Improved example following review.

Author: jm-franc

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

@@ -18,21 +18,36 @@ 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
+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 84%, and the desired utilization is 80%, the
-HPA will ask for \\(100 \times (84/80)\\)) replicas).
+Let's say you have a web application running in a Kubernetes cluster with 50
+replicas. You configure the Horizontal Pod Autoscaler (HPA) to scale based on
+CPU utilization, with a target of 75% utilization. Now, imagine that the current
+CPU utilization across all replicas is 90%, which is higher than the desired
+75%. The HPA will calculate the required number of replicas using the formula:
+```math
+desiredReplicas = ceil\left\lceil currentReplicas \times \frac{currentMetricValue}{desiredMetricValue} \right\rceil
+```
+
+In this example:
+```math
+50 \times (90/75) = 60
+```
+
+So, the HPA will increase the number of replicas from 50 to 60 to reduce the
+load on each pod. Similarly, if the CPU utilization were to drop below 75%, the
+HPA would scale down the number of replicas accordingly. The Kubernetes
+documentation provides a
+[detailed description of the scaling algorithm](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#algorithm-details).
 
 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%.
+number of replicas when the current and desired metric values differ by more
+than 10%. In the example above, since the ratio between the current and desired
+metric values is \\(90/75\\), or 20% above target, exceeding the 10% tolerance,
+the scale-up action will proceed.
 
 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
@@ -73,11 +88,6 @@ spec:
       tolerance: 0
 ```
 
-Consider the previous scenario where the ratio of current to desired metric
-values is \\(84/80\\), a 5% increase. With the default 10% scale-up tolerance,
-no scaling occurs. However, with the HPA configured as shown, featuring a 0%
-scale-up tolerance, the 5% increase triggers scaling.
-
 ## I want all the details!
 
 Get all the technical details by reading