Keep track of latest changes on the Hugging Face Hub
You can now attach any Storage Bucket, model or dataset from the Hub as a local filesystem. It allows you to attach remote storage that is 100x bigger than your local machine's disk. This is also perfect for Agentic storage! Read-write for Storage Buckets, read-only for models and datasets.
Get started by installing hf-mount from the GitHub repo: https://github.com/huggingface/hf-mount
You can now set your Spaces to protected, making them private on Hugging Face while still keeping their URL publicly accessible.
This is useful for deploying production-ready demos or internal tools without exposing model weights, prompts, or proprietary logic.
Combined with custom domains you can also host your website on Hugging Face.
When AI agents such as Cursor or Claude Code fetch a Hugging Face Papers page, Markdown versions are served automatically, saving tokens and improving efficiency — e.g. huggingface.co/papers/2601.15621.md.
A new hugging-face-paper-pages skill for AI agents lets agents search papers by title, author, or semantic similarity, read their content, and discover linked models, datasets, and Spaces on the Hub.
User and organization settings now include a Repositories page to visualize repository storage consumption. This update makes it easier to monitor usage, understand how storage is distributed across repositories, and manage resources more effectively.
Buckets bring mutable, non-versioned object storage to the Hub, available for users and organizations using your existing storage plan. Upload training checkpoints, intermediate artifacts, logs and processed data shards without version control overhead. Manage them from the hf CLI, Python, JavaScript or the Hub API directly.
Built on Xet, uploads and downloads are deduplicated and fast. Buckets also support CDN pre-warming to place your data close to compute (AWS and GCP are supported at launch).
Read the blog post and the Storage Buckets Documentation to get started.
