The article shows how packing specialized, retrieved knowledge into smaller AI agents can match or beat huge frontier models. It explains a structure—raw source extractions, concept entries, theses and a startup primer—plus embedding‐powered retrieval to feed just the right context at query time.

Karpathy proposes replacing on-the-fly retrieval with an LLM-maintained markdown wiki that ingests sources, compiles structured pages, and self-updates through ingest, query, and lint cycles. This approach builds a persistent, compounding knowledge base without vectors or re-retrieval, though it currently lacks enterprise controls.