optimizing the whole thing together. And to the last point, you know, how do we as as experts encode our expertise, we again think it's through environments. Like building environments, at least good environments, is really really hard work. You know, it's you have to be there's of course like the data that goes in that's proprietary, but you know, designing the the verifiable metrics is maybe the easy part, but then we're also doing this qualitative rubrics, where we kind of look at the traces and say, you know, what makes a good researcher, what's the thought process, and we can grade each roll out on, you know, how well it's following our research process. And so, having these good rubrics that become the signal for the model to learn, we feel is really how we're we're building our own expertise into the system. And so, just to kind of conclude the the 2.0 version is really having this strict environment and eval setup, and you know, whatever lives in the middle, we almost in the limit kind of don't care about. We can initialize it to Alpha Lab 1.0, but this it should really be this self-improving system.