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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.

This is from man bash:

A pipeline is a sequence of one or more commands separated by one of the control operators | or |&. The format for a pipeline is:

[ time [ -p ]] [ ! ] command [ [ | | |& ] command2 ... ]

This is the only place in which ! can appear. It never prefixes commands, it prefixes pipelines.

PostgreSQL is Enough

See the new site: https://postgresisenough.dev

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

`$HOME`, Not So Sweet `$HOME`

Preface
1 What is $HOME to you?
2 Setup
- 2.1 Linux
2.1.4 xdg-user-dirs

	#!/bin/sh
	# ISC License

	# Copyright (c) 2026 Alexandre Gomes Gaigalas <alganet@gmail.com>

	# Permission to use, copy, modify, and/or distribute this software for any
	# purpose with or without fee is hereby granted, provided that the above
	# copyright notice and this permission notice appear in all copies.

	# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES

	"""
	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

	;; nano-emacs.el --- NANO Emacs (minimal version) -- lexical-binding: t --

	;; Copyright (c) 2025 Nicolas P. Rougier
	;; Released under the GNU General Public License 3.0
	;; Author: Nicolas P. Rougier <nicolas.rougier@inria.fr>
	;; URL: https://github.com/rougier/nano-emacs

	;; This is NANO Emacs in 256 lines, without any dependency
	;; Usage (command line): emacs -Q -l nano.el -[light\|dark]

	#include <stdint.h>
	#include <stdlib.h>
	#include <stdbool.h>
	#include <windows.h>
	#pragma comment( lib, "user32.lib" )
	#pragma comment( lib, "gdi32.lib" )

	#define SCRW 640
	#define SCRH 480

	(advice-add 'org-babel-C-execute
	:filter-args
	(defun sloth/org-babel-C-execute/filter-args (args)
	(when-let* ((params (cadr args))
	(stdin (cdr (assoc :stdin params)))
	(res (org-babel-ref-resolve stdin))
	(stdin (org-babel-temp-file "c-stdin-")))
	(with-temp-file stdin (insert res))
	(let* ((cmdline (assoc :cmdline params))
	(cmdline-val (or (cdr cmdline) "")))