setup: complete wiki scaffold with schema, seed files, sample article, search script, and workflow docs

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+# LLM Wiki: A New Approach to Knowledge Management
+
+Source: Andrej Karpathy's blog / GitHub Gist (2026)
+
+## Summary
+
+Andrej Karpathy introduced the concept of an "LLM Wiki" — a persistent, structured knowledge base built and maintained by an LLM agent. Unlike traditional RAG (Retrieval-Augmented Generation) systems that search raw documents at query time, an LLM Wiki is compiled once and kept current.
+
+## Key Concepts
+
+### The Problem with RAG
+Traditional RAG retrieves from raw documents every time you ask a question. Results depend heavily on chunking strategy, embedding quality, and retrieval parameters. Good results are not retained.
+
+### The LLM Wiki Approach
+Instead of retrieving at query time, the LLM reads sources once and builds a structured wiki:
+- **Raw sources** are stored in `raw/` (immutable)
+- **Compiled knowledge** lives in `wiki/` as interlinked markdown pages
+- **A schema file** (`AGENTS.md`) defines conventions and workflows
+
+### Three Workflows
+1. **Ingest**: Add source to `raw/`, LLM compiles into wiki (~10-15 pages per source)
+2. **Query**: LLM reads `index.md`, finds relevant pages, synthesizes answer with citations
+3. **Lint**: Periodic audit for contradictions, orphans, stale claims
+
+### Compounding Knowledge
+Good answers get filed back into the wiki as new pages. The wiki gets richer with every source added and every question asked. This creates a compounding knowledge effect.
+
+## Technical Implementation
+- Works with any LLM that can read/write files
+- Tested with Claude Code, Codex CLI, Cursor
+- Also works with local models via Ollama (Gemma, Llama, etc.)
+- No special infrastructure needed — just a git repository of markdown files
+- Optional: qmd for enhanced search at scale (100+ sources)
+
+## Key Advantages
+- No embedding models or vector databases needed
+- Full version control via git
+- Human-in-the-loop curation
+- Knowledge compounds over time
+- Works offline / on-premise