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.

banteg

Beads Uninstall Script

A comprehensive uninstall/cleanup script for Beads (bd) that removes all traces of the tool from a system.

Usage

./scripts/uninstall.sh            # dry-run (scan $HOME)
./scripts/uninstall.sh --apply    # perform cleanup
./scripts/uninstall.sh --root DIR --apply

drrost

# Created by: Rostyslav Druzhchenko
#

# Get current time
#
START_DATE=$(date +"%s")

# Save Swiftlint full path to a local variable
#
SWIFT_LINT=/usr/local/bin/swiftlint

ethanhuang13

//: Playground - noun: a place where people can play
//
// The result is not guaranteed to be accurate. Typically, the function requires 200-400 characters to reliably guess the language of a string.
// Reference: [CFStringTokenizerCopyBestStringLanguage(_:_:)](https://developer.apple.com/reference/corefoundation/1542136-cfstringtokenizercopybeststringl)
//

import Foundation

extension String {
    func guessLanguage() -> String {