A GitHub research repo claims transformer training directly on Apple Neural Engine through reverse-engineered private APIs, with dynamic weights, IOSurface I/O, and small model benchmarks. It is careful about its own limits, including low peak utilization, CPU fallbacks for gradients and masking, and private API breakage risk, which makes it more useful than the usual miracle-hardware post.