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We’re open-sourcing Drex-Max v1 The most capable Multimodal Decision Model ! And yes: it runs 100% locally on Apple Silicon (M4 & M5) Enjoy Building with Nace! Cloud Inference: https://t.co/fm3GXfjaqx Local Inference: https://t.co/9nwcBn7c2D https://t.co/Wgu8P4zByR
We just merged the first protein sequence design model RFD4-Proteina in Diffusers 🔥 I hope this pushes the state of open BioML further. Kudos to @NVIDIAAI and Baker Lab for shipping this! Check out the docs to know more: https://t.co/9EkDKglFRj https://t.co/4h4EfOEOoy
This is really cool work on our synthetic hospital benchmark. For patient diagnosis, this shows that small models + SFT + the right tools > GPT-6 Luna. Interesting is that the tools help small models, but not big models. So gaps in capability can be replaced by tools
spent a few days trying to train a tiny model to diagnose patients on the Synthetic Hospital environment out of CMU. RL didn't help much, but some SFT and a simple search tool got qwen3-4b to perform as well as a ~1T param model this is because the agent is more than just the m
S4VY is an incredible advancement in the field of real-time 4D joint segmentation and depth prediction! We’ll be releasing the code and checkpoints soon, so stay tuned for more updates.
1/9 Introducing S4VY: Segment Anything, now in 4D 🧵 From any set of RGB observations (a video, a few scattered frames, or an unordered photo collection), S4VY segments every object in 4D, each with one consistent identity across space and time. https://t.co/2kO0wmHnj4
Open weights models fall further behind in v5. The leader, Kimi K3.1 Pro (weights not yet released), scores 46, 12 points behind the leader, compared with a 3-point gap in v4.3. Of models with released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) score highest at 32, and Claude Haiku 5.5 (max) outscores all of them. The gap is widest on HLE-Diamond and Terminal-Bench-Science, where Kimi K3.1 Pro scores 37% and 34%, compared with 68% for Claude Opus 5.5 (max) and 63% for GPT-6 Astra (max).
Gemini 4 Argon leads GPU optimization on Isoquant's new Inference Engineering Benchmark: a 4.38× mean speedup across 9 tasks, ahead of GPT-6 Astra (3.71×) and Muse Wonder 1.0 (3.66×). We evaluate how well AI models improve inference performance across 15 tasks. 🧵 https://t.co/WJ6jXiAvqR
Drop 1/15: Introducing Joy, a dual-channel conversational dataset. Real, unscripted conversations between two people, each on their own track. Built to teach voice models how people actually talk: when to listen, when to jump in, when to say “hmm”. 100,000 hours. 50,000 speakers. 10 languages.
wtf is a "decision model"? Like architectural details, not hand-wavy explanations. Come learn from @barrowjoseph with me. https://t.co/JbixiT9oyN
One broadcast clip → solved the camera → reconstructed the trajectory → physics for every thruster pulse → a WebXR replay you can step into. I recreated the Roadster ramp stunt with IWSDK. Try it: https://t.co/dfXytTHyA7 Code: https://t.co/2X4dcnOxWF https://t.co/MgTqdE6Joa
Silt is a design canvas wired into your real codebase. Each project is a repo, so your design system, tokens and code conventions sit in the agent's context while you design. That one idea changes everything the agent can do for you. https://t.co/93xQckyfSW
