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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.

@marcan
marcan / bloom.py
Last active February 29, 2024 19:55
Simple Bloom filter implementation in Python 3 (for use with the HIBP password list)
#!/usr/bin/python3
#
# Simple Bloom filter implementation in Python 3
# Copyright 2017 Hector Martin "marcan" <marcan@marcan.st>
# Licensed under the terms of the MIT license
#
# Written to be used with the Have I been pwned? password list:
# https://haveibeenpwned.com/passwords
#
# Download the pre-computed filter here (968MB, k=11, false positive p=0.0005):