Merge pull request #3125 from ArmDeveloperEcosystem/main

Production update

Author: jasonrandrews

.wordlist.txt

@@ -5957,4 +5957,17 @@ Performix
 ArmPerformix
 callees
 parallelizable
-performix
+performix
+GREENGUARD
+MyGreengrassCoreGroup
+Pico's
+SCVD
+VCC
+Vivado's
+devcontainer
+nss
+osRtxIdleThread
+osRtxTimerThread
+piezo
+safetyisolation
+solderless

assets/contributors.csv

@@ -118,4 +118,5 @@ Asier Arranz,NVIDIA,,asierarranz,,asierarranz.com
 Prince Agyeman,Arm,,,,
 Parichay Das,,parichaydas,parichaydas,,
 Johnny Nunez,NVIDIA,johnnynunez,johnnycano,,
-Raymond Lo,NVIDIA,raymondlo84,raymondlo84,,
+Raymond Lo,NVIDIA,raymondlo84,raymondlo84,,
+Kavya Sri Chennoju,Arm,kavya-chennoju,kavya-sri-chennoju,,

content/install-guides/codex-cli.md

@@ -195,6 +195,8 @@ To analyze a local codebase, use a `-v` argument to mount a volume to the Arm MC
 
 Replace the path `/Users/yourname01/yourlocalcodebase` with the path to your local codebase.
 
+##### **Option 1: edit the Codex configuration file**
+
 Add the following to your `~/.codex/config.toml` file:
 
 ```toml
@@ -215,6 +217,14 @@ startup_timeout_sec = 60
 The section must be named `mcp_servers` with an underscore. Using `mcp-servers` or `mcpservers` will cause Codex to ignore the configuration.
 {{% /notice %}}
 
+##### **Option 2: add from the Codex CLI**
+
+You can also add the Arm MCP server from the Codex CLI, after starting `codex` run:
+
+```bash
+codex mcp add arm-mcp -- sh -lc 'docker run --rm -i -v "$PWD:/workspace" --name arm-mcp armlimited/arm-mcp:latest'
+```
+
 ### How do I verify the Arm MCP server is working?
 
 Start Codex CLI and list the tools from the MCP server to verify it is working:

content/install-guides/container.md

@@ -60,13 +60,13 @@ Go to the [GitHub Releases page](https://github.com/apple/container/releases) an
 For example:
 
 ```bash
-wget https://github.com/apple/container/releases/download/0.4.1/container-0.4.1-installer-signed.pkg
+wget https://github.com/apple/container/releases/download/0.11.0/container-0.11.0-installer-signed.pkg
 ```
 
 Install the package:
 
 ```bash
-sudo installer -pkg container-0.4.1-installer-signed.pkg -target /
+sudo installer -pkg container-0.11.0-installer-signed.pkg -target /
 ```
 
 This installs the Container binary at `/usr/local/bin/container`.
@@ -90,7 +90,7 @@ container --version
 Example output:
 
 ```output
-container CLI version 0.4.1 (build: release, commit: 4ac18b5)
+container CLI version 0.11.0 (build: release, commit: d9b8a8d)
 ```
 
 ## Build and run a container

content/learning-paths/cross-platform/tinkerblox_ultraedge/_index.md

@@ -2,6 +2,8 @@
 title: Deploy Tinkerblox UltraEdge HPC-I for AI and mixed workloads on Arm
 minutes_to_complete: 60 
 
+description: Learn how to deploy Tinkerblox UltraEdge HPC-I for edge AI and mixed workloads on Arm platforms, including installation and configuration on Debian, Ubuntu, and Yocto systems.
+
 who_is_this_for: This is an advanced topic for business, R&D, and engineering teams seeking to optimize CPU and GPU infrastructure utilization while reducing total cost of ownership on edge and constrained environments. It's ideal for innovation and development teams building next-generation AI workloads using alternative runtime environments and packaging technologies.
 
 learning_objectives:

content/learning-paths/cross-platform/tinkerblox_ultraedge/background.md

@@ -71,7 +71,7 @@ UltraEdge orchestrates edge-native execution for high-performance compute infras
 
 UltraEdge is composed of layered systems, each responsible for a distinct aspect of execution and orchestration:
 
-![UltraEdge high-level architecture diagram showing layered execution systems for edge AI and mixed workloads alt-txt#center](https://raw.githubusercontent.com/Tinkerbloxsupport/arm-learning-path-support/main/static/images/High-level%20architecture%20diagram.png "UltraEdge high-level architecture")
+![UltraEdge high-level architecture diagram showing layered execution systems for edge AI and mixed workloads#center](https://raw.githubusercontent.com/Tinkerbloxsupport/arm-learning-path-support/main/static/images/High-level%20architecture%20diagram.png "UltraEdge high-level architecture")
 
 UltraEdge organizes functionality into five specialized layers. Each layer is responsible for a specific aspect of workload execution and orchestration:
 

content/learning-paths/cross-platform/tinkerblox_ultraedge/debian_installation.md

@@ -17,13 +17,13 @@ Follow these steps to initialize and register your device within the **Uncloud**
 * **Provision a New Device:**
     * Go to **Device Management** > **New Device**.
 
-![Device Management](https://raw.githubusercontent.com/Tinkerbloxsupport/arm-learning-path-support/main/static/images/Device_managment.png)
+![Screenshot of the Uncloud Dashboard showing the Device Management section with the navigation menu highlighting the option to create a new device#center](https://raw.githubusercontent.com/Tinkerbloxsupport/arm-learning-path-support/main/static/images/Device_managment.png "Device Management interface")
 
-![Creating New Device](https://raw.githubusercontent.com/Tinkerbloxsupport/arm-learning-path-support/main/static/images/creating_new_device.png)
+![Screenshot of the Uncloud interface showing the New Device creation form where you can configure device settings and register a new edge device#center](https://raw.githubusercontent.com/Tinkerbloxsupport/arm-learning-path-support/main/static/images/creating_new_device.png "Creating a new device")
 
 * Click the **three dots (options menu)** next to your device entry and select **Initialize**.
 
-![Initialize Device](https://raw.githubusercontent.com/Tinkerbloxsupport/arm-learning-path-support/main/static/images/Initialize%20.png)
+![Screenshot of the Uncloud Dashboard showing the device options menu with the Initialize option highlighted to begin the device setup process#center](https://raw.githubusercontent.com/Tinkerbloxsupport/arm-learning-path-support/main/static/images/Initialize%20.png "Initialize device option")
 
 * **Install prerequisites:**
 
@@ -39,7 +39,7 @@ sudo apt install -y curl jq
 
     Copy the generated device installation command or details from the **Uncloud** portal to your clipboard.
 
-![Installation Command](https://raw.githubusercontent.com/Tinkerbloxsupport/arm-learning-path-support/main/static/images/Initialize%20_command.png)
+![Screenshot of the Uncloud portal showing the device initialization screen with the installation command displayed that you need to copy and execute on your target device#center](https://raw.githubusercontent.com/Tinkerbloxsupport/arm-learning-path-support/main/static/images/Initialize%20_command.png "Installation command")
 
 You should be able to locate and copy the specific installation command appropriate for your account. Here is an example:
 

content/learning-paths/cross-platform/topdown-compare/1c-compare-arch.md

@@ -62,7 +62,7 @@ On Arm Neoverse V2, equivalent lost work appears under Bad Speculation through `
 | Frontend vs Backend Balance | Measured by slot distribution from `IDQ_UOPS_NOT_DELIVERED` and `CYCLE_ACTIVITY` counters | Measured using `STALL_SLOT_FRONTEND` and `STALL_SLOT_BACKEND` |
 | Speculative Execution Impact | Explicit "Bad Speculation" slot fraction + `MACHINE_CLEARS.*` | Derived from `BR_MIS_PRED`, `BR_MIS_PRED_RETIRED`, and `OP_SPEC/OP_RETIRED` ratios |
 | Cache and Memory Hierarchy | Layered: L1 → L2 → L3 → DRAM via `CYCLE_ACTIVITY` events | Effectiveness groups: L1I/L1D/L2/LL with MPKI and hit/miss ratios |
-| Stall Accounting Granularity | Strict hierarchical attribution (single bottleneck per slot) | Flexible overlap across groups—multiple concurrent stall sources possible |
+| Stall Accounting Granularity | Strict hierarchical attribution (single bottleneck per slot) | Flexible overlap across groups - multiple concurrent stall sources possible |
 | Metric Normalization | All metrics normalized to total slots = machine_width × CPU cycles | All metrics normalized to total slots = CPU cycles × 8 (rename width) |
 
 ## Practical cross-platform guidance

content/learning-paths/cross-platform/topdown-compare/_index.md

@@ -3,6 +3,8 @@ title: Compare Arm Neoverse and Intel x86 top-down performance analysis with PMU
 
 minutes_to_complete: 30
 
+description: Learn how to compare Arm Neoverse and Intel x86 top-down performance analysis methodologies using PMU counters, Linux Perf, and topdown-tool to identify bottlenecks across architectures.
+
 who_is_this_for: This is an advanced topic for software developers and performance engineers who want to understand the similarities and differences between Arm Neoverse and Intel x86 top-down performance analysis using PMU counters, Linux Perf, and the topdown-tool. 
 
 learning_objectives:

content/learning-paths/cross-platform/vectorization-comparison/_index.md

@@ -3,6 +3,8 @@ title: "Migrate x86-64 SIMD to Arm64"
 
 minutes_to_complete: 30
 
+description: Learn how to migrate x86-64 SIMD code to Arm64 by mapping Intel SSE/AVX to Arm Neon, SVE, and SME, with code examples and migration strategies using autovectorization or intrinsics.
+
 who_is_this_for: This is an advanced topic for developers migrating vectorized (SIMD) code from x86-64 to Arm64.
 
 learning_objectives: