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.

vialuch

在 ChatGPT 对话里做一个动态状态栏

通用教程与实现思路。作者：Dan & Joy

效果思路很简单：让模型调用一个只读 MCP 工具，工具返回 mood / place / focus / note 等结构化数据；ChatGPT 再把这份数据交给一个内嵌 HTML 组件。组件可以用 CSS 做动画，用 JavaScript 更新时间、展开面板和处理点击，也可以把用户刚刚操作后的状态带到下一轮对话。

它不是修改 ChatGPT 原生界面，也不是普通 Markdown 图片，而是一个运行在对话消息里的 MCP Apps 组件（iframe）。示例字段和文案都只是占位，每个人都可以按照自己的角色、项目或使用场景替换。

1. 架构

mjasnikovs

#!/usr/bin/env bash
set -euo pipefail
cd "$(dirname "$0")"

export CUDA_MALLOC_ASYNC_SUPPORTED=1
export GGML_CUDA_FORCE_MMQ=1

IMAGE="${IMAGE:-llama-turboquant:cuda}"
HOST_PORT="${HOST_PORT:-8080}"
NETWORK="${NETWORK:-runner-network}"

gvoze32

To convert audio mp3 to MP4 by ffmpeg, use the following command

ffmpeg -f lavfi -i color=c=black:s=1280x720:r=5 -i audio.mp3 -crf 0 -c:a copy -shortest output.mp4

Description

This generates mp4 formatted video with blank black background with the color source filter instead of using an image.

Since it is just black video this is one case with lossless mode (-crf 0) will have a smaller file size than the default lossy mode.

leonid-ed

/*
    An example of using raw sockets.

    You can capture packets by tcpdump:
        tcpdump -X -s0 -i lo -p udp
 */

#include <stdlib.h>
#include <unistd.h>
#include <stdio.h>

rex

Awesome Libraries of Awesomeness

Interface

• Summernote - Amazing WYSIWYG editor with badass plugin architecture
• Medium Editor - Clone of Medium's incredible WYSIWYG editor
• Slack Themes - Themes for Slack. Duh.
• Multi-Level Push Menu - Sexy sidebar library that displays nested shelves
• Awesomeplete - Sexy autocomplete with zero dependencies

rex

[
  "swapping space and time",
  "downloading golf balls",
  "warming up reactor",
  "reticulating splines",
  "searching for the answer to life, the universe, and everything",
  "counting backwards from infinity",
  "pay no attention to the man behind the curtain",
  "Calculating gravitational constant in your bay",
  "following the white rabbit",

rex

window.css_refresh_index = 0;
const begin = () => {
  var nodes = document.querySelectorAll('link');
  [].forEach.call(nodes, function (node) {
      node.href += '?___ref=0';
  });
}

const refresh = () => {
  if (!window.css_refresh_index) {

rex

Role: You are an expert AI-assisted software engineer. Your task is to analyze a given codebase and generate a set of robust, best-practice AGENTS.md files to help other AI coding agents understand and contribute to this project effectively.

Objective: Create one AGENTS.md file at the project root and, where appropriate, additional AGENTS.md files in key subdirectories (like packages in a monorepo or distinct modules like /frontend and /api). These files will serve as the primary instruction set for AI agents.

Process:

1. Analyze Codebase Structure:

   • Scan the entire file tree to identify the project's overall architecture.

• Identify the primary package.json, pyproject.toml, go.mod, pom.xml, build.gradle, requirements.txt, or similar dependency/build file to understand the project's main language, framework, and build system.