kubelet now allows the use of XFS quotas (on XFS and suitably configured ext4fs filesystems) to monitor storage consumption for ephemeral storage.  This method of monitoring consumption, which is currently available only for `emptyDir` volumes, is faster and more accurate than the old method of walking the filesystem tree. Note that it does not enforce limits, it only monitors consumption. To utilize this functionality, set the feature gate `LocalStorageCapacityIsolationFSQuotaMonitoring=true`. For...

* Removed metrics api group ([#26073](https://github.com/kubernetes/kubernetes/pull/26073), [@piosz](https://github.com/piosz))
* Fixed check in kubectl autoscale: cpu consumption can be higher than 100%. ([#26162](https://github.com/kubernetes/kubernetes/pull/26162), [@jszczepkowski](https://github.com/jszczepkowski))

- Reduced CPU and memory consumption of `kube-apiserver` if OpenAPI V2 will not be accessed by any client. Also improved performance of the apiserver on installation of many CRDs. ([#118212](https://github.com/kubernetes/kubernetes/pull/118212), [@Jefftree](https://github.com/Jefftree))...

- Services implemented a field selector for the ClusterIP and Type fields.
  The Kubelet uses this field selector to avoid monitoring Headless Services, which helps reduce memory consumption. ([#123905](https://github.com/kubernetes/kubernetes/pull/123905), [@aojea](https://github.com/aojea)) [SIG Apps, Node and Testing]

- Scheduler ComponentConfig fields are now pointers ([#83619](https://github.com/kub...

### Promoted core CSI Migration to Stable

- A new set of alpha metrics are reported by the Kubernetes scheduler under the `/metrics/resources` endpoint that allow administrators to easily see the resource consumption (requests and limits for all resources on the pods) and compare it to actual pod usage or node capacity. ([#94866](https://github.com/kubernetes/kubernetes/pull/94866), [@smarterclayton](https://github.com/smarterclayton)) [SIG API Machinery, Instrumentation,...

        `True`,`tf.data` will now automatically add a `prefetch` transformation
        to datasets that end in synchronous transformations. This enables data
        generation to be overlapped with data consumption. This may cause a
        small increase in memory usage due to buffering. To enable this
        behavior, set `inject_prefetch=True` in
        `tf.data.experimental.OptimizationOptions`.

## Known Issues

* Improved **correctness** in corner cases.
* **Safer** types.
* Better **performance** and **less energy** consumption.
* Better **extensibility**.
* etc.

...all this while keeping the **same Python API**. In most of the cases, for simple models, you can simply upgrade the Pydantic version and get all the benefits. 🚀