Andrej Karpathy LLM Wiki — AI skill for Claude Code

Karpathy-Inspired LLM Knowledge Base

**English** | [简体中文](./README.zh-CN.md)

One `CLAUDE.md` = a self-maintaining local knowledge base. No backend, no vector DB, no RAG framework.

curl -fsSL https://raw.githubusercontent.com/zhurudong/andrej-karpathy-llm-wiki/main/install.sh | bash -s my-kb

After that one line, open your LLM CLI inside `my-kb/` and say `ingest https://example.com/article` — you now have a knowledge base the LLM organizes, indexes, and queries for you. Everything is plain markdown; open it with any editor.

Inspired by Andrej Karpathy's gist: .

Why

Most "personal knowledge base" solutions take one of two paths:

• Note-taking apps (Notion / Obsidian / Logseq) — great for storage and browsing, but tagging, linking, and organizing is all manual.
• RAG / vector search — great for Q&A over large corpora, but needs embedding services, a vector store, and an ingestion pipeline. Heavy, fragile, opaque.

This project takes a third path: **let the LLM do the organizing, use markdown files as the substrate, use wiki-links as the graph, and use an LLM CLI as the runtime.**

• Raw articles are immutable; LLM-generated summaries / entities / concepts can be recompiled anytime
• Everything is plain markdown — works with any editor, Git, grep
• The knowledge graph emerges naturally from [[wiki-link]] — no graph DB
• Switching LLM tools requires zero data migration — the rules live in CLAUDE.md

Typical use cases:

• Reading papers — drop an arXiv link; the LLM generates a summary and links it to existing concepts
• Following a field — ingest industry blogs regularly; overviews/ organically form topic surveys
• Archiving your own thinking — ask questions, let the LLM store synthesized answers in synthesis/, building your own opinion library
• Team collaboration — push to Git; teammates maintain the same knowledge base with their own LLM CLIs