A guide to better DE-Integration for the GNU Guix Package Manager.
This has been tried and tested (successfully) in the following desktop environments:
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
أكبر وهم نقع فيه هو افتراض أن النماذج اللغوية (LLMs) "تُفكر" وتحلل بمجرد أن نطلب منها ذلك. في الواقع، الخوارزميات كسولة ومبرمجة على "الاستدعاء الاحتمالي السريع" وإرضاء المستخدم، مما يؤدي إلى إجابات سطحية، هلوسة، وموافقة عمياء.
هندسة التلقينات (Prompt Engineering) ليست مجرد كتابة للطلبات، بل هي "هندسة قيود". هذا المستودع يضم 5 بروتوكولات احترافية تعمل كمثبطات اصطناعية تعطل الاندفاع والكسل في الذكاء الاصطناعي، وتُجبره على استخدام كامل طاقته التحليلية. باستخدام هذه التلقينات، أنت تتحول إلى "طبيب أعصاب" لهذه الخوارزميات، تعالج اضطراباتها الإدراكية وتوجهها نحو أقصى درجات الكفاءة.
Raycast has a feature to import/export preferences in a .rayconfig file format. This isn't ideal for use in a dotfiles-esque Git repository since it would be difficult to compare changes with a compressed file. Also, unfortunately, syncing is only possible on a pro subscription and only through Raycast's cloud service.
To get a more useful JSON file from the .rayconfig (example shown in example.rayconfig.json), you will have to first have to export a decrypted version. Go to Settings > Extensions > Raycast > Export Settings & Data and under "Export Password," which by default is 12345678, you will want to delete that.
After that, run the "Export Settings & Data" command. You should be able to export all of the preferences (Settings, Extensions, Floating Notes, Script Directories, Snippets) except Quicklinks without setting a password.
Then, in terminal, run gzip --decompress --keep --suffix .rayconfig NAME_OF_FILE.rayconfig and you should get a JSON file similar to example.rayconfig.json.
After that,
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Adapted to gist from https://unix.stackexchange.com/questions/344225/how-to-install-linux-on-the-playstation-2
Credits to Alison E.E.
Looking to learn about game development? Are you a Linux enthusiast looking to test the claim that "Linux runs on everything"? Perhaps you are a software developer who is looking to release for multiple architectures, and you don't have another MIPS Little Endian machine on-hand for testing your programme. Whatever your situation there are a surprising number of reasons to install Linux on a Playstation 2, even sixteen years after it's release (boy do I feel old all of a sudden.), yet an equally surprising lack of documentation about it or how to install it.
Now don't get me wrong, if you want to use the original Sony Linux Kit, or one of it's updated open source releases on a fat PS2 with a network adapter and an IDE hard disk you can find plenty of info. However th
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
A self-hosted, compounding-memory AI assistant running on a Raspberry Pi.
NanoClaw is a personal AI assistant built on Anthropic's Claude that runs entirely on a Raspberry Pi. It connects to messaging channels (WhatsApp, Telegram, Slack, Discord), processes voice and images, schedules recurring tasks, and — unlike a standard chatbot — accumulates knowledge over time through a structured memory system.
