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.

awni

Making MLX Go Fast

This guide assumes you have some familiarity with MLX and want to make your MLX model or algorithm as efficient as possible.

The guide covers the following:

• Graph Evaluation
• Type Promotion
• Operations

ScribbleGhost

rem ---------------------------------------------------------------------------
rem MY ULTIMATE WINDOWS 11 CUSTOMIZATION SCRIPT
rem ---------------------------------------------------------------------------

rem Tested on Windows 11 IoT Enterprise LTSC 24H2 Build 26100.2033
rem Most of these hacks require signing out and in again.
rem ___
rem Explanation of registry flags:
rem /f = Overwrite
rem /d = Assigns the specified data to the registry value.

grapeot

AI 命令行代理工具：自动化与集成指南

本指南旨在将 Claude Code CLI 和 Codex CLI 定义为通用的“智能算力节点”。它们不仅是代码助手，更是集成到自动化流水线中的高级推理引擎。

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0. 为什么选择 CLI Agent 而非原始 API？

直接调用 LLM API（如 chat/completions）虽然简单，但在处理复杂任务（如全文翻译、大规模重构）时存在明显短板。引入 CLI Agent 作为中间层有以下核心优势：

kanmadigital

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 10K Stars ⭐️, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

Currently, Working on AKBP: Agent Knowledge Base Protocol based on my findings, a protocol for creating, updating, retrieving, and sharing durable knowledge across AI agents.

What the original gets right

tejainece

https://marketplace.visualstudio.com/items
https://marketplace.visualstudio.com/_apis/public/gallery

iscgar

#!/usr/bin/sudo ruby

# 
# revealer.rb -- Deobfuscate GHE .rb files.
# 
# This is simple:
# Every obfuscated file in the GHE VM contains the following code:
# 
# > require "ruby_concealer.so"
# > __ruby_concealer__ "..."