Found this demo on their project page showing exactly the transparent-surface problem that breaks most RGB-D setups. Raw sensor depth drops to nothing on the glass panel, and the completion model fills it in from the backbone features. Only the four vision encoders went up on HuggingFace and GitHub this week under Apache-2.0; the depth completion weights themselves are not released. Their paper lists NYUv2 RMSE of 0.296 for the flagship ViT-g, and they report 2.552 on KITTI, trailing both DINOv3-7B and V-JEPA 2.1. For actual robotics work this is the exact failure mode that makes wine glasses and steel cabinets a consistent headache for grasp pipelines. Curious how people see validating these depth numbers when the completion weights are not available for independent testing. submitted by /u/Savings-Display5123 [link] [Kommentare]
https://www.wearedevelopers.com/world-congress Why Attend? AI is already inside the development workflow. The hard questions are still open: how to build, what to automate, what to trust, what to secure, what to buy, how to operate it in production. WeAreDevelopers World Congress is where developers, platform teams, security teams, and engineering leaders discuss what actually works. submitted by /u/semanticweb [link] [Kommentare]
I've been running a lot of comparative evals across recent model releases—both API and open-weight—and there's a pattern I can't unsee. After a certain number of turns, or when you push into niche territory, the outputs start converging. Same cadence. Same hedging phrases. Same blind spots. It's not full collapse. It's a kind of... homogenization. A creep. My working theory: we're deep enough into the synthetic data flywheel now that we're seeing the first-generation effects. Not model collapse in the catastrophic sense, but a gradual loss of "texture" across models that share overlapping synthetic ancestry. I've been calling this EchoCreep in my notes. The slow, creeping homogenization of model behavior driven by shared synthetic data lineage. Has anyone else been tracking this? Is there a formal term yet? If not, what are you seeing in your evals that fits this pattern? I'm especially interested in: Concrete eval metrics that might capture it Whether fine-tuning on entirely human-curated data clears it If you've seen it worsen between checkpoint versions any feedback would be appreciated? Thanks submitted by /u/BCondor3 [link] [Kommentare]
Building meaningful relationships with customers through support didn't turn out as I'd hoped