Rephrased Tekton paragraph

Signed-off-by: Stefano Fioravanzo <[email protected]>

_posts/2024-07-22-kubeflow-1.9-release.md

@@ -48,9 +48,9 @@ We made a huge progress towards KFPv1 feature parity by adding more Kubernetes r
 
 The Pipelines Tekton backend has been [merged](https://github.com/kubeflow/pipelines/pull/10678) into the main Kubeflow Pipelines repository. You can now choose what workflow engine to use from the same Pipelines version. This proves the extensibility and flexibility of the KFP v2 architecture, which encourages other contributors to bring support for other workflow engines.
 
-Compared to Argo Workflows, Tekton provides better extensibility thanks to pipeline definitions and reusable components that facilitate easier customization and integration into various CI/CD systems, making it adaptable for diverse machine learning workflows. Tekton also optimizes resource usage and execution efficiency, enhancing scalability for large-scale machine learning workloads compared to Argo Workflows, resulting in faster execution and better resource management.
+Both Argo Workflows and Tekton provide unique advantages. Argo Workflows is known for its simplicity and ease of use, making it a popular choice for many users. Tekton offers extensive customization options with its pipeline definitions and reusable components, which can be advantageous for integrating into various CI/CD systems. Depending on your specific requirements and preferences, you can leverage the strengths of either Argo Workflows or Tekton to optimize your machine learning workflows.
 
-In this [blog post](https://developer.ibm.com/blogs/awb-tekton-optimizations-for-kubeflow-pipelines-2-0/) you can find more details about the benefits of running KFP with Tekton instead of Argo Workflows. 
+In this [blog post](https://developer.ibm.com/blogs/awb-tekton-optimizations-for-kubeflow-pipelines-2-0/), you can find more details about the benefits of running KFP with either Tekton or Argo Workflows.
 
 ### Argo Workflows Upgrade