Convert external learn.arm.com links to internal format

Co-authored-by: jasonrandrews <4744588+jasonrandrews@users.noreply.github.com>

Author: Copilot

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/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

content/learning-paths/cross-platform/memory-latency/_index.md

@@ -38,7 +38,7 @@ shared_between:
 further_reading:
     - resource:
         title: Write a Dynamic Memory Allocator
-        link: https://learn.arm.com/learning-paths/cross-platform/dynamic-memory-allocator/
+        link: /learning-paths/cross-platform/dynamic-memory-allocator/
         type: website
     - resource:
         title: Memory Latency

content/learning-paths/cross-platform/multiplying-matrices-with-sme2/_index.md

@@ -52,7 +52,7 @@ further_reading:
         type: documentation
     - resource:
         title: Port Code to Arm Scalable Vector Extension (SVE)
-        link: https://learn.arm.com/learning-paths/servers-and-cloud-computing/sve
+        link: /learning-paths/servers-and-cloud-computing/sve
         type: website
     - resource:
         title: Introducing the Scalable Matrix Extension for the Armv9-A Architecture
@@ -72,7 +72,7 @@ further_reading:
         type: blog
     - resource:
         title: Build adaptive libraries with multiversioning
-        link: https://learn.arm.com/learning-paths/cross-platform/function-multiversioning/
+        link: /learning-paths/cross-platform/function-multiversioning/
         type: website
     - resource:
         title: SME Programmer's Guide

content/learning-paths/cross-platform/multiplying-matrices-with-sme2/overview.md

@@ -47,4 +47,4 @@ On SVE and SME:
 - [Arm Scalable Matrix Extension (SME) Introduction (Part 1) - Zenon Xiu](https://community.arm.com/arm-community-blogs/b/architectures-and-processors-blog/posts/arm-scalable-matrix-extension-introduction)
 - [Arm Scalable Matrix Extension (SME) Introduction (Part 2) - Zenon Xiu](https://community.arm.com/arm-community-blogs/b/architectures-and-processors-blog/posts/arm-scalable-matrix-extension-introduction-p2)
 - [Matrix-matrix multiplication. Neon, SVE, and SME compared (Part 3)](https://community.arm.com/arm-community-blogs/b/architectures-and-processors-blog/posts/.matrix-matrix-multiplication-neon-sve-and-sme-compared)
-- [Learn about function multiversioning - Alexandros Lamprineas, Arm](https://learn.arm.com/learning-paths/cross-platform/function-multiversioning/)
+- [Learn about function multiversioning - Alexandros Lamprineas, Arm](/learning-paths/cross-platform/function-multiversioning/)