Update following review.

Author: jm-franc

content/en/blog/_posts/XXXX-XX-XX-hpa-configurable-tolerance.md

@@ -6,75 +6,72 @@ slug: hpa-configurable-tolerance
 # the release comms team can confirm which date has been assigned
 #
 # PRs to remove the draft marker should be opened BEFORE release day
-draft: true 
+draft: true
 math: true # for formulae
 date: XXXX-XX-XX
-author: "Jean-Marc François"
+author: "Jean-Marc François (Google)"
 ---
 
 This post describes _configurable tolerance for horizontal Pod autoscaling_,
 a new alpha feature first available in Kubernetes 1.33.
 
 ## What is it?
 
-[Horizontal Pod autoscaling](/docs/tasks/run-application/horizontal-pod-autoscale/) (HPA) is a
-well-known Kubernetes feature that
-allows your workload to automatically resize by adding or removing replicas
-based on resource utilization.
+[Horizontal Pod autoscaling](/docs/tasks/run-application/horizontal-pod-autoscale/)
+(HPA) is a well-known Kubernetes feature that allows your workload to
+automatically resize by adding or removing replicas based on resource
+utilization.
 
 To decide how many replicas a workload requires, users configure their HPA
 with a metric (e.g. CPU utilization) and an expected value for this metric (e.g.
 80%). The HPA updates the number of replica based on the ratio between the
 current and desired metric value. (For example, if there are currently 100
 replicas, the CPU utilization is 88%, and the desired utilization is 80%, the
-HPA will ask for `100 * (88/80)` replicas).
+HPA will ask for \\(100 \times (88/80)\)) replicas).
 
 In order to avoid replicas being created or deleted whenever a small metric
-fluctuation occurs, Kubernetes applies a form of hysteresis: it only changes the number of replicas
-when the the current and desired metric values
-differ by more than 10%.
+fluctuation occurs, Kubernetes applies a form of hysteresis: it only changes the
+number of replicas when the the current and desired metric values differ by more
+than 10%.
 
-This default tolerance of 10% is cluster-wide; in older Kubernetes releases, it could not be fine-tuned. It's
-a suitable value for most usage, but too coarse for large deployments, where a
-10% tolerance represents tens of pods. As a result,
-users have long [asked](https://github.com/kubernetes/kubernetes/issues/116984)
-to be able to tune this value.
+This default tolerance of 10% is cluster-wide; in older Kubernetes releases, it
+could not be fine-tuned. It's a suitable value for most usage, but too coarse
+for large deployments, where a 10% tolerance represents tens of pods. As a
+result, users have long
+[asked](https://github.com/kubernetes/kubernetes/issues/116984) to be able to
+tune this value.
 
-In Kubernetes v1.33,
-``
-this is now possible.
+In Kubernetes v1.33, this is now possible.
 
 ## How do I use it?
 
-Just add the tolerance you want an HPA to use to your `HorizontalPodAutoscaler`
-resource.
+Enable the `HPAConfigurableTolerance` feature flag in your Kubernetes 1.33
+cluster, then add the tolerance you want an HPA to use to your
+HorizontalPodAutoscaler object.
 
 Tolerances appear under the `spec.behavior.scaleDown` and
 `spec.behavior.scaleUp` fields and can thus be different for scale up and scale
 down. A typical usage would be to specify a small tolerance on scale up (to
 react quickly to spikes), but lower on scale down (to avoid adding and removing
 replicas too quickly in response to small metric fluctuations).
 
-For example, an HPA with a tolerance of 5% on scale-down, and no
-tolerance on scale-up, would look like the following:
+For example, an HPA with a tolerance of 5% on scale-down, and no tolerance on
+scale-up, would look like the following:
 
 ```yaml
 apiVersion: autoscaling/v2
 kind: HorizontalPodAutoscaler
 metadata:
   name: my-app
 spec:
-   …
+  ...
   behavior:
     scaleDown:
       tolerance: 0.05
     scaleUp:
       tolerance: 0
 ```
 
-Note: This feature is in alpha in Kubernetes 1.33, gated by the
-`HPAConfigurableTolerance` flag.
-
 ## I want all the details!
 
 Get all the technical details by reading