rohitg00

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, 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.

What the original gets right

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.

karpathy

LLM Wiki

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.

The core idea

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.

NeoFusion

Installing NTKDaemon without Rosetta

Problem description

Installer still requires Rosetta, while Native Access and NTKDaemon work on Apple Silicon.

Solution

Patch installer package, adding <options hostArchitectures="x86_64,arm64"/> and removing check in installation script.

cameronwlewis

Play Europa Universalis V 1.3 on your Mac — with 3D terrain 🗺️

For Apple Silicon Macs (M1–M5). Free. No CrossOver license needed.

EU5 has no Mac version, and the popular CrossOver/Wine recipes broke with patch 1.3 "Pavia" (the game hangs or silently dies). This guide gets EU5 1.3 running natively-fast on your Mac — including the 3D terrain, which even working setups usually have disabled — and with a fully working, visible Steam client (no black window, no engine-swapping).

It was worked out on an M5 Max running macOS 26, and every step below was validated on

cmer

🧠 Ultimate LLM Codebase Brain Dump Prompt

This prompt is designed for GPT-5, Claude Opus 4.1, Cursor, Claude Code, and cursor-agent to perform a deep, structured analysis of any codebase without pasting the entire repo into chat.

It guides the LLM to:

• Browse and discover the codebase using built-in repo tools
• Read only the necessary files in intelligent order
• Analyze the system at high, mid, and low levels
• Identify features, their business purposes, how they work, and how they interact
• Output a complete master knowledge document inside codebase-analysis-docs/

ChristopherA

yt-dlp

A youtube-dl fork with additional features and fixes. Download videos from YouTube and other websites. More information at https://github.com/yt-dlp/yt-dlp

• Download a video or playlist (with the default options from command below):

yt-dlp "https://www.youtube.com/watch?v=oHg5SJYRHA0"

imba-tjd

云

• IaaS指提供系统（可以自己选）或者储存空间之类的硬件，软件要自己手动装。PaaS提供语言环境和框架（可以自己选）。SaaS只能使用开发好的软件（卖软件本身，如税务会计、表格文字处理）。BaaS一般类似于非关系数据库，但各家不通用
• 云服务的特点：零前期成本 & 按需付费 & 弹性（类似于租，可随时多加、退掉；但没有残值）、高可用（放在机房中，不同AZ间水电隔离）

如果你想补充内容，建议优先给 free-for-dev 提PR，还能混个高星repo的contributor，没必要加到本列表里。
If you want to make improvements, I would recommend you contributing to free-for-dev rather than this list.

其他人的集合

joshbuchea

Semantic Commit Messages

See how a minor change to your commit message style can make you a better programmer.

Format: <type>(<scope>): <subject>

<scope> is optional

Example

andrealaforgia

Prova Language Specification

Version 0.4 (draft). Status: design complete for a v1 compiler; open items listed in section 15. Changes from 0.1 are recorded in Appendix B, from 0.2 in Appendix C, and from 0.3 in Appendix D.

1. Purpose and founding principle

Prova is a programming language designed for code generation by large language models. Its founding principle:

A Prova program is a claim plus its evidence. The compiler rejects claims without evidence.