Multi-step logic is hard to implement reliably. I want to understand why.
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I trained a Keras model that gives good validation accuracy.After saving and loading it, the predictions become completely wrong.Even training samples are misclassified.Nothing crashes, but the outputs no longer make sense.
My model recognizes actions well in static camera videos.When the camera pans or shakes, predictions become unstable.The action is the same.Only the camera motion changes.
Early gains were easy, but progress has slowed significantly.Most basic controls are already in place.I’m trying to understand how teams continue improving beyond this point.
In single-org projects, small shortcuts often feel harmless and efficient.When the same solution must work across many orgs, those shortcuts quickly become liabilities.This transition forces a deeper level of architectural discipline.
