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diff --git a/blocks/highlighted-tutorials.mdx b/blocks/highlighted-tutorials.mdx
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--- a/blocks/highlighted-tutorials.mdx
+++ b/blocks/highlighted-tutorials.mdx
@@ -1,19 +1,19 @@
-import plausible from "./assets/scaleway-plausible-tutorial-time.webp"
-import sentry from "./assets/scaleway-sentry-tutorial-time.webp"
+import kapsule from "./assets/scaleway-kubernetes-kapsule-tutorial-time.webp"
+import object from "./assets/scaleway-object-storage-tutorial-time.webp"
 
 <Grid>
   <Card
-    image={plausible}
-    title="Running web analytics with Plausible on Ubuntu Linux"
-    tags="Analytics Plausible Ubuntu"
-    label="Open Plausible tutorial"
-    url="/tutorials/plausible-analytics-ubuntu"
+    image={kapsule}
+    title="Deploying a demo application on Scaleway Kubernetes Kapsule"
+    tags="Kubernetes Kapsule"
+    label="Open Kubernetes Kapsule tutorial"
+    url="/tutorials/deploy-demo-application-kubernetes-kapsule/"
   />
   <Card
-    image={sentry}
-    title="Configuring Sentry error tracking"
-    tags="Sentry"
-    label="Open Sentry tutorial"
-    url="/tutorials/sentry-error-tracking"
+    image={object}
+    title="Build and deploy an MkDocs static website with GitHub Actions CI/CD"
+    tags="MkDocs"
+    label="Open MkDocs tutorial"
+    url="/tutorials/deploy-automate-mkdocs-site/"
   />
 </Grid>
\ No newline at end of file
diff --git a/tutorials/deploy-demo-application-kubernetes-kapsule/index.mdx b/tutorials/deploy-demo-application-kubernetes-kapsule/index.mdx
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index 0000000000..fa9be0a25a
--- /dev/null
+++ b/tutorials/deploy-demo-application-kubernetes-kapsule/index.mdx
@@ -0,0 +1,287 @@
+---
+title: Deploying a demo application on Scaleway Kubernetes Kapsule
+description: This page shows you how to deploy a demo application on Scaleway Kubernetes Kapsule
+tags: Kubernetes Kapsule k8S
+products:
+  - kubernetes
+dates:
+  validation: 2025-08-13
+  posted: 2025-08-13
+  validation_frequency: 12
+---
+import Requirements from '@macros/iam/requirements.mdx'
+
+
+# Deploy an intermediate workload on Scaleway Kubernetes Kapsule
+
+This tutorial guides you through deploying a demo application (`whoami`) on Scaleway Kubernetes Kapsule. You will create a managed Kubernetes cluster, deploy a sample application, configure an ingress controller for external access, set up auto-scaling, and test the setup. 
+This tutorial is designed for users with a basic understanding of Kubernetes concepts like pods, deployments, services, and ingress.
+
+<Requirements />
+
+- [Owner](/iam/concepts/#owner) status or [IAM permissions](/iam/concepts/#permission) allowing you to perform actions in the intended Organization
+- A [Scaleway API key](/iam/how-to/create-api-keys/) for details.
+- Installed the tools `kubectl`, `scw`, and `helm` on your local computer
+- Basic familiarity with Kubernetes concepts (Pods, Deployments, Services, Ingress).
+
+## Configure Scaleway CLI
+
+Configure the [Scaleway CLI (v2)](https://github.com/scaleway/scaleway-cli) to manage your Kubernetes Kapsule cluster.
+
+1. Install the Scaleway CLI (if not already installed):
+
+   ```bash
+   curl -s https://raw.githubusercontent.com/scaleway/scaleway-cli/master/scripts/get.sh | sh
+   ```
+
+2. Initialize the CLI with your API key:
+
+   ```bash
+   scw init
+   ```
+
+   Follow the prompts to enter your `SCW_ACCESS_KEY`, `SCW_SECRET_KEY`, and default region (e.g., `pl-waw` for Warsaw, Poland).
+
+## Create a Kubernetes Kapsule cluster
+
+Create a managed Kubernetes cluster using the Scaleway CLI.
+
+1. Run the following command to create a cluster with a single node pool:
+
+   ```bash
+   scw k8s cluster create name=demo-cluster version=1.32.7 pools.0.size=2 pools.0.node-type=DEV1-M pools.0.name=default pools.0.min-size=1 pools.0.max-size=3 pools.0.autoscaling=true region=pl-waw
+   ```
+
+   - `version=1.32.7`: Specifies a [recent Kubernetes version](/kubernetes/reference-content/version-support-policy/#scaleway-kubernetes-products).
+   - `pools.0.size=2`: Starts with two nodes.
+   - `pools.0.min-size=1`, `pools.0.max-size=3`, `pools.0.autoscaling=true`: Enables node auto-scaling.
+   - `region=pl-waw`: Deploys in the Warsaw region.
+
+2. Retrieve the cluster ID and download the kubeconfig file:
+
+   ```bash
+   CLUSTER_ID=$(scw k8s cluster list | grep demo-cluster | awk '{print $1}')
+   scw k8s kubeconfig get $CLUSTER_ID > ~/.kube/demo-cluster-config
+   export KUBECONFIG=~/.kube/demo-cluster-config
+   ```
+
+   <Message type="tip">
+    Alternatively, you can copy the cluster ID from the output after cluster creation and install the kubeconfig file using the following command:
+        ```bash
+        scw k8s kubeconfig install <CLUSTER_ID>
+        ```
+    </Message>
+
+3. Verify cluster connectivity:
+
+   ```bash
+   kubectl get nodes
+   ```
+
+   Ensure all nodes are in the `Ready` state.
+
+## Deploy a sample application
+
+Deploy the [whoami](https://github.com/traefik/whoami) application (a well-known demo application to test cluster deployments) using a Kubernetes Deployment and Service.
+
+1. Create a file named `whoami-deployment.yaml` with the following content:
+
+   ```yaml
+   apiVersion: apps/v1
+   kind: Deployment
+   metadata:
+     name: whoami
+     namespace: default
+   spec:
+     replicas: 2
+     selector:
+       matchLabels:
+         app: whoami
+     template:
+       metadata:
+         labels:
+           app: whoami
+       spec:
+         containers:
+         - name: whoami
+           image: traefik/whoami:latest
+           ports:
+           - containerPort: 80
+           resources:
+             requests:
+               cpu: "100m"
+               memory: "128Mi"
+             limits:
+               cpu: "200m"
+               memory: "256Mi"
+   ---
+   apiVersion: v1
+   kind: Service
+   metadata:
+     name: whoami-service
+     namespace: default
+   spec:
+     selector:
+       app: whoami
+     ports:
+     - protocol: TCP
+       port: 80
+       targetPort: 80
+     type: ClusterIP
+   ```
+
+2. Apply the configuration:
+
+   ```bash
+   kubectl apply -f whoami-deployment.yaml
+   ```
+
+3. Verify the deployment and service:
+
+   ```bash
+   kubectl get deployments
+   kubectl get pods
+   kubectl get services
+   ```
+
+## Configure an ingress controller
+
+Expose the `whoami` application externally using an [Nginx ingress controller](/kubernetes/reference-content/lb-ingress-controller/).
+
+<Message type="note">
+    Before proceeding, ensure the [Helm package manager](/tutorials/kubernetes-package-management-helm/) is installed on your local machine. If it is not already installed, you will need to set it up first.
+</Message>
+
+1. Install the Nginx ingress controller using Helm:
+
+   ```bash
+   helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
+   helm repo update
+   helm install ingress-nginx ingress-nginx/ingress-nginx --namespace ingress-nginx --create-namespace
+   ```
+
+2. Create a file named `whoami-ingress.yaml` with the following content:
+
+   ```yaml
+   apiVersion: networking.k8s.io/v1
+   kind: Ingress
+   metadata:
+     name: whoami-ingress
+     namespace: default
+     annotations:
+       nginx.ingress.kubernetes.io/rewrite-target: /
+   spec:
+     ingressClassName: nginx
+     rules:
+     - host: whoami.example.com
+       http:
+         paths:
+         - path: /
+           pathType: Prefix
+           backend:
+             service:
+               name: whoami-service
+               port:
+                 number: 80
+   ```
+
+3. Apply the Ingress configuration:
+
+   ```bash
+   kubectl apply -f whoami-ingress.yaml
+   ```
+
+4. Retrieve the external IP of the Ingress controller:
+
+   ```bash
+   kubectl get svc -n ingress-nginx ingress-nginx-controller
+   ```
+
+## Set up auto-scaling
+
+Configure [Horizontal Pod Autoscaling (HPA)](https://www.scaleway.com/en/blog/understanding-kubernetes-autoscaling/) to dynamically scale the `whoami` application based on CPU usage.
+
+1. Create a file named `whoami-hpa.yaml` with the following content:
+
+   ```yaml
+   apiVersion: autoscaling/v2
+   kind: HorizontalPodAutoscaler
+   metadata:
+     name: whoami-hpa
+     namespace: default
+   spec:
+     scaleTargetRef:
+       apiVersion: apps/v1
+       kind: Deployment
+       name: whoami
+     minReplicas: 2
+     maxReplicas: 5
+     metrics:
+     - type: Resource
+       resource:
+         name: cpu
+         target:
+           type: Utilization
+           averageUtilization: 70
+   ```
+
+2. Apply the HPA configuration:
+
+   ```bash
+   kubectl apply -f whoami-hpa.yaml
+   ```
+
+3. Verify the HPA status:
+
+   ```bash
+   kubectl get hpa
+   kubectl describe hpa whoami-hpa
+   ```
+
+## Test the application
+
+1. Get the Ingress controller’s external IP:
+
+   ```bash
+   INGRESS_IP=$(kubectl get svc -n ingress-nginx ingress-nginx-controller -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
+   ```
+
+2. Test the application by sending an HTTP request (replace `whoami.example.com` with your domain or use the IP directly):
+
+   ```bash
+   curl -H "Host: whoami.example.com" http://$INGRESS_IP
+   ```
+
+3. Simulate load to trigger auto-scaling (optional):
+
+   ```bash
+   kubectl run -i --tty load-generator --image=busybox --restart=Never -- /bin/sh -c "while true; do wget -q -O- http://whoami-service.default.svc.cluster.local; done"
+   ```
+
+4. Open another terminal and monitor pod scaling:
+
+   ```bash
+   kubectl get pods -w
+   kubectl get hpa -w
+   ```
+
+## Clean up
+
+Delete the cluster to avoid unnecessary costs.
+
+1. Delete the cluster:
+
+   ```bash
+   scw k8s cluster delete $CLUSTER_ID
+   ```
+
+2. Confirm the cluster is deleted:
+
+   ```bash
+   scw k8s cluster list
+   ```
+
+## Conclusion
+
+This tutorial has guided you through the full lifecycle of a Kubernetes deployment, from creating a cluster and deploying an application to configuring ingress, enabling autoscaling, performing load testing, monitoring performance, and cleaning up resources. 
+You have completed the first steps to effectively manage cloud-native applications on Scaleway, with a focus on both manual resource control and automated scaling to build resilient, efficient, and scalable systems.
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