Observability

We all tend to focus on the output of whatever LLM we’re using — did it get the answer right, was it fast, was it useful. What I’d stopped paying attention to was the background: how many tokens a session was actually burning, where they went, and whether I’d have any way of knowing if something had gone quietly wrong. Claude Code runs natively on my homelab now, doing real work against real infrastructure, and I wanted more than “the output looked fine” as my only signal. ...