LLM Wiki cc
A personal Wikipedia an LLM maintains for you
LLM Wiki cc launched on May 31, 2026 with 7 votes, #1,574 of 20,538 launches that month and more than 90% of that year's launches.
What it does
Drop in papers, notes, PDFs, or URLs and an agent compiles them into a cross-linked markdown wiki you own. Each new source makes every page richer, not just adds one. Open source, local-first, free to start with local Ollama models. An implementation of Karpathy's LLM Wiki pattern. Artificial Intelligence, Open Source, Developer Tools, Productivity, Notes
Local-first, open-source knowledge base. Drop in articles, papers, notes, PDFs, or URLs — an agent compiles them into a cross-linked markdown wiki you fully own. Install free via npm or use the hosted version (coming soon).
Drop in articles, papers, notes, PDFs, or URLs. An agent reads them and writes a cross-linked markdown wiki you fully own. Knowledge compounds — each new source makes every page richer, not just one new page longer. Pick OpenRouter (frontier cloud models) or run local models via Ollama — your choice, per operation. Drop a source (text / PDF / URL / image). The LLM reads it alongside your existing wiki, writes new pages, updates older pages where context shifts, refreshes the index, logs the change. Each ingest is a refactor pass — not an append. One-shot Q&A against the whole wiki with cited pages. 'Save as wiki page' promotes useful answers into permanent entries. For ongoing…from llmwiki.cc
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LLM Wiki – Open-Source Implementation of Karpathy's LLM WikiApr 2026 · llmwiki.app · ▲6Free, open-source implementation of Karpathy's LLM Wiki. Upload documents and build a compounding wiki directly via Claude.
A Karpathy-style LLM wiki your agents maintain (Markdown and Git)Apr 2026 · github.com · ▲260I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet. It runs locally in ~/.wuphf/wiki/ and you can git clone it out if you want to take your knowledge with you. The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard. I…
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