Pawprint Studio's open-world creature-catching RPG Aniimo launches day one on GeForce NOW, enabling streaming on Steam Deck and newly supported Firefox without waiting for a 45 GB download.
GitHub used the Copilot app and Copilot CLI to completely rewrite the Copilot agent runtime from TypeScript into over 800,000 lines of production Rust. AI agents wrote most of the code, delivered incrementally across 128 PRs, improving performance by several orders of magnitude.
NVIDIA submitted preview results for Vera Rubin NVL72 to MLPerf Inference v6.1 for the first time, achieving throughput up to 3.7 times that of GB300 NVL72 on Qwen3-VL and up to 2.5 times on DeepSeek-R1.
Emerald AI, Google and NVIDIA announced the AI Energy Management Alliance (AEMA) to encourage data centres to dynamically adjust electricity use to grid conditions.
OpenAI described how ChatGPT Work and Codex analytics help teams understand AI usage and spending, identify training needs and connect adoption to business outcomes.
A University of Manchester team used NVIDIA Earth-2's generative Earth-2 CorrDiff model and one year of hourly UK pollution data to train a nationwide air-pollution model at 2–3-square-kilometre resolution, completing training in two days on one eight-GPU Isambard-AI node.
NVIDIA introduced DSX at AI Infra Summit to improve AI-factory token throughput within a fixed power budget. Lambda validated DSX MaxLPS on a five-rack, 19-node HGX B200 cluster, running 19 nodes within the same budget as 16 at full power. Cluster token throughput rose 24%, from around four million to five million tokens per second, with performance per watt up 23%.
NVIDIA announced several advances for Vera Rubin and the DSX platform at AI Infra Summit, focusing on energy-efficiency improvements in token throughput per megawatt.
Children's Hospital of Philadelphia (CHOP) uses MONAI, the open-source medical imaging framework co-founded by NVIDIA, to reduce paediatric heart modelling from four hours to seconds, already supporting complex ventricular septal defect surgical planning. CHOP is working with NVIDIA to integrate Newton, an open-source physics engine based on NVIDIA Warp, into SlicerHeart, reducing cardiac-device simulation from up to four hours to near real time.
GitHub's Japan and Korea marketing leads used Copilot to automate event operations. GitHub Issues are work units, Issue forms collect structured fields and labels trigger Actions workflows to generate UTM links, landing pages and invitation emails and clean registrant lists.
OpenAI evolved Habitat from a Python library into a globally distributed storage platform supporting one billion ChatGPT users and 22 million requests per second, meeting the scaling demands of ChatGPT's online storage.
Mistral and Cloudera partnered to integrate Mistral models into Cloudera's hybrid data platform. Enterprises can run inference in private or public clouds, on-premises and fully air-gapped environments with full control. They can train custom models on proprietary data in controlled environments while retaining ownership of data and resulting intelligence. Cloudera runs 30 exabytes of customer-managed data.
TRL v1.14's AsyncGRPOTrainer now supports training and synchronising only LoRA adapters to vLLM. A rank-1 adapter is just a few MB and can transfer through a Storage Bucket mounted to each job, without NCCL.
GitHub released a research preview of Project HydraFusion, using runtime multi-model orchestration to deliver frontier-level coding through Copilot. Users can enable it via /experimental in GitHub Copilot CLI, paying each model's standard rates for tokens actually consumed.
Hugging Face launched NeoMME in 260M and 800M sizes. A single bidirectional Transformer handles text tokens and 32×32 image patches, trained from scratch with a masked discrete diffusion objective rather than pretrained vision towers or causal language models.
Hugging Face’s WebAI team released @huggingface/kernels, a lightweight library for loading and running optimised WebGPU kernels from the Hugging Face Hub. An initial collection of 207 kernels is also available as separate repositories under Apache-2.0.
Mistral and HUMAIN announced a strategic partnership covering infrastructure, advanced models and deployment, initially focusing on cybersecurity and voice, with frontier models strong in Arabic planned. Worth hundreds of millions of euros, it includes exploring HUMAIN data centres and joint market strategies for regulated Saudi industries.
Papers with Code uses hybrid retrieval, with Hugging Face Jobs generating embeddings in batches, Storage Buckets providing persistent transfer and Inference Endpoints supplying low-latency query embeddings. It covers over 110,000 papers from arXiv and Daily Papers.
Liquid AI released DSpark draft model checkpoints for LFM2.5-1.2B-Instruct, LFM2.5-2.6B and LFM2.5-8B-A1B. Speculative decoding accelerates decoding without changing output quality, increasing throughput by up to 3.18 times on GPUs and 2.87 times on devices.
A Hugging Face blog describes a constraint-aware GPU allocator compared with FIFO under identical hardware and workloads. Across five real contention scenarios, GPU utilisation rose from 52–85% to 72–88%, while priority-weighted output improved 24.6–105.1%, averaging 52%.
A Hugging Face blog tutorial demonstrates a streaming data loop for Strands Robots. The same Robot() object records demonstrations, syncs them to a Storage Bucket, streams training data from the Hub and deploys the checkpoint back to hardware, keeping the LeRobot disk format unchanged throughout.
Hugging Face's ALTK-Evolve and ACE both provide agent memory without weight updates, but differ in delivery: ACE injects the full playbook at every step, while ALTK-Evolve retrieves a small number of strongly supported guidelines for each task.
Mistral announced three AI sovereignty initiatives. Mistral Regional Endpoints are generally available, letting customers choose inference in Europe or the US. Mistral Priority Tier is in public preview, offering committed service levels, custom rate limits and uptime SLAs for critical workloads.
NVIDIA released Magpie TTS Multilingual, a 364M-parameter open-weight speech synthesis model supporting English, Spanish, French, German, Italian, Vietnamese, Chinese, Hindi and Japanese, plus newly added Modern Standard Arabic, Korean and Brazilian Portuguese, for 12 languages in total.
Baseten became a new Inference Provider on Hugging Face Hub, initially offering conversation and text generation with open-weight models including Kimi K3, DeepSeek V4 Flash and GLM-5.2.
Hugging Face's blog argues AI's next real constraint is GPU utilisation, rather than intelligence. GPUs are billed by calendar hours but produce only in compute hours, mirroring the cost structure of grounded aircraft.
NVIDIA introduced Cosmos-H-Dreams, a real-time action-conditioned generative simulator for surgical robots. It runs through FlashDreams on one RTX PRO 6000 GPU and supports closed-loop interaction by humans and policies.
IBM Research shared its experience building model routing into agent systems on the Hugging Face blog, noting that most routers treat model selection as classification when it is actually a systems-optimisation problem.
Google DeepMind launched the ATL Saathi pilot in India, a Gemini-powered web app providing Atal Tinkering Labs educators with a 24/7 lesson preparation and training assistant, initially covering 100 schools.
The third Hugging Face PyTorch profiling instalment traces attention implementation with a profiler. Comparing naive attention with in-place masking shows replacing masked_fill with masked_fill_ removes one GPU Memcpy kernel per forward pass.
Mistral Studio now provides central management of Prompts and Skills, with version histories, clear ownership and traceability. Immutable versions, rollback, tags and audit logs make AI behaviour governable and discoverable, while Observability traces production outputs to their versions. Skills can be invoked directly from Studio as MCP servers, ensuring production executes the same governed asset.
Hugging Face announced that vLLM’s transformers modelling backend now matches or exceeds the throughput of vLLM’s handwritten native implementations across several LLM architectures. It uses torch.fx for static graph analysis and ast to rewrite source code, dynamically applying inference-related layer fusion at runtime to match custom-code performance.
Hugging Face and Amazon SageMaker AI introduced deep-link integration. Clicking Customize on SageMaker AI or Deploy on SageMaker AI on supported model pages opens SageMaker Studio with the model preloaded and environment configured automatically.
Google Research published the first large-scale real-world study in Nature Cities, redirecting under 2% of trips to alternative routes through Google Maps changes across ten US cities for six months. Median target-road speed rose around 2% and median fuel consumption rates fell 0.5–1.0%, potentially saving thousands of tonnes of CO2e per city annually.
At Build 2026, Microsoft announced Foundry Managed Compute and a Hugging Face model collection, with weekly updates to open-weight models and one-click deployment to Foundry's managed GPU platform.
As inference costs fell from around $30 per million tokens for GPT-4-class models in early 2023 to under $1 today, and below $0.10 at some providers, UC Berkeley's Aditya G. Parameswaran and colleagues proposed three directions for data systems in the agent era: For Agents, Of Agents and By Agents.
Hugging Face and SkyPilot released an integration allowing a Hugging Face Bucket or any model, dataset or Space repository to be mounted into SkyPilot tasks with an hf:// URL and an existing HF_TOKEN, running across more than 20 clouds, Kubernetes, Slurm and local environments.
Hugging Face introduced a new kernel repository type for Kernels, along with trusted publishers and code signing. By default, only trusted publishers' kernels load; other sources require explicit trust_remote_code=True.
Google Research published an architecture attaching multi-token prediction (MTP) heads to frozen Gemini Nano v3 models, accelerating on-device inference without changing backbone weights. It has shipped with the Pixel 9 and 10 series.
Google Research proposed linear elastic caching, modelling page eviction as a ski-rental problem and using shallow decision trees to predict page TTLs, dynamically resizing caches to minimise total ownership cost. After months in Spanner production, memory fell 15.5%, cache misses rose only 5.5%, TCO dropped around 5% and actual I/O cost impact was just 0.5%. It was also validated on several public cache traces.