Jason Davis jasondavis

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.

type

project

status

🟡

description

Miscellaneous personal tasks

aliases

#p/personal

I use the project file to track tasks for a given project. I use tags like #p/personal to associate a task to a project. The alias in the YAML front matter allows me to navigate to this tasks page by clicking on the tag from a tasks query.

	/**
	* File: GitHubFolderToGist-v1.1.2.js
	* Version: 1.1.2
	* Description:
	* Select a folder or ZIP archive from iOS Files and publish its contents as
	* a GitHub Gist. Nested paths are flattened into Gist-safe filenames.
	*
	* Features:
	* - Select an iOS Files folder or a .zip archive.
	* - Recursively includes files from nested folders.

	import json
	import os
	import sys
	import getpass
	from datetime import datetime, timezone

	import praw
	from praw.models import MoreComments

	"""
	The most atomic way to train and inference 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

	"""
	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

	<%*
	let raindrop = tp.user.raindrop;
	let collections = await raindrop.collections();
	let collection = await tp.system.suggester((item) => item.title, collections);
	let raindrops = await raindrop.raindrops(collection._id);
	let drop = await tp.system.suggester((item) => item.title, raindrops);
	drop = await raindrop.raindrop(drop._id);

	let title = drop.title
	let url = drop.link

	#!/bin/bash

	# 1: Define backup filenames with timestamps.
	db_backup_name="wp-db-backup-$(date +%d-%m-%Y-%H.%M).sql.gz"
	wpfiles_backup_name="wp-content-backup-$(date +%d-%m-%Y-%H.%M).tar.gz"

	# 2: Database connection info.
	db_name="wordpress"
	db_username="wordpress"
	db_password="wordpress123"

	function getNumOfGames(campaign) {
	let numOfGames = app.plugins.plugins.dataview.api
	.pages(`"ttrpgs/${campaign}"`)
	.where(page => {
	if (page.type === 'session') {
	if (page.campaign === campaign) {
	return true;
	}
	}
	}).length