Uh, you might think of like how how do I get started with this? Well, you you don't always have to start fresh, but you could start fresh. There is no harm in having a catalog that is just empty in the beginning and as more and more profiling happens, uh, more uh, powerful the coding agents that are referring it could become. But there are few, many in fact, sources of how you could public sources of how you could improve your coding agents. If you're into C++ optimizations, Jeff Dean had this wonderful blog post around how to look for certain optimization opportunities in your C++ code. PyTorch has a torch fix code repo where it catalogs several anti-patterns that could help optimize kernels and the model graph of your PyTorch model. And then you might have your own performance playbooks as well. Typically, as your organization grows and your enterprise software evolves, you end up noting down certain patterns and anti-patterns. Those all could become the bootstrapping point for that uh catalog Git repo that I mentioned that can act as a centralized um place for all uh catalog all patterns and anti-patterns to be noted.