LLM Knowledge Bases

You rarely ever write or edit the wiki manually, it's the domain of the LLM.

Describes a personal research workflow where raw source documents are compiled by an LLM into a markdown wiki, maintained through index files, health checks, generated outputs, and lightweight tools rather than a heavyweight RAG stack.

This is the Extended Frontier applied to knowledge work: the model's capability comes from a maintained corpus, indexes, summaries, visual outputs, and health checks that make research cumulative instead of ephemeral.

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Karpathy describes a knowledge-work harness, not just a note-taking habit. Raw sources go into one directory; an LLM incrementally compiles them into a markdown wiki with summaries, backlinks, concept pages, index files, and derived visualizations. Obsidian becomes the human-facing IDE, while the LLM owns most direct edits to the wiki. The important move is that research outputs are not terminal chat answers. They become files: markdown notes, Marp slides, matplotlib images, search indexes, and follow-up articles that can be filed back into the corpus. Each query can make the next query easier because the knowledge base itself accumulates structure. For the library, this is a clean example of capability as artifact maintenance. Karpathy expected to need "fancy RAG," but at roughly 100 articles and 400K words, LLM-maintained summaries and index files were enough. The boundary condition matters: the system works because the scale is still small enough for source-aware traversal and because the artifacts are legible. Extended Frontier Read The raw model is not the unit of analysis. The useful system is model plus: • a raw source archive, • a compiled markdown wiki, • index and summary files, • Obsidian as inspection surface, • generated outputs that feed back into the wiki, • health checks over consistency and missing data, • small custom tools such as a wiki search engine. This belongs beside harness entries, but it broadens the frame from coding agents to research agents. The same pattern appears: make the environment legible, let the model act on files, inspect the result, repair the substrate, and let work accumulate. Open Questions • At what corpus size does this stop working without stronger retrieval infrastructure? • Which health checks are most predictive of useful future Q&A? • Does finetuning on the wiki improve capability, or does it destroy the inspectability and repairability that make the workflow valuable?

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