MoonMath.ai

WorldJen is a high-performance benchmarking stack for video models Today we’re releasing the WJ SDK + CLI 🚀 Run fast, reproducible evals to compare checkpoints and rank against other models Get started: PyPI: pypi.org/project/worldj… Blog: moonmath.ai/posts/speed-an… Evaluate LTX2 on your Mac: docs.worldjen.com/tutorial-ltx2

@ItayAmir8 @MokadyRon @bria_ai_ You are the 👑

@MokadyRon @bria_ai_ It was great working with you guys @moonmathai !!

Making models run fast at inference requires optimizing the entire AI stack. It was great partnering with MoonMath to take @bria_ai_ 's Fibo to the next level of speed. Unlike standard models, Fibo consists of a Reasoner (VLM) and a Renderer (Flow Matching), requiring both to be optimized at the algorithm, deployment, and kernel levels. And most importantly it was great to work with @moonmathai Read more in the new blog post

We accelerated Fibo on Hopper, Instinct & DGX Spark (Blackwell)! Awesome model and awesome team.

@OPatashnik @DecartAI @ingeration @Lightricks Explore talks from last event: youtube.com/playlist?list=…

📢Announcing the 2nd Generative Media TLV meetup, April 15th. Tel Aviv. Speakers: @OPatashnik Tel Aviv Uni Essam Qeys , @DecartAI @ingeration , @Lightricks no fluff ! Sign up: luma.com/pnrjjkyf

“24 FPS” ≠ real-time Example (seaweed-apt.com/2): - 24 FPS (this is throughput!) - ~160ms latency → ~4 frames delay That’s not interactive! FrameCommit moves latent video models in that direction. True frame-by-frame real-time a-la @DecartAI

8/ A final pixel-mismatch loss penalizes decoded outputs that disagree with already committed frames, reducing jitter. The proposal is simple: keep the latent video model, but change the conditioning/inference loop so it behaves like a 1-pixel-frame-per-step live-stream system.

7/ Training starts from a StreamDiffusionV2 / CausVid checkpoint. The new cross-attention layers are randomly initialized, and α is annealed from 1 -> 0.5 so the model gradually learns to use committed-frame conditioning before standard fine-tuning.

1/ New post: FrameCommit: Journey From Wan to Decart LSD, Part1 The target for live-stream video is not just high FPS but ~40 ms input-to-output latency per visible pixel frame. moonmath.ai/posts/framecom…

The Quantization Handbook 🪄 The first SoK for diffusion quantization 📘Read: moonmath.ai/assets/posts/q…

This week we lost Chuck Norris, or so reality claims. In his honor, here are 10 facts about Chuck Norris and AI. Feel free to add your own 1. Chuck Norris won all reward models 2. AI trains on Nvidia because it cannot keep up training with Chuck Norris 3. When a model looks at Chuck Norris, it backpropagates 4. Chuck Norris defeated AlphaGo in Go 5. RLHF was invented by Chuck Norris, feedback originally was his kick 6. Your AI agent will go to sleep before Chuck Norris 7. Chuck Norris models don't need training, they have zero loss 8. Chuck Norris can get as many H100 as he want 9. AI never hallucinates when speaking with Chuck Norris, it’s just sometimes afraid to tell the truth 10. Chuck Norris can compile flashattention so quickly, nvcc asks him for advice h/t my team, Thank you Chuck, we'll always remember♥️

had to integrate Recraft the moment v4 dropped. more coming soon 👀