AI inference is rapidly moving out of the data center and onto local machines. With hardware like the upcoming Mac Studio M5 Ultra, it’s already possible to run top open models locally at performance levels approaching systems like ChatGPT. At the same time, companies like SK Hynix and Micron Technology are pushing memory bandwidth forward, making edge inference increasingly practical. But the software layer hasn’t caught up yet. We have great building blocks (e.g., OpenClaw), but they don’t yet provide the reliability guarantees you’d expect from production systems like Temporal Technologies—things like durable execution, failure recovery, and long-running workflow management. So I built MirrorNeuron: https://www.mirrorneuron.io GitHub: https://github.com/MirrorNeuronLab MirrorNeuron is an open-source runtime for AI agents that need to run continuously and reliably on edge or local environments. The focus is simple: long-running, stateful agent workflows fault tolerance and recovery by default scheduling + orchestration primitives for agents designed for real-world conditions (not just demos) The idea is that as AI moves onto personal machines and edge devices, we’ll need something closer to a “workflow OS” for agents—not just prompt loops or scripts. Curious how others are thinking about this space—especially around reliability and long-running agent systems.

If you’re building in this space or want to collaborate, feel free to reach out: homerquan@gmail.com