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I open-sourced a tool that compiles raw documents into an AI-navigable wiki with persistent memory; runs 100% locally
After seeing Karpathy's tweet about using LLMs to build personal wikis from research documents, I realized I'd already been using something similar like this internally for our R D. So I cleaned it up and open-sourced it. What it does: You drop a folder of raw documents (PDFs, papers, notes, code, 60+ formats) and the LLM compiles them into a structured markdown wiki with backlinked articles, concept pages, and a master index. It then compresses everything into a .aura archive optimized for RAG retrieval (~97% smaller than raw source data). How it works: pip install aura-research research init my-project # copy docs into raw/ research ingest raw/ research compile research query "your question" Key design decisions: No embeddings, no vector databases. Uses SimHash + Bloom Filters instead. Z
🔥 teng-lin/notebooklm-py
Unofficial Python API and agentic skill for Google NotebookLM. Full programmatic access to NotebookLM's features—including capabilities the web UI doesn't expose—via Python, CLI, and AI agents like Claude Code, Codex, and OpenClaw. — Trending on GitHub today with 138 new stars.
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