provide a real fix that uh an engineer could approve and merge. So, first finding uh the the O of N squared um uh example that I just mentioned, it uh once it knew uh that this is a problem, and looking at the profile profiling data, it knows that this is consuming 8.8% of the CPU time during that uh uh period of profiling. So, for us, uh the coding agent not just ended up finding the problem, given enough instructions as a form of let's say skill or prompt, uh you could actually have it uh do those four steps that I just mentioned, which is checking out the Git repo, uh finding where that code is implemented, and actually uh sending a code review out. All of it could be done uh in a very large code base with powerful enough code agents in less than 5 minutes. Um and in this case, uh we also, which I'll talk about a little bit in the later slides, uh were able to identify what is the savings uh if you were to uh reimplement it with a uh with an optimized implementation in terms of both CPU as well as latency savings. Uh I I've shown the numbers here of our real production uh savings that we were able to observe uh through this uh exercise.