Yeah, I mean it is been an interesting trade off for us where I think the way that I would describe it is in our product, certainly the large bulk of the features that we build are for this kind of making pull requests and engineering teams use case. But I think basically our kind of general stance with all the others is obviously if folks want to use Devin for that, that's great and we want to just make sure that they're fully aware about the limitations and about where things can get caught up. It is funny with AI and especially because I would say one of, I would say the most common pieces of advice out there I would say is focus on a really niche cohort. Do things that don't scale, make one use case that's really great and then you grow from there. And I think that's great advice across the board. But yeah, it's kind of interesting because I think with generative AI, you naturally see this where a lot of product experiences can turn out to be more general. And so it's an interesting trade-off for us. This is something that we still always go back and forth on and how much do we want to do more to support all the other kind of use cases out there to handle other things that folks might want to do with Devin.
But yeah, it's kind of interesting because I think with generative AI, you naturally see this where a lot of product experiences can turn out to be more general.