Verified Spec-Driven Development (VSDD) is a unified software engineering methodology that fuses three proven paradigms into a single AI-orchestrated pipeline:
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| Description: 'GPU instance for Ollama with flexible instance type selection (P4d A100 or G5 A10G)' | |
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| Default: 'g5.12xlarge' | |
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| - 'g5.2xlarge' # 1x A10G (24GB) - $1.006/hr | |
| - 'g5.4xlarge' # 1x A10G (24GB) - $1.624/hr |
It's 7th of May 2025, and I'm leaving. Before I go, I'd like to share my vision of the future. If you're reading this in 2030, you probably wish you'd read it five years earlier. Consider this a time capsule from a future that arrived faster than expected.
Let’s rewind. It’s hard to believe developers once worked without AI. In domains like embedded systems, mining infrastructure, and medical software, adoption was slower due to regulation and legacy constraints. But everywhere else, including browsers, APIs, simulations, and cloud environments where AI coding agents took over.
Why did AI take over coding so quickly? One word: economics. In 2021, a junior developer in San Francisco earned over USD $260,000 to build basic prototypes. Managing 100 of them cost over $26 million per year. By 2025, an AI-powered team could deliver equivalent output at less than six percent of that cost. Quality improved, fatigu
Managing multiple repositories that serve distinct but interconnected purposes can be a challenging task 😓. This scenario is common when an organization wishes to contribute to an open-source project 🌐 while also maintaining an internal space for private collaboration 🔒.
The challenge here lies in keeping these repositories in sync 🔄 without manual intervention, ensuring that changes made to one repository 📁 are accurately reflected in the other 📁.
In this step-by-step tutorial 📃, we'll walk through a use case where a customer wants to contribute to an open-source project 💻 and, at the same time, collaborate internally within their organization 🏢.
We will:
Python script to get all the programming languages in a GitHub organization and return a total number of repos using the programming language.
Hard requirements: 🐍
read:org and repo:status permissions. 🔐
Based on https://docs.getutm.app/guides/windows/
brew install aria2 cabextract wimlib cdrtools minacle/chntpw/chntpw;bash uup_download_macos.sh - this will produce an ISO;
| #!/bin/bash | |
| # Make sure you change this script chmod to executable | |
| # Set your GitHub username and access token | |
| USERNAME={REPLACE_ME} | |
| ACCESS_TOKEN={REPLACE_ME} | |
| # Set the path to the directory where you want to store your repositories | |
| LOCAL_REPO_DIR=/path/to/your/local/repo/directory |
One of the biggest issues with using a self hosted GitHub runner is that actions that require downloading large amounts of data will bottleneck at the network. [actions/cache][1] does not support locally caching objects and artifacts stored on GitHub's servers will require a lot of bandwidth to fetch on every job. We can, however, set up a content proxy using [Squid][2] with SSL bumping to locally cache requests from jobs.
A major challenge is that [actions/cache][1] uses Azure storage APIs which makes HTTP range requests. While Squid supports range requests, it is not good at caching them. There is an option, range_offset_limit none which, according to the [documentation][3]:
A size of 'none' causes Squid to always fetch the object from the beginning so it may cache the result. (2.0 style)
However, after extensive debugging, I discovered that the feature does not work if the client closes the connection. When range_offset_limit is set, Squid will make a full request to the server,