karpathy

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.

kizbitz

#!/bin/bash

# Example for the Docker Hub V2 API
# Returns all images and tags associated with a Docker Hub organization account.
# Requires 'jq': https://stedolan.github.io/jq/

# set username, password, and organization
UNAME=""
UPASS=""
ORG=""

lrascao

dash_id=xxxx
api_key=xxx
app_key=xxx

# 1. export
curl -X GET "https://app.datadoghq.com/api/v1/dash/${dash_id}?api_key=${api_key}&application_key=${app_key}" > dash.json

# 2. edit dash.json
move "graphs", "title", "description" up one level in the json hierarchy, from being beneath "dash" to being at the same level