Addressed comments

Author: mortent

content/en/blog/_posts/2026/dra-136-update.md

@@ -1,6 +1,6 @@
 ---
 layout: blog
-title: "Kubernetes v1.36: DRA has graduated to GA"
+title: "Kubernetes v1.36: More Drivers, New Features, and the Next Era of DRA"
 slug: dra-136-updates
 draft: true
 date: XXXX-XX-XX
@@ -14,6 +14,12 @@ continues to mature, bringing a wave of feature graduations, critical usability
 improvements, and new capabilities that extends the flexibility of DRA to native
 resources like memory and CPU, and support for ResourceClaims in PodGroups.
 
+We have also seen significant momentum in driver availability. Both the
+[NVIDIA GPU](https://github.com/NVIDIA/k8s-dra-driver-gpu)
+and Google TPU DRA drivers are being transferred to the Kubernetes project, joining the
+[DRANET](https://github.com/kubernetes-sigs/dranet)
+driver that was added last year.
+
 Whether you are managing massive fleets of GPUs, need better handling of failures,
 or simply looking for better ways to define resource fallback options, the upgrades
 to DRA in 1.36 have something for you. Let's dive into the new features and graduations!
@@ -37,7 +43,7 @@ scheduling flexibility and cluster utilization.
 
 As DRA becomes the standard for resource allocation, bridging the gap with legacy systems
 is crucial. The DRA
-[Extended Resource](https://kubernetes.io/docs/concepts/scheduling-eviction/dynamic-resource-allocation/#device-taints-and-tolerations)
+[Extended Resource](https://kubernetes.io/docs/concepts/scheduling-eviction/dynamic-resource-allocation/#extended-resource)
 feature allows users to request resources via traditional extended resources on a Pod.
 This allows for a gradual transition to DRA, meaning application developers and
 operators are not forced to immediately migrate their workloads to the ResourceClaim
@@ -71,6 +77,23 @@ devices or FPGAs—are fully prepared. By explicitly modeling resource readiness
 prevents premature assignments that can lead to Pod failures, ensuring a much more robust
 and predictable deployment process.
 
+**Resource Health Status (Beta)**
+
+Knowing when a device has failed or become unhealthy is critical for workloads running on
+specialized hardware. With
+[Resource Health Status](/docs/concepts/scheduling-eviction/dynamic-resource-allocation/#device-health-monitoring),
+Kubernetes now exposes device health information directly in the Pod Status through the
+`allocatedResourcesStatus` field. When a DRA driver detects that an allocated device
+has become unhealthy, it reports this back to the kubelet, which surfaces it in each
+container's status.
+
+In 1.36, the feature graduates to beta (enabled by default) and adds an optional `message`
+field providing human readable context about the health status, such as error details or
+failure reasons. DRA drivers can also configure per device health check timeouts,
+allowing different hardware types to use appropriate timeout values based on their
+health reporting characteristics. This gives users and controllers crucial visibility
+to quickly identify and react to hardware failures.
+
 ## New Features
 
 Beyond stabilizing existing capabilities, v1.36 introduces foundational new features
@@ -100,10 +123,25 @@ performance tuning.
 
 One of the most requested features from cluster administrators has been better visibility
 into hardware capacity. The new
-[Resource Availability Visibility](add_link_here)
-feature introduces robust mechanisms to query and expose the total capacity, allocated
-usage, and available pool of DRA resources across the cluster. This unlocks better
-integration with dashboards and capacity planning tools.
+[DRAResourcePoolStatus](/docs/concepts/scheduling-eviction/dynamic-resource-allocation/#resource-pool-status)
+feature allows you to query the availability of devices in DRA resource pools. By creating a
+`ResourcePoolStatusRequest` object, you get a point-in-time snapshot of device counts
+— total, allocated, available, and unavailable — for each pool managed by a given
+driver. This enables better integration with dashboards and capacity planning tools.
+
+**List Types for Attributes**
+
+With
+[List Types for Attributes](/docs/concepts/scheduling-eviction/dynamic-resource-allocation/#list-type-attributes),
+DRA can represent device attributes as typed lists (int, bool, string, and version), not
+just scalar values. This helps model real hardware topology, such as devices that belong
+to multiple PCIe roots or NUMA domains.
+
+This feature also extends `ResourceClaim` constraint behavior to work naturally
+with both scalar and list values: `matchAttribute` now checks for a non-empty
+intersection, and `distinctAttribute` checks for pairwise disjoint values.
+It also introduces `includes()` function in DRA CEL, which lets device selectors keep working
+more easily when an attribute changes between scalar and list representations.
 
 **Device Allocation Ordering through Lexicographical Ordering**
 
@@ -130,5 +168,5 @@ A good starting point is joining the WG Device Management
 [meetings](https://docs.google.com/document/d/1qxI87VqGtgN7EAJlqVfxx86HGKEAc2A3SKru8nJHNkQ/edit?tab=t.0#heading=h.tgg8gganowxq),
 which happen at US/EU and EU/APAC friendly time slots.
 
-Not all enhancement ideas are tracked as issues yet, so come talk to us if you wantto help or have some ideas yourself!
+Not all enhancement ideas are tracked as issues yet, so come talk to us if you want to help or have some ideas yourself!
 We have work to do at all levels, from difficult core changes to usability enhancements in kubectl, which could be picked up by newcomers.