Today, we're launching shift. We're starting by cleaning your apartment in New York City, for free. Here's how it works. Book a shift cleaning. A vetted shift operator comes to your home wearing one of our devices. They clean. They leave. You pay nothing. In exchange, we record the cleaning. Robotics is being built on data about how people do daily tasks, and the value of that recording is what funds the service. Anything personal in it is anonymized before the recording is processed. By now, you have heard about the shift to AI more times than you can count. About the shift toward you, the part where you actually feel it, you have heard almost nothing. Shift is what starts to make it concrete, in specific cities, with specific services. Today, cleaning in New York. Soon, handymen, repairs, and errands across the globe. And this is just one side of shift, with more on the way. Comment “shift” and we’ll send you an early access link.

Two days since DFlash was released, and @_dcw02 (on @modal research) already shipped support for it in SGLang. Why are we so excited about this? Diffusion speculators let us get *way* higher tok/s than auto-regressive models. E.g. we're seeing a 4.73x boost with H200s FA3 already — with still more improvements to come! Reach out to us if we can help you get this in prod today, and huge thanks to @zhijianliu_ and team for coming up with this technique.