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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.

@burnash
burnash / get_oauth2_token.py
Last active August 11, 2021 21:16
Simple command line script to fetch a Google API's access token.
'''
This script will attempt to open your webbrowser,
perform OAuth 2 authentication and print your access token.
It depends on two libraries: oauth2client and gflags.
To install dependencies from PyPI:
$ pip install python-gflags oauth2client
@quchen
quchen / trolling_haskell
Last active December 25, 2025 23:55
Trolling #haskell
13:15 <xQuasar> | HASKELL IS FOR FUCKIN FAGGOTS. YOU'RE ALL A BUNCH OF
| FUCKIN PUSSIES
13:15 <xQuasar> | JAVASCRIPT FOR LIFE FAGS
13:16 <luite> | hello
13:16 <ChongLi> | somebody has a mental illness!
13:16 <merijn> | Wow...I suddenly see the error of my ways and feel
| compelled to write Node.js!
13:16 <genisage> | hi
13:16 <luite> | you might be pleased to learn that you can compile
| haskell to javascript now