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Knowledge Powerhouse

Copyright © 2017 Knowledge Powerhouse

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By u/angkitbharadwaj

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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.

Git Commit Guidelines

This document defines how commits should be made in this repository.

The goal is to keep the Git history clear, searchable, consistent, and useful to future developers. A commit should explain what changed and, when necessary, why it changed.

When to Make Commits

People

`:bowtie:`	😄 `:smile:`	😆 `:laughing:`
😊 `:blush:`	😃 `:smiley:`	☺️ `:relaxed:`
😏 `:smirk:`	😍 `:heart_eyes:`	😘 `:kissing_heart:`
😚 `:kissing_closed_eyes:`	😳 `:flushed:`	😌 `:relieved:`
😆 `:satisfied:`	😁 `:grin:`	😉 `:wink:`
😜 `:stuck_out_tongue_winking_eye:`	😝 `:stuck_out_tongue_closed_eyes:`	😀 `:grinning:`
😗 `:kissing:`	😙 `:kissing_smiling_eyes:`	😛 `:stuck_out_tongue:`

Hey

The journey from the first simple bots to superhuman AI like Libratus and Pluribus is a fascinating 27-year story of academic rivalries, commercial scandals, and massive algorithmic breakthroughs. It's a perfect case study in AI for imperfect information games.

I've written up a little history, from the first academic attempts in 1998 to the modern "GTO Solvers." Grab a coffee, this is a long one.

	"""
	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

	Latency Comparison Numbers (~2012 except when noted)
	----------------------------------
	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

	html {
	--crust: #dce0e8;
	--mantle: #e6e9ef;
	--base: #eff1f5;
	--surface0: #ccd0da;
	--surface1: #bcc0cc;
	--surface2: #acb0be;
	--overlay0: #9ca0b0;
	--overlay1: #8c8fa1;
	--overlay2: #7c7f93;



	#EXTM3U

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	#EXTINF:-1 tvg-logo="https://goo.gl/IQS0pJ" group-title="Notícias",GLOBO NEWS
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