Karpathy's LLM Wiki Pattern — Original Reference

Source: https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f

Core Insight

"The wiki is a persistent, compounding artifact. The knowledge is compiled once and then kept current, not re-derived on every query."

Human curates sources and asks questions; LLM maintains the knowledge system. Obsidian becomes the IDE, the LLM becomes the programmer, and the wiki becomes the codebase.

Why This Beats RAG

Traditional RAG rediscovers knowledge on every query — it searches raw sources, pulls relevant chunks, and synthesizes an answer from scratch. The LLM Wiki compiles knowledge once into maintained pages, so queries hit pre-synthesized, cross-referenced content.

Key Operations

Operation	What it does	When to use
Ingest	Read new sources, extract key information, update 10-15 wiki pages, maintain consistency	When new documents arrive
Query	Answer questions against compiled wiki with citations	When the user asks something
Lint	Identify contradictions, orphaned pages, stale claims, missing cross-references	Periodic maintenance

Recommended Tools

Obsidian — IDE for browsing and exploring the wiki
Web Clipper — Browser extension for converting articles to markdown
Marp — Markdown-based slide decks from wiki content
Dataview — Obsidian plugin for querying page metadata
qmd — Local search engine with BM25/vector hybrid search

Applications

Personal tracking (goals, psychology, self-improvement)
Research (building comprehensive understanding over weeks/months)
Book annotation (companion wikis with characters, themes, plot connections)
Team/business (wikis from Slack threads, meeting transcripts)
Due diligence, competitive analysis, trip planning

Community Extensions Worth Knowing

Provenance tracking — Record which source files produced each claim, detect staleness through content hashing
Hierarchical inheritance — Parent-child page relationships instead of flat indexing
Decision records — Capture why the wiki evolved, not just what changed
Two-tier LLMs — Local models for sensitive data, cloud for the rest
Graph databases — Typed ontologies instead of markdown links

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