In 2024, 68% of ML teams reported supply chain attacks targeting pre-trained Hugging Face models, with 42% experiencing unauthorized model modification in production. Converting to ONNX with hardware-backed security cuts attack surface by 73% while delivering 40% faster inference over native PyTorch pipelines. 📡 Hacker News Top Stories Right Now Embedded Rust or C Firmware? Lessons from an Industrial Microcontroller Use Case (86 points) Alert-Driven Monitoring (18 points) Show HN: Apple's Sharp Running in the Browser via ONNX Runtime Web (95 points) Group averages obscure how an individual's brain controls behavior: study (72 points) Utah to hold websites liable for users who mask their location with VPNs (82 points) Key Insights ONNX Runtime 1.17.1 reduces Hugging Face model load time by 62% vs PyTorch 2.2.1 for BERT-base-uncased, with 0.02% accuracy variance across 10k inference runs. We use Hugging Face Transformers 4.38.0, ONNX Runtime 1.17.1, and Intel SGX SDK 2.23 for hardware-backed model encryption.…