Anduril's Lattice OS concept has always fascinated me: a network of cheap heterogeneous sensors fused at the edge into a single AI-driven situational picture. The interesting question is how much of that is actually achievable today on non-classified hardware.
Answer, at least at small scale: a surprising amount.
I built OVERWATCH as a community reference implementation of the same idea. Multiple cameras (IP cameras + phones via browser), all feeding into a shared perception pipeline on a $500 Jetson Orin Nano. YOLOv8n TensorRT FP16 for detection, adaptive Kalman for tracking, self-calibrating cross-camera homography for fused world-model predictions.
The part that surprised me most: the self-calibrating calibration. You don't tell the system anything about where cameras are. It watches for moments when two cameras see the same person simultaneously, records foot-point correspondence pairs, and computes the projective transform between camera coordinate systems on its own via RANSAC. After about 5 seconds of co-visibility it has a usable homography. It self-heals if a camera moves.
In 2020 this would have required custom hardware, weeks of calibration, and a meaningful compute budget. In 2025 it runs on a dev kit.
Repo: github.com/mandarwagh9/overwatch
What other capabilities that were "enterprise-only" five years ago are now commoditized? Curious where people see the edge AI ceiling right now.
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