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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.

@golbin
golbin / ssh-start-menu.sh
Last active April 21, 2026 01:08
SSH login tmux session picker
#!/bin/sh
set -eu
# Show this menu only for an SSH login with a terminal, and never inside tmux.
if [ -z "${SSH_TTY:-}" ] || [ -n "${TMUX:-}" ] || [ ! -t 0 ]; then
exit 0
fi
case "${LC_ALL:-${LC_CTYPE:-${LANG:-}}}" in
*UTF-8*|*utf8*|*utf-8*) ;;
@jinjier
jinjier / javdb-top250.md
Last active April 21, 2026 01:08
JavDB top 250 movies list. [Updated on 2026/01]
"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.
@karpathy
"""
import os # os.path.exists
import math # math.log, math.exp