Agents

TL;DR I run a growing fleet of autonomous agents — homelab ops, trading research, content generation. Most blow up the first few times they try anything new. I needed a way to decide what an agent is allowed to do without asking me, and what still requires a human checkpoint. The answer is a four-rung trust ladder — supervised, monitored, trusted, full autonomy. Agents earn rungs through track record, not promises. Demotions are possible and routine. The framework took the question “should this agent be allowed to do X” out of my head every single time and turned it into a policy I can apply consistently. ...

TL;DR I run 3-5 Claude Code sessions in parallel at staggered cadences. They coordinate through a shared #mat-claude-sessions Mattermost channel plus a small coordination board file. Each session announces what it’s about to touch, claims it, and announces when it’s done. Conflicts are rare; throughput is dramatically higher than running one session at a time and waiting. Why parallel A single Claude Code session running a long task — refactor across a few repos, work through a debugging session, draft a blog post — is mostly me waiting. The model is fast but tasks are bounded by my decisions, my reviews, and my edits. If I’m waiting on Session A to finish a build, Session B can be drafting something unrelated. Session C can be running a slow eval. The bottleneck stops being the model and becomes my own attention rotation. ...

Heads up: this post mentions Claude. If you want to try it, I've got a referral link — it gives us both a bit of extra credit, no pressure: claude.ai via my referral. TL;DR Most AI tooling still treats an LLM like a search bar — you prompt, it answers, the loop ends. Useful, but not what I wanted. For my homelab’s ops + trading intelligence platform (OpenClaw), I needed agents that could run for hours, do real work against a real cluster, and then tap me on the shoulder when they found something I should see. Claude turned out to be the model I kept coming back to for the “thinking” layer — it’s both comfortable with long tool-use chains and happy to write structured output a human won’t need to decode. This is a tour of how I’ve actually wired that up: k3s CronJobs doing the heavy lifting, LiteLLM as the routing layer, Slack as the interrupt bus, and named cat-bot personas so I can tell at a glance who’s knocking. ...