copilot review updates

Author: DougAnsonAustinTX

content/learning-paths/automotive/tinkerblox_ultraedge/_index.md

@@ -10,12 +10,10 @@ minutes_to_complete: 60
 who_is_this_for: Business teams targeting lower TCO of computing infrastructure through higher utilization of CPU and/or CPU-GPU install bases. R&D and Engineering teams looking for most efficient use of CPU/CPU-GPU infrastructure. Innovation teams looking to maximize edge resources to host new-age AI on constrained environments. Development teams looking at alternative packaging tech – run-time environments to build next generation workloads.
 
 learning_objectives:
-  -   Understand the layered architecture of UltraEdge  **core**, **boost**, and **prime**.
+  -   Understand the layered architecture of UltraEdge **core**, **boost**, and **prime**
   -   Build applications using the **UltraEdge MicroStack**
-  -   Deploy the MicroPacs on **Linux-based compute systems** and scale to
-      cloud or data-centre environments.
-  -   Optimize performance for **edge-cloud scenarios**, enabling near
-      real-time data flows.
+  -   Deploy the MicroPacs on **Linux-based compute systems** and scale to cloud or data-center environments
+  -   Optimize performance for **edge-cloud scenarios**, enabling near real-time data flows
 
 
 prerequisites:

content/learning-paths/automotive/tinkerblox_ultraedge/background.md

@@ -22,7 +22,7 @@ Together, these systems deliver up to **30× faster startup times** and **3.8×
 
 This learning path introduces the architecture, principles, and components that make UltraEdge a high-performance execution fabric for modern edge infrastructure. 
 
-### Ultraedge Overview
+### Ultraedge overview
 
 UltraEdge was built with the vision of **orchestrating the edge-native execution fabric for high-performance compute infrastructure**
 
@@ -60,40 +60,40 @@ Provides control-plane visibility, diagnostics, and telemetry for operational in
 
 Demonstrable reduction in CPU/GPU cluster costs through faster startup, higher utilization, and reduced runtime overhead. 
 
-### UltraEdge High-Level Architecture
+### UltraEdge high-level architecture
 
 UltraEdge is composed of layered systems, each responsible for a distinct aspect of execution and orchestration:
 
 ![High-level Architecture diagram](https://raw.githubusercontent.com/Tinkerbloxsupport/arm-learning-path-support/main/static/images/High-level%20architecture%20diagram.png)
 
 ---
 
-#### 1. UltraEdge Core Layer
+#### UltraEdge Core Layer
 *Manages the foundational execution fabric, including:*
 * Compute infrastructure management
 * Service orchestration and lifecycle management
 * Rule-engine orchestration
 * Data-flow management across workloads
 
-#### 2. UltraEdge Boost Layer
+#### UltraEdge Boost Layer
 *Provides performance-critical acceleration, including:*
 * Low-level optimized routines
 * FFI (Foreign Function Interface) integrations
 * Dynamic connectors and southbound protocol adapters
 
-#### 3. UltraEdge Prime Layer
+#### UltraEdge Prime Layer
 *Implements workload intelligence and orchestration logic, including:*
 * Business logic execution
 * Trigger and activation sequences
 * AI and mixed workload coordination
 
-#### 4. UltraEdge Dock
+#### UltraEdge Dock
 *Provides workload and cluster orchestration through:*
 * Kubernetes-based stacks
 * Slurm-based scheduling environments
 
-#### 5. UltraEdge Edge-Cloud Connect Layer
+#### UltraEdge Edge-Cloud Connect Layer
 *Enables data movement and observability, including:*
-* Data streaming to databases (e.g., InfluxDB, SQLite)
+* Data streaming to databases (for example, InfluxDB, SQLite)
 * Diagnostics, logging, and telemetry outputs
 

content/learning-paths/automotive/tinkerblox_ultraedge/debian_installation.md

@@ -6,7 +6,7 @@ weight: 5
 layout: "learningpathall"
 ---
 
-### Installation Process for UltraEdge on Ubuntu/Debian
+### Installation process for UltraEdge on Ubuntu/Debian
 
 Follow these steps to initialize and register your device within the **Uncloud** ecosystem:
 
@@ -25,17 +25,17 @@ Follow these steps to initialize and register your device within the **Uncloud**
 
 ![Initialize Device](https://raw.githubusercontent.com/Tinkerbloxsupport/arm-learning-path-support/main/static/images/Initialize%20.png)
 
-3. **Install some prerequsiites:**
+3. **Install prerequisites:**
 
-    * First install some prerequisites:
+    Install the required prerequisites:
 
 ```bash
 sudo apt update 
 sudo apt install -y curl jq
 ```
 
 
-4.  **Retrieve Installation Command Details:**
+4.  **Retrieve installation command details:**
 
     * Copy the generated device installation command or details from the **Uncloud** portal to your clipboard.
 
@@ -49,7 +49,7 @@ sudo DEVICE_ID="5b3ff290-0c88-4cd9-8ef7-08de0bded9df" KEY="TB.ApiKey-mlBZgDFc7qy
 
 Run your specific installer command in your Ubuntu/Debian SSH shell and initialize the agent to install UltraEdge.
 
-### Activation of the UltraEdge Agent
+### Activation of the UltraEdge agent
 
 On the first boot, the agent will automatically generate a file named
 `activation_key.json` at the path: 
@@ -74,7 +74,7 @@ receive license key (which includes license metadata).
         sudo systemctl start tbx-agent.service
 ```
 
-### Manual Running
+### Manual running
 
 -   Binary path: `/bin/tbx-agent`
 
@@ -87,37 +87,37 @@ receive license key (which includes license metadata).
 -   To stop, press <span class="kbd">Ctrl</span> +
     <span class="kbd">C</span> once.
 
-## MicroPac Installation
+## MicroPac installation
 
 MicroPac is the core tooling used to build and manage **MicroStack** (general microservices) and **NeuroStack** (AI-native services). 
 
-* **Platform Agnostic:** MicroPac is not restricted to a specific operating system; it is fully compatible with both **Debian** and **Yocto** environments, providing a consistent execution layer across different Linux distributions.
+* Platform agnostic: MicroPac is not restricted to a specific operating system; it is fully compatible with both **Debian** and **Yocto** environments, providing a consistent execution layer across different Linux distributions.
 
-* **Build System:** To create a service, the system utilizes a **MicroPacFile** (the declarative configuration) and the **MicroPac Builder** (the high-performance packaging engine).
+* Build system: To create a service, the system utilizes a **MicroPacFile** (the declarative configuration) and the **MicroPac Builder** (the high-performance packaging engine).
 
-* **Validation:** The ecosystem includes a **MicroPac Validator**, which verifies the integrity and security of the package created by the builder to ensure it is ready for edge deployment.
+* Validation: The ecosystem includes a **MicroPac Validator**, which verifies the integrity and security of the package created by the builder to ensure it is ready for edge deployment.
 
-#### System Requirements
+#### System requirements
 
 -   Linux host (aarch64)
 -   Sudo permissions
 -   Overlay filesystem support
 -   Internet connection
 
-#### Required Packages
+#### Required packages
 
 ```bash
     sudo apt-get update
     sudo apt-get install -y tar curl qemu-user-static binfmt-support
 ```
 
-### Cross-Architecture Support
+### Cross-architecture support
 
 The **MicroPacFile** is the central declarative configuration used by the builder to define the environment and behavior of your service. This configuration is essential for orchestrating both **MicroStack** (general microservices) and **NeuroStack** (AI/ML) services.
 
-* **Multi-Language Support:** You can configure MicroPacFiles for applications written in **Python, C, and C++**, making it highly versatile for both high-level AI workloads and low-level embedded system tasks.
+* Multi-language support: You can configure MicroPacFiles for applications written in **Python, C, and C++**, making it highly versatile for both high-level AI workloads and low-level embedded system tasks.
 
-* **Unified Workloads:** It bridges the gap between complex ML models and resource-constrained embedded software, ensuring consistent execution across diverse hardware. 
+* Unified workloads: It bridges the gap between complex ML models and resource-constrained embedded software, ensuring consistent execution across diverse hardware. 
 
 
 To build MicroPac for different architectures:
@@ -137,9 +137,9 @@ To build MicroPac for different architectures:
         sudo apt install ./<package_name>.deb
 ```
 
-### MicroPac File Schema file creation/setup
+### MicroPac file schema file creation/setup
 
-#### File Placement
+#### File placement
 For the MicroPac Builder to function correctly, the **MicroPacFile** must be placed in the root directory alongside your source code and dependency files. 
 
 
@@ -158,7 +158,7 @@ video_cv/
 
 Place a `MicroPacFile` in your project directory as mentioned in above example.
 
-```console
+```yaml
     name: nginx
     version: 1.0.0.0
     target: aarch64
@@ -196,9 +196,9 @@ Place a `MicroPacFile` in your project directory as mentioned in above example.
       mode: host
       name: nginx-net
 ```
-### Configuration Fields
+### Configuration fields
 
-#### Required Fields
+#### Required fields
 
 -   **name**: Application name (≤ 10 characters)
 -   **version**: Application version
@@ -208,7 +208,7 @@ Place a `MicroPacFile` in your project directory as mentioned in above example.
 -   **entry**: Entry point command
 -   **mode**: single-run
 
-#### Optional Fields
+#### Optional fields
 
 -   **env**: Environment variable
 -   **buildSteps**: Array of build instructions
@@ -218,7 +218,7 @@ Place a `MicroPacFile` in your project directory as mentioned in above example.
 -   **createdBy**: maintainer of the application
 -   **description**: description of the application
 
-### Building the MicroPac
+### Build the MicroPac
 
 Navigate to your project directory and execute:
 
@@ -228,7 +228,7 @@ Navigate to your project directory and execute:
 
 This generates a file named `<project_name>.mpac`.
 
-### Verifying the Micropac Setup
+### Verify the Micropac setup
 
 To confirm that the Micropac has been generated properly, follow these steps:
 

content/learning-paths/automotive/tinkerblox_ultraedge/instance.md

@@ -8,7 +8,7 @@ layout: learningpathall
 
 ## Overview
 
-In this section, you provision a Google Axion C4A virtual machine on Google Cloud Platform (GCP) using the `c4a-standard-32` machine type, which provides 16 vCPUs and 128 GB of memory. This size image will be used in the next steps to build our YOCTO based image. 
+In this section, you provision a Google Axion C4A virtual machine on Google Cloud Platform (GCP) using the `c4a-standard-32` machine type, which provides 16 vCPUs and 128 GB of memory. This VM size is required for building a Yocto-based image in the next steps. 
 
 {{% notice Note %}}
 For general guidance on setting up a Google Cloud account and project, see the Learning Path [Getting started with Google Cloud Platform](/learning-paths/servers-and-cloud-computing/csp/google/).
@@ -25,30 +25,30 @@ To create a virtual machine using the C4A instance type:
   - Set **Series** to **C4A**.
   - Select **c4a-standard-32** as the machine type.
 
-![Google Cloud Console VM creation page with the C4A machine series selected and the c4a-standard-32 machine type highlighted alt-text#center](images/gcp-vm.png "Creating a Google Axion C4A virtual machine in the Google Cloud Console")
+![Google Cloud Console VM creation page with the C4A machine series selected and the c4a-standard-32 machine type highlighted#center](images/gcp-vm.png "Creating a Google Axion C4A virtual machine in the Google Cloud Console")
 
 - Under **OS and storage**, select **Change**, and then choose an Arm64-based operating system image.
   - For this Learning Path, select **Ubuntu 22.04 LTS**.
   - Increase **Size (GB)** from **10** to **100** to allocate sufficient disk space.
   - Select **Choose** to apply the changes.
 
-![Selecting Ubuntu LTS 22.04 with 100GB disk space highlighted alt-text#center](images/gcp-ubuntu.png "Selecting Ubuntu 22.04 LTS with 100GB disk space")
+![Selecting Ubuntu LTS 22.04 with 100GB disk space highlighted#center](images/gcp-ubuntu.png "Selecting Ubuntu 22.04 LTS with 100GB disk space")
 
 - Under **Networking**, enable **Allow HTTP traffic** and **Allow HTTPS traffic** to simplify access for later Kubernetes testing.
 - Select **Create** to launch the virtual machine.
 
-![Configuring the required networking settings alt-text#center](images/gcp-network.png "Configuring the VM network")
+![Configuring the required networking settings#center](images/gcp-network.png "Configuring the VM network")
 
 After the instance starts, click **SSH** next to the VM in the instance list to open a browser-based terminal session.
 
-![Google Cloud Console VM instances list with the SSH button highlighted for a running C4A instance alt-text#center](images/gcp-ssh.png "Connecting to a running C4A virtual machine using SSH")
+![Google Cloud Console VM instances list with the SSH button highlighted for a running C4A instance#center](images/gcp-ssh.png "Connecting to a running C4A virtual machine using SSH")
 
 A new browser window opens with a terminal connected to your virtual machine.
 
-![Browser-based terminal window showing a command prompt on a SUSE Linux VM running on Google Axion C4A alt-text#center](images/gcp-shell.png "Terminal session connected to the virtual machine")
+![Browser-based terminal window showing a command prompt on an Ubuntu Linux VM running on Google Axion C4A#center](images/gcp-shell.png "Terminal session connected to the virtual machine")
 
 {{% notice Note %}}
 This is a rather expensive VM given its vCPU and memory usage. It is highly recommended to delete this VM once you have completed this learning path!
 {{% /notice %}}
 
-Next, lets build our Yocto image on this VM instance.
+Next, build the Yocto image on this VM instance.

content/learning-paths/automotive/tinkerblox_ultraedge/running.md

@@ -13,7 +13,7 @@ To practice the commands below, install and run a sample microservice on your de
 
 https://github.com/Tinkerbloxsupport/arm-learning-path-support/blob/main/static/Sample_ms/dte_nn.mpac
 
-### Tinkerblox CLI Usage Guide
+### Tinkerblox CLI usage guide
 
 Tinkerblox Command Line Interface for managing the Edge Agent and
 microservices.
@@ -35,23 +35,23 @@ microservices.
 
 ### Usage
 
-#### Check CLI Connection Status
+#### Check CLI connection status
 
 ```bash
     sudo tinkerblox-cli status
 ```
 
 *Displays whether the CLI is connected to the Edge Agent.*
 
-#### Microservice Management
+#### Microservice management
 
 Manage microservices running on the Edge platform.
 
 **Syntax:**
 
         sudo tinkerblox-cli microboost <command> [options]
 
-##### Available Commands
+##### Available commands
 
 * **install**
     Installs a microservice. You must provide the path to the MPAC file as an argument.
@@ -83,7 +83,7 @@ Manage microservices running on the Edge platform.
 
         sudo tinkerblox-cli microboost uninstall <id>
 
-#### Diagnostics Management
+#### Diagnostics management
 
 Run diagnostics on the Edge platform.
 
@@ -115,21 +115,21 @@ Run diagnostics on the Edge platform.
 
  The following sections describe common errors you may encounter while running CLI commands or deploying microservices, along with guidance on how to resolve them. These are general errors and are not limited to any specific scenario; they may occur while executing commands or managing the system.
 
-**Permission Denied**
+Permission denied
 
 -   Ensure `sudo` privileges.
 -   Check directory ownership and permissions.
 -   Verify overlay filesystem support.
 
-**Directory Creation Failed**
+Directory creation failed
 
 -   Check disk space.
 -   Verify parent directory permissions.
 -   Ensure the path is valid.
 
-**Cross-Architecture Build Issues**
+Cross-architecture build issues
 
-Cross-architecture issues typically occur when binaries are built for a different CPU architecture than the host system.They are not related to yocto build issue. 
+Cross-architecture issues typically occur when binaries are built for a different CPU architecture than the host system. They are not related to yocto build issues. 
 
 -   Verify QEMU installation: