#ai

A deep technical look at the multi-agent workflow that scraped the entire awesome-selfhosted dataset — nine runs, every architectural failure, exact agent counts and token numbers per run, and how it ended with Python bypassing agents entirely.

I had Portainer running but it made figuring things out harder, not easier. And there was no single place to just click a link and open a service. I described the problem to Claude Code and it built me exactly what I needed.

I bought a NUC to run local LLMs and escape API costs. Then I tested local, free cloud, and paid cloud against the same prompts. The result wasn't 'pay for inference' or 'run it yourself' — it was that 'fast and free' isn't a quadrant that exists right now, and that has implications for anyone planning a personal AI setup.

I've spent months building Android apps — todo, drawing, live wallpaper, launcher, Kotlin Multiplatform, full-stack with Ktor — entirely with Claude Code, without writing a line myself. This is an honest account of what worked, what didn't, what it cost, and what I think it means for how software gets built next.

I had two old Intel NUCs, an obsession with AI agents that actually do things, and a decision to make about which machine gets which job. This is how I split them — and why the answer wasn't obvious.

Part 1 of 2: An experiment in using an AI coding agent to autonomously generate, publish, and document a complete Android analytics library — from a technical design doc to a live JitPack release.