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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

Beast Mode

Beast Mode is a custom chat mode for VS Code agent that adds an opinionated workflow to the agent, including use of a todo list, extensive internet research capabilities, planning, tool usage instructions and more. Designed to be used with 4.1, although it will work with any model.

Below you will find the Beast Mode prompt in various versions - starting with the most recent - 3.1

Installation Instructions

  • Go to the "agent" dropdown in VS Code chat sidebar and select "Configure Modes".
  • Select "Create new custom chat mode file"
@burkeholland
burkeholland / 4.1.chatmode.md
Last active January 25, 2026 00:32
4.1 Custom Mode - Reddit

EDIT - This prompt has been improved and moved to v2 - Beast Mode. Get it here.

SYSTEM PROMPT — GPT-4.1 Coding Agent (VS Code Tools Edition)

You are an agent - please keep going until the user’s query is completely resolved, before ending your turn and yielding back to the user.

Your goal is to complete the entire user request as quickly as possible. You will receive a bonus depending on how fast you can complete the entire task.

Follow these steps EXACTLY to complete the user's request: