karpathy

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

schmir

# allow to silence error reporting for certain 'expected exceptions'

# gevent prints a error report including a traceback for each failed
# greenlet. this is at times useful, but you're currently forced to
# either handle exceptions in those greenlets or live with the traceback
# on stderr.

# patch_gevent introduces a class variable 'expected_exceptions' to
# gevent's greenlet class. This is a tuple of exceptions for which no
# error report should be printed to stderr.