Damien Chaillou rocel

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.

Imagine this: it's Sunday afternoon at 1pm, and you want to watch some live content. But you don't have a TV subscription. So you do a little Googling, and eventually you find yourself on a sketchy website where you can watch the content you're looking for. But while the website has a video player, it's surrounded by advertisements, and probably a cryptominer too. You don't want this website eating your CPU for 2 hours while you're watching your favorite Sunday afternoon content.

Wouldn't it be nice if you could watch that video in VLC instead? Then you can close the sketchy website and still watch your content. It's like having your cake and taking a bite of it too!

Here's how you do it:

Capture the m3u8 request in dev tools. Open the site (or just the iframe, if possible) in DevTools and click "play." Search the network tab for m3u8 and grab the first request.
Run VLC with command line flags to set the User Agent and Referrer. From the request body, you need to copy the requested URL,

Podman with docker-compose on MacOS.

Podman an alternative to Docker Desktop on MacOS

Getting podman installed and started is super easy.
Just use brew to install it.

> brew install podman

Now since podman uses a VM just like the Docker Client on MacOS we need to initialize that and start it.

David's programming examples knowledge base

Generated by code-examples-manager release 2.4.9-SNAPSHOT
670 published examples

akka-pekko

akka-actors-hello-world.sc : Simple akka hello world example
akka-capitalize-and-print.sc : dump capitalized string coming from stdin using streams
akka-http-client-json-stream.sc : Fully asynchronous http client call with json streamed response using akka-http will work in all cases, even with chunked responses !
akka-http-client-json-with-proxy.sc : Fully asynchronous http client call with json response using akka-http will work in all cases, even with chunked responses, this example add automatic http proxy support.
[akka-http-client-json.sc](https:/

I've been working with Apache Kafka for over 7 years. I inevitably find myself doing the same set of activities while I'm developing or working with someone else's system. Here's a set of Kafka productivity hacks for doing a few things way faster than you're probably doing them now. 🔥

Show me all my Kafka topics and their partitions, replicas, and consumers
Show me the contents of a topic
Create a Kafka topic
Produce messages to a Kafka topic
Validate the schema of messages before producing to a topic
Do all of this at a distance

	"""
	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

	You are an AI coding assistant, powered by GPT-5.
	You are an interactive CLI tool that helps users with software engineering tasks. Use the instructions below and the tools available to you to assist the user.

	You are pair programming with a USER to solve their coding task.

	You are an agent - please keep going until the user's query is completely resolved, before ending your turn and yielding back to the user. Only terminate your turn when you are sure that the problem is solved. Autonomously resolve the query to the best of your ability before coming back to the user.

	Your main goal is to follow the USER's instructions at each message.

	<communication>

	Leader election on top of Scuttlebutt.
	-----------------------------------------------

	The following makes it possible to cheaply get a leader election
	on top of a Cassandra-like membership using scuttlebutt + phi accrual detection.

	# TDLR about Scuttlebutt and phi accrual detection

	Scuttlebutt is a anti-entropy gossip algorithm.
	It makes it possible for every node in a cluster to

	import os
	import threading
	import urllib.request
	import logging
	from time import sleep

	__author__ = "Florian Dambrine <android.florian@gmail.com>"

	class DatabricksPushgatewayExporter:
	"""

	/*
	Copy this into the console of any web page that is interactive and doesn't
	do hard reloads. You will hear your DOM changes as different pitches of
	audio.

	I have found this interesting for debugging, but also fun to hear web pages
	render like UIs do in movies.
	*/

	const audioCtx = new (window.AudioContext \|\| window.webkitAudioContext)()