a little bit as >> Yeah, yeah, it's a it's a great question. So, I think we're seeing all sorts of models of the closed frontier models and the open models get increasingly better at writing kernels. So, we use models all the time when we are developing kernels and and and writing the optimization frameworks. I think the the interesting thing that we are starting to look at is this benchmark that that we recently released called um parallel kernel bench. So, it actually has a bunch of unsolved problems in it. So, we went around surveyed all the different ways they can serve model inference. And one of the interesting things that we found is that there's a lot of things that we can think of that would actually speed models up that there don't exist good kernels for. So, one of the reasons that we put that benchmark out was, you know, one thing that people worry about is like bench maxing or or overfitting to particular benchmarks. One of our intentions with this benchmark was that if you overfit to it, that's great cuz we'll go take those kernels and use them to to to accelerate the the the the inference and the development. >> Yeah, this is a really interesting point. A lot of people have problems with bench maxing, but the way I think about it is if researchers like you put all the really useful benchmarks out and we bench max on all of them and everything is in distribution, then that's a perfect world, right? That's a very useful model that we can then use.