ArmDeveloperEcosystem · pareenaverma · Jan 22, 2025 · Jan 13, 2025 · Jan 21, 2025 · Jan 21, 2025
diff --git a/.github/workflows/production.yml b/.github/workflows/production.yml
@@ -22,3 +22,4 @@ jobs:
     secrets:
       AWS_OIDC_ROLE: ${{ secrets.AWS_OIDC_ROLE_PRODUCTION }}
       HUGO_LLM_API: ${{ secrets.HUGO_LLM_API }}
+      HUGO_RAG_API: ${{ secrets.HUGO_RAG_API }}
diff --git a/assets/events.csv b/assets/events.csv
@@ -10,6 +10,6 @@ SUSECon,2,2025-03-10,2025-03-14,Orlando,United States,FALSE,Example description.
 Embedded World,1,2025-03-11,2025-03-13,Nuremburg,Germany,FALSE,"Embedded World offers insight into the world of embedded systems, from components and modules to operating systems, hardware and software design, M2M communication, and more.",https://www.embedded-world.de/en,Embedded and Microcontrollers; Automotive
 FOSSAsia,2,2025-03-13,2025-03-15,Bangkok,Thailand,TRUE,Example description. Example description. Example description. Example description. Example description.,https://events.fossasia.org/,Servers and Cloud Computing; AI; IoT
 NVIDIA GTC,1,2025-03-17,2025-03-21,San Jose,United States,TRUE,"Nvidia GTC is a global artificial intelligence conference for developers that brings together developers, engineers, researchers, inventors, and IT professionals. ",https://www.nvidia.com/gtc/,ML
-GDC,1,2025-03-17,2025-03-21,San Fransisco,United States,FALSE,"The Game Developers Conference (GDC) is the worldÕs premier event for developers who make the games we love. GDC is the destination for creativity, innovation, and excellence.",https://gdconf.com/,"Mobile, Graphics, and Gaming"
+GDC,1,2025-03-17,2025-03-21,San Fransisco,United States,FALSE,"The Game Developers Conference (GDC) is the world's premier event for developers who make the games we love. GDC is the destination for creativity, innovation, and excellence.",https://gdconf.com/,"Mobile, Graphics, and Gaming"
 ATO AI,2,2025-03-17,2025-03-18,Durham,United States,,Example description. Example description. Example description. Example description. Example description.,https://allthingsopen.ai/,AI
 KubeCon EU,2,2025-04-01,2025-04-04,London ,United Kingdom,TRUE,Example description. Example description. Example description. Example description. Example description.,https://events.linuxfoundation.org/kubecon-cloudnativecon-europe/,Servers and Cloud Computing
diff --git a/content/learning-paths/cross-platform/daytona/_review.md b/content/learning-paths/cross-platform/daytona/_review.md
diff --git a/content/learning-paths/cross-platform/github-arm-runners/_images/lscpu.png b/content/learning-paths/cross-platform/github-arm-runners/_images/lscpu.png
diff --git a/content/learning-paths/cross-platform/github-arm-runners/_index.md b/content/learning-paths/cross-platform/github-arm-runners/_index.md
@@ -10,14 +10,14 @@ learning_objectives:
     - Use GitHub Actions to automate image builds.
 
 prerequisites:
-    - A GitHub account with a Team or Enterprise Cloud plan.
+    - A GitHub account (a Team or Enterprise Cloud plan is required for private repositories).
     - A Docker Hub account.
 
 author_primary: Jason Andrews
 
 ### Tags
 skilllevels: Introductory
-subjects: Containers and Virtualization
+subjects: CI-CD
 armips:
     - Neoverse
 operatingsystems:

diff --git a/content/learning-paths/cross-platform/github-arm-runners/_next-steps.md b/content/learning-paths/cross-platform/github-arm-runners/_next-steps.md
@@ -17,14 +17,14 @@ recommended_path: "/learning-paths/cross-platform/docker-build-cloud/"
     # General online references                 (type: website) 
 
 further_reading:
+    - resource:
+        title: Linux arm64 hosted runners now available for free in public repositories
+        link: https://github.blog/changelog/2025-01-16-linux-arm64-hosted-runners-now-available-for-free-in-public-repositories-public-preview/
+        type: documentation
     - resource:
         title: Using GitHub-hosted runners
         link: https://docs.github.com/en/actions/using-github-hosted-runners
         type: documentation
-    - resource:
-        title: Supercharge your CI/CD with Arm Runners in GitHub Actions
-        link: https://www.youtube.com/watch?v=vrr_OgMk458
-        type: video
     - resource:
         title: Arm64 on GitHub Actions Powering faster, more efficient build systems
         link: https://github.blog/2024-06-03-arm64-on-github-actions-powering-faster-more-efficient-build-systems/

diff --git a/content/learning-paths/cross-platform/github-arm-runners/actions.md b/content/learning-paths/cross-platform/github-arm-runners/actions.md
@@ -1,14 +1,14 @@
 ---
 title: "Run GitHub Actions jobs on the Arm-hosted runner"
 
-weight: 3
+weight: 5
 
 layout: "learningpathall"
 ---
 
 ## Use GitHub Actions
 
-You can use GitHub Actions to build multi-architecture images by using your new Arm-hosted runner alongside a standard runner by creating a workflow file.
+You can use GitHub Actions to build multi-architecture images by creating a workflow file and using Arm-hosted runners.
 
 ## Create a new GitHub repository
 

diff --git a/content/learning-paths/cross-platform/github-arm-runners/intro.md b/content/learning-paths/cross-platform/github-arm-runners/intro.md
@@ -0,0 +1,27 @@
+---
+title: "Build options for multi-architecture container images"
+
+weight: 2
+
+layout: "learningpathall"
+---
+
+## How can I build multi-architecture container images?
+
+Building multi-architecture container images for complex projects is challenging.
+
+There are two common ways to build multi-architecture images, and both are explained in [Learn how to use Docker](/learning-paths/cross-platform/docker/).
+
+### Use instruction emulation
+
+The first method uses instruction emulation. You can learn about this method in [Build multi-architecture images with Docker buildx](/learning-paths/cross-platform/docker/buildx/). The drawback of emulation is slow performance, especially for complex builds which involve tasks such as compiling large C++ applications. 
+
+### Use a manifest and multiple computers
+
+The second method uses multiple computers, one for each architecture, and joins the images to create a multi-architecture image using Docker manifest. You can learn about this method in [Use Docker manifest to create multi-architecture images](/learning-paths/cross-platform/docker/manifest/). The drawback of the manifest method is its complexity as it requires multiple systems.
+
+### Arm-hosted runners
+
+Arm-hosted runners from GitHub provide a way to create multi-architecture images with higher performance and lower complexity compared to the two methods described above. 
+
+When you use Arm-hosted runners, you don't need to worry about self-hosting (managing servers) or instruction emulation (slow performance).
diff --git a/content/learning-paths/cross-platform/github-arm-runners/public-repos.md b/content/learning-paths/cross-platform/github-arm-runners/public-repos.md
@@ -0,0 +1,50 @@
+---
+title: "Arm-hosted runners for public repositories"
+
+weight: 3
+
+layout: "learningpathall"
+---
+
+## What are Arm-hosted runners?
+
+Runners are the machines that execute jobs in a GitHub Actions workflow. An Arm-hosted runner is a runner that is managed by GitHub and uses the Arm architecture. This means that you don't need to provide a server to run Actions workflows. GitHub provides the system and runs the Action workflows for you.
+
+Arm-hosted runners are available for public and private repositories. 
+
+If you have a free plan, Arm-hosted runners are available in public repositories at no cost, subject to standard usage limits. 
+
+You can use Arm-hosted runners in private repositories with a Teams or Enterprise Cloud account (covered on the next page).
+
+## What kind of server hardware is used by Arm-hosted runners?
+
+You may have software that relies on Arm architecture features. Arm-hosted runners are powered by Cobalt 100 processors, based on the Arm Neoverse N2. The free runners have 4 vCPUs and Armv9-A features including Scalable Vector Extension 2 (SVE2). 
+
+The output of the `lscpu` command is below.
+
+![Arm-hosted runner info #center](_images/lscpu.png)
+
+## What do I need to change in my workflow to use Arm-hosted runners?
+
+To use Arm-hosted Linux runners, use the `ubuntu-22.04-arm` and `ubuntu-24.04-arm` labels in your public repository workflow runs.
+
+For example, if you have a workflow file with:
+
+```console
+runs-on: ubuntu-24.04
+```
+
+You can replace it with the Arm-hosted runner:
+
+```console
+runs-on: ubuntu-24.04-arm
+```
+
+## How can I find out more about the software installed on the Arm-hosted runners?
+
+You can look at the [GitHub Actions Partner Images repository](https://github.com/actions/partner-runner-images/) for information about the runner images and installed software.
+
+You can also use the repository to report issues or request additional software be added to the images. 
+
+
+
diff --git a/content/learning-paths/cross-platform/github-arm-runners/runner.md b/content/learning-paths/cross-platform/github-arm-runners/runner.md
@@ -1,36 +1,16 @@
 ---
-title: "Create a new Arm-hosted runner"
+title: "Create a new Arm-hosted runner for private repositories"
 
-weight: 2
+weight: 4
 
 layout: "learningpathall"
 ---
 
-## How can I build multi-architecture container images?
+## Can I use Arm-hosted runners for private repositories?
 
-Building multi-architecture container images for complex projects is challenging.
+Yes, you can use Arm-hosted runners in private repositories.
 
-There are two common ways to build multi-architecture images, and both are explained in [Learn how to use Docker](/learning-paths/cross-platform/docker/).
-
-The first method uses instruction emulation. You can learn about this method in [Build multi-architecture images with Docker buildx](/learning-paths/cross-platform/docker/buildx/). The drawback of emulation is slow performance, especially for complex builds which involve tasks such as compiling large C++ applications. 
-
-The second method uses multiple machines, one for each architecture, and joins the images to create a multi-architecture image using Docker manifest. You can learn about this method in [Use Docker manifest to create multi-architecture images](/learning-paths/cross-platform/docker/manifest/). The drawback of the manifest method is its complexity as it requires multiple machines.
-
-Arm-hosted runners from GitHub provide a way to create multi-architecture images with higher performance and lower complexity compared to the two methods described above. 
-
-When you use Arm-hosted runners, you don't need to worry about self-hosting (managing servers) or instruction emulation (slow performance).
-
-## What are Arm-hosted runners?
-
-Runners are the machines that execute the jobs in a GitHub Actions workflow. An Arm-hosted runner is a runner that is managed by GitHub and uses the Arm architecture. This means that you don't need to provide a server to run Actions workflows. GitHub provides the system and runs the Action workflows for you.
-
-Arm-hosted runners are available for Linux and Windows. 
-
-This Learning Path uses Linux. 
-
-{{% notice Note %}}
 You must have a Team or Enterprise Cloud plan to use Arm-hosted runners.
-{{% /notice %}}
 
 Two types of GitHub-hosted runners are available; standard runners, and larger runners. Larger runners are differentiated from standard runners because users can control the amount of RAM, the number of CPUs, and configure the allocated disk space. Larger runners have additional options for a static IP address and the ability to group runners and control settings across the runner group. Currently, Arm-hosted runners are a type of larger runner.
 

diff --git a/content/learning-paths/laptops-and-desktops/windows_armpl/_review.md b/content/learning-paths/laptops-and-desktops/windows_armpl/_review.md
diff --git a/...ning-paths/servers-and-cloud-computing/cca-veraison/attestation-verification.md b/...ning-paths/servers-and-cloud-computing/cca-veraison/attestation-verification.md
@@ -11,12 +11,12 @@ layout: learningpathall
 
 Linaroâs verification service is implemented using components from the open source [Veraison](https://github.com/veraison) project.
 
-The URL for reaching this experimental verifier service is http://veraison.test.linaro.org:8080.
+The URL for reaching this experimental verifier service is https://veraison.test.linaro.org:8443.
 
 To check that you can reach the Linaro attestation verifier service, run the following command:
 
 ```bash
-curl http://veraison.test.linaro.org:8080/.well-known/veraison/verification
+curl https://veraison.test.linaro.org:8443/.well-known/veraison/verification
 ```
 
 This is a simple call to query the well-known characteristics of the verification service. If it succeeds, it will return a JSON response that looks something like this:
@@ -67,15 +67,15 @@ The easiest way to do this is to use the `jq` utility.
 You can save the public key by repeating the curl command from the previous step and use `jq` to filter the response down to just the public key part. Save it into a file called `pkey.json`:
 
 ```bash
-curl -s -N http://veraison.test.linaro.org:8080/.well-known/veraison/verification | jq '."ear-verification-key"' > $HOME/pkey.json
+curl -s -N https://veraison.test.linaro.org:8443/.well-known/veraison/verification | jq '."ear-verification-key"' > $HOME/pkey.json
 ```
 You have now saved the public key of the verification service. You are now ready to submit the CCA example attestation token to the service and get an attestation result.
 
 ## Submit the CCA Example Token to the Verification Service
 To submit the example CCA attestation token to the verification service, you will need to use the `evcli` tool once again. First, configure the correct API endpoint for the Linaro verifier service:
 
 ```bash
-export API_SERVER=http://veraison.test.linaro.org:8080/challenge-response/v1/newSession
+export API_SERVER=https://veraison.test.linaro.org:8443/challenge-response/v1/newSession
 ```
 
 Now submit the token using the following command. The output of this command is an attestation result, which will be saved in a file called `attestation_result.jwt`:

diff --git a/content/learning-paths/servers-and-cloud-computing/gh-runners/train-test.md b/content/learning-paths/servers-and-cloud-computing/gh-runners/train-test.md
@@ -17,7 +17,7 @@ https://github.com/Arm-Labs/gh_armrunner_mlops_gtsrb
 ```
 Fork the repository, using the **Fork** button:
 
-![#fork](/images/fork.png)
+![#fork](images/fork.png)
 
 Create a fork within a GitHub Organization or Team where you have access to Arm-hosted GitHub runners. 
 

diff --git a/content/learning-paths/servers-and-cloud-computing/gh-runners/workflows.md b/content/learning-paths/servers-and-cloud-computing/gh-runners/workflows.md
@@ -58,13 +58,13 @@ Navigate to the **Train Model** workflow under the `Actions` tab.
 
 Press the `Run workflow` button and run the workflow on the main branch. 
 
-![Train_workflow](/images/train_run.png)
+![Train_workflow](images/train_run.png)
 
 The workflow starts running. It takes about 8 minutes to complete. 
 
 Click on the workflow to see the output from each step of the workflow. 
 
-![Actions_train](/images/actions_train.png)
+![Actions_train](images/actions_train.png)
 
 Expand on the `Run training script` step to see the training loss per epoch followed by `Finished Training`.
 
@@ -86,7 +86,7 @@ Finished Training
 
 Confirm the model is generated and saved as an artifact in the job's overview.
 
-![#artifact](/images/artifact.png)
+![#artifact](images/artifact.png)
 
 This trained model artifact is used in the next step. 
 
@@ -143,7 +143,7 @@ Complete the steps below to modify the testing workflow file:
 
 4. Copy the 11-digit ID number from the end of the URL in your browser address bar.
 
-![#run-id](/images/run-id.png)
+![#run-id](images/run-id.png)
 
 5. Navigate back to the **Code** tab and open the file `.github/workflows/test-model.yml`. 
 
@@ -168,7 +168,7 @@ The workflow starts running.
 
 Click on the workflow to view the output from each step.
 
-![Actions_test](/images/actions_test.png)
+![Actions_test](images/actions_test.png)
 
 Click on the **Run testing script** step to see the accuracy of the model and a table of the results from the PyTorch profiler. 
 

diff --git a/content/learning-paths/servers-and-cloud-computing/intro/_next-steps.md b/content/learning-paths/servers-and-cloud-computing/intro/_next-steps.md
@@ -16,8 +16,8 @@ recommended_path: "/learning-paths/servers-and-cloud-computing/csp/"
 
 further_reading:
     - resource:
-        title: Where to Buy (Ampere Computing)
-        link: https://amperecomputing.com/where-to-buy
+        title: Ampere Computing
+        link: https://amperecomputing.com/developers/
         type: website
 
 # ================================================================================

diff --git a/content/learning-paths/servers-and-cloud-computing/rag/_demo.md b/content/learning-paths/servers-and-cloud-computing/rag/_demo.md
@@ -2,7 +2,7 @@
 title: Run a llama.cpp chatbot powered by Arm Kleidi technology
 
 overview: | 
-  This Arm learning path shows how to use a single c4a-standard-64 Google Axion instance -- powered by an Arm Neoverse CPU -- to build a simple "Token as a Service" RAG-enabled server, used below to provide a chatbot to serve a small number of concurrent users.
+  This Arm learning path shows how to use a single c4a-highcpu-72 Google Axion instance -- powered by an Arm Neoverse CPU -- to build a simple "Token as a Service" RAG-enabled server, used below to provide a chatbot to serve a small number of concurrent users.
 
   This architecture would be suitable for businesses looking to deploy the latest Generative AI technologies with RAG capabilities using their existing CPU compute capacity and deployment pipelines. It enables semantic search over chunked documents using FAISS vector store. The demo uses the open source llama.cpp framework, which Arm has enhanced by contributing the latest Arm Kleidi technologies. Further optimizations are achieved by using the smaller 8 billion parameter Llama 3.1 model, which has been quantized to optimize memory usage.