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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.

"""
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
@thomwolf
thomwolf / fast_speech_text_speech.py
Last active January 14, 2025 12:13
speech to text to speech
""" To use: install LLM studio (or Ollama), clone OpenVoice, run this script in the OpenVoice directory
git clone https://github.com/myshell-ai/OpenVoice
cd OpenVoice
git clone https://huggingface.co/myshell-ai/OpenVoice
cp -r OpenVoice/* .
pip install whisper pynput pyaudio
"""
from openai import OpenAI
import time
@wangruohui
wangruohui / Install NVIDIA Driver and CUDA.md
Last active January 24, 2026 19:55
Install NVIDIA Driver and CUDA on Ubuntu / CentOS / Fedora Linux OS
@kevin-smets
kevin-smets / iterm2-solarized.md
Last active April 2, 2026 23:07
iTerm2 + Oh My Zsh + Solarized color scheme + Source Code Pro Powerline + Font Awesome + [Powerlevel10k] - (macOS)

Default

Default

Powerlevel10k

Powerlevel10k