Daniel Patrick Fahey daniel-fahey

Minimal i3 Ubuntu 18.04

TL,DR

A fully fonctional and good-looking linux for less than 256mb of ram

Update:

-> you can use this one witch allow push request and better share :D <-

Installing Cool-Retro-Term on Windows10

First of all, this document is just a recompilation of different resources that already existed on the web previously that I personally tested some ones did work and other not. I liked the idea to make a full guide from start to end so all of you could also enjoy playing with cool-retro-term on windows 10. Personally I installed it on a windows 10 pro version. Fingers crossed!

Scaling Mastodon down

There is already a guide on scaling your Mastodon server up. This is a short guide on scaling your Mastodon server down.

I.e., maybe you want to run a small instance of <100 active users, and you want to keep your cloud costs reasonable. So you might be running everything on a single machine, with limited memory and CPU. (In my case, I was using a t3.medium instance with 2 vCPUs and 4GB of RAM.) How do you do this?

Note that I'm not a Ruby or Sidekiq expert, and most of this stuff I figured out through trial and error.

PostgreSQL is Enough

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

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

	#! /bin/bash

	# This script will export the json contents of an Azure Search instance into a JSON array.
	# The script creates local files under the directory it is executed. The result is saved to a newly created local file.
	# The script depends on `curl` and `jq` utilities.
	# Arguments: $1 : azure search service name, $2: azure search index name, $3: azure search admin auth key.

	set -e -o pipefail

	serviceName="$1"


	{ config, pkgs, lib, ... }:
	{
	imports = [
	<nixpkgs/nixos/modules/virtualisation/linode-image.nix>
	];

	services.nextcloud = {
	enable = true;
	package = pkgs.nextcloud27;