complex work. It's kind of cool and something that just hasn't really been written about a lot. Like this is actually like a new form of test time compute. Like when we talk about the scaling laws and kind of we talk about the model getting more intelligent over time, historically, it's been a function of the size of the neural net, the amount of training data, and the number of flops that you put in to the training. And then recently, we also added test time compute. So this is essentially a fancy way a researcher way of saying how many tokens does it generate. And now dynamic workflows are essentially a new way to orchestrate test time compute. And it's a new way to kind of really, really ramp up the amount of test time compute that you use to do a really hard task. So this all very long way to say this is one way to launch thousands of agents in a way that is productive and efficient. A second way to do it is loops and routines. Loop is essentially a cron job that's running locally for Quad. Routine is the same thing, but it's running the Quad in the cloud. So you can close your laptop. And this is like slightly different because for a dynamic workflow, it's one task and you break it up into chunks. For loops and routines, it's one task that is repetitive, that doesn't share context, but it might share memory. And you kind of do this like over and over. You can do it like maybe every hour, every 5 minutes, every day. And so the thing that we've started doing is um we actually have Quad maintaining itself now. And the way we do this is we have a Slack channel where we just had Quad start a bunch of different routines to maintain its own code base. And we actually do this for the CLI, for the iOS app, for the Android app, uh for the desktop app. And you for example, one routine is clean up dead code. This is a single prompt. It's like one sentence. Quad runs this every day. It'll look for dead code across all the code bases using static and dynamic analysis. We didn't prompt that. It just kind of figured it out. And it'll put up a pull request every day to the weak the dead code. Another example is shipping experiments that should go out. Um so the experiment's already out to 100%. It'll delete it from the code base and it will just ship it. Another one is writing tests for areas of the code base that need test coverage. Another one is deleting tests that don't need to be there cuz you know, they were kind of useless tests added by older models or added by people at some point. One that one that I really love is this um I forgot what we called it. I think we called it abstraction police. And the idea is there are often in a big code base, there's kind of the same abstraction and it appears multiple times and if you kind of squint it actually maybe should just be the same abstraction, but kind of over time for whatever reason you rebuilt it multiple ways in different parts of the code base. So Quad kind of goes out every day across all our code bases, it finds these nearly duplicated abstractions and it unifies them. And so now we have every day maybe 20 or 30 of these routines. It's running across all of our code bases and it's not totally there yet, but we're on the path to fully automating the maintenance of our apps by doing this. And this is again hundreds of agents running every day, sometimes thousands of agents every day. It's doing the work of you know, dozens or hundreds of engineers. This is kind of what it used to take to do this kind of work. And this means that engineers can just like do the thing they actually want to do, which is ship new product and talk to users and do stuff that's actually