Ksenia_TuringPost

@ClementDelangue @huggingface Foresaw that in December :) turingpost.com/p/fod131

Nvidia just crossed Google as the biggest org on @huggingface with 3,881 team members on the hub. I'm officially calling it: Nvidia is the new American king of open-source AI!

@Lockhead @NVIDIAGTC @wschenk Thank you!

@TheTuringPost @NVIDIAGTC @wschenk Happy Birthday. 🎂🎉🎊

It was a busy week @NVIDIAGTC! Celebrating my birthday on the road 🎉

@abhishek__AI @NVIDIAGTC @wschenk Thank you, Abhishek!

@TheTuringPost @NVIDIAGTC @wschenk Happy Birthday, Ksenia 🎂 Years of consistency with Turing Post well deserved. Big fan!

NVIDIA's Nemotron 3 is an architectural response to the 2 pressures: - Long-context cost as agentic interactions scale - Repeated reasoning cost from invoking full models for small subtasks Nemotron 3 proposes several design decisions to solve this: ▪️ Hybrid architecture: Transformer + Mamba 2 layers for efficient long-context processing ▪️ Mixture-of-Experts (MoE) and LatentMoE on top of it to get cheaper experts ▪️ Multi-token prediction ▪️ NVFP4 precision = 4.75 bits used for inference and pre-training, allowing Nemotron pre-training dataset achieve up to 4× faster convergence than standard open web datasets. This is all about one key idea – "Acceleration is intelligence" Here is the tech stack explained and what the Nemotron Coalition is – NVIDIA has just announced that this alliance of leading players like Cursor, Mistral, Black Forest Labs, etc., is gathering to develop the Nemotron family of open models → turingpost.com/p/nemotroncoal…

A new paper from @ylecun and others – V-JEPA 2.1 It changes the recipe of V-JEPA so the model learns both: • Global semantics – what is happening in the scene • Dense spatio-temporal structure – where things are and how they move The idea is to supervise not just masked tokens but the visible ones too There are 4 key ingredients for V-JEPA 2.1: - Dense prediction loss on both masked and visible tokens - Deep self-supervision across intermediate layers - Modality-specific tokenizers (2D for images, 3D for videos) within a shared encoder - Model + data scaling The workflow turns into: masked image/video → encode visible tokens → predict latent representations for both masked and visible tokens → supervise at multiple layers Here are the details:

NemoClaw – NVIDIA’s contribution to the emerging OpenClaw ecosystem and one of the biggest announcements at NVIDIA GTC It's a framework for long-running autonomous agents. ▪️ The idea: Install OpenClaw together with Nemotron models and OpenShell (NVIDIA’s new security runtime) in a single command. NemoClaw gives agents a sandboxed execution environment that: - runs OpenClaw inside a secure container – OpenShell - enforces policies on network, filesystem, and processes - routes all model calls via NVIDIA cloud - provides CLI tools to manage agents In other words, NVIDIA is no longer aiming only to power the model. It wants to sit under the agent itself.

OpenViking – filesystem memory for AI agents It gives agents a structured navigable context system that: - replaces flat vector storage with a filesystem (viking://) - unifies memory, resources, and skills - loads context in layers (L0/L1/L2) to save tokens - retrieves info via directory-aware search (not flat RAG) - makes retrieval traceable and debuggable → So it's a combination of structured navigation + semantic (embedding-based) retrieval This approach delivers better retrieval accuracy, up to 80–96% lower token cost and self-improving memory over time.

@ylecun Follow @TheTuringPost for more. Get deep analysis, guides & breakdowns of what AI is about now. Join 100,000+ readers from top AI labs, VC funds & universities.: turingpost.com/subscribe

V-JEPA 2.1 = V-JEPA made denser, deeper, more multimodal and more scalable arxiv.org/abs/2603.14482 Code: github.com/facebookresear…

8. So V-JEPA 2.1 looks strong across both prediction and dense visual understanding (even with the encoder kept frozen) Some of the results: • +20% robot grasping success over V-JEPA 2 in zero-shot real-world manipulation • 10× faster navigation planning, with 5.687 ATE on Tartan Drive And new SOTA: • 7.71 mAP on Ego4D short-term object interaction anticipation • 40.8 Recall@5 on EPIC-KITCHENS action anticipation