knew what good code looks like, it would probably write it in the first place. Uh and review agents and throwing more tokens at the problem, it can raise the floor, um but we're still constrained by what we can teach during RL. Um and so I will I will posit that for now we're stuck reading the code, uh but we can still move pretty fast. And of course there's a world where this is solved uh in the future, and if you want to just keep yolowing prompts until you get to GPT-7 and you don't have to think about this, by all means, please. Uh but bitter lesson be damned, we've got some problems to solve. So let's engineer our way out of this. Um so turning the lights back on, we're going to put the code review back. Uh we're going to embrace this approach of like how do we plan up front to reduce the chance that we have a long or difficult review process. We're going to find leverage. We're going to use AI to help with this. Um the first thing we're going to do is we're going to do some sort of product review, understanding what problem we're solving, what's the desired behavior, maybe looking at mock-ups. Here's a product review I was working on yesterday with a mock-up of a new feature. Once we have our product review, we're going to By the way, we don't small stuff still just go straight to the agent. But once we have the product review, we're going to also do architecture, system architecture. A lot of people have been doing this for a while, component contracts, data models, constraints. This is an example of a doc that we build to understand how these systems are going to fit together and what's like the high-level picture of it. From there, we do something that I think is really under-emphasized in agentic coding these days, which is program design. I think people assume that once you get the architecture right, the model can just cook. But we we often look into the types and the method signatures, the program layout and the call stacks. So here's some examples. I don't think you'll be able to read this one, but this is like the level of abstraction we're at. It's how we're actually going to lay this stuff out and how these systems are going to interact. Dylan Mulroy from Cloudflare talks a lot about how he's using these call graphs as part of his planning process. I think this is exactly right. And then once we've done the product program design, we can do this thing called vertical slices, which is the order of implementation, multi-repo coordination, how we're going to build this across our entire system, and how are we going to check it along the way. I've talked a little bit about how models have horizontal plans. I won't go too deep into it. If you want to learn more about this, you can go watch our talk from AI Engineer Miami. Couple shots of a doc like this going through the tests and the steps in between each phase.