I keep running into the same surprise: I sit down to make a model better, and the lever that actually moves the result is somewhere else.
In Text-Conditioned IMLE, the whole study was gated by nearest-neighbor matching. Until FAISS and a fused distance made it cheap, no conditioning experiment was runnable at all. Then, the moment it was fast, the matching stopped being where the difficulty lived.
In Dynamic Radiant Foam, multi-view training never finished, and not because the motion model was wrong. Re-triangulating the Voronoi mesh every step, plus the quiet assumption that the whole video fits in memory, is what ran out the clock.
In Virtual Ring Try-On, the jump from 5 to 30 FPS came from stabilization and deleting frame conversions, not from a better hand tracker.
The model is usually the part I understand best, so it draws my attention, while the constraint that actually caps me sits in the plumbing around it: the matching, the data structure, the latency budget. So I now try to find the binding constraint before touching the network. Often the boring fix is the big one, which is its own note: Cheap structure beats heavy machinery. And when the model genuinely is the problem, it is usually the representation that is wrong rather than the capacity, the point behind Every conditioning choice is a hidden assumption.