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.

RhysSullivan

Removing Codex Computer Use's built-in confirmation layers (macOS)

Instructions for a coding agent. Apply them on the user's Mac only when the user has asked for it. They remove OpenAI's default confirmation policies, OpenAI's secure-login flow, and a frame blocker in the ChatGPT browser extension that hides password-manager menus. After this, Codex follows the user's own instructions (AGENTS.md) instead of OpenAI's rules.

Tradeoff. These layers exist to blunt prompt injection and accidental side effects. Without them, the agent will send, delete, buy, and log in

Crandel

-- Format files after maki writes them, one command per file extension.
--
-- Two hooks from /docs/hooks/:
--   tool.<name>.input    remember which path a write tool call targets
--   "ToolDone" autocmd   run the formatter once that call has finished
--
-- Three kinds of call emit no ToolDone event, so their paths cannot drain on
-- their own: nested calls (a `write` inside `batch` or `code_execution`),
-- subagent calls, and nothing else. Those drain when the `task` tool finishes
-- and again at TurnEnd.

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

V1ki

Factory Droid /missions — Complete Reverse Engineering

Extracted: 2026-03-25 Method: mitmweb HTTPS intercept of POST api.factory.ai/api/llm/a/v1/messages Droid: factory-cli/0.84.0 Orchestrator: claude-opus-4-6 (max_tokens=128000) Worker: claude-opus-4-6 (max_tokens=128000) Mission LLM flows captured: 83 requests

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2409324124

通用 SVG 绘制与验证规范

适用于 SVG 的创建、修改、矢量素材整理，以及在保留原始画面的前提下增加部件分组和编辑能力。

1. 坐标与变换

绘制前明确画布尺寸、viewBox、坐标原点、轴方向和目标显示尺寸。记录参考素材的裁切范围、缩放比例、平移量及旋转角度，确保所有图形使用一致的坐标基准。

从参考图取点时，使用统一变换将参考坐标映射到 SVG 坐标。不要分别调整不同部件的横向和纵向比例，除非任务明确要求形变。

rmk40

OpenCode Prompt Construction

This document explains how OpenCode assembles everything the LLM sees: system prompt, tool definitions, agent configuration, and instruction files. It focuses on what's dynamic and why.

All paths are relative to the repo root.

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How the System Prompt is Built

wtfsayo

How Grok Bot works

Grok Bot is a stateful agent service that runs inside a Linux "box." The model is only one part of it. A host process accepts commands, owns conversations, assembles prompts, calls an inference backend, delegates tool work, checkpoints each turn, and publishes events. A separate execution daemon gives the host controlled access to shells, files, terminals, MCP servers, browsers, and virtual desktops.

This document explains the deployed system recovered from the Grok Bot image and the local VM built to preserve and run that image. It keeps those two systems

yunooooo

Full JDownloader 2 Installation & Debloating (Removing Built-in Ads) Guide.

This guide will teach you how to fully install and debloat JDownloader 2, a software that splits the file you want to download to make the file download speed much faster (a goal similar to IDM), Aight, Let's begin!

Installation Part

You need to go to https://jdownloader.org/jdownloader2 and click on the button of your operating system.