diff --git a/_posts/2021-04-23-kubeflow-1.3-release.md b/_posts/2021-04-23-kubeflow-1.3-release.md
index 272e3e18..78d3e1f9 100644
--- a/_posts/2021-04-23-kubeflow-1.3-release.md
+++ b/_posts/2021-04-23-kubeflow-1.3-release.md
@@ -22,7 +22,7 @@ The Kubeflow user community is growing quickly, which was demonstrated in our re
 
 ## Streamlined ML workflows delivered via new UIs
 
-Data scientists will like the new and updated user interfaces (UIs) for Katib, TensorBoard, Persistent Volumes, Pipelines and Kale. These new UIs address many of the ML tasks that are time consuming and technically challenging. The UIs reduce the need for a data scientist to learn kfctl or docker CLI commands.   
+Data scientists will like the new and updated user interfaces (UIs) for Katib, TensorBoard, Persistent Volumes, Pipelines and Kale. These new UIs address many of the ML tasks that are time consuming and technically challenging. The UIs reduce the need for a data scientist to learn kubeflow or docker CLI commands.   
 
 Below please find details on the UIs’ benefits for ML workflows:
 
@@ -37,7 +37,7 @@ Below please find details on the UIs’ benefits for ML workflows:
 *   Kubeflow Pipelines (KFP)
     *   The KFP UI has been reorganized for a more unified experience (PR [4925](https://github.com/kubeflow/pipelines/pull/4925)), and includes the ability to manage recurring runs via new “JobsList” and “AllJobslist” pages (PR [5131](https://github.com/kubeflow/pipelines/pull/5131)) and simplified view of dependency graphs.
 
-Beyond the UIs, data scientists can also tie Notebooks with Serving more closely than ever before. In addition to the aforementioned integration with TensorBoard, Kubeflow Notebooks also now support first class deployments with TensorFlow 2.0, PyTorch, VS Code and RStudio.
+Beyond the UIs, data scientists can also tie Notebooks with Serving more closely than ever before. In addition to the aforementioned integration with TensorBoard, Kubeflow Notebooks also now support first class deployments with JupyterLab, VS Code and RStudio.
 
 KFServing enhancements include simplified canary rollouts with traffic splitting at the Knative revisions level. It also delivers extended ML framework support for:
 
@@ -122,7 +122,7 @@ We are pleased to announce that the user documentation on Kubeflow.org has also
 
 ## Simplified installation and improved documentation 
 
-ML Engineers, who are installing Kubeflow, have a clear path to installation success as Kubeflow 1.3 includes new manifests and upgraded Istio support. For more information on installation patterns for each distribution, please visit the [Getting Started](https://www.kubeflow.org/docs/started/installing-kubeflow/) page on Kubeflow.org. If you are supporting a distribution or just interested in low-level details, please review the Kubeflow 1.3 Manifest [readme](https://github.com/kubeflow/manifests/tree/v1.3.0-rc.0#readme).  
+ML Engineers, who are installing Kubeflow, have a clear path to installation success as Kubeflow 1.3 includes new manifests and upgraded Istio support. For more information on installation patterns for each distribution, please visit the [Getting Started](https://www.kubeflow.org/docs/started/installing-kubeflow/) page on Kubeflow.org. If you are supporting a distribution or just interested in deploying Kubeflow on your own, please review the Kubeflow 1.3 Manifests [readme](https://github.com/kubeflow/manifests/tree/v1.3.0-rc.0#readme).  
 
 ## Kubeflow 1.3 tutorials