Quick version in case you missed it: instead of using RAG to re-chunk your sources every time you ask a question, you compile each source once into a persistent markdown wiki. The LLM extracts concepts, writes entity and concept pages, updates cross-references, flags contradictions, and maintains the whole thing. Future queries read the pre-synthesized wiki. The part that clicked for me: the reason most of us abandon our second brains is that backlink and cross-reference upkeep is boring. The LLM doesn't care. It's happy to touch fifteen pages in one pass. I spent a couple of weeks turning Karpathy's pattern into a Claude Code plugin that actually scales (atomic pages, sharded indexes, BM25 fallback past ~300 pages). It also runs in Codex, Cursor, Gemini CLI, Pi, and OpenClaw through the skills CLI. Install in Claude Code: /plugin marketplace add praneybehl/llm-wiki-plugin /plugin install llm-wiki@llm-wiki Or in any other supported agent: npx skills add praneybehl/llm-wiki-plugin -a <your-agent> Five slash commands (init, ingest, query, lint, stats), stdlib-only Python, no dependencies. Plays well with Obsidian if you want the graph view. Repo: https://github.com/praneybehl/llm-wiki-plugin Karpathy's gist: https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f Curious if anyone here has tried the pattern themselves. What did you ingest first, and what broke before it worked?

My wifey Simcha-Chaya filled in for me (with some minor trolling at the end from hubby) on the YouTube channel to show off Higgsfield AI's new Cinematic Studio feature! https://youtu.be/KYISSt99vho?si=bSvsyA3ehTxWDA6V ( Find crazyyy value by typing in search: #MAX_tips# )

Hello everyone, Since joining this community (by chance - not because of joining!) I've completely stopped my YouTube journey. I'm back on the horse and created a video I'm really proud of. However, I'm RUBBISH!!!! at thumbs, so I'd really welcome your thoughts/ideas/help! Feel free to tell me why my thumbs are rubbish and better: what I should do instead! Thanks all, Jamie