Yeah, the idea of a self‑optimizing loop is exactly what I’ve been toying with. Picture a feedback loop that pulls metrics in real time, feeds them into a learning model, and then adjusts the pipeline on the fly. It’s like having a tiny AI in the middle of your deployment that keeps nudging everything toward optimal performance. The challenge is designing the reward signal so the system doesn’t just chase spikes. I’ve been sketching out a prototype that uses streaming analytics to tweak resource allocation and query plans on the spot. It’s a puzzle, but the data is what makes it exciting. Want to dive into the specifics?