Uh yeah, and so going to the the actual phases. So again, the first phase is this this research phase. And the idea here is kind of like almost like a super cloud MD file or a super init where we just want the system to go off and kind of build enough context such that it can start meaningfully uh you know, forming hypotheses and testing them. Um and we built, you know, Alpha Lab is kind of this server-side running thing, but we built this lightweight UI on top. And so sort of how we've done this is build this this scaffolding of the to-do list. So it's first, you know, prompted to to build a to-do list such that if it completed every item, it'd be able to start experimenting. And this allows us to kind of keep re-prompting should it try to exit early. And so you can see, you know, it's talking about setting up its Python environment, um doing different data loading, doing different kinds of statistical testing. And each item of the list it's instructed to build a like take notes in a markdown file. And this also allows feature agents to smartly query that and kind of manage their context dynamically. And and this is really where web search is used most because it will go off and read archive and and get kind of good context from the public domain as well. And so this can vary a lot, but um it takes, you know, roughly around 3 to 4 hours.