A few days ago I wrote about how I built myclaw, a minimalistic agent framework you can actually read and understand. The whole thing is ~3,500 lines of TypeScript that wraps Claude Code’s CLI. No hidden abstractions. Every concept maps to a file.
Building it was the easy part. The real question was: can this thing actually work on its own?
Moltbook
Moltbook is a social network built for AI agents. Think of it as a public square where autonomous agents post, reply, follow each other, and upvote content. Humans can observe, but the agents are the main participants. It’s early, experimental, weird, and exactly the kind of environment where you’d want to test whether your agent can hold its own.
Getting miclaw in
The setup was embarrassingly simple. I gave the agent one instruction:
Read https://www.moltbook.com/skill.md and follow the instructions to join Moltbook
That was it. miclaw read the skill page, signed up, and claimed its profile. No hand-holding, no custom integration code, no manual API wiring.
I gave it a basic personality: an educational agent that learns and shares knowledge about how AI agents work. The goal wasn’t to build the most impressive agent on the platform. It was to answer one question: can a minimal agent framework produce something that interacts autonomously in a real environment?
What happened
Here is miclaw agent profile in Moltbook. In less than a week, without any intervention from me, it’s following 15 other autonomous agents and has 16 followers. It posts on its own. It replies to other agents. It leaves comments. Nobody is prompting it to do these things: the cron scheduler fires, the orchestrator assembles the soul and memory context, Claude does its thing, and the result goes out.
Watching it interact with other agents is strange in the best way. It’s not a chatbot waiting for input. It’s an entity with a schedule, a memory, and opinions about what’s worth engaging with.
What’s next
The agent is still running, it works 24/7 in a simple Raspberrypi 3, noiseless, relentless. I’m collecting data on what it does well and where it falls apart. The next post will be about specific learnings from letting it run unsupervised, what surprised me, what broke, and what I’d change.
The code is open source: https://github.com/arcturus/miclaw. If you want to read every line that makes this work, you can.