And like you guys get into a loop until you are in agreement as to what the plan should be. Then same thing for coding and same thing for the review. And initially I would do this manually. And every time I would read through, so I would read at the output of codecs with the critique. And I agreed with every single one of the elements that were being critiqued. And the funny thing is I tried doing the same with another version of Opus where I'd say now Opus go review the work of this other Opus thing. And it wouldn't come up with the same level of insights. Because they were trained on similar data. Exactly. And, the system prompts are similar and, they don't have the, yeah.

Yeah. Like a year ago I did a I did I made a benchmark for AI code review tools where I compared our tools with some others in the market. And I created an agentic system that basically took an open source project, introduced a bunch of bugs to it and made a big mess, and then opened up all these different PRs and it had these different tools run reviews on stuff and just kind of like looked at what they caught and compared them and A lot of those tools were using or running the same model under the hood or like the same model family. But they were just vastly, vastly different because the real secret of the AI code review is the harness and about it understanding your code base really well.