Writing

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.

For over a decade, sleeping late was just how I was wired. Quiet nights, no interruptions, my own pace. Then life required 6AM, every day — and I had nothing to give. This is what I did about it, what actually worked, and what didn't.

I put the Nexus backend under four progressively harder k6 load tests — smoke, stress, spike, and soak — while watching every CPU cycle and megabyte on Polaris. 300 concurrent users, zero failures, 40% CPU headroom. Here's what a NUC can actually handle.

The last post ended with Polaris's role shifting to embeddings and batch work. What actually happened was more interesting — it became a proper personal server in a single afternoon. nginx, WireGuard, Docker, GitLab Runners, and a weekly AI-powered security audit.

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.