acmerfight

🧠 Karpathy "LLM Wiki" 方法论深度解读

Andrej Karpathy — OpenAI 联合创始人、前 Tesla AI 负责人、"Vibe Coding" 提出者
原始推文：2026 年 4 月 2-3 日（因时区差异，不同来源记录为 2 日或 3 日） · GitHub Gist（5000+ ⭐）
本文基于原始推文与 Gist 原文，经多源交叉验证

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〇、一句话概括

flashblaze

import type { CustomToolFactory } from '@oh-my-pi/pi-coding-agent';

const factory: CustomToolFactory = (pi) => {
  const z = pi.zod;

  return {
    name: 'agy_web_search',
    label: 'AGY Web Search',
    description:
      'Search and retrieve public web evidence through Antigravity CLI. MUST be used for unknown-URL web research instead of native OMP web search providers. Returns source URLs and page excerpts for the calling model to synthesize; use read for known URLs and browser for interactive pages.',

fredjoseph

Config files

To configure Zsh for your user’s session, you can use the following files:

• $ZDOTDIR/.zshenv
• $ZDOTDIR/.zprofile
• $ZDOTDIR/.zshrc
• $ZDOTDIR/.zlogin
• $ZDOTDIR/.zlogout

By default, Zsh will try to find the user’s configuration files in the $HOME directory. You can change it by setting the environment variable $ZDOTDIR.

yowmamasita

Stremio Offline Downloads — Expo Android App

Project Brief

Build an Expo (React Native) Android app that runs a local Stremio addon server on the device, aggregating streams from 75+ Stremio addons (based on AIOStreams core logic), with an added offline download manager for saving content to local storage.

Stremio connects to http://127.0.0.1:3000/manifest.json — the addon runs entirely on-device, no external server needed.

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HaleTom

#!/bin/bash
# Update the bt-tracker= line in aria2.conf
# Any bt-tracker= lines are removed and and a new one added at the bottom of the file

# Updates at:  https://gist.github.com/HaleTom/fe873dc2f3c5bd14f7418efefc2b91a8
# Inspiration: https://github.com/wuyuansushen/aria2c_TrackersList

set -euo pipefail
shopt -s failglob

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.

NicolasCARPi

VibeCoded AI-Slop License v1.0

Copyright (c) [YEAR] [AUTHOR]

This software, source code, documentation, configuration, generated artifacts,
and whatever else happened to end up in this repository (collectively, the
"Software") is released under the VibeCoded AI-Slop License v1.0.

1. Permission

WerWolv

import std.core;
import std.io;
import type.time;
import std.sys;

using int = s32;
using long = s64;

struct SevenBitEncodedIntByte {
    u8 byte;