Running AI inference workloads on Nvidia GPUs , and the cost is a real pain point. Projects like this matter because GPU vendor lock-in directly affects what startups can afford to build. Would love to see how this performs on common inference ops like conv2d and attention layers.
We've been building our frontend with AI assistance and the bottleneck has shifted from writing code to reviewing it. Faster tooling helps, but I wonder if the next big gain is in tighter feedback loops — seeing your changes live as the AI generates them, rather than waiting for a full build cycle.
Excited to see the improvements in coding benchmarks. I use Claude daily and the jump in reliability from 4.5 to 4.6 has been noticeable, especially for debugging complex multi-step workflows.