The heroes behind deployment are often underrated. A strong, general model is not enough to get robots into production. Anyone who has really deployed knows that most of the important lessons only show up in the field — and you need serious infrastructure to capture them. At Dyna, we’ve spent a lot of time building observability, auto-labeling, feedback, and evaluation systems that turn deployment into a continuous learning loop, with humans guiding the loop where they add the most value. That loop is critical. It tells us where models actually fail, what needs to be solved fundamentally, and where our foundational research should go next — instead of patching problems one deployment at a time. Deployment isn’t just a business use case for us. It’s what powers the data and feedback flywheel behind scalable robotics. More here: There’s probably a lot more behind it than you’d expect.