Grok Build is pretty good at optimizing my code in one shot. Prompt: I want you to optimize it entirely on GPU to speed it up. Measure two metrics: the result must compare with the golden image (CPU) and be nearly identical (PSNR > 40dB), with fast pixels per second. Make a plan to a) write GPU equivalent code, b) write a benchmark suite to measure PSNR and pixels per second, c) execute various optimization strategies. Go!

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As eval is downstream of everything, it determines whether you will spend your time optimizing the right metrics. The current gap between academia and industry AI labs is the attitude toward eval. In academia, the eval set is very hard to change since a) you need to explain why your eval is better and b) you need to benchmark against your cited works with the new eval and show that your work is superior. Doing both a and b at the same time invites risky rebuttal, even if you are doing a good job on a. It is far easier to benchmark against the eval set that everyone has agreed upon. In contrast, in industry AI labs, customer feedback is your eval set and it keeps changing to cover the long tail that you could never think of during years of PhD programs. If the loss functions are not a good proxy for customer feedback, then you change them until both are aligned. Thus, academia might train students who are very good at hill climbing but inexperienced in building eval sets that capture hard real cases. To move the needle, building the right eval set matters the most.