Chris Scott CyberStrike

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.

	"""
	The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp

	<?php
	/*
	* custom pagination with bootstrap .pagination class
	* source: http://www.ordinarycoder.com/paginate_links-class-ul-li-bootstrap/
	*/

	function bootstrap_pagination( $custom_query = null, $echo = true ) {
	global $wp_query;

	$pagination = '';

	# http://docs.rubygems.org/read/chapter/11
	---
	gem: --no-ri --no-rdoc
	benchmark: false
	verbose: true
	update_sources: true
	sources:
	- http://gems.rubyforge.org/
	- http://rubygems.org/
	backtrace: true

	@mobileWeb = /Mobile\|iP(hone\|od\|ad)\|Android\|BlackBerry\|IEMobile\|Kindle\|NetFront\|Silk-Accelerated\|(hpw\|web)OS\|Fennec\|Minimo\|Opera M(obi\|ini)\|Blazer\|Dolfin\|Dolphin\|Skyfire\|Zune/i.test(navigator.userAgent)

	// Usage: if @mobileWeb then ...