engineering, y'all. So, let's talk about control theory. Control theory is all about how we drive a dynamic system, which would be your codebase, towards some desired, stable, or optimal end state, right? You have a sensor that measures the current state of the world. You have your set point, right? The desired state of the world. And the difference between those two things is your measured error. You have a controller that reads that measured error and turns it into a control signal about an incremental change to apply to the system. We have an actuator that applies that change to the system, which is undergoing disturbances in the meantime. And then we remeasure, recompute our measured error, and we're back where we started. Now, this sounds really complicated, and it can be. I have a twin brother, actually, who's an aerospace engineer. This is how they keep fighter jets from falling out of the sky. Uh but uh it's probably a little bit simpler than most of y'all think. Does anyone have one of these? Uh a thermostat uses a control loop, right? Uh for for any of our European friends in the audience, this part of something we have uh here in the States. It's called air conditioning. And uh most of us probably actually use control loops on a daily basis, right? Kubernetes auto scaling systems are built on control loops. Infrastructure as code uses a desired state, current state, iterative change like control loop pattern. Postgres is auto vacuum and React's virtual DOM both use approximate control loops. Control loops are ideal when we have a system that we want to change, a problem we can measure, and a way to get feedback on the result of that change. Like good software engineers have always been taught to do, control loops change a system incrementally instead of just trying to get straight to the end state immediately all at once and risk blowing everything up, right? They help us to avoid over steering and destabilizing the system and it minimizes risk. So, control loops are the opposite of