The maintained version of this guide now lives at zero8.dev/blog/completely-uninstall-logitech-options-plus-macos — same cleanup, with fixes (this gist originally had a ~-inside-quotes bug on the SentryCrash line, corrected below) and kept up to date as Logitech moves paths around.

Logitech Options+ (and its predecessor Logitech Options) installs far more than a mouse/keyboard driver. For what should be a simple button remapping tool, it drops files across 6+ system directories, installs background daemons, phones home via crash reporting (Sentry), and even bundles an AI Prompt Builder — none of which you asked for.

Here's what's actually running on your Mac after a "simple" Logitech Options+ install:

(I keep delivering this as a quip, here's a bit more)

First order of business - is 'tool' going to have an interesting, distinguishing and thus illuminative meaning or be a mere platitudinous stand-in for "anything useful"?

We'll save tool as "anyone/thing that can be exploited" for the AI titans, for whom VCs, politicians, wannabe-titan-CEOs, software devs, rural towns etc are all 'tools'. The pockets on the front of my shirts are useful (I need to keep my reading glasses handy), but they are not tools. Similarly, fast food drive-thru windows are useful, but are not tools (more later).

Some definitions of 'tool':

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.

PI is a TypeScript toolkit for building AI agents. It's a monorepo of packages that layer on top of each other: pi-ai handles LLM communication across providers, pi-agent-core adds the agent loop with tool calling, pi-coding-agent gives you a full coding agent with built-in tools, session persistence, and extensibility, and pi-tui provides a terminal UI for building CLI interfaces.

These are the same packages that power OpenClaw. This guide walks through each layer, progressively building up to a fully featured coding assistant with a terminal UI, session persistence, and custom tools.

By understanding how to compose these layers, you can build production-grade agentic software on your own terms, without being locked into a specific abstraction.

Pi was created by @badlogicgames. This is a great writeup from him that explains some of the design decisions made when creating it.

Six design concepts, twelve before/after prompts, and what to look for in the results.

“Beautiful,” “professional,” and “eye-catching” tell an image model how you want to feel about the result. They leave a lot of the visual decisions open.

More useful questions: What should someone notice first? What should the empty space do? How can the composition explain the idea? What shapes should communicate the personality?

The examples below are from my Better AI Images video. Use them as a small design vocabulary you can borrow for your own work.

name: tufte-viz description: | Ideate and critique data visualizations using Edward Tufte's principles from "The Visual Display of Quantitative Information." Use this skill when: (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays

by Flamerix AI · MIT License

Tested on Linux, Claude Code 2.1.257–2.1.312; macOS reported working by others (see Tested versions). The binary is not modified (BUN_OPTIONS=--preload). An update can silently undo it, so see Verify.

Every ~30 seconds, Claude Code re-sends each background agent's full transcript to the model just to fill that agent's status line in the agents panel. no-agent-summary.js is a Bun --preload