The latest version of Ray, a distributed computing framework, has been released, marking an important step in its evolution by removing the default Redis dependency, opening the door to better support for fault tolerance and high availability in future releases. The new release also introduces a more intuitive documentation structure, organized around three primary use cases: Ray ML, Ray Core, and Ray Clusters. Additionally, Ray is now stable for Python 3.9, allowing users to run it in production with confidence. With these changes, Ray aims to improve its performance, scalability, and usability, providing a better experience for developers and researchers alike.