Aron Reis aronreisx

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.

Open git bash with admin privilege.

cd "C:/Program Files/Git/usr/share/mintty"
mkdir -p emojis
cd emojis
curl https://raw.githubusercontent.com/wiki/mintty/mintty/getemojis > getemojis
./getemojis -d

Open Automator.app
Create new Quick Action
Select Run AppleScript
Add this:

set inputVolume to input volume of (get volume settings)
if inputVolume = 0 then
	set inputVolume to 100
	display notification "Volume set to 100" with title "✅ Microphone is on"

	import { Prisma, PrismaClient } from '@prisma/client';
	import { exec } from 'child_process';
	import * as util from 'util';

	const execPromisify = util.promisify(exec);
	const prisma = new PrismaClient();

	const tables = Prisma.dmmf.datamodel.models
	.map((model) => model.dbName)
	.filter((table) => table);