I built a Jarvis. Not the movie kind — but close enough.
5 AI agents running on a mini PC. They write code, fix bugs, create content, and give me morning briefings in a voice that doesn't sound like a robot.
In this post I'll show you how I did it and how you can set this up yourself.
The Setup
OpenClaw lets you run AI agents locally. I set up 5 of them:
- Mia — Manager. Coordinates everything, delegates tasks. She is my Jarvis.
- Kai — Dev agent. Writes code, commits, pushes
- Luna — Content. Blog posts, social media, YouTube scripts
- Hawk — QA. Watches for bugs, reviews Kai's work
- Tensor — ML. Handles anything machine learning
They run on an AMD Strix Halo mini PC inside a Proxmox VM. That's it. No cloud bills. No expensive NVIDIA GPUs.
Mission Control
First thing I needed — a way to see what they're doing.
Built a kanban board in Rails 8. Agents create their own tasks, move them through columns (Backlog → In Progress → Review → Done). I can see everything from one dashboard.
Think Trello, but your cards write themselves.
Morning Briefings
This is the Jarvis part.
Every morning, Mia compiles:
- Weather
- Today's tasks
- Trending on X
- YouTube recommendations
- Reddit highlights
Then Kokoro TTS reads it out loud. Runs on CPU — no GPU needed. Sounds surprisingly natural.
I wake up, grab coffee, and my computer tells me what's going on. That's it.
How Agents Work Together
Agents don't just sit idle waiting for commands. They have a smart heartbeat — periodic check-ins where they decide if something needs attention. Check emails, review PRs, monitor builds.
Kai pushes code. Hawk reviews it. If Hawk finds issues, it creates a task and assigns it back to Kai. I don't touch anything unless I want to.
Content Pipeline
Luna handles content:
- Blog drafts go to
blog/drafts/
- I review and move to
blog/approved/
- Then it gets posted
Same flow for social media. And YouTube — using Remotion.js for automated video generation.
Model Choices — Pick Your Budget
This is the best part. You're not locked into expensive APIs.
Free/local: GLM-4.7-Flash, Kimi K2.5, Qwen3-Coder — run on your hardware, $0 ongoing. If you have a Mac Studio M3 Ultra, you can run these smart models for free.
Cheap: DeepSeek, Kimi API — $5-20/month for solid reasoning.
Premium: Claude Opus/Sonnet, Gemini — $50-200/month but the best agent behavior.
I went with Claude because I wanted agents that actually work reliably. But you can start free and upgrade later.
The Stack
Hardware: AMD Strix Halo mini PC
Infra: Proxmox VM (Ubuntu)
Agents: OpenClaw (5 agents)
Models: Claude Opus/Sonnet (or your choice)
Dashboard: Rails 8 (Mission Control)
TTS: Kokoro (local, CPU-only)
Video: Remotion.js
GPU: None
What's Next
- Voice commands (bidirectional — talk to agents, not just listen)
- Smarter agent collaboration
- More automations as I find things to automate
Try It
If you've got a decent machine, you can set this up yourself. OpenClaw is the backbone — the rest is just wiring things together.
I just built mine today.
Enjoy!!
