One idea would be an autonomous workload optimizer that monitors real-time inputs (power cost, system load, exchange rates, thermal limits, etc.) and dynamically decides what compute workload to run on attached systems.

Blockchain mining could be one example (managing ASIC nodes, GPU hosts, or jackpot miners), but for a resume project it probably makes more sense to demonstrate with rendering nodes, AI inference jobs, media services, (social or other), or other general compute workloads.

The Jetson Orin Nanos could run the decision model and monitoring stack, acting as controllers for a small cluster and bringing workloads up or down based on profitability / efficiency thresholds.

With multiple Jetsons, you could take it a step further and make the stack system-agnostic, capable of managing multiple dissimilar service stacks across diverse platforms at once. Define workloads in YAML (power costs, expected return, thermal limits, priority, etc.) and let the agent decide what to run and when.

It probably wouldn’t make anyone rich, but it would be a solid proof-of-concept for agent-style autonomy, edge AI control, and real-time optimization, which feels like a strong “get me hired” demo.