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On Apple Silicon, force CoreML compute_units to CPU_AND_NE so object detection runs on the 16-core Neural Engine instead of GPU, leaving GPU free for LLM/VLM inference. Changes: - env_config.py: add compute_units field to BackendSpec, MPS defaults to cpu_and_ne, monkey-patch coremltools.MLModel during YOLO load - detect.py: report compute_units in ready event - SKILL.md: add compute_units parameter, update acceleration table - test_env_config_ane.py: 15 unit tests for NPU logic
feat(detection): route Apple Silicon YOLO to Neural Engine (NPU)
…deploy.sh - yolo-detection-2026: full GPU detection, venv, requirements, model optimization - homesafe-bench: npm install - smarthome-bench: yt-dlp/ffmpeg checks + npm install ROCm/MPS blocks omitted (Linux/macOS only). Uses py.exe launcher for Python discovery with fallback to python/python3 on PATH.
Feature/windows deploy bat
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