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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.

@ityonemo
ityonemo / test.md
Last active May 8, 2026 13:37
Zig in 30 minutes

A half-hour to learn Zig

This is inspired by https://fasterthanli.me/blog/2020/a-half-hour-to-learn-rust/

Basics

the command zig run my_code.zig will compile and immediately run your Zig program. Each of these cells contains a zig program that you can try to run (some of them contain compile-time errors that you can comment out to play with)

@skeeto
skeeto / README.md
Last active September 23, 2024 02:16
AI driving simulation
@bishboria
bishboria / springer-free-maths-books.md
Last active April 19, 2026 15:50
Springer made a bunch of books available for free, these were the direct links