Merge remote-tracking branch 'upstream/main' into review3

Author: jasonrandrews

content/install-guides/streamline-cli.md

@@ -52,7 +52,7 @@ Streamline CLI tools are supported on the following Arm CPUs:
 
 Use the Arm Sysreport utility to determine whether your system configuration supports hardware-assisted profiling. Follow the instructions in [Get ready for performance analysis with Sysreport][1] to discover how to download and run this utility.
 
-[1]: https://learn.arm.com/learning-paths/servers-and-cloud-computing/sysreport/
+[1]: /learning-paths/servers-and-cloud-computing/sysreport/
 
 The `perf counters` entry in the generated report indicates how many CPU counters are available. The `perf sampling` entry indicates if SPE is available. You will achieve the best profiles in systems with at least 6 available CPU counters and SPE.
 

content/learning-paths/automotive/openadkit1_container/3_setup_openadkit.md

@@ -15,7 +15,7 @@ The example has been tested on [AWS EC2](https://aws.amazon.com/ec2/) and an [Am
 
 ## Installation
 
-You need Docker to run Open AD Kit. Refer to the [Docker install guide](https://learn.arm.com/install-guides/docker/) to learn how to install Docker on an Arm platform.
+You need Docker to run Open AD Kit. Refer to the [Docker install guide](/install-guides/docker/) to learn how to install Docker on an Arm platform.
 
 First, verify Docker is installed on your development computer by running:
 

content/learning-paths/automotive/openadkit2_safetyisolation/2_data_distribution_service.md

@@ -17,7 +17,7 @@ In modern vehicles, multiple sensors such as LiDAR, radar, and cameras must cont
 DDS ensures these components share data seamlessly and in real time, both within the vehicle and across infrastructure such as V2X systems, including traffic lights and road sensors.
 
 {{% notice Tip %}}
-To get started with open-source DDS on Arm platforms, see the [Installation Guide for CycloneDDS](https://learn.arm.com/install-guides/cyclonedds).
+To get started with open-source DDS on Arm platforms, see the [Installation Guide for CycloneDDS](/install-guides/cyclonedds).
 {{% /notice %}}
 
 

content/learning-paths/automotive/openadkit2_safetyisolation/3_container_spliting.md

@@ -10,7 +10,7 @@ layout: learningpathall
 
 Now that you’ve explored the concept of a safety island, a dedicated subsystem responsible for executing safety-critical control logic, and learned how DDS (Data Distribution Service) enables real-time, distributed communication, you’ll refactor the original OpenAD Kit architecture into a multi-instance deployment.
 
-The predecessor Learning Path, [Deploy Open AD Kit containerized autonomous driving simulation on Arm Neoverse](http://learn.arm.com/learning-paths/automotive/openadkit1_container/), showed how to  deploying three container components on a single Arm-based instance, to handle:
+The predecessor Learning Path, [Deploy Open AD Kit containerized autonomous driving simulation on Arm Neoverse](/learning-paths/automotive/openadkit1_container/), showed how to  deploying three container components on a single Arm-based instance, to handle:
 - The simulation environment
 - Visualization
 - Planning and control

content/learning-paths/automotive/openadkit2_safetyisolation/4_multiinstance_executing.md

@@ -86,7 +86,7 @@ On the Simulation and Visualization node, execute:
 
 Once both machines are running their launch scripts, the Visualizer container exposes a web-accessible interface at: http://6080/vnc.html.
 
-Open this link in your browser to observe the simulation in real time. The demo closely resembles the output in the [previous Learning Path, Deploy Open AD Kit containerized autonomous driving simulation on Arm Neoverse](http://learn.arm.com/learning-paths/automotive/openadkit1_container/4_run_openadkit/). 
+Open this link in your browser to observe the simulation in real time. The demo closely resembles the output in the [previous Learning Path, Deploy Open AD Kit containerized autonomous driving simulation on Arm Neoverse](/learning-paths/automotive/openadkit1_container/4_run_openadkit/). 
 
 ![Distributed OpenAD Kit simulation running on two Arm-based instances with visualizer and simulator coordination over DDS alt-text#center](split_aws_run.gif "Visualizer output from a distributed OpenAD Kit simulation showing ROS 2 modules running across two cloud instances using DDS communication.")
 

content/learning-paths/cross-platform/kleidiai-explainer/_index.md

@@ -11,7 +11,7 @@ learning_objectives:
     - Run a basic C++ matrix multiplication example to showcase the speedup that KleidiAI micro-kernels can deliver.
 
 prerequisites:
-    - An Arm-based Linux machine that implements the Int8 Matrix Multiplication (*i8mm*) architecture feature. The example in this Learning Path is run on an AWS Graviton 3 instance. Instructions on setting up an Arm-based server are [found here](https://learn.arm.com/learning-paths/servers-and-cloud-computing/csp/aws/).
+    - An Arm-based Linux machine that implements the Int8 Matrix Multiplication (*i8mm*) architecture feature. The example in this Learning Path is run on an AWS Graviton 3 instance. Instructions on setting up an Arm-based server are [found here](/learning-paths/servers-and-cloud-computing/csp/aws/).
     - A basic understanding of linear algebra terminology, such as dot product and matrix multiplication.
 
 author: Zach Lasiuk

content/learning-paths/cross-platform/kleidiai-explainer/page2.md

@@ -19,7 +19,7 @@ There are essentially two types of KleidiAI micro-kernels today:
 ![KleidiAI stuff](KleidiAI-src.JPG "KleidiAI src directory")
 
 ### What are the quantization levels that KleidiAI supports?
-KleidiAI has multiple matrix multiplication micro-kernels, and dynamic quantization routines, to optimally support all model quantization levels. To learn more about model quantization and how selecting the right quantization level affects your AI-based application, refer to [this Learning Path](https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama-cpu/llama-chatbot#quantization-format).
+KleidiAI has multiple matrix multiplication micro-kernels, and dynamic quantization routines, to optimally support all model quantization levels. To learn more about model quantization and how selecting the right quantization level affects your AI-based application, refer to [this Learning Path](/learning-paths/servers-and-cloud-computing/llama-cpu/llama-chatbot#quantization-format).
 
 KleidiAI currently has three matrix multiplication directories that each handle input/output types differently, and which will evolve to broaden the reach of their support:
 

content/learning-paths/cross-platform/kleidiai-explainer/page3.md

@@ -25,7 +25,7 @@ This reference code is functionally-identical to KleidiAI's micro-kernels, and i
 Follow these steps to build and run the KleidiAI library and example script:
 
 1. Create an Ubuntu 24.04 Arm Linux machine on an AWS EC2 instance. 
-For more details view the Learning Path on [setting up AWS EC2 Graviton instances](https://learn.arm.com/learning-paths/servers-and-cloud-computing/csp/aws/). Use an M7g-medium instance type, which uses the Graviton 3 SoC supporting the *i8mm* Arm architecture feature. The 1 CPU and 4 GB of RAM in the M7g-medium are sufficient for this basic example run.
+For more details view the Learning Path on [setting up AWS EC2 Graviton instances](/learning-paths/servers-and-cloud-computing/csp/aws/). Use an M7g-medium instance type, which uses the Graviton 3 SoC supporting the *i8mm* Arm architecture feature. The 1 CPU and 4 GB of RAM in the M7g-medium are sufficient for this basic example run.
 
 2. Initialize your system by installing essential packages:
 ```bash

content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/how-to-9.md

@@ -27,8 +27,8 @@ layout: learningpathall
 	--output_name="llama3_kv_sdpa_xnn_qe_4_32.pte"
 ```
 
--	Build the Llama Runner binary for [Android](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/5-run-benchmark-on-android/).
--	Build and Run [Android](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/6-build-android-chat-app/).
+-	Build the Llama Runner binary for [Android](/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/5-run-benchmark-on-android/).
+-	Build and Run [Android](/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/6-build-android-chat-app/).
 -	Open Android Studio and choose "Open an existing Android Studio project" to navigate to examples/demo-apps/android/LlamaDemo and Press Run (^R) to build and launch the app on your phone.
 -	Tap the Settings widget to select a model, configure its parameters, and set any prompts.
 -	After choosing the model, tokenizer, and model type, click "Load Model" to load it into the app and return to the main Chat activity.

content/learning-paths/cross-platform/mcp-ai-agent/intro-to-mcp-uv.md

@@ -46,7 +46,7 @@ Each component in the diagram plays a distinct role in enabling AI agents to int
 - The **Remote services** are external APIs the server can call on the host’s behalf.
 
 {{% notice Learning Tip %}}
-Learn more about AI Agents in the Learning Path [Deploy an AI Agent on Arm with llama.cpp and llama-cpp-agent using KleidiAI](https://learn.arm.com/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/).
+Learn more about AI Agents in the Learning Path [Deploy an AI Agent on Arm with llama.cpp and llama-cpp-agent using KleidiAI](/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/).
 {{% /notice %}}
 
 ## Section summary