And one design choice we made, so the harness is actually all our own code. So we decided not to use any off-the-shelf agent framework. Um we wrote it all and really Claude wrote it all and I think like in the era of of Claude code, I I like this approach of kind of building your own from scratch because you get sort of max freedom and max like, you know, you're you're free to kind of tweak anything you care about. Um and so, you know, all the tool calls are done with these like functional tool calling and that also allows us to nicely really be provider agnostic. So OpenAI Anthropic or open source providers, it's very easy to to adapt the the harness to any any of those. As far as the actual tools, there's there's several but the sort of three main ones are one full shell access, so it can write any bash command. So this is how it's writing code, setting up its Python environment, editing code, running code. Um another important one is web search and this allows it to go read, you know, archive or technical blogs or anything like that and get sort of up to speed at um you know, at least in the public domain like sort of what's the state of the art methods. And then the third one is um we use Slurm to manage our GPU cluster, but the the higher level idea is just a a nice abstraction where the model can say like I'm training a a fairly big model. I need 4 H100s and this many CPUs and just kind of write the config config and submit the job and not have to worry about like doing hardware orchestration.