate to next", systemImage:"arrow.forward")}Button{letdestinations=(2...5).map{Destination(id: $0, title:"Screen #\($0)")}
path.append(contentsOf: destinations)} label:{Label("Navigate to \"5\"", systemImage:"arrow.forward")}}.navigationBarTitleDisplayMode(.inline).navigationTitle("NavigationPath inspection").inspectableNavigationDestination(for:Destination.self){DestinationView(destination: $0, path: $path)}.inspectableNavigationDestination(for:AlternativeDestination.self){AlternativeDestinationView(destination: $0, path: $path)}}.buttonStyle(.borderedProminent).safeAreaInset(edge:.bottom){
lastComponentOverlay
}.task{runPseudoTests()}}varlastComponentOverlay:someView{
// We observe the current last element of the path, extracted from the inspectable path
VStack(spacing:8){Text("Last element of path").textCase(.uppercase).foregroundStyle(.secondary)Text(path.last.map(String.init(describing:))??"nil").font(.footnote.monospaced()).fontWeight(.semibold).frame(maxWidth:.infinity, alignment:.leading)if !path.isEmpty {Button{
isModalPresented =true} label:{Text("Show NavigationPath")}.buttonStyle(.bordered)}}.font(.footnote).frame(maxWidth:.infinity).padding().background(.ultraThinMaterial.shadow(.drop(radius:6)),
in:RoundedRectangle(cornerRadius:11)).padding(.horizontal).animation(.spring(dampingFraction:0.7), value:(path.last as?Destination)?.id).sheet(isPresented: $isModalPresented){if path.isEmpty {VStack{Text("The path is empty")Button("Close"){ isModalPresented =false}}.presentationDetents([.medium])}else{NavigationStack{List{ForEach(Array(zip(0..., path)), id: \.0){ index, value inHStack{Text("\(index)")Text(String(describing: value))}}.onDelete{ offsets in
path.remove(atOffsets: offsets)}
// This is glitchy in SwifUI Previews
.onMove{ source, destination in
path.move(fromOffsets: source, toOffset: destination)}}.safeAreaInset(edge:.bottom){if path.count >1{Button{
// Not animating unfortunately, likely by design for deep-linking
withAnimation{
path.shuffle()}} label:{Label("Shuffle", systemImage:"dice").frame(maxWidth:.infinity).frame(minHeight:33)}.buttonStyle(.borderedProminent).padding(.horizontal)}}.environment(\.editMode,.constant(.active)).navigationTitle("NavigationPath").navigationBarTitleDisplayMode(.inline)}.presentationDetents([.medium,.large])}}}}structDestinationView:View{vardestination:Destination@Bindingvarpath:NavigationPath.Inspectablevarbody:someView{letnextDestination=Destination(
id: destination.id +1,
title:"Screen #\(destination.id +1)")letnextAlternativeDestination=AlternativeDestination(
id: destination.id +1,
title:"Alternative Screen #\(destination.id +1)")List{NavigationLink("Navigate to \(destination.id +1)",
value: nextDestination)NavigationLink("Alternative destination \(destination.id +1)",
value: nextAlternativeDestination)}.safeAreaInset(edge:.top){HStack{Button{
path.append(nextDestination)} label:{Label("Navigate to \(destination.id +1)", systemImage:"arrow.forward")}if path.count >1{Button{withAnimation{
path.shuffle()}} label:{Label("Shuffle", systemImage:"dice")}}}}.navigationTitle(destination.title)}}structAlternativeDestinationView:View{vardestination:AlternativeDestination@Bindingvarpath:NavigationPath.Inspectablevarbody:someView{letnextDestination=Destination(
id: destination.id +1,
title:"Screen #\(destination.id +1)")letnextAlternativeDestination=AlternativeDestination(
id: destination.id +1,
title:"Alternative Screen #\(destination.id +1)")List{NavigationLink("Navigate to \(destination.id +1)",
value: nextDestination)NavigationLink("Alternative destination \(destination.id +1)",
value: nextAlternativeDestination)}.scrollContentBackground(Color.yellow).safeAreaInset(edge:.top){HStack{Button{
path.append(nextDestination)} label:{Label("Navigate to \(destination.id +1)", systemImage:"arrow.forward")}if path.count >1{Button{withAnimation{
path.shuffle()}} label:{Label("Shuffle", systemImage:"dice")}}}}.navigationTitle(destination.title)}}structContentView_Previews:PreviewProvider{staticvarpreviews:someView{ContentView()}}
Xcode 14 brings a new look to the preview canvas. The pin control is now in the upper left corner and works as before allowing you navigate to different source files while pinning the preview in the canvas. Next to the pin control are the new page controls.
Unlike most types in SwiftUI, ImageRenderer is not a struct, it is a class. And not just any class, it is an ObservableObject. That means it has a publisher you can subscribe to. All published events by the renderer, mean that the image changed.
A type-erased list of data representing the content of a navigation stack.
You can manage the state of a NavigationStack by initializing the stack with a binding to a collection of data. The stack stores data items in the collection for each view on the stack. You also can read and write the collection to observe and alter the stack’s state.
When a stack displays views that rely on only one kind of data, you can use a standard collection, like an array, to hold the data. If you need to present different kinds of data in a single stack, use a navigation path instead. The path uses type erasure so you can manage a collection of heterogeneous elements. The path also provides the usual collection controls for adding, counting, and removing data elements.
The new UICalendarView added to UIKit in iOS 16 looks great but there’s not a SwiftUI equivalent. Here’s how I got a SwiftUI app to show the calendar based on custom dates and update the calendar when dates change.
In the past, if we wanted to convert a SwiftUI view into an image we would wrap the view in a representable, and then use UIKit/AppKit to build our image. With the new renderers that is not longer necessary, but the approach is totally different and there is a whole set of considerations we need to make in order to be successful.
The idea of UI being a pure function of the model seems so obviously incorrect, and leads to such a plethora of problems, that it is a bit puzzling how one could come up with it in the first place, and certainly how one would stick with it in face of the avalanche of problems that just keeps coming. A part of this is certainly the current unthinking infatuation with functional programming ideas. These ideas are broadly good, but not nearly as widely or universally applicable as some of their more starry-eyed proponents propose (I hesitate to use the word "think" in this context).
View models and functional UI look like solutions, and they are indeed effective ways of managing complexity by making all the constituent state visible and enumerated. But in my experience they also encourage a way of programming where you bind as much as possible, and the problem with that is that, as the title of the linked post notes, UIs are not pure functions of the models.
Create executable bundles to extend the functionality of other apps by presenting a user interface.
Extensions are executable code bundles, in one app that perform functions in a second, host app. Host apps declare extension points that control the kinds of functionality its extensions can implement. Extensions allow iOS and Mac apps to include code that runs inside system apps. For example, Messages provides extension points so apps can create iMessage Apps. Messages automatically finds extension bundles that target its extension points and makes them available in its app drawer. A Mac app can also declare its own extension points so that other apps can extend the Mac app’s functionality.
Languages that have a rather low barrier to entry often struggle when it comes to performance because too much is abstracted from the programmer to make things simple. Therefore in those languages, the key to unlock performance is often to write some of the code in C, collaterally abandoning the safety of the higher-level language.
Swift on the other hand lets you unlock best of both worlds: performance and safety. Naturally not all Swift code is magically fast and just like everything else in programming performance requires constant learning.
Johannes discusses one aspect of what was learned during SwiftNIO development. He debunks one particular performance-related myth that has been in the Swift community ever since, namely that classes are faster to pass to functions than structs.
I've built a batteries-included Store that comes with everything you'll need out of the box called Boutique to be the foundation for that data. Boutique does no behind the scenes magic and doesn't resort to shenanigans like runtime hacking to achieve a great developer experience.
A SwiftUI view that displays the phrase someone uses to invoke an App Shortcut.
Use a SiriTipView to display the spoken phrase for the intent you specify. Include an instance of your intent when you create the view, and bind the view to a Boolean to handle the view’s presentation. The following example shows how to configure a button for a reorder intent and bind it to an isInserted variable.
The Web Authentication API (also known as WebAuthn) is a specification written by the W3C and FIDO, with the participation of Google, Mozilla, Microsoft, Yubico, and others. The API allows servers to register and authenticate users using public key cryptography instead of a password.
SwiftUI is the declarative data-driven framework allowing us to build complex user interfaces by defining the data rendering on the screen. Navigation was the main pain point of the framework from the very first day. Fortunately, things have changed since WWDC 22, and SwiftUI provides the new data-driven Navigation API.
To help future us (and you!), we’ve copied every question/answer from the lounges of special interest to us: SwiftUI and design. I bet we’ll be referencing them throughout development, and we expect many others to do too. So many valuable insights and tips!
App Clip diagnostics checks App Clip experiences that use physical codes, Safari and iMessage, and it will also check your universal link associated domains configuration. This simple new tool makes it so much easier to get your configuration right.
Don't be captured by CAPTCHAs! Private Access Tokens are a powerful alternative that help you identify HTTP requests from legitimate devices and people without compromising their identity or personal information. We'll show you how your app and server can take advantage of this tool to add confidence to your online transactions and preserve privacy.
Join us as we explore one of the core concepts in Swift concurrency: isolation of tasks and actors. We'll take you through Swift's approach to eliminating data races and its effect on app architecture. We'll also discuss the importance of atomicity in your code, share the nuances of Sendable checking to maintain isolation, and revisit assumptions about ordering work in a concurrent system.
Background Tasks help apps respond to system events and keep time-sensitive data up to date. Learn how you can use the SwiftUI Background Tasks API to handle tasks succinctly. We'll show you how to use Swift Concurrency to handle network responses, background refresh, and more — all while preserving performance and power.
Learn how the Xcode build system extracts maximum parallelism from your builds. We'll explore how you can structure your project to improve build efficiency, take you through the process for resolving relationships between targets' build phases in Xcode, and share how you can take full advantage of available hardware resources when compiling in Swift. We'll also introduce you to Build Timeline — a powerful tool to help you monitor your build efficiency and performance.
Learn how you can set up complex Swift projects for debugging. We'll take you on a deep dive into the internals of LLDB and debug info. We'll also share best practices for complex scenarios such as debugging code built on build servers or code from custom build systems.
Starting from iOS 16 we can present resizable sheets natively in SwiftUI. In this article we'll look into what we can achieve with the new APIs and what limitations they have in comparison with UIKit.
Learn how you can use Swift 5.7 to design advanced abstractions using protocols. We'll show you how to use existential types, explore how you can separate implementation from interface with opaque result types, and share the same-type requirements that can help you identify and guarantee relationships between concrete types.
Passkeys are a replacement for passwords. They are faster to sign in with, easier to use, and much more secure.
Passkeys are a replacement for passwords that are designed to provide websites and apps a passwordless sign-in experience that is both more convenient and more secure. Passkeys are a standard-based technology that, unlike passwords, are resistant to phishing, are always strong, and are designed so that there are no shared secrets. They simplify account registration for apps and websites, are easy to use, and work across all of your Apple devices, and even non-Apple devices within physical proximity.
SwiftUI now offers powerful tools to level up your layouts and arrange views for your app's interface. We'll introduce you to the Grid container, which helps you create highly customizable, two-dimensional layouts, and show you how you can use the Layout protocol to build your own containers with completely custom behavior. We'll also explore how you can create seamless animated transitions between your layout types, and share tips and best practices for creating great interfaces.
Use navigation links, stacks, destinations, and paths to provide a streamlined experience for all platforms, as well as behaviors such as deep linking and state restoration.
A view that displays a root view and enables you to present additional views over the root view.
Use a navigation stack to present a stack of views over a root view. People can add views to the top of the stack by clicking or tapping a NavigationLink, and remove views using built-in, platform-appropriate controls, like a Back button or a swipe gesture. The stack always displays the most recently added view that hasn’t been removed, and doesn’t allow the root view to be removed.
Create accessory widgets that appear on the iPhone Lock Screen and as complications on Apple Watch.
Starting with iOS 16 and watchOS 9, WidgetKit allows you to extend the reach of your app to the Lock Screen on iPhone and to the watch face as complications on Apple Watch. They are constantly visible, display your app’s most relevant, glanceable content, and let people quickly access your app for more details.
Generics are a fundamental tool for writing abstract code in Swift. Learn how you can identify opportunities for abstraction as your code evolves, evaluate strategies for writing one piece of code with many behaviors, and discover language features in Swift 5.7 that can help you make generic code easier to write and understand.
The recipe for a great app begins with a clear and robust navigation structure. Join the SwiftUI team in our proverbial coding kitchen and learn how you can cook up a great experience for your app. We'll introduce you to SwiftUI's navigation stack and split view features, show you how you can link to specific areas of your app, and explore how you can quickly and easily restore navigational state.
Eliminate passwords for your users when they sign in to apps and websites.
Passkeys use iCloud Keychain public key credentials, eliminating the need for passwords. Instead, they rely on biometric identification such as Touch ID and Face ID in iOS, or a specific confirmation in macOS for generating and authenticating accounts.
Discover how to improve your app's build and runtime linking performance. We'll take you behind the scenes to learn more about linking, your options, and the latest updates that improve the link performance of your app.
Learn how we've optimized the Swift and Objective-C runtimes to help you make your app smaller, quicker, and launch faster. Discover how you can get access to efficient protocol checks, smaller message send calls, and optimized ARC simply when you build your app with Xcode 14 and update your deployment target.
Discover distributed actors — an extension of Swift's actor model that simplifies development of distributed systems. We'll explore how distributed actor isolation and location transparency can help you avoid the accidental complexity of networking, serialization, and other transport concerns when working with distributed apps and systems.
Learn how the Xcode build system extracts maximum parallelism from your builds. We'll explore how you can structure your project to improve build efficiency, take you through the process for resolving relationships between targets' build phases in Xcode, and share how you can take full advantage of available hardware resources when compiling in Swift. We'll also introduce you to Build Timeline — a powerful tool to help you monitor your build efficiency and performance.
Learn how you can create integrated content experiences across multiple devices. We'll introduce you to DeviceDiscoveryUI, which makes it easy to set up communication pathways and connect Apple TV with other devices like iPhone, iPad and Apple Watch. We'll also explore common scenarios and provide best practices to help you enable frictionless device-to-device connectivity.
Learn how to take advantage of the power of SwiftUI in your UIKit app. Build custom UICollectionView and UITableView cells seamlessly with SwiftUI using UIHostingConfiguration. We'll also show you how to manage data flow between UIKit and SwiftUI components within your app. To get the most out of this session, we encourage basic familiarity with SwiftUI.
Send live and on‐demand audio and video to iPhone, iPad, Mac, Apple Watch, Apple TV, and PC with HTTP Live Streaming (HLS) technology from Apple. Using the same protocol that powers the web, HLS lets you deploy content using ordinary web servers and content delivery networks. HLS is designed for reliability and dynamically adapts to network conditions by optimizing playback for the available speed of wired and wireless connections.
Since its introduction in 2009, HTTP Live Streaming (HLS) has enabled the delivery of countless live and on‐demand audio and video streams globally. With the introduction of a new Low-Latency mode, latencies of less than two seconds are now achievable over public networks at scale, while still offering backwards compatibility to existing clients. Learn about how to develop and configure your content delivery systems to take advantage of this new technology.
If your app sends or receives data over the network, it’s critical to preserve the privacy and integrity of a person’s information and protect it from data breaches and attacks. You should use the Transport Layer Security (TLS) protocol to protect content in transit and authenticate the server receiving the data.
When you connect through TLS, the server provides a certificate or certificate chain to establish its identity. You can further limit the set of server certificates your app trusts by pinning their public-key identities in your app. Here’s how to get started.
There may come a time when you need to know whether or not a view is being rendered in a preview or not, or rather if an Xcode process is running for an SwiftUI preview or not.
In SwiftUI, identity holds the key to understanding how the rendering system works. A View's identity tells SwiftUI which of that View's values correspond to the same rendered view over time. This identity has strong implications for correctness, performance, and as we will see, transitions.
An approach here is to have multiple copies of your repository. Then you don’t need to worry about stashing any un-comitted work and you can dodge the aforementioned Xcode woes. Alas this also means you need to keep these two copies of the repo up to date. Turns out that git actually supports this out of the box without having to tend to your multiple repo copies manually. The answer is git worktrees🌲.
So yesterday I learned something that blew my mind. If you're in Interface Builder and you copy a UI element, you can paste it into an image editor like Photoshop and you get just the UI element with full transparency! 🤯
We will first look into a number of examples of such cases where it is useful to know how the SwiftUI render loop works. Then we will explore the render loop in more detail and ask questions such as: when exactly is the body of a SwiftUI view evaluated. Not "when" as in under what circumstances, but as in: at which point in time? Is a view always drawn on screen immediately after a body is evaluated? How related are body evaluation and screen rendering even? We sometimes use the word "render" for evaluating a body of a view, does that even make sense?
Although you can use asset catalogs to define colors for your apps and frameworks, there may come a time when you have to create colors from hex codes, for instance when fetching colors from a web api.
You may find it strange that neither UIKit, AppKit nor SwiftUI has initializers for creating colors with hex codes, and you’d be right to think so. Since Apple provides initializers that let you define red, green, blue and alpha values separately, it’s a bit strange.
@MainActor is a Swift annotation to coerce a function to always run on the main thread and to enable the compiler to verify this. How does this work? In this article, I’m going to reimplement @MainActor in a slightly simplified form for illustration purposes, mainly to show how little “magic” there is to it.
To inject code into an application the first step is to recompile the file being injected.The correct command for this is extracted the Xcode “.xcactivitylog” files and theresulting object file is linked to a dynamic library that is loaded into the application.
What can we do? In this post I go over some techniques I've been playing with. They're largely hacks, so please only read for enjoyment and not edification. :)
Xcode alone can fill the RAM on a 16GB system. Yeah, okay, half of that is "Cached Files" (not app memory) so the effect is subtle. Incremental builds are 5% slower compared to a 32GB system but clean builds are about the same.
Overall SwiftUI has been well received after its introduction. However, something most developers stumble upon quickly is how to structure non-trivial applications. One option is to just stick to MVC and get a reasonably clean architecture that isn’t full of hacks.
As developers have started adopting the new Swift Concurrency functionality introduced in Swift 5.5, a key area of interest has been around how this works with the Combine framework and how much of existing Combine based functionality can be replaced with async/await, AsyncSequence etc based code. In particular there has been some discussion around how Combine Publishers can be replaced by AsyncSequence and, in that context, one noted gap in initial offerering was difference in the range of operators available for both approaches. There have been attempts already to close that gap using, for example, projects like AsyncExtensions but, with the announcment recently of Swift Async Algorithms package, we will now have a more standard approach to supporting some of those missing operators. In this article I’m going to outline an example of how existing Combine Publisher based code can be replaced with code based on use of AsyncSequence and Swift Async Algorithms.
Periodic reminder that you probably don’t want the “concurrent” flag on dispatch queues in general, but you especially don’t want the popular “barrier async set, sync get” atomic property antipattern. It’s like using a 747 to go to the house next door.
The SwiftUI layout system is more predictable and easier to understand than UIKit layout system. But this does not mean how it works is entirely straightforward.
For newcomers with no preconception of how layout historically worked on Apple platforms, official documentation about the SwiftUI layout system might namely be incomplete or obscure. The number of views and modifiers, as well as their various behaviors, can be quite overwhelming. Even for seasoned UIKit developers it can be difficult to figure out how SwiftUI layout system works, as its core principles are quite different from UIKit well-known concepts of Auto Layout constraints, springs and struts.
This article explores the essential rules and behaviors of the SwiftUI layout system and explains how you should reason about it. It also introduces a formalism that helps characterize views and their sizing behaviors in general. It finally provides a list of the sizing behaviors for most SwiftUI built-in views.
Optimizing performance is definitely one of the most interesting topics, not only on iOS, but software development in general. There are many thought provoking challenges, accompanied with a detective work to find the places that stand between your product and the optimal and performant user experience.
For debugging SwiftUI performance issues, I usually use the following profiling templates:
Animation hitches — for detecting the hitches.
SwiftUI — for counting how many times the view has been redrawn.
Time Profiler — for checking which methods took the most time to execute.
CRDTs, which I will simply call ‘replicating types’ from here on, are data types that have the ability to merge themselves when changes are made on different devices, in order to reach a consistent state. They have built-in logic which allows them to be updated independently, and then merged back together in a completely deterministic way, such that all syncing devices end up with the same value.
Xcode Cloud is a continuous integration and delivery service built into Xcode and designed expressly for Apple developers. It accelerates the development and delivery of high-quality apps by bringing together cloud-based tools that help you build apps, run automated tests in parallel, deliver apps to testers, and view and manage user feedback.
One of the challenging parts of SwiftUI is really understanding the way it manages view state (for example, through @State and @StateObject). In theory, it's pretty simple: anytime you want associated view state you just create a property with @State and you're done.
The build system and Swift compiler have a new mode that better utilizes available cores, resulting in faster builds for Swift projects. The mode is opt-in, and you can enable it globally.
Not sure what colors to use in your designs or where to use them? Happy Hues is a color palette inspiration site that acts as a real world example as to how the colors could be used in your design projects.
Usually, your journey will start by detecting that an allocation test regressed. So let's first start by looking at how to run the allocation counter tests.
It is said that use of Canvas to create complex shapes can provide better performance in SwiftUI. This article compares performance of scrolling through multiple instances of the same card pattern created using shape, canvas or image.
In this post, you will learn about Combine operators: what they are, how they work, and how refactoring our code into a custom Combine operator will make it easier to reason about and more reusable at the same time.
Developer Experience is about creating an environment in which a developer can do their best work. DX is the context in which developer productivity can be unleashed, in which individual needs can be successfully balanced with those of the engineering team. DX is about developer feelings – it is a sociotechnical system which should consider every touchpoint a developer interacts with to plan and produce software, from learning a platform to the very first line of code all the way through its promotion into production. From documentation to SDKs to version control to monitoring and observability, everything a developer touches will have an impact on their productivity. Culture too, helps to define a good DX of the lack of it.
Debugging SwiftUI views is an essential skill to own when writing dynamic views with several redrawing triggers. Property wrappers like @State and @ObservedObject will redraw your view based on a changed value. In many cases, this is expected behavior, and things look like they should. However, in so-called Massive SwiftUI Views (MSV), there could be many different triggers causing your views to redraw unexpectedly.
If you work with SwiftUI, or have even just tried SwiftUI previews, then you’ve seen this annoying message: Automatic preview updating paused. To some devs it happens all the time and is extremely frustrating. In this article I’ll explain why this happens and how it can be solved. Let’s dig in!
I’ve been using this tool called Bloaty McBloatface1 to attribute the contribution of each swift module or file to our app’s binary. And it has worked out really well for me, the CLI tool is super fast, gives lots of information, supports diffing and has so many options that configurations and options that it’s difficult to explore it all in a single blog post.
In this post, we will talk about sharing resources between modules and separating the localization of feature modules. Swift Packages support localization out of the box. We added the defaultLocalization parameter to the package declaration in the example above. This parameter is necessary if you want to support localization. Now you can create en.lproj, es.lproj, or any-locale-identifier.lproj folders in your module to place your Localizable.strings files with particular translations. Remember that you still need to specify the correct bundle whenever you want to access the localization of the current module.
It has mutable state, but = creates a copy of that state that doesn't share anything with the original
It has mutable atate and = doesn't prevent sharing, but you are synchronizing access somehow (e.g. @MainActor, COW, atomics, private queues, locks)
As a rule of thumb, most structs/enums can be Sendable as long as all of their stored properties have Sendable types; if you try to conform a struct and that isn’t the case, you’ll get an error unless you use @unchecked.
Resetting the Bluetooth Module in that method is simply just no longer an option. It is just not part of macOS Monterey. For reference, I wrote this user tip back in April 2021, prior to the release of macOS Monterey: Use "Reset the Bluetooth module" to Fix Constant Bluetooth Disconnections
NVRAM (nonvolatile random-access memory) is a small amount of memory that your Mac uses to store certain settings and access them quickly. PRAM (Parameter RAM) stores similar information, and the steps for resetting NVRAM and PRAM are the same.
Settings that can be stored in NVRAM include sound volume, display resolution, startup-disk selection, time zone, and recent kernel panic information. The settings stored in NVRAM depend on your Mac and the devices that you're using with your Mac. If you experience issues related to these settings or others, resetting NVRAM might help. For example, if your Mac starts up from a disk other than the one selected in Startup Disk preferences, or a question mark icon briefly appears before your Mac starts up, you might need to reset NVRAM.
How to reset NVRAM: Option keyplusCommand keyplusP keyplusR key
A plugin is the way that the extensible build tools feature provides us to define what commands we want to run alongside, before or after (not available yet) our builds.
Release your apps that aren’t suited for public distribution as unlisted on the App Store, discoverable only with a direct link. Unlisted apps don’t appear in any App Store categories, recommendations, charts, search results, or other listings. They can also be accessed through Apple Business Manager and Apple School Manager. Apps for specific organizations, special events, or research studies, or apps used as employee resources or sales tools are good candidates for unlisted distribution.
By defining a cascading action type, and a helper modifier to handle the chain building, we have essentially introduced a variant of the observer pattern in SwiftUI.
Being able to simulate situations where your app goes out of memory is incredibly useful when you’re working on features that rely on your app being resumed in the background even when it’s out of memory. Background uploads and downloads are just some examples of when this trick is useful.
Learn about the dyld dynamic linker used on Apple platforms, how it's changed over the years, and where it's headed next. Find out how improved tooling makes it easier to optimize your app's launch time, and see how new changes coming in dyld will bring even further launch time improvements.
Method dispatch is a term referring to mechanisms by which the program determines which operation should be executed (by operation, I mean a set of instructions). There are times we expect a method behavior to be determined only at runtime. This motivation give rise to different mechanisms of dispatching a method, each of which has its own pros and cons.
Iterating over a sequence of values over time is now as easy as writing a “for” loop. Find out how the new AsyncSequence protocol enables a natural, simple syntax for iterating over anything from notifications to bytes being streamed from a server. We'll also show you how to adapt existing code to provide asynchronous sequences of your own.
Find out how structs, classes, protocols, and generics are implemented in Swift. Learn about their relative costs in different dimensions of performance. See how to apply this information to speed up your code.
The goal is to use the minimum number of abstractions and make the code easy to understand and extend. I’m going with Apple technologies exclusively for this project: URLSession, Codable, Async/Await, and Actors.
Pulse is a powerful logging system. Record and inspect network requests and logs right from your iOS app using Pulse Console. Share and view logs in Pulse macOS app. Logs are recorded locally and never leave your device.
Sourcery Pro is a Mac App that extends Xcode with the ability to create your own live templates that understand your code structure. Do you hate writing same repetitive Swift patterns over and over again? Now you don’t need to, it’s just a shortcut away.
The ability to debug complex crashes is not something immediate. Keep this out of your expectations: there's no magical instrument that you forgot to run that will give you the output you're expecting. When it comes to complex crashes, what we need to do instead is prepare our environment so that these issues are better understood when they arrive, making them more actionable. Let's see how to do that!
Everything in this post can be found in excruciating detail in GNU ld’s documentation; lld accepts basically the same syntax. There’s no spec, just what your linker happens to accept.
In iOS 15 and later, the system may, depending on device conditions, prewarm your app — launch nonrunning application processes to reduce the amount of time the user waits before the app is usable.
Separate the simulator and device architectures into separate binaries such that the size of each sliced up binary is less than the whole. This is easy to do using the new-ish XCFramework packaging.
So you have a running program. Functions call other functions, so you need a mechanism to save the caller’s state while the callee runs, and then restore that state when the callee completes. Some of this is defined by ABIs, but even with a defined ABI there’s a lot of wiggle room for the callee to mess around.
Designing for user privacy is important. Most Apple devices contain personal data that the user doesn’t want to expose to apps or to external entities. If your app accesses or uses data inappropriately, the user might stop using your app and even delete it from their device.
Access user or device data only with the user’s informed consent obtained in accordance with applicable law. In addition, take appropriate steps to protect user and device data, and be transparent about how you use it.
The Swift reference has a chapter discussing stable attributes. This document is intended to serve as a counterpart describing underscored attributes, whose semantics are subject to change and most likely need to go through the Swift evolution process before being stabilized.
Technically speaking, reference counting is a form of garbage collection, but I get what you mean. Since there are multiple forms of GC, I'll assume that you mean a generational mark and sweep algorithm like you’d see in a Java implementation.
It's worth remembering Swift's implementation of ARC is still far from optimal. Swift 5.3's optimizer significantly reduces the number of ARC calls in optimized code; we've seen up to 2x improvements in hot parts of SwiftUI and Combine without code changes. There will continue to be ARC optimizer improvements as we propagate OSSA SIL through the optimizer pipeline as well. (However, the main benefit of ARC over other forms of GC will always be lower-overhead interop with non-GC-managed resources, such as the large volumes of C, C++, and growing amount of value-oriented Swift code that implements the lower level parts of the OS, rather than the performance of the heap management itself.)
LLVM provides both a textual form that looks roughly like a C-family programming language, and a binary form (bitcode, in LLVM’s parlance) that’s denser and (ostensibly) faster to parse. The two forms are semantically identical, and trivial to produce and convert between.
The advantage we have over objc_msgSend is that as humans (or as a compiler), we have static type information and an understanding of the surrounding code which lets us guess, statically and with high accuracy, what the target is. In fact, we can just speculate that the call will go to the predicted method, and, taking a leaf out of objc_msgSend’s book, wrap a direct call to it in the barest minimum of checking to make sure that we were correct in our prediction.
The Swift runtime keeps a metadata record for every type used in a program, including every instantiation of generic types. These metadata records can be used by (TODO: reflection and) debugger tools to discover information about types.
There's a good chance that you're using SwiftUI and that you're not quite sure how and when SwiftUI determines which views should redraw. There's an equal likelihood that your setup isn't as performant as you might think but you're just not seeing any issues yet.
SwiftUI's Environment is one of the most important pillars of Apple's declarative UI framework. There are many aspects of this infrastructure: in this article, let's review all environment values that SwiftUI offers.
View._printChanges()
This static function was added in iOS 15 and is perhaps the most widely known one, seeing as Apple folk are encouraging people to use it. You can use it print out to Xcode debug console what triggered SwiftUI to evaluate the body function of your view.
Learn about the basics of object lifetimes and ARC in Swift. Dive deep into what language features make object lifetimes observable, consequences of relying on observed object lifetimes and some safe techniques to fix them.
Learn to use the Instruments Network template to record and analyze your app's HTTP traffic. We'll show you how to explore and visualize the behavior of sessions, tasks, and individual HTTP requests to ensure data is transmitted efficiently and respects people's privacy.
Peek behind the curtain into the core tenets of SwiftUI philosophy: Identity, Lifetime, and Dependencies. Find out about common patterns, learn the principles that drive the framework, and discover how you can use them to guarantee correctness and performance for your app.
Discover how you can understand and diagnose memory performance problems with Xcode. We'll take you through the latest updates to Xcode's tools, explore Metrics, check out the memgraph collection feature in XCTest, and learn how to catch regressions using a Performance XCTest.
Discover how Xcode can automatically track down infinite loops, unused code, and other issues before you even run your app. Learn how, with a single click, Xcode can analyze your project to discover security issues, logical bugs, and other hard-to-spot errors in Objective-C, C, and C++. We'll show you how to use the static analyzer to save you time investigating bug reports and improve your app's overall quality.
Breakpoints can help you debug issues by allowing you to pause and inspect problems in the middle of a process. Discover the latest improvements to breakpoints in Xcode including column and unresolved breakpoints. We'll also go over best practices for general breakpoints and LLDB tips and tricks.
Discover how to distribute your apps directly to App Store Connect and all the distribution methods supported in Xcode. Explore how to automate distribution for your apps, and learn about improvements to the distribution workflow like cloud signing, app record creation, and build number management.
Working with more complex Xcode projects? You've come to the right place. Discover how you can configure your project to build for multiple Apple platforms, filter content per-platform, create custom build rules and file dependencies, and more. We'll take you through multi-platform framework targets, detail how to optimize your project and scheme configuration, and show you how to make effective use of configuration settings files. We'll explore configuring schemes for parallel builds and implicit dependencies, script phases, custom build rules, setting up input and output file dependencies, build phase file lists, and deduplicating work via aggregate targets. Lastly, find out more about the build settings editor, how levels work, and configuration settings file syntax.
When you have code that needs to run at the same time as other code, it's important to choose the right tool for the job. We'll take you through the different kinds of concurrent tasks you can create in Swift, show you how to create groups of tasks, and find out how to cancel tasks in progress. We'll also provide guidance on when you may want to use unstructured tasks.”
iCloud Private Relay is an iCloud+ service that prevents networks and servers from monitoring a person's activity across the internet. Discover how your app can participate in this transition to a more secure and private internet: We'll show you how to prepare your apps, servers, and networks to work with iCloud Private Relay.
Discover how memory graphs can be used to get a close up look at what is contributing to an app's memory footprint. Understand the true memory cost of an image. Learn some tips and tricks for reducing the memory footprint of an app.
Discover how you can achieve maximum performance and insightful debugging with your app. Symbolication is at the center of tools such as Instruments and LLDB to help bridge the layers between your application's runtime and your source code. Learn how this process works and the steps you can take to gain the most insight into your app.
Some problems are easier to solve by creating a customized programming language, or “domain-specific language.” While creating a DSL traditionally requires writing your own compiler, you can instead use result builders with Swift 5.4 to make your code both easier to read and maintain. We'll take you through best practices for designing a custom language for Swift: Learn about result builders and trailing closure arguments, explore modifier-style methods and why they work well, and discover how you can extend Swift's normal language rules to turn Swift into a DSL. To get the most out of this session, it's helpful (though not necessary) to have some experience writing SwiftUI views. You won't need to know anything about parser or compiler implementation.
I've tried to make it clear in the title, but hopefully you haven't gotten to this page thinking that Apple provides a built-in way to add custom key mappings in Xcode 13's new Vim Mode. Unfortunately, this functionality just isn't easily configurable. Instead, this is one of those “but everything's a little configurable with enough swizzling & pointer arithmetic” kind of posts.
Did you know you can connect the Xcode debugger to a running process? You can also have Xcode wait for a process to launch before connecting. This helps when debugging issues when your App is launched in response to an external event such as a notification. We can also use it to peek at some of the built-in Apps in the simulator.
If you look up linear types online, you'll find a lot of unhelpful definitions, like a linear type is a type that can't be aliased, can't be cloned, and must be "used" exactly once.
That's somewhat unhelpful because, as Vale shows us, you can have a linear struct without any of the above restrictions.
You can read/modify it as much as you like.
You can copy it.
You can make aliases to it.
So for now, let's use a less correct but more helpful definition: A linear struct must eventually be explicitly destroyed.
In other words, a linear struct can't just go out of scope. When the user lets a linear struct go out of scope, the compiler gives an error.
This might seem like a weird restriction, but if we use it in a certain way, it can unlock some rather amazing capabilities:
Keep your caches consistent with your data
Prevent zombie temporary states
Prevent concurrency bugs and ensure messages are handled
Help with database consistency; prevent forgotten updates
Prevent certain kinds of memory leaks (even GC or Rust leaks!)
Prevent accidentally canceling an ongoing thread or connection
Prevent an accidental rollback of a perfectly good transaction
The following sections are general guidelines that describe fundamental Toolbar layout and design principles for Mac applications. Following these guidelines will help you create functional and aesthetically pleasing toolbars that are easy for Mac users to understand and use.
This document will reference a hypothetical Email application to illustrate key points in designing a Toolbar. It will heavily reference classes, structs, and properties in NSToolbar and NSTitlebarAccessoryViewController.
Bazzite is a cloud native image built upon Fedora Atomic Desktops that brings the best of Linux gaming to all of your devices — including your favorite handheld.
You can use GitHub Copilot Chat in GitHub to answer general questions about software development, or specific questions about the issues or code in a repository.
You can ask a general question about software development that is not focused on a particular context, such as a repository or a knowledge base.
Depending on the question you ask, and your enterprise and organization settings, Copilot may respond using information based on the results of a Bing search. By using Bing search, Copilot can answer a broad range of tech-related questions with up-to-date details based on information currently available on the internet.
Copilot allows you to use natural language questions to explore repositories on GitHub. This can help you get a better understanding of where specific aspects of a codebase are implemented.
Wyze Cameras are one of the best in the market While I was renovating my home, I decided to invest in the Wyze Cam V3 for my HomeKit ecosystem. Unfortunately, it doesn’t come with native HomeKit compatibility. But that's okay because I had already found a way to make my Wyze with HomeKit, even though it wasn't officially supported.
Have you ever wondered how to control your Wyze devices using Apple Home App and Siri? Let’s find the possible ways to add smart home devices to Apple HomeKit.
In simple terms, Homebridge is a bridge between smart home devices and the Apple Homekit. It is a lightweight Node JS server. We can integrate our smart home devices using Homebridge that don’t support Apple HomeKit
Control your Homebridge HomeKit accessories
Set up automation using your iPhone, Apple iWatch, iPad, and Mac.
Create secure control and powerful automation with addition to the home hub (HomePod, Apple TV, or iPad) even when you're not at home.
The Athom Bridge is a standout feature of our Homebridge server, offering the ability to add any smart home device to HomeKit. With easy installation, a secure option, LAN connectivity, and plugin support, the Athom Bridge provides a reliable and convenient solution for integrating non-native HomeKit devices.
Take advantage of seamless control with Siri and the Apple Home app. Stay on track with Siri's assistance and effortlessly manage all your smart home devices. Upgrade your smart home experience today by choosing our specially compiled and pre-flashed Homebridge server.
The HomeKit Bridge integration allows you to make your Home Assistant entities available in Apple HomeKit, so they can be controlled from Apple’s Home app and Siri; even if those devices do not natively support HomeKit.
Unleashing the Power of Large-Scale Unlabeled Data
Depth Anything is trained on 1.5M labeled images and 62M+ unlabeled images jointly, providing the most capable Monocular Depth Estimation (MDE) foundation models with the following features:
zero-shot relative depth estimation, better than MiDaS v3.1 (BEiTL-512)
zero-shot metric depth estimation, better than ZoeDepth
optimal in-domain fine-tuning and evaluation on NYUv2 and KITTI
We also upgrade a better depth-conditioned ControlNet based on our Depth Anything.
A trustworthy, industrial-strength interactive theorem prover and dependently-typed programming language for mechanised reasoning in mathematics, computer science and more.
The Rocq Prover is an interactive theorem prover, or proof assistant. This means that it is designed to develop mathematical proofs, and especially to write formal specifications: programs and proofs that programs comply to their specifications. An interesting additional feature of Rocq is that it can automatically extract executable programs from specifications, as either OCaml or Haskell source code.
Learn the powerful secrets of Apple’s software debugger, LLDB!
In Advanced Apple Debugging & Reverse Engineering, you’ll come to realize debugging is an enjoyable process to help you better understand software. Not only will you learn to find bugs faster, but you’ll also learn how other developers have solved problems similar to yours.
You’ll also learn how to create custom, powerful debugging scripts that will help you quickly find the secrets behind any bit of code that piques your interest.
After reading this book, you’ll have the tools and knowledge to answer even the most obscure question about your code — or someone else’s.
This book is for intermediate-to-advanced iOS/macOS developers who are already familiar with either Swift or Objective-C and want to take their debugging skills to the next level.
If you’re reading this book then hopefully you’re already on board with this idea, but let’s recap anyway.
The typical practice with passwords is to remember a few different ones and re-use them widely. (Password managers support generating random passwords, but people mostly don’t.) Sites must store hashes of these passwords to recognize them, but most passwords have too little entropy to resist brute-forcing when the hashes leak. (The website haveibeenpwned.com now has records of about 13.5 billion accounts that have been found in account database leaks from nearly 800 websites.)
When a password database leaks, not only can any successfully cracked passwords be used immediately to sign in to that site but, because of password re-use, those users’ accounts on other sites may also be compromised.
Next, because users typically remember their passwords, they can be tricked into entering them on lookalike websites. These “phishing” attacks are common, effective, and can have global implications when used to interfere in elections.
Lastly, passwords can leak from many other parts of the software stack. Facebook inadvertently logged hundreds of millions of passwords over many years, and Javascript-injection attacks can exfiltrate anything entered on a site, including passwords.
This book is about using public key signature schemes to try and build a better system of authentication. These schemes have names like ECDSA, RSA, and ML-DSA. They vary in how large their outputs are, how fast they operate, and whether they’re resistant to (still theoretical) large quantum computers. In this book we’ll consider them only in the abstract—the construction of public key signature schemes is a deep and fascinating topic, but we will cover none of it here.
Evaluate foundation models, including custom and imported models, to find models that fit your needs. You can also evaluate your retrieval or end-to-end RAG workflow in Amazon Bedrock Knowledge Bases.
Build a board game for visionOS from scratch using TabletopKit. We'll show you how to set up your game, add powerful rendering using RealityKit, and enable multiplayer using spatial Personas in FaceTime with only a few extra lines of code.
To add your gesture recognizer to a SwiftUI view, create an instance of UIGestureRecognizerRepresentable and use the gesture(\_:) modifier to attach it. The system calls the methods of your representable instance at appropriate times to create and update the gesture recognizer.
The following example shows the inclusion of a custom MyGestureRecognizer structure in the view hierarchy.
Because your UIGestureRecognizerRepresentable is a struct, it can use the environment, have data dependencies, and is more similar to views in SwiftUI. The system will call the appropriate methods on your instance to propagate the latest data.
Private Cloud Compute (PCC) delivers groundbreaking privacy and security protections to support computationally intensive requests for Apple Intelligence by bringing our industry-leading device security model into the cloud. Whenever possible, Apple Intelligence processes tasks locally on device, but more sophisticated tasks require additional processing power to execute more complex foundation models in the cloud. Private Cloud Compute makes it possible for users to take advantage of such models without sacrificing the security and privacy that they expect from Apple devices.
We designed Private Cloud Compute with core requirements that go beyond traditional models of cloud AI security:
Stateless computation on personal user data: PCC must use the personal user data that it receives exclusively for the purpose of fulfilling the user’s request. User data must not be accessible after the response is returned to the user.
Enforceable guarantees: It must be possible to constrain and analyze all the components that critically contribute to the guarantees of the overall PCC system.
No privileged runtime access: PCC must not contain privileged interfaces that might enable Apple site reliability staff to bypass PCC privacy guarantees.
Non-targetability: An attacker should not be able to attempt to compromise personal data that belongs to specific, targeted PCC users without attempting a broad compromise of the entire PCC system.
Verifiable transparency: Security researchers need to be able to verify, with a high degree of confidence, that our privacy and security guarantees for PCC match our public promises.
This guide is designed to walk you through these requirements and provide the resources you need to verify them for yourself, including a comprehensive look at the technical design of PCC and the specific implementation details needed to validate it.
Transform your videos into breathtaking 3D experiences with Holo Vids. Advanced real-time AI projects any video into 3D no matter how it was recorded.
Watch videos with true depth perception that makes scenes feel real. Holo Vids features 3 display modes:
Hologram display projects the video into your room so you can walk around it, view it from any angle, and even carry it around!
Two Spatial video modes that work when facing the video. One mode gives a gentle 3D effect while the other is more intense.
Enjoy natural, comfortable 3D without special recording equipment. Perfect for home videos, streaming clips, travel memories, and more.
Simple to use: Just open any video and let Holo Vids play it back in an immersive spatial experience. (There is no need to convert the video upfront or do anything fancy.)
Ever dreamed of shooting webs with your hands? Now it visually comes true! Dive into Spatial Web Shooter to experience the excitement of interacting with virtual props and your real surroundings using spider webs. Explore Free Play mode or take on thrilling challenges!
Bash stack is an HTTP server and framework for building modern web applications (in bash).
Bash stack uses file-based routing. All of the application routes should exist as .sh files in the pages/ folder of the project.
Whenever an HTTP request is made, the framework will locate the corresponding script and execute it. Anything written to stdout by the script will be written to the HTTP response body.
Bash stack also pairs well with htmx, which is included by default. We strongly recommend familizarizing yourself with their examples before proceeding.
With Jetpack Compose for XR, you can declaratively build your spatial UI and layout using familiar Compose concepts such as rows and columns. This lets you extend your existing Android UI into 3D space or build entirely new immersive 3D applications.
If you are spatializing an existing Android Views-based app, you have several development options. You can use interoperability APIs, use Compose and Views together, or work directly with the SceneCore library. See our guide to working with views for more details.
The Jetpack XR SDK lets you build immersive XR experiences using modern tools like Kotlin and Compose, as well as previous generation tools such as Java and Views. You can spatialize your UI, load and render 3D models and semantically understand the real world.
If you already have a mobile or large screen app on Android, Jetpack XR SDK extends your app into a new dimension by spatializing existing layouts and enhancing your experiences with 3D models and immersive environments. See our quality guidelines for our recommendations on spatializing your existing Android app.
Redefine how users interact with your app with Android XR — from focused productivity to immersive entertainment. Android XR apps become part of your user's environment and create expansive experiences for watching, learning, and getting things done.
Monospace fonts are dear to many of us. Some find them more readable, consistent, and beautiful, than their proportional alternatives. Maybe we’re just brainwashed from spending years in terminals? Or are we hopelessly nostalgic? I’m not sure. But I like them, and that’s why I started experimenting with all-monospace Web.
On this page, I use a monospace grid to align text and draw diagrams. It’s generated from a simple Markdown document (using Pandoc), and the CSS and a tiny bit of Javascript renders it on the grid. The page is responsive, shrinking in character-sized steps. Standard elements should just work, at least that’s the goal. It’s semantic HTML, rendered as if we were back in the 70s.
All right, but is this even a good idea? It’s a technical and creative challenge and I like the aesthetic. If you’d like to use it, feel free to fork or copy the bits you need, respecting the license. I might update it over time with improvements and support for more standard elements.
Despite the fact that SQLite’s file-based nature makes working with it extremely simple, SQLite grew a considerable amount of tunables over the years, which make getting top performance out of it non-obvious (the SQLite numbers on the benchmark above are after tuning). For maximum performance, users have to choose WAL mode over journal mode, disable POSIX advisory locks, etc.
Limbo, while maintaining compatibility with SQLite’s bytecode and file format, drops a lot of the features that we consider less important for modern environments (including SQLite’s “amalgamation”, the build system that generates a single C file), providing a better out-of-the-box experience.
PreferenceKeys in SwiftUI provide a mechanism for passing data up the view hierarchy. Again, they’re quite useful when we need to communicate information from child views to parent views without relying on bindings or state variables.
While they might seem a bit complex at first, once you understand how they work, you will like using them to build complex components. Not to mention they’re used extensively in many of SwiftUI’s built-in views and can make your custom views more flexible and reusable.
Remember, the key points are:
They allow data to flow up the view hierarchy. Always think child -> parent
They’re defined by implementing the PreferenceKey protocol.
Child views set preferences using the preference modifier.
Parent views read preferences using the onPreferenceChange modifier.
Spatial Analogue is the easiest way to quickly visualize your 3D designs and collaborate with your team in full spatial context—all without having to write any code or publish your own app.
Native RealityKit: Create native experiences without writing a single line of code. Publish directly to the Analogue app on Apple Vision Pro for instant viewing.
Spatial Collaboration: Use SharePlay and Spatial Personas to review designs in real-time, enjoying face-to-face interactions no matter where team members are.
Vantage Points: Create and navigate through various viewpoints of your designs. Review every detail from every angleVantage Points.
Asset Variants: Quickly test different versions of the same asset. Experience real-time A/B testing, as if everyone is in the same room.
Team Projects: Organize your team and share 3D designs with ease. Manage permissions to ensure the right people have the right access.
Instead of manually testing each preview, you can write a unit test that automatically runs every preview in your app or framework. Just link the SnapshotPreviews package to your test target and copy the following code:
import SnapshottingTests
// This is an XCTestCase you can run in Xcode
finalclassMyPreviewTest:PreviewLayoutTest{}
Test functions will be automatically added for every preview in the app. You can easily view the results in Xcode or automate it to run as part of CI using Fastlane or xcodebuild.
SwiftUI 16 makes it easier than ever before to implement hero animations that allow us to implement UIs that delight our users. With just three lines of code, we were able to add a transition that looks similar to the App Store’s Today view hero animation. And with a little bit of extra effort, we were able to improve the user experience even more.
Swift is Apple’s recommended language for app development, and with good reason. Its safety, efficiency, and expressiveness have made it easier than ever to build fast, polished, and robust apps for the Apple ecosystem. Recent stories about Swift on Windows and Swift on the Playdate highlight developers’ desire to take advantage of Swift on other platforms too. In this series, we explore writing native Swift apps for Android with Skip.
Since its 1.0 release earlier this year, Skip has allowed developers to create cross-platform iOS and Android apps in Swift and SwiftUI by transpiling your Swift to Android’s native Kotlin language. Now, the Skip team is thrilled to give you the ability to use native, compiled Swift for cross-platform development as well.
Part 1 of this series described bringing a native Swift toolchain to Android. Being able to compile Swift on Android, however, is only the first small step towards real-world applications. In this and upcoming installments, we introduce the other pieces necessary to go from printing “Hello World” on the console to shipping real apps on the Play Store:
Integration of Swift functionality like logging and networking with the Android operating system.
Bridging technology for using Android’s Kotlin/Java API from Swift, and for using Swift API from Kotlin/Java.
The ability to power Jetpack Compose and shared SwiftUI user interfaces with native Swift @Observables.
Xcode integration and tooling to build and deploy across both iOS and Android.
By integrating Embedded Swift with SDL3 on the ESP32-C3 and ESP32-C6, you can leverage modern programming languages and desktop-class libraries to develop rich graphical applications for embedded systems. The use of ESP-BSP simplifies targeting multiple boards, making your applications more portable.
We encourage you to explore the GitHub repository for the full source code and additional details.
These days, websites—or web apps if you prefer—tend to use one of two navigation schemes:
The navigation scheme browsers provide by default—that is, you enter a URL in your browser's address bar and a navigation request returns a document as a response. Then you click on a link, which unloads the current document for another one, ad infinitum.
The single page application pattern, which involves an initial navigation request to load the application shell and relies on JavaScript to populate the application shell with client-rendered markup with content from a back-end API for each "navigation".
The benefits of each approach have been touted by their proponents:
The navigation scheme that browsers provide by default is resilient, as routes don't require JavaScript to be accessible. Client-rendering of markup by way of JavaScript can also be a potentially expensive process, meaning that lower-end devices may end up in a situation where content is delayed because the device is blocked processing scripts that provide content.
On the other hand, Single Page Applications (SPAs) may provide faster navigations after the initial load. Rather than relying on the browser to unload a document for an entirely brand new one (and repeating this for every navigation) they can offer what feels like a faster, more "app-like" experience—even if that requires JavaScript to function.
In this post, we're going to talk about a third method that strikes a balance between the two approaches described above: relying on a service worker to precache the common elements of a website—such as header and footer markup—and using streams to provide an HTML response to the client as fast as possible, all while still using the browser's default navigation scheme.
The first agentic IDE, and then some. The Windsurf Editor is where the work of developers and AI truly flow together, allowing for a coding experience that feels like literal magic.
Welcome to Category Theory in Programming, a journey into the conceptual world where mathematics meets software development. This tutorial is designed for Racket programmers who are curious about the mathematical ideas underlying computational systems. It offers insights into how familiar programming concepts can be reinterpreted through the lens of category theory, and even goes further to directly borrow from category theory, using programming language constructs to describe these abstract concepts.
Category theory, a branch of mathematics that deals with abstract structures and relationships, may seem esoteric at first glance. However, its principles are deeply intertwined with the concepts and patterns we encounter in programming. Through this tutorial, we aim to bridge the gap between these two worlds, offering a unique perspective that enriches the programmer’s toolkit with new ways of thinking, problem-solving, and system design.
Why study category theory as a programmer? The answer lies in the abstraction and generalization capabilities provided by category theory. It allows us to see beyond the specifics of a particular programming language, problem, or system, revealing the underlying structures that are common across different domains. By identifying connections between a system and the constructs of category theory, you can leverage existing categorical results and structures to expand and improve the system, applying well-established theories and techniques to refine and extend your design. This tutorial aims to open the door to this broader perspective, enriching your approach to programming.
As you embark on this journey, keep in mind that the real value of understanding category theory in the context of programming is not merely in acquiring new knowledge but in developing a new way of thinking about problems or systems. We encourage you to approach the material with an open mind and to explore how the concepts presented here can be applied or extended in your programming endeavors.
Category Theory in Programming is an invitation to explore, to question, and to discover. It is a starting point for a deeper inquiry into the vast and fascinating intersection of mathematics and programming. We hope this tutorial will inspire you to delve further into both fields, exploring new ideas and forging connections that will enhance your work as a programmer.
Not realizing that view initializers—not just the body property—are in the hot path can quickly get you into hot water, particularly if you’re doing any expensive work or are trying to use RAII with a value in a view property. So, what follows are some general recommendations for avoiding those issues that are as close to universally correct as I’m willing to claim in writing.
While many examples of the matchedGeometryEffect() modifier focus on hero animations, it can also be applied in other contexts, like my custom segmented control. Here, it's not used to transition geometry when one view disappears and another appears. Instead, it matches the geometry of a non-source view to that of the source view while both remain present in the view hierarchy simultaneously.
The subdivision technique works by recursively subdividing a polygon mesh, adding new vertices based on existing ones to create smoother surfaces. This process can be executed at the modeling stage (i.e., modifiers in Blender, dynamic topology tools, etc.) or at runtime, which potentially unlocks another technique called Adaptive Level of Detail, which allows engines to dynamically adjust the level of detail based on the camera’s distance from the object (pretty much foveation for geometry). With this benefit, runtime support for subdivision on modern engines is considered crucial, and starting with visionOS 2, macOS 15, iOS 18, and iPadOS 18, RealityKit gets support of this feature at the API level.
RealityKit's SubD implementation not only creates smoother geometry at a lower cost but also aligns with visionOS's trend of incorporating USD standard features. Here's an extract of the invaluable doc Validating feature support for USD files.
Use AirPlay to stream or share content from your Apple devices to your Apple TV, AirPlay-compatible smart TV, or Mac. Stream a video. Share your photos. Or mirror exactly what's on your device's screen.
iOS 18 introduced a powerful new feature: the ability to animate UIKit views using SwiftUI animation types. This bridges the gap between the two frameworks even further, allowing us to bring the flexibility and expressiveness of SwiftUI animation system into UIKit-based projects.
SwiftUI animation API makes it simple to define animations and manage their timing and repetition. By using SwiftUI animations in UIKit, we can create smoother, more cohesive animations across our entire app, improving the overall experience for users.
Swift has 6 access levels ranging from open to private. Typically if you do not want consumers of your code to access a function or type, you can mark it private. However, Apple frameworks written in Swift, particularly SwiftUI, contain APIs that are meant to be used by other Apple frameworks, but not by 3rd party apps. This is achieved by limiting when the code can be seen at compile time, but still allowing it to be found at link time. In this post we’ll look at how you can still call these functions in your own code to use features that are not typically available.
To summarize, calling an external function from your code means it must be defined in three places:
A .swiftinterface file for the compiler to recognize it
A .tbd file for the linker to recognize it
The exported symbols of the binary at runtime for dyld to recognize it
Discover why the operating system terminated your app when available memory was low.
iOS, iPadOS, watchOS, and tvOS have a virtual memory system that relies on all apps releasing memory when the operating system encounters memory pressure, where available memory is low and the system can’t meet the demands of all running apps. Under memory pressure, apps free memory after receiving a low-memory notification. If all running apps release enough total memory to alleviate memory pressure, your app will continue to run. But, if memory pressure continues because apps haven’t relinquished enough memory, the system frees memory by terminating applications to reclaim their memory. This is a jetsam event, and the system creates a jetsam event report with information about why it chose to jettison an app.
Jetsam event reports differ from crash reports because they contain the overall memory use of all apps and system processes on a device, they’re in JSON format, and they don’t contain the backtraces of any threads in your app. If the system jettisons your app due to memory pressure while the app is visible, it will look like your app crashed. Use jetsam event reports to identify your app’s role in jetsam events, even if your app didn’t get jettisoned.
If the system runs low on free memory and is unable to reclaim memory by terminating suspended apps, UIKit sends a low-memory warning to running apps. UIKit delivers low-memory warnings in the following ways:
When your app receives a low-memory warning, free up as much memory as possible, as quickly as possible. Remove references to images, media files, or any large data files that already have an on-disk representation and can be reloaded later. Remove references to any temporary objects that you no longer need. If active tasks might consume significant amounts of memory, pause dispatch queues or restrict the number of simultaneous operations that your app performs.
On 13 November 1961, the Oceanic building at London Airport opened to handle long-haul flight departure. In 1979, German publisher Ravensburger brought out a game designed to help children learn to count. Around Christmas 2023, I stumbled across a copy of that vintage game. The type on the box caught my eye, and that’s where this story began.
The letterforms resembled those of Helvetica. As the corners were soft, I initially thought it might be its Rounded version. However, the typeface featured a much larger x-height, the capitals were less wide, and the glyphs also had white bits in some places, yielding a highlight effect. I had never seen this design before. My first suspicion was that it might be a Letraset face, as this would have fitted in well with the release date of the game. Unfortunately, I couldn’t find a match in a catalog by this manufacturer of rub-down type, so I contacted Florian Hardwig, who had often helped me with type research in the past. Florian was able to identify the mystery typeface. He found it in a catalog published in 1985 by Layout-Setzerei Stulle, a typesetting service in Stuttgart, Germany. Named Airport Spotlight, it’s a derivative of Airport, a typeface that Matthew Carter had designed in the early 1960s for signs at London Airport. The Stulle catalog also showed other variants, such as Airport Round, the stencilled Airport Stamp, and Carter’s original style, here listed as Airport halbfett.
Up to this point, I hadn’t really looked into the history of London Airport – since 1966 known as Heathrow Airport – and its design language in any depth, not least because there isn’t a great deal of material on the subject. To my knowledge, there are only two books on this typeface. One is “Airport Wayfinding” by Heike Nehl and Sibylle Schlaich from 2021. The other is “A Sign System Manual” by Theo Crosby, Alan Fletcher and Colin Forbes from 1970. I found the topic quite intriguing and began to do more research. As a first step, I got my hands on the two books. The former is easy to get hold of. The latter not so much: considered a design classic today, it has become a sought-after item. And when a copy does pop up, booksellers ask several hundred euros for it. Despite the limited supply, I eventually managed to find a comparably inexpensive copy from a private seller in the UK. Thanks to the help of a good friend, the book was brought from London to Antwerp by train and then sent to me in Germany by mail. The effort was more than worth it. The content and typography of the book are superb. In a next step, I contacted Matthew Carter to find out more about his Airport and how it came into being.
Understanding how developers problem-solve within ecosystems of practice, tooling, and social contexts is a critical step in determining which factors dampen, aid or accelerate software innovation. However, industry conceptions of developer problem-solving often focus on overly simplistic measures of output, over-extrapolate from small case studies, rely on conventional definitions of “programming” and short-term definitions of performance, fail to integrate the new economic features of the open collaborative innovation that marks software progress, and fail to integrate rich bodies of evidence about problem-solving from the social sciences. We propose an alternative to individualistic explanations for software developer problem-solving: a Cumulative Culture theory for developer problem-solving. This paper aims to provide an interdisciplinary introduction to underappreciated elements of developers’ communal, social cognition which are required for software development creativity and problem-solving, either empowering or constraining the solutions that developers access and implement. We propose that despite a conventional emphasis on individualistic explanations, developers’ problem-solving (and hence, many of the central innovation cycles in software) is better described as a cumulative culture where collective social learning (rather than solitary and isolated genius) plays a key role in the transmission of solutions, the scaffolding of individual productivity, and the overall velocity of innovation.
Auto-renewable subscriptions can be priced by App Store country or region (also known as storefronts.) You can choose from 800 price points in each currency, with the option for the Account Holder to submit a request for additional higher price points.
After you set a starting price for your auto-renewable subscription, you can schedule one future price change at a time, per country or region. If you schedule a second price change when you already have a change scheduled, the first scheduled change will be overwritten. You also have the option to preserve prices for existing subscribers.
The Metal shading language is a powerful C++ based language that allows apps to render stunning effects while maintaining a flexible shader development pipeline. Discover how to more easily build and extend your render pipelines using Dynamic Libraries and Function Pointers. We'll also show you how to accelerate your shader compilation at runtime with Binary Function Archives, Function Linking, and Function Stitching.
A description of a new stitched function.
A MTLFunctionStitchingGraph object describes the function graph for a stitched function. A stitched function is a visible function you create by composing other Metal shader functions together in a function graph. A function stitching graph contains nodes for the function’s arguments and any functions it calls in the implementation. Data flows from the arguments to the end of the graph until the stitched function evaluates all of the graph’s nodes.
The graph in the figure below constructs a new function that adds numbers from two source arrays, storing the result in a third array. The function’s parameters are pointers to the source and destination arrays, and an index for performing the array lookup. The graph uses three separate MSL functions to construct the stitched function: a function to look up a value from an array, a function that adds two numbers together, and a function that stores a value to an array.
Create a MTLFunctionStitchingGraph object for each stitched function you want to create. Configure its properties to describe the new function and the nodes that define its behavior, as described below. To create a new library with these stitched functions, see MTLStitchedLibraryDescriptor.
By exploring the view concepts and response update mechanisms in SwiftUI, developers should grasp the following key points:
Response code does not necessarily cause the view declaration value to be re-computed: Adding response logic in view code does not mean that the view declaration value will be re-evaluated as a result.
Re-computation of the view declaration value requires event triggering: SwiftUI only re-evaluates the view declaration value under specific conditions (such as state changes). This process must be triggered by some event.
Handle the construction process of view types carefully: Avoid performing time-consuming or complex operations in the constructor of view types. Because regardless of whether the view declaration value needs to be re-computed, SwiftUI may create instances of the view type multiple times.
Optimize the computation process of the view declaration value: The computation of the view declaration value is a recursive process. By appropriate optimization, such as reducing unnecessary nested computations, you can effectively reduce computational overhead.
Rationally split the view structure: Encapsulating the view declaration in a separate view type allows SwiftUI to better identify which views do not need to be re-computed, thereby improving the efficiency of view updates.
We’ve seen how injecting objects at different levels of the view hierarchy can accommodate various use cases, from global state shared across the app to context-specific state scoped to certain views. By carefully designing your environment objects based on bounded contexts and separating concerns, you can avoid common pitfalls like unnecessary re-evaluations or tightly coupled components.
It’s essential to strike a balance when using environment objects—leveraging their power for deeply nested view hierarchies while avoiding overuse that could lead to unwieldy dependencies. By passing only the necessary data to subviews and utilizing SwiftUI’s diffing mechanism, you can optimize performance and maintain a clear data flow.
Ultimately, using @Environment effectively can lead to modular, testable, and maintainable SwiftUI applications. With these tools in your development toolkit, you can confidently architect AI systems or any complex application that requires shared state management, ensuring scalability and elegance in your design.
Although Swift’s Automatic Reference Counting memory management model doesn’t require us to manually allocate and deallocate memory, it still requires us to decide exactly how we want our various objects and values to be referenced.
While it’s common to hear over-simplified rules like “Always use `weak references within closures”, writing well-performing and predictable apps and systems often requires a bit more nuanced thinking than that. Like with most things within the world of software development, the best approach tends to be to throughly learn the underlying mechanics and behaviors, and then choose how to apply them within each given situation.
Increasingly, the cyberinfrastructure of mathematics and mathematics education is built using GitHub to organize projects, courses, and their communities. The goal of this book is to help readers learn the basic features of GitHub available using only a web browser, and how to use these features to participate in GitHub-hosted mathematical projects with colleagues and/or students.
The first post of this series explored how to take advantage of Dev Containers, a useful VS Code feature that enables to run and debug Swift command line or server apps in a Linux container.
In this post you will take it a step further by having a more complex scenario: instead of storing the todos temporarily in memory, the app will store them in a PostgreSQL database. And to test it locally, you won’t need to install and run a Postgres server directly in your machine.
The UIKit combination of UICollectionView and the UICollectionViewFlowLayout gives a lot of flexibility and control to build grid-like flow layouts. How do we do that with SwiftUI?
Everything You Need to Know About Live Activities and Dynamic Island in iOS
With the release of iOS 16, Apple introduced Live Activities, and later with iPhone 14 Pro, the Dynamic Island—two powerful tools that allow us to present real-time, glanceable updates directly on the Lock Screen and at the top of the screen on the Dynamic Island. These features are designed to keep users informed about ongoing activities, like delivery tracking, live sports scores, or wait times, without requiring them to unlock their devices or open the app.
In this two-part guide, we’ll discuss everything you need to know to integrate Live Activities and Dynamic Island effectively in your iOS app. We'll detail each step from understanding design constraints to setting up a Live Activity, handling updates, and adding interactions.
While everyone who writes Swift code will use Swift Macros, not everyone should write their own Swift Macros. This book will help you determine whether writing Swift Macros is for you and show you how the best ways to make your own.
You'll create both freestanding and attached macros and get a feel for when you should and shouldn't create them, which sort of macro you should create, and how to use SwiftSyntax to implement them. Your macros will accept parameters when appropriate and will always include tests. You'll even learn to create helpful diagnostics for your macros and even FixIts.
It’s like an invisible world that always surrounds us, and allows us to do many amazing things: It’s how radio and TV are transmitted, it’s how we communicate using Wi-Fi or our phones. And there are many more things to discover there, from all over the world.
In this post, I’ll show you fifty things you can find there — all you need is this simple USB dongle and an antenna kit!
A trace trap or invalid CPU instruction interrupted the process, often because the process violated a requirement or timeout.
A trace trap gives an attached debugger the chance to interrupt the process at a specific point in its execution. On ARM processors, this appears as EXC_BREAKPOINT (SIGTRAP). On x86_64 processors, this appears as EXC_BAD_INSTRUCTION (SIGILL).
The Swift runtime uses trace traps for specific types of unrecoverable errors — see Addressing crashes from Swift runtime errors for information on those errors. Some lower-level libraries, such as Dispatch, trap the process with this exception upon encountering an unrecoverable error, and log additional information about the error in the Additional Diagnostic Information section of the crash report. See Diagnostic messages for information about those messages.
If you want to use the same technique in your own code for unrecoverable errors, call the fatalError(\_:file:line:) function in Swift, or the __builtin_trap() function in C. These functions allow the system to generate a crash report with thread backtraces that show how you reached the unrecoverable error.
An illegal CPU instruction means the program’s executable contains an instruction that the processor doesn’t implement or can’t execute.
Sudden app crashes are a source of bad user experience and app review rejections. Learn how crash logs can be analyzed, what information they contain and how to diagnose the causes of crashes, including hard-to-reproduce memory corruptions and multithreading issues.
This is a really promising development. 32GB is just small enough that I can run the model on my Mac without having to quit every other application I’m running, and both the speed and the quality of the results feel genuinely competitive with the current best of the hosted models.
Given that code assistance is probably around 80% of my LLM usage at the moment this is a meaningfully useful release for how I engage with this class of technology.
