MicroSafe-RL started from a simple but critical question: **What happens when AI makes a wrong decision in the physical world?** In software, errors are tolerable. In embedded systems — robotics, aerospace, industrial control — they can cause real damage. I wanted a solution that doesn’t rely on heavy models, cloud calls, or probabilistic guarantees. So I built a **deterministic safety layer** that runs directly on hardware. The goal was very specific: * sub-microsecond response * zero dynamic memory * mathematically predictable behavior During development, the approach evolved from a basic statistical filter into a **hybrid safety metric** combining: * real-time signal instability (σ / entropy) * deviation from learned baseline * dynamic response to sudden changes The result is a minimal, O(1) system that acts as a **shield between AI decisions and physical actuators**. It’s designed for environments where failure is not an option. I’m sharing it open for research and experimentation — and I’m especially interested in feedback from people working in: * embedded systems * robotics * safe reinforcement learning * aerospace / industrial control Curious to hear how others approach safety at the edge.

MicroSafe-RL

Prevent AI Failures in the Physical World in Microseconds

Prevent AI Failures in the Physical World in Microseconds