Karpathy's setup keeps a 400K-word research knowledge-base without RAG for LLM query. Dump sources into raw/. Let an LLM turn them into linked Markdown. Let it add summaries, concepts, and backlinks. View it in Obsidian. Ask the wiki questions with an LLM. Let it make notes, slides, or charts. Feed those outputs back into the wiki. Run checks for gaps and errors.
Andrej Karpathy
@karpathy
LLM Knowledge Bases
Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating
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