• Screen sharing on Hyprland (Arch Linux)
  • Introduction
  • Install PipeWire and friends
  • Install xdg-desktop-portal and friends
    • Option #1: xdg-desktop-portal-hyprland (recommended)
    • Option #2: xdg-desktop-portal-wlr (not recommended)

• Remove other portal implementations

1. Vitest

npm install --save-dev vitest jsdom

2. React Testing Library

This service will use the same remote name you specified when using rclone config create. If you haven't done that yet, do so now.

Next, create the mountpoint for your remote. The service uses the location ~/mnt/<remote> by default.

mkdir ~/mnt/dropbox

Partitioning scheme

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.

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.

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, 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.

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.

(I'm enjoying doing these raw, barely edited writeups; I hope they're useful to you too)

This is my own writeup on feature flags; for a deep dive I'd recommend something like Martin Fowler's article (https://martinfowler.com/articles/feature-toggles.html).

So. Feature flags. The basic idea that you'll store configuration/values on a database/service somewhere, and by changing those values, you can change the user experience/features for a user on the fly.

Let's say that you're building a new feature, called 'new-button' which changes the color of buttons, which is currently red, to blue. Then you'd change code that looks like this -