I went into this build expecting the AI to be the hard part. It was not. The model did what models do. The hard part was everything around it: auth that would not stay logged in, a VM that booted with no network, a node that powered itself off, a single character of YAML that took down an entire Arg

The on-call seat is a staffing problem before it is a tooling problem. Somebody has to be awake enough at 3am to look at an alert and ask the only question that matters: is this real, or is this noise? In my homelab I am the only somebody. So I wanted something to sit in that seat with me, not a lou

When I decided to put an AI engineer in my homelab cluster, the first real question was not "can it do the work." It was "how much do I trust it, and what catches it when I am wrong about that." That is a governance question, not a coding one. And governance is squarely in the i

A persistent homelab operator that forgets is a goldfish. Brilliant for one turn, then blank. It investigates a disk alert beautifully, closes the ticket, and three weeks later it meets the same alert as a total stranger. No memory of the last time, no idea it already wrote a runbook for exactly thi

My homelab grew past the point where I could babysit it. More nodes, more Grafana panels, more alerts firing at hours when I would rather be asleep. At the same time, every customer conversation I sit in at work circles back to the same thing: who watches the cluster at 3am, and can any of that be h