Two devs built an offline YOLO26 posture detector in 7 days — here's what broke

A two-person team in Austin, TX — engineer Igor Eduardo and field specialist Lexi Armstrong — built an on-device posture detection tool called SENTINEL during the webAI YOLO26 MLX Build Challenge in May 2026. The single-file Python app uses the YOLO26n model with a bounding-box heuristic to classify people as standing, sitting, or lying, running at roughly 16 FPS on an M4 Mac with Wi-Fi physically disabled to guarantee zero network egress. The project drew on Eduardo's healthtech background in patient triage and Armstrong's experience with denied-communications environments, targeting mass-casualty and industrial security scenarios. Two major technical failures shaped the build: OpenCV 4.13 crashed reliably when camera capture ran off the main thread on macOS 26, and a silent hang was traced to lazy evaluation in the yolo-mlx Boxes proxy rather than MLX inference itself. An earlier attempt to fine-tune the model on labeled posture data also failed to converge and was abandoned within the seven-day window.