Add descriptions to various learning paths for clarity and context

madeline-underwood · madeline-underwood · commit 4cd969aaa851 · 2026-04-15T06:16:35.000+01:00
- Updated the description for the "Install Ubuntu on ChromeOS Crostini as an LXC container" learning path.
- Enhanced the "Build Robot Simulation and Reinforcement Learning Workflows with Isaac Sim and Isaac Lab on DGX Spark" path with a detailed description.
- Added a description for the "Unlock quantized LLM performance on Arm-based NVIDIA DGX Spark" learning path.
- Included a description for the "Build a RAG pipeline on Arm-based NVIDIA DGX Spark" learning path.
- Updated the "Build an offline voice chatbot with faster-whisper and vLLM on DGX Spark" path with a new description.
- Added a description for the "Run AI models with Docker Model Runner" learning path.
- Enhanced the "Develop cross-platform desktop applications with Electron on Windows on Arm" path with a description.
- Updated the "Automate Windows on Arm Builds with GitHub Arm-hosted Runners" learning path with a new description.
- Added a description for the "Create Linux virtual machines with Hyper-V" learning path.
- Enhanced the "Get started with Laptops and Desktops" path with a new description.
- Updated the "Build and test KleidiCV on macOS" learning path with a description.
- Added a description for the "Build a Windows on Arm native application with clang" learning path.
- Enhanced the "Adding Memory Tagging to a Dynamic Memory Allocator" path with a new description.
- Updated the "Install Arch Linux with the i3 window manager on a Pinebook Pro" learning path with a description.
- Added a description for the "Fine-tune PyTorch models on DGX Spark" learning path.
- Enhanced the "Use Self-Hosted Arm64-based runners in GitHub Actions for CI/CD" path with a new description.
- Updated the "Create OpenCV applications on Windows on Arm" learning path with a description.
- Enhanced the "Measure application resource and power usage on Windows on Arm with FFmpeg and PowerShell" path with a new description.
- Updated the "Automate Windows on Arm virtual machine deployment with QEMU and KVM on Arm Linux" learning path with a description.
- Added a description for the "Use Arm64EC with Windows 11 on Arm" learning path.
- Enhanced the "Port Applications to Arm64 using Arm64EC" path with a new description.
- Updated the "Build and run a native Windows on Arm Qt application" learning path with a description.
- Enhanced the "Run ASP.NET Core Web Server on Arm64" path with a new description.
- Updated various markdown files for clarity and consistency in language.
diff --git a/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md b/content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Install Ubuntu on ChromeOS Crostini as an LXC container
 
+description: Learn how to create and run Ubuntu containers on ChromeOS Crostini using LXC with file sharing and GUI application support on Arm-based Chromebooks.
+
 minutes_to_complete: 60
 
 who_is_this_for: This Learning Path is for software developers who want to install Ubuntu and other Linux distributions on their Arm-based Chromebook with ChromeOS file sharing and GUI support.
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Build Robot Simulation and Reinforcement Learning Workflows with Isaac Sim and Isaac Lab on DGX Spark
 
+description: Learn how to build and deploy high-fidelity robotic simulations and reinforcement learning pipelines using Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark with Grace-Blackwell architecture.
+
 minutes_to_complete: 90
 
 who_is_this_for: This is an advanced topic for robotics developers, simulation engineers, and AI researchers who want to run high-fidelity robotic simulations and reinforcement learning (RL) pipelines using NVIDIA Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark system powered by the Grace–Blackwell (GB10) architecture.
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Unlock quantized LLM performance on Arm-based NVIDIA DGX Spark
 
+description: Learn how to build and optimize quantized LLMs using llama.cpp on NVIDIA DGX Spark with Grace-Blackwell architecture, leveraging Armv9 SIMD acceleration.
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for AI practitioners, performance engineers, and system architects who want to learn how to deploy and optimize quantized large language models (LLMs) on NVIDIA DGX Spark systems powered by the Grace-Blackwell (GB10) architecture.
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md
@@ -1,5 +1,8 @@
 ---
 title: Build a RAG pipeline on Arm-based NVIDIA DGX Spark
+
+description: Learn how to build a Retrieval-Augmented Generation (RAG) pipeline on NVIDIA DGX Spark combining Arm Grace CPU orchestration with Blackwell GPU-accelerated inference using llama.cpp.
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an advanced topic for developers who want to build a Retrieval-Augmented Generation (RAG) pipeline on the NVIDIA DGX Spark platform. You'll learn how Arm-based Grace CPUs handle document retrieval and orchestration, while Blackwell GPUs speed up large language model inference using the open-source llama.cpp REST server. This is a great fit if you're interested in combining Arm CPU management with GPU-accelerated AI workloads.
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/3_fasterwhisper.md b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/3_fasterwhisper.md
@@ -248,7 +248,7 @@ This demo shows the real-time transcription pipeline in action, running on an Ar
 
 Notice the clean terminal output and low latency, demonstrating how the pipeline is optimized for local, real-time voice recognition on resource-efficient hardware.
 
-![Real-time speech transcription demo with volume visualization alt-txt#center](fasterwhipser_demo1.gif "Real-time speech transcription with audio volume bar")
+![Animated terminal session demonstrating real-time speech-to-text transcription on DGX Spark. The terminal displays timestamped transcribed text as someone speaks into a USB microphone, with a volume visualization bar at the bottom showing live audio input levels.#center](fasterwhipser_demo1.gif "Real-time speech transcription with audio volume bar")
 
 The device runs audio capture and transcription in parallel. Use `threading.Thread` to collect audio without blocking, store audio frames in a `queue.Queue`, and in the main thread, poll for new data and run STT.
 
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/6_chatbot_contextaware.md b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/6_chatbot_contextaware.md
@@ -304,7 +304,7 @@ Once both the STT and LLM services are live, you'll be able to speak naturally a
 
 ### Demo: Multi-turn voice chatbot with context memory on DGX Spark
 
-![Animated terminal session showing real-time speech-to-text transcription and AI responses in a multi-turn customer service conversation, with a volume bar at the bottom indicating live audio input levels from a microphone alt-txt#center](fasterwhipser_vllm_demo2.gif "Full function voice-to-AI with volume bar")
+![Animated terminal showing an offline voice assistant in action on DGX Spark. The session displays multi-turn customer service conversation with real-time speech transcription and AI responses. A volume bar at the bottom indicates live microphone input levels throughout the interaction.#center](fasterwhipser_vllm_demo2.gif "Full function voice-to-AI with volume bar")
 
 This demo showcases a fully offline voice assistant that combines real-time transcription (via faster-whisper) and intelligent response generation (via vLLM). Running on an Arm-based DGX Spark system, the assistant captures live audio, transcribes it, and generates context-aware replies using a local language model, all in a seamless loop.
 
diff --git a/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md b/content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Build an offline voice chatbot with faster-whisper and vLLM on DGX Spark
 
+description: Learn how to build an offline voice assistant combining speech-to-text via faster-whisper and text generation via vLLM on Arm-based DGX Spark for privacy-focused deployments.
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an advanced topic for developers and ML engineers who want to build private, offline voice assistant systems on Arm-based servers such as DGX Spark.
diff --git a/content/learning-paths/laptops-and-desktops/docker-models/_index.md b/content/learning-paths/laptops-and-desktops/docker-models/_index.md
@@ -1,6 +1,7 @@
 ---
 title: Run AI models with Docker Model Runner
 
+description: Learn how to run pre-trained AI models locally using Docker Model Runner and build containerized applications integrating large language models.
 
 minutes_to_complete: 45
 
diff --git a/content/learning-paths/laptops-and-desktops/electron/_index.md b/content/learning-paths/laptops-and-desktops/electron/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Develop cross-platform desktop applications with Electron on Windows on Arm
 
+description: Learn how to develop and build cross-platform desktop applications using the Electron Framework on Windows on Arm devices.
+
 minutes_to_complete: 30
 
 who_is_this_for: This learning path is for developers who want to learn how to develop cross-platform desktop applications using the Electron Framework on Windows on Arm (WoA).
diff --git a/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md b/content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Automate Windows on Arm Builds with GitHub Arm-hosted Runners
 
+description: Learn how to automate Windows application builds on Arm architecture using GitHub Arm-hosted runners and GitHub Actions workflows.
+
 minutes_to_complete: 20
 
 who_is_this_for: This introductory tutorial is for software developers looking to automate Windows application builds on Arm architecture using GitHub Actions.
diff --git a/content/learning-paths/laptops-and-desktops/hyper-v/_index.md b/content/learning-paths/laptops-and-desktops/hyper-v/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Create Linux virtual machines with Hyper-V
 
+description: Learn how to create and manage Arm-based Linux virtual machines using Hyper-V on Windows on Arm devices.
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for software developers who want to use Linux virtual machines with Windows on Arm devices. 
diff --git a/content/learning-paths/laptops-and-desktops/intro/_index.md b/content/learning-paths/laptops-and-desktops/intro/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Get started with Laptops and Desktops
 
+description: Learn where the Arm architecture is used in desktop and laptop computers and find hardware for software development on Arm platforms.
+
 minutes_to_complete: 10
 
 who_is_this_for: This is an introductory topic for developers working on laptops and desktops and new to the Arm architecture.
diff --git a/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md b/content/learning-paths/laptops-and-desktops/kleidicv-on-mac/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Build and test KleidiCV on macOS
 
+description: Learn how to build, test, and verify KleidiCV with Scalable Matrix Extensions (SME) on Apple Silicon Macs for accelerated computer vision performance.
+
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for software developers who want to build and test KleidiCV on macOS.
diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md b/content/learning-paths/laptops-and-desktops/llvm_putty/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Build a Windows on Arm native application with clang
 
+description: Learn how to configure the LLVM toolchain with Visual Studio to build native Windows on Arm applications using the open-source PuTTY project.
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for software developers doing native development on Windows on Arm computers.
diff --git a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Adding Memory Tagging to a Dynamic Memory Allocator
 
+description: Learn how to apply Arm Memory Tagging Extension (MTE) to protect dynamic memory allocations and prevent common memory use errors.
+
 minutes_to_complete: 120
 
 who_is_this_for: This is an advanced topic for software developers who want to learn how to use the Memory Tagging Extension (MTE) to protect dynamic memory allocations.
diff --git a/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/how-to-3.md b/content/learning-paths/laptops-and-desktops/memory-tagged-dynamic-memory-allocator/how-to-3.md
@@ -103,8 +103,8 @@ different tags to each allocation (and assign tag `0` to free space).
 
 ### Accessing Range Headers
 
-The ranges have headers that are stored in tagged memory. This means we cannot simply implement a pointer that pointed to one
-range and expect it to be able to read from a subsequent range.
+The ranges have headers that are stored in tagged memory. This means a pointer that pointed to one
+range cannot be used to read from a subsequent range.
 
 For example, if we have done one allocation, the ranges might be:
 ```text
diff --git a/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md b/content/learning-paths/laptops-and-desktops/pinebook-pro/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Install Arch Linux with the i3 window manager on a Pinebook Pro
 
+description: Learn how to install and configure Arch Linux for Arm with the i3 window manager and Neovim editor on the Pinebook Pro laptop.
+
 minutes_to_complete: 120 
 
 who_is_this_for: This is an advanced topic for developers who want to use the Pinebook Pro as an Arm Linux development machine. 
diff --git a/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md b/content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Fine-tune PyTorch models on DGX Spark
 
+description: Learn how to fine-tune large language models using PyTorch and Hugging Face on NVIDIA DGX Spark to improve domain-specific accuracy.
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an advanced topic for AI developers and ML engineers who want to fine-tune large language models using PyTorch and Hugging Face on the NVIDIA DGX Spark platform.
diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Use Self-Hosted Arm64-based runners in GitHub Actions for CI/CD
 
+description: Learn how to create a CI/CD pipeline in GitHub using self-hosted Arm64 runners to build and push Docker images to DockerHub.
+
 minutes_to_complete: 20
 
 who_is_this_for: This Learning Path is for software developers and IT practitioners who want to learn how to use GitHub Actions for CI/CD purposes.
diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/how-to-1.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/how-to-1.md
@@ -14,7 +14,7 @@ Before completing this Learning Path you can complete the Hello World [example](
 
 You will extend that knowledge here with a comprehensive set of operations critical for real-world application deployment:
 
-1. *Environment Set up*: There are detailed steps on preparing the self-hosted runner with the necessary tools such as .NET SDK and Docker, which are essential for building real applications, not just running simple scripts.
+1. *Environment Set up*: There are detailed steps on preparing the self-hosted runner with the necessary tools such as .NET SDK and Docker, which are essential for building real applications, not running simple scripts.
  
 2. *Complex Workflow Configuration*: This Learning Path demonstrates how to use GitHub Actions to manage complex workflows involving multiple jobs and steps, along with job dependencies, which are crucial for sophisticated CI/CD pipelines.
 
diff --git a/content/learning-paths/laptops-and-desktops/win-opencv/_index.md b/content/learning-paths/laptops-and-desktops/win-opencv/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Create OpenCV applications on Windows on Arm
 
+description: Learn how to build the OpenCV library for Windows on Arm devices and develop computer vision applications using OpenCV.
+
 minutes_to_complete: 90
 
 who_is_this_for: This is an advanced topic for software developers who want to build and develop applications on Windows on Arm devices using OpenCV.
diff --git a/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md b/content/learning-paths/laptops-and-desktops/win-resource-ps1/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Measure application resource and power usage on Windows on Arm with FFmpeg and PowerShell
 
+description: Learn how to measure application resource usage, benchmark video encoding tasks, and monitor CPU, memory, and power consumption on Windows on Arm using FFmpeg and PowerShell.
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for developers who want to measure resource usage of applications on Windows on Arm devices using FFmpeg.
diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md b/content/learning-paths/laptops-and-desktops/win11-vm-automation/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Automate Windows on Arm virtual machine deployment with QEMU and KVM on Arm Linux
 
+description: Learn how to automate Windows on Arm VM creation on Arm Linux systems using QEMU, KVM, and Bash scripts for development and testing.
+
 minutes_to_complete: 90
 
 who_is_this_for: This is an introductory topic for developers and system administrators who want to automate Windows on Arm virtual machine (VM) creation on Arm Linux systems using QEMU and KVM.
diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/understanding-scripts-2.md b/content/learning-paths/laptops-and-desktops/win11-vm-automation/understanding-scripts-2.md
@@ -34,7 +34,7 @@ This modular approach allows you to understand each component while maintaining
 
 ## Virtual machine creation
 
-The creation script, `create-win11-vm.sh` is responsible for building a complete Windows 11 on Arm VM from scratch. It handles everything from directory setup to Windows installation, with each step clearly defined and independently executable.
+The creation script, `create-win11-vm.sh` is responsible for building a complete Windows 11 on Arm VM from scratch. It handles everything from directory setup to Windows installation, with each step defined and independently executable.
 
 The script handles resource detection and allocation, provides unattended Windows installation, and has a flexible command line to change default values.
 
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Use Arm64EC with Windows 11 on Arm
 
+description: Learn how to build native Arm applications and migrate x86/x64 applications to Arm using Arm64EC on Windows on Arm devices.
+
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for software developers who want to use Arm64EC with Windows on Arm devices. 
diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Port Applications to Arm64 using Arm64EC
 
+description: Learn how to port Qt-based Python desktop applications with C/C++ dependencies to Arm64 using Arm64EC on Windows on Arm.
+
 minutes_to_complete: 90
 
 who_is_this_for: This is an introductory topic for developers who want to learn how to port their applications to Arm64 using Arm64EC. 
diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md b/content/learning-paths/laptops-and-desktops/win_arm_qt/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Build and run a native Windows on Arm Qt application 
 
+description: Learn how to build and run Qt-based desktop applications on Windows on Arm and investigate native Arm64 performance improvements.
+
 minutes_to_complete: 20
 
 who_is_this_for: This is an introductory topic for software developers who want to use the native performance of the Qt framework for building desktop applications on Windows on Arm (WoA).
diff --git a/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md b/content/learning-paths/laptops-and-desktops/win_asp_net8/_index.md
@@ -1,6 +1,8 @@
 ---
 title: Run ASP.NET Core Web Server on Arm64
 
+description: Learn how to build and run an ASP.NET Core 8 web server application with Web API and dependency injection services on Windows on Arm.
+
 minutes_to_complete: 30
 
 who_is_this_for: This is an advanced topic for developers who are interested in building a web server for a headless IoT applications.
diff --git a/content/learning-paths/laptops-and-desktops/win_asp_net8/how-to-1.md b/content/learning-paths/laptops-and-desktops/win_asp_net8/how-to-1.md
@@ -191,7 +191,7 @@ app.MapControllers();
 app.Run();
 ```
 
-This approach ensures that a single instance of the TemperatureSensor class is available to the entire application. Any web API controller requiring access to that sensor can simply use the constructor injection.
+This approach ensures that a single instance of the TemperatureSensor class is available to the entire application. Any web API controller requiring access to that sensor can use the constructor injection.
 
 You will also use the Swagger toolset. As shown above, the default ASP.NET Core Web API project template also registers two services, `EndpointsApiExplorer` and `SwaggerGen`:
 
diff --git a/content/learning-paths/laptops-and-desktops/win_asp_net8/how-to-2.md b/content/learning-paths/laptops-and-desktops/win_asp_net8/how-to-2.md
@@ -8,7 +8,7 @@ layout: "learningpathall"
 ---
 
 ## Objective
-In this section, you will build and run the Arm64.HeadlessIoT project you have just created.
+In this section, you will build and run the Arm64.HeadlessIoT project you created.
 
 ## Building and running the application
 To build and run the application proceed as follows. 
@@ -56,7 +56,7 @@ The web server outputs an emulated sensor reading which in this case reported a
 
 ## Test the POST request
 
-To test the POST method of the `IoTController` class, use curl, Postman, or Swagger. Swagger is the simplest to use as it's available in the project you just created.
+To test the POST method of the `IoTController` class, use curl, Postman, or Swagger. Swagger is the simplest to use as it's available in the project you created.
 
 To access Swagger, type http://localhost:<port>/swagger in the web browser's address bar. This URL takes you to the following screen:
 
diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/create-rule.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/create-rule.md
@@ -56,6 +56,6 @@ This query ensures that only temperature readings are sent to the AWS Lambda fun
 
 ![fig8](figures/08.webp)
 
-This means that the AWS Lambda function you have just created has an input trigger set to AWS IoT. Specifically, the AWS Lambda function is invoked whenever a new message is sent to the 'Emulators/Weather/SensorReadings' topic.
+This means that the AWS Lambda function you created has an input trigger set to AWS IoT. Specifically, the AWS Lambda function is invoked whenever a new message is sent to the 'Emulators/Weather/SensorReadings' topic.
 
 You are now ready to implement and test the Lambda function.
diff --git a/content/learning-paths/laptops-and-desktops/win_cef/how-to-1.md b/content/learning-paths/laptops-and-desktops/win_cef/how-to-1.md
@@ -225,7 +225,7 @@ Re-run the application and you will see the following:
 
 This example demonstrates the versatility and power of CEF applications. One of their key strengths is the ability to modify the rendered content dynamically. This means you can change what's displayed in the application, like updating the HTML or CSS, without having to rebuild the entire application each time. This feature significantly streamlines the development process, making it quicker and more efficient.
 
-Let's leverage this capability to enhance our application. You will focus on styling the application by updating the HTML and CSS, providing a more engaging and visually appealing user interface. This step will show how easily you can improve the look and feel of your CEF application with just a few modifications to the web content.
+Let's leverage this capability to enhance our application. You will focus on styling the application by updating the HTML and CSS, providing a more engaging and visually appealing user interface. This step will show how easily you can improve the look and feel of your CEF application with a few modifications to the web content.
 
 ### Style the application
 Add `styles.css` file to modify the appearance of the application:
diff --git a/content/learning-paths/laptops-and-desktops/win_net_maui/how-to-1.md b/content/learning-paths/laptops-and-desktops/win_net_maui/how-to-1.md
@@ -56,7 +56,7 @@ Your project should be now ready. Next, open the Solution Explorer (View -> Solu
 ![fig6](figures/06.webp)
 
 ## Understanding the project structure
-The .NET MAUI project template is structured to enable the development of cross-platform applications using a single codebase, targeting Android, iOS, macOS, and Windows. Therefore, the project we have just created contains the following elements:
+The .NET MAUI project template is structured to enable the development of cross-platform applications using a single codebase, targeting Android, iOS, macOS, and Windows. Therefore, the project you created contains the following elements:
 1. **MauiProgram.cs** - contains the entry point for .NET MAUI application. It's where you configure and set up the app, including services, dependencies, and the main app configuration. It typically contains the CreateMauiApp method, which builds and returns an instance of MauiApp class.
 2. **App.xaml** and **App.xaml.cs** - these files define the application-level resources and the App class, which is derived from Application. This class serves as the central point for managing the app's lifecycle and its main interface.
 3. **Platforms Folder** - this folder contains platform-specific code and resources. It is organized into subfolders for each target platform. Here we have: Android, iOS, MacCatalyst, Tizen, and Windows. These folders can contain platform-specific initialization code, icons, splash screens, and other resources. For example, the Android subfolder contains the MainActivity.cs file, which represents the main activity class that runs when the application starts on an Android device.
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/spe-feature.md b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/spe-feature.md
@@ -9,7 +9,7 @@ layout: learningpathall
 
 ## Introduction
 
-The SPE feature is a subset of the Sampling feature that we just discussed.
+The SPE feature is a subset of the Sampling feature previously discussed.
 
 {{% notice Note %}}
 This section requires hardware that supports the SPE specification to function properly.
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/windowsperf_sampling_cpython.md b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/windowsperf_sampling_cpython.md
diff --git a/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/windowsperf_sampling_cpython_example_2.md b/content/learning-paths/laptops-and-desktops/windowsperf_sampling_cpython/windowsperf_sampling_cpython_example_2.md
diff --git a/content/learning-paths/laptops-and-desktops/wsl2/_index.md b/content/learning-paths/laptops-and-desktops/wsl2/_index.md
