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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.

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.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

	//If you are planing to use the CySharp Unitask , this also handles the return to the Main thread !
	//using Cysharp.Threading.Tasks;
	using System;
	using System.Collections.Generic;

	//you can use the event bus with any sort of data type, even structs (in my opinion is better)
	public struct ContextOnSelectedGameHubItem
	{
	public string GuID;
	}

	RAR registration data
	WinRAR
	Unlimited Company License
	UID=4b914fb772c8376bf571
	6412212250f5711ad072cf351cfa39e2851192daf8a362681bbb1d
	cd48da1d14d995f0bbf960fce6cb5ffde62890079861be57638717
	7131ced835ed65cc743d9777f2ea71a8e32c7e593cf66794343565
	b41bcf56929486b8bcdac33d50ecf773996052598f1f556defffbd
	982fbe71e93df6b6346c37a3890f3c7edc65d7f5455470d13d1190
	6e6fb824bcf25f155547b5fc41901ad58c0992f570be1cf5608ba9

	@echo off
	REM 注意：此脚本需要在执行的计算机具备UPS为前提的条件下进行，且目标IP设备不能具有UPS。
	REM 此批处理可以检测目标IP是否因为停电而失效，一旦失效则在180秒后关机。
	REM 需将此批处理写入计划任务才能使用，建议每隔60秒执行一次。
	REM 若不知道如何配置计划任务，请参阅 https://twitter.com/chenshaoju/status/973109556640333825
	REM 若参照60秒执行一次的话，在120至180秒内若恢复供电（能Ping通），则会取消关机。
	REM 若路由器或需要检测的目标IP不是 192.168.1.1 ，请手动修改下方的 192.168.1.1 IP地址。
	REM 关机等待时间不建议设置太短，避免手动开机但目标IP未启动时又立刻自动关机。

	REM 若只需要监控一个设备（默认），请勿注释下一行。