Thanks for the thoughtful comment! We totally agree—chasing hyper-optimization can sometimes lead to overengineered solutions with marginal gains. We’ve seen this firsthand while training our models. One example: we once explored a complex multi-layer architecture for a recommendation system, aiming for a 2% performance boost. But after profiling, we found a simpler ensemble approach delivered nearly the same results with half the compute cost and way less maintenance overhead. Would love to hear about any examples you’ve come across! What’s a time your team nailed that performance-complexity balance?