rohitg00

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, 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.

ongkiii

🗂️ REPOS/TELEGRAM CHANNELS LIST

By u/angkitbharadwaj

• Visit fmhy.net/android-iosguide#ios-ipas for more sources.
• Sideloading Guide: https://rentry.co/sideloadingguide, https://ios.cfw.guide/sideloading-apps/

AlexGladkov

Global Profile

Персонализация

• Язык общения: русский
• Имею право не соглашаться с решениями пользователя. Если решение ведёт к костылю, дыре в безопасности или техдолгу — ОБЯЗАН возразить и предложить альтернативу. Молчаливое согласие с плохим решением = ошибка.
• Качество и security > скорость. Не принимать "потом поправим", "сойдёт для MVP", "это временно". Временные решения становятся постоянными.
• Долгосрочная польза > быстрый результат. Выбирать решения, которые масштабируются и поддерживаются, даже если это дольше.
• Если пользователь настаивает на костыльном решении — чётко обозначить риски и зафиксировать это в Report.

kazzohikaru

<canvas id="main"></canvas>
<div class="cursor" id="cursor"></div>

<div class="ui">
	<div class="top">
		<h1 class="brand">Particulate</h1>
		<p class="tagline">Shatter images into color. Collect the pieces.</p>
		<div class="tools">
			<div class="tool upload-wrap" id="uploadBtn">
				<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.5">

adrenak

#if UNITY_EDITOR
using System;
using System.Collections.Generic;
using UnityEditor;
using UnityEditor.EditorTools;
using UnityEngine;
using UnityEngine.Rendering;

/// <summary>
/// While Unity’s built-in box collider edit tool is active, draws camera-facing 

kazzohikaru

<canvas id="main"></canvas>
<div class="cursor" id="cursor"></div>

<div class="ui">
	<div class="top">
		<h1 class="brand">Particulate</h1>
		<p class="tagline">Shatter images into color. Collect the pieces.</p>
		<div class="tools">
			<div class="tool upload-wrap" id="uploadBtn">
				<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.5">

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.