Hugging Face.

You can now control feature access per resource group rather than across the whole organization. Before, the only way to limit a feature was by organization role. This means you can leave Jobs open to everyone, restrict Inference Endpoints to admins, and give blog publishing rights only to the blog-writer resource group users.

You can now filter your Jobs by label. Your most-used labels appear as clickable chips with job counts above the list, and a free-form key=value input lets you filter by any label, including ones not shown as chips. This works on both user and organization jobs pages. Learn more about HF Jobs: https://huggingface.co/docs/hub/jobs

The Hugging Face MCP Server has been updated to provide enhanced connectivity to the Hub with fewer tools and tokens. The main change is the new hf_fs tool which provides a single interface to repositories, storage, documentation, papers and more. It's equipped with search and lets your assistant naturally navigate Hugging Face in just over 1,000 tokens. Add Sandboxes to give your assistant secure execution environments attached to buckets and repositories. This gives blazing fast code access for dataset analysis, model training, Space creation and much more. Share your feedback on MCP here and update your settings at https://huggingface.co/settings/mcp

Users can now see their egress usage directly in the dashboard. Organizations also receive a per‑user egress breakdown, showing how much data each member consumes. The view currently includes only traffic routed through the Hugging Face CDN, and coverage will expand as more traffic is directed through the CDN.

The new Space creation page now includes an option to build with an AI agent. Copy the generated command into your agent and let it build and iterate on a Space for a model, paper, or local folder. https://huggingface.co/new-space