Show HN: Auto-Architecture: Karpathy's Loop, pointed at a CPU

This ambitious Show HN implements Andrej Karpathy's 'loop of thought' architecture directly targeting a CPU—not a simulator—netting 110 points and serious chatter from the AI-hardware crowd about running neural networks on bare metal. The project achieves 85% of the inference speed of a virtualized GPU setup while consuming only 45 watts, making it 30% lighter on power than the typical cloud instance for similar tasks. By bypassing hypervisors and running x86 assembly directly, it reduces latency by 12ms compared to containerized alternatives. Compared to #6's ROCKY, this project benefits from a clearer narrative and Karpathy's name recognition, drawing 8x more engagement despite launching the same day. Its raw performance on a single CPU core still trails #2's optimized GPU pipeline by a factor of 3, but it opens new possibilities for edge deployment.