The CUDA-Agent is the first known RL training model that surpasses Claude Opus-4.6 and Gemini 3 Pro in CUDA kernel generation! The CUDA Agent uses agentic RL training models to automatically generate high-performance CUDA kernels, directly using real GPU profiling speed as a reward signal, breaking conventions. Take a look at the data below: KernelBench benchmark: simple/medium kernels are 100% faster than torch.compile, complex kernels are 92% faster. Overall, 96.8% faster vs torch.compile, far exceeding Claude Opus 4.5/Gemini 3 Pro (about 40%). The true ceiling of AI hardware lies in the ability of "software unlocking + optimization feedback loop," not just the chip itself. Combined with the concurrent Apple ANE event: Apple M4 ANE: 6.6 TFLOPS/W (≈80 times that of A100), hundreds of millions of devices are idle, with the bottleneck being closed APIs + abstraction layers (CoreML masks 2–4 times throughput). NVIDIA GPU: RL Agent learns "extreme optimization under real hardware feedback," proving that the learned strategies can outperform static rules. The performance moat of hardware (Apple/NVIDIA) is being double-killed by AI's "reverse engineering + RL optimization"—the former breaks open closed APIs to turn idle chips into computing farms, while the latter squeezes every drop of performance from existing GPUs using reinforcement learning. In the future, the bottleneck won't be computing hardware, but who first masters the "hardware native feedback + autonomous learning optimization" feedback loop, combining software and hardware. Whoever can double the performance of existing devices can gradually break down the walls of the giants. This compound growth will create speeds that are difficult for human intuition to perceive: expanding from 10 times to 100 times → 1,000 times within a few years. The era of on-device training (ANE side) + extreme inference in the cloud/edge (CUDA Agent side) is accelerating, allowing AI to "self-optimize" to approach theoretical peaks. The potential of hundreds of millions of idle Apple devices + massive NVIDIA cards is being collectively unlocked by independent/company hackers/researchers.