Today we are releasing JST-2, the world's first full-stack physics-native world foundation model. Many world models learn in pixel/video space. We went back to first principles and built the entire model stack in 3D. Generate a 3D scene in seconds and expand it in real time. Create characters with motion built in, all through one general architecture.

I truly believe conversational AI will drive the biggest educational leap of our generation. 1-on-1 tutoring is the gold standard for education, but it doesn't scale. AI can change that with truly active conversational learning. Annie and team set out to solve this with @gakkuai, and they've done a great job. Gakku is an AI mentoring app for K-12 students. Rather than doing the work for them, the voice chat coach guides students through 10 min essay writing "missions" that cover brainstorming, drafting, and revising. It actively trains kids to generate their own ideas, structure logical arguments, and articulate themselves clearly. If you have school-aged kids, I highly recommend checking it out: gakku.ai

Only 7 days left to save up to 80% on Flova × Seedance 2.5. Grab the offer and bring your ideas to life!

Every recruiter search creates a ranking. Most people have never seen one they're in. Today you can see yours @metix_ai. Paste your LinkedIn and see where you rank against people with your title, in your area, at your experience level. You don't need to sign up. go.metix.ai/recruiters-view/

$52M seed round. 15M+ users. But this is what we're most proud of yet. Introducing Drama 3: the most emotive and controllable TTS model in the world And yes, it's open-weight. You can now control how even a single word should sound, change its tone, and shift your voice mid-sentence. Uncle Roger was impressed too. Try it here: s.fish.audio/rqh0l8 Comment “DRAMA” within the next 48 hours for 1 month free. If you're an enterprise your first month is on us: s.fish.audio/qz9r0t

Introducing SubQ Snap, the first long-context decision model for real enterprise work. Snap is the only decision model or LLM where compute per token stays constant as context grows. • 384K tokens of multimodal context, 12x larger than Jev. • 61.7 on Decision Index v0.2.1 vs Jev’s 57.9, outperforming on 13 of 16 enterprise benchmarks. •Processes 100K tokens and returns a decision in under one second. •$0.05/MM input tokens. Output is free. Available today to anyone. The first 10,000 sign-ups get 100M free input tokens. We’ll likely hit capacity fast and will add users as we add compute. Compute offers welcome 🙂. Latency will be variable as we tune the platform to this new model and category of users. Model speeds are 80ms for minimal context, 250ms for 32k, 660ms for 100k, and 2.5 seconds for 384k. Visit: subq.ai/

I have spent more than a decade studying causality. For me, causality is about understanding the rules that govern how actions change the world, and reasoning about their consequences before acting. Scaling has made today's models remarkably capable at spotting patterns and predicting what comes next. A system that acts in the physical world needs more. It has to ask what will happen if it acts, check the outcome, and adapt when reality doesn't match the plan. In the physical world, trial and error can be costly or unsafe, and an early mistake can carry through the rest of a task. When we started Aether AI, robotics was the obvious place to begin. CRIS-0 is our first integrated system built on these ideas. It tracks the conditions that matter for a task, predicts how each action will change them, and replans when something unexpected happens. In our demo, that means taking a metal can off a plate before heating it, and telling an empty can from an unfinished drink while tidying a room. Physical AI is just our first proving ground. We're building towards causal intelligence that also works for forecasting, scientific discovery, and more. The technical write-up is linked in the comments. x.com/AetherLab_AI/status/21…

I spent the morning running cost projections for live interactive video, and the math on conventional models is completely broken for production. That is why @VivixLabs_HQ's W1 model caught my attention. At $0.003/sec, I can trigger nearly 77 equal-length W1 generations for the API cost of a single Seedance 2.5 call. When you build applications where video generates non-stop as users interact, unit economics dictate whether your project survives. This affordability is what makes W1’s identity as a real-time interactive video model practically usable. API access is active today, though browser playground tools are currently offline generation only while real-time updates roll out. Looking at their 4-way comparison with Seedance 2.5, Kling 3.0, and H3 Max fal featuring the slow-motion sneeze window shatter, W1 holds its visual fidelity remarkably well. You still see minor physical artifacts in high-density glass debris while full-scale model training progresses, but solving the real-time inference cost curve is the exact infrastructure breakthrough I was waiting for. The numbers that put W1 on my production shortlist 👇 platform.vivix.ai/vivix-w1-m…