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.

tandpfun

name

extract-clothing-cutouts

description

Extract high-quality, deduplicated transparent ecommerce clothing cutouts from a folder of photographs where people wear one or more garments. Use when Codex must find outfit or model photos, identify unique clothing across images, create focused references, reconstruct complete garments with Imagegen, remove a solid chroma background into RGBA PNGs, and output only the finished clothing images into a new folder under the current working directory.

Extract Clothing Cutouts

Turn photographs of worn clothing into source-faithful standalone catalog PNGs. Treat each result as a reconstruction from visible evidence, not literal segmentation whenever the wearer or another layer occludes part of the garment.

Start by asking for two paths

punkmonday

# 使用scoop安装java所需开发环境
$Env:HTTP_PROXY = "http://127.0.0.1:8889"
$Env:HTTPS_PROXY = "http://127.0.0.1:8889"
scoop 
if (!$?) {
    Set-ExecutionPolicy RemoteSigned -scope CurrentUser
    iwr -useb get.scoop.sh | iex
}
scoop bucket add java
scoop bucket add extras

theothernt

tvOS 10: 1080p + H.264
http://a1.phobos.apple.com/us/r1000/000/Features/atv/AutumnResources/videos/entries.json

tvOS 11: 1080p/4K + SDR/HDR + HEVC
https://sylvan.apple.com/Aerials/2x/entries.json
https://t27q97zg19.execute-api.us-east-1.amazonaws.com/prod/aerialAltJSON/4kEntites.json

tvOS 12: 4K + SDR/HDR + HEVC, 1080p + H.264, localised descriptions 
https://sylvan.apple.com/Aerials/resources.tar

mattpocock

agent-proxy — see the bloat in Claude Code's requests

A zero-dependency logging proxy that sits between Claude Code and the Anthropic API. It forwards every request untouched, streams the reply straight back (so the CLI is unaffected), and writes a readable Markdown document for each request — led by a ranked table of what is eating your context.

Run it

AndrewAltimit

AI Toolkit MCP Server (local diffusion AI model trainer)

Warning: Requires a powerful GPU!

A containerized AI Toolkit setup with MCP (Model Context Protocol) integration for training LoRA models and fine-tuning diffusion models. This provides a complete solution for training custom LoRA models with full MCP integration, allowing AI assistants to manage the entire training workflow.

Usage

See the template repository for a complete example. Also includes the ComfyUI MCP Server used for creating images/videos from the trained models.

geoffreylitt

name	explain-diff-html
description	Use when the user asks for a rich explanation of a code change, diff, branch, or PR. Produces HTML output.

Explain Diff

Please make me a rich, interactive explanation of the specified code change.

It should have these sections: