Mark Lapierre mlapierre

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.

Stuff I wish I'd known sooner about service workers

I recently had several days of extremely frustrating experiences with service workers. Here are a few things I've since learned which would have made my life much easier but which isn't particularly obvious from most of the blog posts and videos I've seen.

I'll add to this list over time – suggested additions welcome in the comments or via twitter.com/rich_harris.

Use Canary for development instead of Chrome stable

~~Chrome 51 has some pretty wild behaviour related to console.log in service workers. Canary doesn't, and it has a load of really good service worker related stuff in devtools.~~

Git Cheat Sheet

Commands

Getting Started

git init

or

May 8, 2018

I wrote this four years ago, so instead use this command:

$ docker rmi $(docker images -q -f dangling=true)

	library(rtweet) #rtweet API creds should already be set up
	library(stringi)
	library(dplyr)

	friends = get_friends(user="noamross")
	followers = get_followers("noamross")
	tweeps_id = distinct(bind_rows(friends, followers))
	tweeps_info = lookup_users(tweeps_id$user_id)

	# A regex for a visit to Durham