From edf737c5ae06a309259e533b781d079acd4b76d9 Mon Sep 17 00:00:00 2001 From: Usamah Zaheer Date: Tue, 29 Sep 2026 12:43:50 +0100 Subject: [PATCH 001/109] Use released dependencies in the MobileSAM Learning Path Keep the documented example and source-built Python package aligned while replacing the dated TorchAO nightly with released dependencies. Document the FVP runtime prerequisite and the newly verified Linux end-to-end results. Authored with assistance from OpenAI Codex. --- .../deploy-mobilesam-on-ethos-u/_index.md | 3 +- .../prepare-the-environment.md | 68 ++++++++++++++++--- .../run-the-example.md | 2 +- .../validate-the-results.md | 6 +- 4 files changed, 64 insertions(+), 15 deletions(-) diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/_index.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/_index.md index 48cdb23715..3dc41540c5 100644 --- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/_index.md +++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/_index.md @@ -15,6 +15,7 @@ learning_objectives: prerequisites: - A Linux development machine with glibc 2.28 or later, or an Apple silicon Mac running macOS 15 or later + - On Linux, libstdc++.so.6 providing GLIBCXX_3.4.26 or later for the Corstone-320 FVP - Familiarity with PyTorch model export and embedded cross-compilation # START generated_summary_faq @@ -91,7 +92,7 @@ operatingsystems: further_reading: - resource: title: ExecuTorch MobileSAM prompt segmentation example - link: https://github.com/pytorch/executorch/tree/main/examples/arm/mobilesam_prompt_segmentation_example_ethos_u + link: https://github.com/pytorch/executorch/tree/5c4d2c4a0150a0809bc77be9a67093f80f60a60f/examples/arm/mobilesam_prompt_segmentation_example_ethos_u type: repository - resource: title: ExecuTorch Arm Ethos-U backend documentation diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md index 2d49958e58..bea6b5d6e6 100644 --- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md +++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md @@ -65,6 +65,8 @@ The check confirms that you are using a supported Linux host or Apple silicon Ma The Python export dependencies require macOS 15 or later on Apple silicon, or glibc 2.28 or later on Linux. The pinned [TOSA tools](https://pypi.org/project/tosa-tools/2026.5.0/#files) set the macOS minimum, and the [PyTorch wheels](https://pypi.org/project/torch/2.14.0/#files) require the Linux glibc version. The preflight check verifies these requirements before you install the dependencies. +On Linux, the Corstone-320 FVP also requires `libstdc++.so.6` providing `GLIBCXX_3.4.26` or later. + ## Clone the ExecuTorch source Clone ExecuTorch, select the revision used by this Learning Path, and initialize its submodules: @@ -81,22 +83,13 @@ Run the remaining commands from the ExecuTorch repository root. The pinned revis ## Create a Python environment -Create and activate a Python 3.12 virtual environment: +Create and activate a fresh Python 3.12 virtual environment for this checkout: ```bash python3.12 -m venv .venv source .venv/bin/activate -python -m pip install --upgrade pip -``` - -Install the current ExecuTorch checkout with its Ethos-U dependencies: - -```bash -./install_executorch.sh --optional-dependency ethos_u ``` -Using the checkout's installer keeps the Python package aligned with the example source. - ## Install the Arm development tools {{% notice Note %}} @@ -115,6 +108,61 @@ The Arm setup script installs the pinned GNU bare-metal toolchain, Ethos-U Vela source examples/arm/arm-scratch/setup_path.sh ``` +## Install released Python dependencies + +The Arm setup script also installs developer packages that this Learning Path does not use. Remove those packages from this fresh, dedicated virtual environment before resolving the model dependencies: + +```bash +python -m pip uninstall -y \ + tosa-adapter-model-explorer ai-edge-model-explorer \ + pte-adapter-model-explorer pytest-timeout +``` + +Install released PyTorch, Torchvision, and TorchAO packages. Linux uses the stable CPU wheel index; macOS uses the Apple silicon wheels from PyPI: + +```bash +case "$(uname -s)" in + Linux) + python -m pip install \ + --index-url https://pypi.org/simple \ + --extra-index-url https://download.pytorch.org/whl/cpu \ + 'torch==2.14.0+cpu' 'torchvision==0.29.0+cpu' 'torchao==0.18.0+cpu' + ;; + Darwin) + python -m pip install --index-url https://pypi.org/simple \ + 'torch==2.14.0' 'torchvision==0.29.0' 'torchao==0.18.0' + ;; + *) + echo "Use a supported Linux host or Apple silicon Mac." >&2 + exit 1 + ;; +esac +``` + +Install the source build dependencies and the `timm` version used by the pinned MobileSAM example: + +```bash +python -m pip install --index-url https://pypi.org/simple \ + 'cmake==3.31.10' 'packaging==26.3' 'setuptools==84.0.0' \ + 'wheel==0.48.0' 'pyyaml==6.0.3' 'zstd==1.5.7.2' \ + 'certifi==2026.7.22' 'patchelf==0.19.1.0; sys_platform == "linux"' \ + 'timm==1.0.7' +``` + +Build and install ExecuTorch from the same checkout used by the example and native runner: + +```bash +CMAKE_ARGS='-DEXECUTORCH_BUILD_MLX=OFF' env -u DEBUG \ + python -m pip install --no-build-isolation '.[ethos_u]' +python -m pip check +``` + +The Ethos-U extra installs Vela 5.1.0 and TOSA tools 2026.5.0 with their required dependencies. Installing it after Arm setup resolves the older FlatBuffers version installed by that script. `pip check` should report `No broken requirements found.` Resolve any reported conflicts before exporting. + +MLX is not used by this Ethos-U workflow; disabling it avoids building an unrelated Apple GPU backend. Removing `DEBUG` from the build environment prevents a shell setting from selecting an unintended debug build. + +Do not run `install_executorch.sh` for this environment: at the pinned revision it requests a dated TorchAO nightly. The commands above use released dependencies while keeping the Python package and native code aligned to the same source. If you rerun Arm setup, repeat the package removal and Python dependency installation before continuing. + ## Verify the environment Confirm that Python imports ExecuTorch: diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/run-the-example.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/run-the-example.md index 558afb5ce1..e937d890e0 100644 --- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/run-the-example.md +++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/run-the-example.md @@ -26,7 +26,7 @@ backends/arm/scripts/build_executor_runner.sh \ '--extra_build_flags=-DSEMIHOSTING=ON -DET_COMPILED_PTE=ON' ``` -The script builds the required ExecuTorch libraries and the runner for Corstone-320. `ET_COMPILED_PTE=ON` embeds `mobilesam.pte` in the Executable and Linkable Format (ELF) file named `arm_executor_runner`, under `arm_test/mobilesam/runner/`. +The script builds the required ExecuTorch libraries and the runner for Corstone-320. The model selected by `--pte` is embedded in the Executable and Linkable Format (ELF) file named `arm_executor_runner`, under `arm_test/mobilesam/runner/`. `SEMIHOSTING=ON` lets the simulated target read input tensors from host files and write its output tensors back to the host. diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md index 03caa505f7..e2ae88cb48 100644 --- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md +++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md @@ -40,10 +40,10 @@ A reference run at the pinned commit produced these scores: | Comparison | IoU | | --- | --- | -| Floating-point and quantized host masks | 0.9550 | -| FVP and quantized host masks | 0.9809 | +| Floating-point and quantized host masks | 0.9436 | +| FVP and quantized host masks | 0.9764 | -This run used Python 3.12.13, PyTorch 2.14.0, Vela 5.1.0, and Corstone-320 FVP 11.31.28 on macOS 26.6.2 with Apple silicon. Treat these scores as examples; use the two `0.9` thresholds to check your own run. +This reference run used Ubuntu 24.04.4 on AArch64 with Python 3.12.3, the PyTorch 2.14.0, Torchvision 0.29.0, and TorchAO 0.18.0 CPU wheels, Vela 5.1.0, TOSA tools 2026.5.0, Arm GNU Toolchain 15.2.Rel1, and Corstone-320 FVP 11.27.25. Treat these scores as examples from this configuration; use the two `0.9` thresholds to check your own run. ## Confirm Ethos-U delegation From dbab932e19e4a76539a2de009a5a111b77478fe5 Mon Sep 17 00:00:00 2001 From: Usamah Zaheer Date: Tue, 29 Sep 2026 12:43:59 +0100 Subject: [PATCH 002/109] Use ExecuTorch 1.5.1 in the Silero VAD Learning Path Align source and released Python packages, resolve dependencies after Arm setup, and clarify host prerequisites. The complete Linux AArch64 export, target build, FVP run and numerical comparison pass without changing model or runtime commands. Authored with assistance from OpenAI Codex. --- .../1-prepare-environment.md | 101 +++++++++++------- .../silero-vad-ethos-u/_index.md | 6 +- 2 files changed, 64 insertions(+), 43 deletions(-) diff --git a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/1-prepare-environment.md b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/1-prepare-environment.md index 8ad19ce477..3dcafee058 100644 --- a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/1-prepare-environment.md +++ b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/1-prepare-environment.md @@ -23,7 +23,7 @@ Use the FVP for functional validation. The FVP's Ethos-U model is cycle accurate ## Check your development machine -Run the following preflight check before downloading the source: +Use Python 3.12 with development headers and virtual-environment support. The release wheels require glibc 2.28 or later on Linux, or macOS 15 or later on Apple silicon. On Linux, the FVP also requires `libstdc++.so.6` providing `GLIBCXX_3.4.26` or later. Run the following preflight check before downloading the source; CMake is installed in the virtual environment later: ```bash case "$(uname -s)/$(uname -m)" in @@ -36,7 +36,7 @@ case "$(uname -s)/$(uname -m)" in ;; esac -for tool in python3 git cmake c++ curl; do +for tool in python3.12 git c++ curl; do command -v "$tool" >/dev/null || { echo "Missing required tool: $tool" >&2 exit 1 @@ -51,34 +51,19 @@ fi printf 'int main() { return 0; }\n' | \ c++ -std=c++17 -x c++ -fsyntax-only - -python3 - <<'PY' -import re -import subprocess -import sys - -if not (3, 10) <= sys.version_info[:2] <= (3, 13): - raise SystemExit("Python 3.10 through 3.13 is required") - -output = subprocess.check_output(["cmake", "--version"], text=True) -version = tuple(map(int, re.search(r"\d+(?:\.\d+)+", output).group().split(".")[:2])) -if version < (3, 24): - raise SystemExit("CMake 3.24 or later is required") - -print(f"Python {sys.version.split()[0]}") -print(output.splitlines()[0]) -PY +python3.12 --version ``` ## Create an isolated ExecuTorch environment -The Silero VAD Ethos-U example is available in the upstream ExecuTorch repository. Use the tested main-branch commit so that the commands and generated artifacts match the Learning Path. +Use the ExecuTorch `v1.5.1` release for both the Python package and the example and runtime source. The commands below pin the release commit and install released dependencies, without using nightly package indexes. -Clone ExecuTorch and check out the tested revision: +Clone ExecuTorch and check out the release commit: ```bash git clone https://github.com/pytorch/executorch.git cd executorch -git checkout 4fd161058ebe2b9d80d11242a9d21811c0e92dac +git checkout 3b60683923245cf472b7323426920e15623ba361 git submodule sync --recursive git submodule update --init --recursive ``` @@ -92,35 +77,17 @@ git rev-parse --short=12 HEAD The expected output is: ```output -4fd161058ebe +3b6068392324 ``` Create and activate a Python virtual environment: ```bash -python3 -m venv .venv +python3.12 -m venv .venv source .venv/bin/activate python -m pip install --upgrade pip ``` -Install ExecuTorch and its Python dependencies: - -```bash -CMAKE_ARGS="-DEXECUTORCH_BUILD_MLX=OFF" \ - env -u DEBUG ./install_executorch.sh \ - --minimal --optional-dependency ethos_u - -python - <<'PY' -import executorch.codegen.tools.selective_build -from executorch.backends.arm.ethosu import EthosUPartitioner -from executorch.backends.arm.quantizer import EthosUQuantizer - -print("ExecuTorch installation verified") -PY -``` - -The installation script initializes the Git submodules that are needed by the build. The script also installs the matching PyTorch and ExecuTorch packages. The command omits the unrelated MLX backend and optional packages used by other examples. - ## Install the Arm backend tools The Arm setup script downloads the Arm GNU Toolchain, Ethos-U Vela compiler, and supporting Python packages. On Linux, the script also installs the Corstone FVPs. Review the license terms presented by the script before using its EULA acceptance option. @@ -152,6 +119,58 @@ command -v FVP_Corstone_SSE-320 The compiler resolves under `examples/arm/arm-scratch/`. On Linux, the FVP also resolves under this directory. On macOS, the FVP resolves under the FVPs-on-Mac wrapper directory. If the compiler is missing, source `examples/arm/arm-scratch/setup_path.sh` again. If the FVP is missing on macOS, add the wrapper directory to `PATH`. +## Install the released Python packages + +The Arm setup script also installs model-viewing and test packages that this Learning Path doesn't use. Remove these packages from the new virtual environment, then install the released ExecuTorch stack. This order lets the package resolver select the FlatBuffers version required by TOSA tools, rather than retaining the older version installed by the setup script. + +```bash +python -m pip uninstall --yes \ + tosa-adapter-model-explorer ai-edge-model-explorer \ + pytest-timeout pte-adapter-model-explorer +``` + +Select your host operating system. Linux uses the released CPU wheels; macOS uses the released Apple silicon wheels: + +{{< tabpane code=true >}} + {{< tab header="Linux" language="bash" >}} +python -m pip install \ + --index-url https://pypi.org/simple \ + --extra-index-url https://download.pytorch.org/whl/cpu \ + "executorch[ethos-u]==1.5.1" \ + "torch==2.14.0+cpu" "torchvision==0.29.0+cpu" "torchao==0.18.0+cpu" \ + "cmake==3.31.10" "zstd==1.5.7.2" + {{< /tab >}} + {{< tab header="macOS" language="bash" >}} +python -m pip install \ + --index-url https://pypi.org/simple \ + "executorch[ethos-u]==1.5.1" \ + "torch==2.14.0" "torchvision==0.29.0" "torchao==0.18.0" \ + "cmake==3.31.10" "zstd==1.5.7.2" + {{< /tab >}} +{{< /tabpane >}} + +Check dependency consistency and the imports used by export and CMake code generation: + +```bash +python -m pip check +python - <<'PY' +import executorch.codegen.tools.selective_build +from executorch.backends.arm.ethosu import EthosUPartitioner +from executorch.backends.arm.quantizer import EthosUQuantizer + +print("ExecuTorch installation verified") +PY +``` + +The expected output is: + +```output +No broken requirements found. +ExecuTorch installation verified +``` + +The `ethos-u` extra supplies Vela and TOSA tools. The Python package and source checkout use the same ExecuTorch release; the following pages build the host quantized operators and bare-metal runtime from that source. If you rerun the Arm setup script, repeat this package-installation section before continuing. + ## Download the model and sample audio Create one workspace for the files generated in this Learning Path: diff --git a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/_index.md b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/_index.md index bba418a85b..69424cbf83 100644 --- a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/_index.md +++ b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/_index.md @@ -13,7 +13,9 @@ learning_objectives: - Validate simulated speech probabilities against a host-generated reference. prerequisites: - - A Linux host using `x86_64` or `arm64`, or an Apple silicon macOS host + - A Linux host using `x86_64` or `arm64` with glibc 2.28 or later, or an Apple silicon host running macOS 15 or later + - On Linux, libstdc++.so.6 providing GLIBCXX_3.4.26 or later for the Corstone-320 FVP + - Python 3.12 with development headers and virtual-environment support - Basic familiarity with PyTorch models and command-line development tools # START generated_summary_faq @@ -95,7 +97,7 @@ further_reading: type: documentation - resource: title: Silero VAD Ethos-U example source - link: https://github.com/pytorch/executorch/tree/main/examples/arm/silero_vad_example_ethos_u + link: https://github.com/pytorch/executorch/tree/3b60683923245cf472b7323426920e15623ba361/examples/arm/silero_vad_example_ethos_u type: repository - resource: title: Silero VAD project From 7a47faec642a58a7c582e907402cc22cbd796db2 Mon Sep 17 00:00:00 2001 From: Usamah Zaheer Date: Tue, 29 Sep 2026 13:54:57 +0100 Subject: [PATCH 003/109] Simplify the MobileSAM dependency setup guidance Trim explanations introduced by the release-dependency update without changing existing checks or execution commands. Fresh Linux AArch64 setup and complete Corstone-320 end-to-end validation pass with the documented thresholds. Authored with assistance from OpenAI Codex. --- .../prepare-the-environment.md | 10 ++++------ .../validate-the-results.md | 2 +- 2 files changed, 5 insertions(+), 7 deletions(-) diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md index bea6b5d6e6..2bedc18169 100644 --- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md +++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md @@ -108,9 +108,9 @@ The Arm setup script installs the pinned GNU bare-metal toolchain, Ethos-U Vela source examples/arm/arm-scratch/setup_path.sh ``` -## Install released Python dependencies +## Install ExecuTorch -The Arm setup script also installs developer packages that this Learning Path does not use. Remove those packages from this fresh, dedicated virtual environment before resolving the model dependencies: +Remove unused developer packages installed by Arm setup: ```bash python -m pip uninstall -y \ @@ -157,11 +157,9 @@ CMAKE_ARGS='-DEXECUTORCH_BUILD_MLX=OFF' env -u DEBUG \ python -m pip check ``` -The Ethos-U extra installs Vela 5.1.0 and TOSA tools 2026.5.0 with their required dependencies. Installing it after Arm setup resolves the older FlatBuffers version installed by that script. `pip check` should report `No broken requirements found.` Resolve any reported conflicts before exporting. +Installing the Ethos-U extra after Arm setup resolves its older FlatBuffers dependency. Before exporting, confirm that `pip check` reports `No broken requirements found.` -MLX is not used by this Ethos-U workflow; disabling it avoids building an unrelated Apple GPU backend. Removing `DEBUG` from the build environment prevents a shell setting from selecting an unintended debug build. - -Do not run `install_executorch.sh` for this environment: at the pinned revision it requests a dated TorchAO nightly. The commands above use released dependencies while keeping the Python package and native code aligned to the same source. If you rerun Arm setup, repeat the package removal and Python dependency installation before continuing. +These commands replace `install_executorch.sh` and its TorchAO nightly with released dependencies. If you rerun Arm setup, repeat the package removal and Python installation steps. ## Verify the environment diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md index e2ae88cb48..748d035352 100644 --- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md +++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md @@ -43,7 +43,7 @@ A reference run at the pinned commit produced these scores: | Floating-point and quantized host masks | 0.9436 | | FVP and quantized host masks | 0.9764 | -This reference run used Ubuntu 24.04.4 on AArch64 with Python 3.12.3, the PyTorch 2.14.0, Torchvision 0.29.0, and TorchAO 0.18.0 CPU wheels, Vela 5.1.0, TOSA tools 2026.5.0, Arm GNU Toolchain 15.2.Rel1, and Corstone-320 FVP 11.27.25. Treat these scores as examples from this configuration; use the two `0.9` thresholds to check your own run. +This reference run used Ubuntu 24.04.4 on AArch64 with Python 3.12.3 and the dependencies from [environment setup](../prepare-the-environment/). Treat these scores as examples; use the two `0.9` thresholds to check your own run. ## Confirm Ethos-U delegation From aaa258e4c8f1786f5c1458aab5ad2d5eaca509ae Mon Sep 17 00:00:00 2001 From: Usamah Zaheer Date: Tue, 29 Sep 2026 13:54:57 +0100 Subject: [PATCH 004/109] Simplify the Silero dependency setup guidance Shorten explanations introduced by the release-dependency update while preserving earlier revision and import checks and all installation and runtime commands. Fresh Linux AArch64 end-to-end validation passes with all 79 speech decisions matching on Corstone-320. Authored with assistance from OpenAI Codex. --- .../1-prepare-environment.md | 19 +++++-------------- 1 file changed, 5 insertions(+), 14 deletions(-) diff --git a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/1-prepare-environment.md b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/1-prepare-environment.md index 3dcafee058..be504cf463 100644 --- a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/1-prepare-environment.md +++ b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/1-prepare-environment.md @@ -56,9 +56,7 @@ python3.12 --version ## Create an isolated ExecuTorch environment -Use the ExecuTorch `v1.5.1` release for both the Python package and the example and runtime source. The commands below pin the release commit and install released dependencies, without using nightly package indexes. - -Clone ExecuTorch and check out the release commit: +Use ExecuTorch `v1.5.1` for the Python package, example, and native runtime. Clone the matching source: ```bash git clone https://github.com/pytorch/executorch.git @@ -119,9 +117,9 @@ command -v FVP_Corstone_SSE-320 The compiler resolves under `examples/arm/arm-scratch/`. On Linux, the FVP also resolves under this directory. On macOS, the FVP resolves under the FVPs-on-Mac wrapper directory. If the compiler is missing, source `examples/arm/arm-scratch/setup_path.sh` again. If the FVP is missing on macOS, add the wrapper directory to `PATH`. -## Install the released Python packages +## Install ExecuTorch -The Arm setup script also installs model-viewing and test packages that this Learning Path doesn't use. Remove these packages from the new virtual environment, then install the released ExecuTorch stack. This order lets the package resolver select the FlatBuffers version required by TOSA tools, rather than retaining the older version installed by the setup script. +Remove the unused model-viewing and test packages installed by Arm setup: ```bash python -m pip uninstall --yes \ @@ -129,7 +127,7 @@ python -m pip uninstall --yes \ pytest-timeout pte-adapter-model-explorer ``` -Select your host operating system. Linux uses the released CPU wheels; macOS uses the released Apple silicon wheels: +Install the released packages for your host. Linux uses CPU wheels; macOS uses Apple silicon wheels. Keep this step after Arm setup to resolve its older FlatBuffers dependency: {{< tabpane code=true >}} {{< tab header="Linux" language="bash" >}} @@ -162,14 +160,7 @@ print("ExecuTorch installation verified") PY ``` -The expected output is: - -```output -No broken requirements found. -ExecuTorch installation verified -``` - -The `ethos-u` extra supplies Vela and TOSA tools. The Python package and source checkout use the same ExecuTorch release; the following pages build the host quantized operators and bare-metal runtime from that source. If you rerun the Arm setup script, repeat this package-installation section before continuing. +The checks should report `No broken requirements found.` and `ExecuTorch installation verified`. These commands replace `install_executorch.sh`, which uses nightly and test indexes. If you rerun Arm setup, repeat this section before continuing. ## Download the model and sample audio From 7d7143456254724268cca7fc4d8cbcac4c0ee70d Mon Sep 17 00:00:00 2001 From: Usamah Zaheer Date: Tue, 13 Oct 2026 15:08:13 +0100 Subject: [PATCH 005/109] Verify MobileSAM setup and clarify FVP validation List the Python build prerequisites, check the exporter imports, and explain runtime delegation and fast-mode timing limitations. Authored with assistance from OpenAI Codex. --- .../deploy-mobilesam-on-ethos-u/_index.md | 1 + .../prepare-the-environment.md | 14 ++++++++++---- .../deploy-mobilesam-on-ethos-u/run-the-example.md | 2 ++ .../validate-the-results.md | 6 ++++++ 4 files changed, 19 insertions(+), 4 deletions(-) diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/_index.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/_index.md index 3dc41540c5..6973fb6bcb 100644 --- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/_index.md +++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/_index.md @@ -16,6 +16,7 @@ learning_objectives: prerequisites: - A Linux development machine with glibc 2.28 or later, or an Apple silicon Mac running macOS 15 or later - On Linux, libstdc++.so.6 providing GLIBCXX_3.4.26 or later for the Corstone-320 FVP + - Python 3.12 with development headers and virtual-environment support - Familiarity with PyTorch model export and embedded cross-compilation # START generated_summary_faq diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md index 2bedc18169..3d9f17e9b5 100644 --- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md +++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md @@ -8,7 +8,7 @@ layout: "learningpathall" ## Check your development machine -Run the following preflight check before downloading the source: +Use Python 3.12 with development headers and virtual-environment support. Run the following preflight check before downloading the source: ```bash case "$(uname -s)/$(uname -m)" in @@ -163,16 +163,22 @@ These commands replace `install_executorch.sh` and its TorchAO nightly with rele ## Verify the environment -Confirm that Python imports ExecuTorch: +Check the ExecuTorch and Ethos-U imports used by the exporter: ```bash -python -c "import executorch; print('ExecuTorch import succeeded')" +python - <<'PYTHON' +import executorch.kernels.quantized +from executorch.backends.arm.ethosu import EthosUPartitioner +from executorch.backends.arm.quantizer import EthosUQuantizer + +print("ExecuTorch Ethos-U imports succeeded") +PYTHON ``` The output is similar to: ```output -ExecuTorch import succeeded +ExecuTorch Ethos-U imports succeeded ``` Check the target compiler and FVP: diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/run-the-example.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/run-the-example.md index e937d890e0..4a4ada8fc3 100644 --- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/run-the-example.md +++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/run-the-example.md @@ -69,6 +69,8 @@ backends/arm/scripts/run_fvp.sh \ The runner reads the tensor specified by `-i input.bin` and writes its first output tensor to `output-0.bin` under `arm_test/mobilesam/io/`. The console transcript is saved to `arm_test/mobilesam/fvp.log`; validation reads the tensor file. +`--fast` selects fast Ethos-U simulation for functional validation. Don't use its cycle counts or performance monitoring unit (PMU) counters to benchmark the model. + Confirm that the run created the output file before continuing: ```bash diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md index 748d035352..647cc27b88 100644 --- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md +++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md @@ -63,6 +63,12 @@ Number of non-delegated nodes: 3 Node counts can vary with dependency versions. Use the values from your export when reporting delegation. +In `arm_test/mobilesam/fvp.log`, also confirm `NPU delegations: 1`. This count records a completed NPU invocation at runtime; the export report shows how the model was partitioned. + +{{% notice Note %}} +The FVP can report `Inference CPU ratio: 100.00 %` and `Inference NPU ratio: 0.00 %` even when the NPU executes the delegated subgraph. These ratios use CPU-side timing, not delegated operator counts. CPU timing isn't cycle accurate on this FVP. Fast mode can also zero PMU event counters and isn't suitable for benchmarking. Use the delegation count and mask comparison to validate this run. +{{% /notice %}} + ## Inspect the visual result Open `fvp_comparison.png` under `arm_test/mobilesam/result/` in an image viewer. The image contains three panels: From 25a662068f0c5a4d15ad6a5b8d358ceb04c35388 Mon Sep 17 00:00:00 2001 From: Usamah Zaheer Date: Tue, 13 Oct 2026 15:08:15 +0100 Subject: [PATCH 006/109] Clarify Silero validation state limitation Explain that the pinned exporter retains state from reference generation and scope the probability comparison to the documented functional check. Authored with assistance from OpenAI Codex. --- .../silero-vad-ethos-u/4-validate-output.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/4-validate-output.md b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/4-validate-output.md index 0e9fa57e5d..92caeaf99c 100644 --- a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/4-validate-output.md +++ b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/4-validate-output.md @@ -60,7 +60,9 @@ Mean abs error: 0.018459 Threshold mismatches at 0.550: 0 ``` -The comparison confirms that the host and FVP produced the same number of finite probabilities, and the numerical differences stay within tolerance. It also confirms that every frame has the same speech or silence decision. +The comparison checks that the host and FVP produced the same number of finite probabilities, that the numerical differences stay within the stated tolerances, and that every frame has the same speech or silence decision. + +The pinned exporter retains the recurrent state left after generating the host reference, so the FVP starts from a different state. Treat this as a functional check of the example clip's speech decisions; the probability errors include that state difference. ## What you've accomplished From c61eb1353c2aa17fc79b6d71a385a9b2550031e7 Mon Sep 17 00:00:00 2001 From: "BarbaraC@Arm" Date: Thu, 15 Oct 2026 10:14:23 -0700 Subject: [PATCH 007/109] Review and improve ML Extensions for Vulkan Learning Path _index.md: - Update LP title and broaden target audience to include AI/ML developers - Clarify Windows x86-64 requirements and consolidate development prerequisites 1-introduction.md: - Clarify the roles of VK_ARM_tensors, VK_ARM_data_graph, and SPIR-V - Broaden the positioning of ML Extensions beyond gaming-only workloads - Improve heading hierarchy and separate the next-steps section - Simplify the transition to hands-on samples and remove premature tooling details 2-ml-ext-for-vulkan.md: - Simplify setup by moving general development tools to prerequisites - Clarify Python virtual environment setup and introduce ML SDK components - Expand Vulkan SDK installation and verification instructions - Update Vulkan Configurator steps for clearer, version-independent layer configuration 4-scenario-runner.md: - Add ML Workload Library for Vulkan to the SDK component overview 5-renderdoc.md: - Simplify RenderDoc introduction and remove duplicated capability descriptions --- .../vulkan-ml-sample/1-introduction.md | 13 +-- .../vulkan-ml-sample/2-ml-ext-for-vulkan.md | 82 ++++++++++--------- .../vulkan-ml-sample/4-scenario-runner.md | 1 + .../vulkan-ml-sample/5-renderdoc.md | 6 +- .../vulkan-ml-sample/_index.md | 14 ++-- 5 files changed, 60 insertions(+), 56 deletions(-) diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/1-introduction.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/1-introduction.md index 278715d573..da2f464a38 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/1-introduction.md +++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/1-introduction.md @@ -12,7 +12,7 @@ Neural graphics combines real-time rendering with machine learning to improve vi ## How do ML Extensions for Vulkan support neural graphics workloads? -The `VK_ARM_data_graph` and `VK_ARM_tensors` extensions bring structured compute graph execution and tensor processing to the Vulkan API. Their data graph pipelines use SPIR-V-defined graphs to execute ML inference workloads efficiently. +The `VK_ARM_tensors` extension introduces support for multidimensional tensor data in Vulkan, while `VK_ARM_data_graph` enables the execution of ML computational graphs using Vulkan data graph pipelines. These pipelines use SPIR-V, Vulkan's intermediate representation for GPU programs, to define and execute ML computational graphs. To help developers adopt these features, the Tensor and Data Graph Vulkan Samples offer hands-on demonstrations. @@ -25,17 +25,18 @@ The samples range from basic configurations to more advanced features, such as t This Learning Path walks you through setting up and running the first sample, `simple_tensor_and_data_graph`. -### Why use ML Extensions for Vulkan in game and graphics development? +## Why use ML Extensions for Vulkan in game and graphics development? As a game developer, you've probably noticed the rising demand for smarter, more immersive graphics — but also the increasing strain on GPU resources, especially on mobile. Vulkan's traditional pipelines give you fine-grained control, but finding the right tooling to integrate machine learning has been a challenge. That’s where the new ML extensions for Vulkan come in. Arm’s `VK_ARM_tensors` and `VK_ARM_data_graph` extensions give you native Vulkan support for executing neural networks on the GPU — using structured tensors and data graph pipelines. Instead of chaining compute shaders to simulate ML models, you can now express them as dataflow graphs in SPIR-V and run them more efficiently. This opens the door to using AI techniques right alongside the graphics pipeline. +Although ML is widely used for image classification and large language models (LLMs), these extensions are particularly useful for game and graphics workloads. They prioritize predictable execution, GPU compatibility, and memory efficiency. Built-in support for tensor formats and pipeline sessions helps you combine traditional Vulkan rendering with machine learning. -Although ML is widely used for image classification and large language models (LLMs), these extensions target game and graphics workloads. They prioritize predictable execution, GPU compatibility, and memory efficiency. Built-in support for tensor formats and pipeline sessions helps you combine traditional Vulkan rendering with machine learning. +## What's next? -Arm provides emulation layers for development on any modern Vulkan-capable hardware, and PyTorch support is available for model conversion workflows. +To help developers explore these capabilities, Arm provides development tools and hands-on examples. The ML Emulation Layers for Vulkan allow developers to experiment with the extensions on Vulkan-capable hardware without native support. -For an example of real-time upscaling, see the Learning Path [**Neural Super Sampling with Unreal Engine**](/learning-paths/mobile-graphics-and-gaming/nss-unreal/). +For an example of neural graphics in practice, see the Learning Path [**Neural Super Sampling with Unreal Engine**](/learning-paths/mobile-graphics-and-gaming/nss-unreal/). -With the Vulkan Samples, you can experiment directly with these ideas. Move on to the next section to set up your machine for running the samples. +In this Learning Path, you'll start with the Vulkan Samples to explore the ML Extensions directly. Continue to the next section to set up your development environment. \ No newline at end of file diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md index 3ae16a03d4..0361d2e473 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md +++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md @@ -12,35 +12,24 @@ To run the Vulkan Samples, you first need to set up your development environment This setup involves two main steps: -- Install the required tools on your development machine. -- Download the ML Emulation Layers for Vulkan, which simulate the `VK_ARM_data_graph` and `VK_ARM_tensors` extensions. +- Create a Python virtual environment and install the ML Emulation Layers for Vulkan. +- Install the Vulkan SDK and configure the emulation layers for Vulkan, which simulate the `VK_ARM_data_graph` and `VK_ARM_tensors` extensions. -## Install required tools for development +## Set up the development environment -Before building and running the samples, install these tools on your development machine: +Open **Windows PowerShell** from the Windows 11 Start menu. Verify that Python 3.10 or later is installed, then navigate to your user home directory: -- CMake (version 3.25 or later) -- Python 3 -- Git - -Install Git with the following command: - -```bash -winget install --id Git.Git -e +```powershell +python --version +cd $HOME ``` -In Visual Studio Code, create a folder for the project and install these Microsoft extensions: - -- C/C++ -- CMake Tools +The following steps create a Python virtual environment and install two packages: -![C/C++ tools extension](images/c-ext.png) +- **ML Emulation Layers for Vulkan:** Part of Arm's open-source **ML SDK for Vulkan**, a toolkit that helps developers build and test neural graphics and AI/ML workloads using the ML Extensions for Vulkan. The emulation layers provide support for these extensions on Vulkan-capable hardware. +- **CMake:** Provides the build tools needed to compile the Vulkan Samples. -![CMake Tools extension](images/cmake-ext.png) - -Create a Python virtual environment and install the required packages: - -```bash +```powershell py -m venv .venv .\.venv\Scripts\Activate.ps1 python -m pip install --upgrade pip @@ -49,7 +38,7 @@ python -m pip install ai-ml-emulation-layer-for-vulkan cmake To verify your installation, run the following commands: -```bash +```powershell cmake --version python --version git --version @@ -60,37 +49,50 @@ Each command prints the installed version of the tool or shows where the appropr ### Install the Vulkan Software Development Kit -Download the Windows installer from [Getting Started with the Windows Vulkan SDK](https://vulkan.lunarg.com/sdk/home). The Vulkan Software Development Kit (SDK) includes **Vulkan Configurator**, which you use to run the emulation layers. +Download and install the Vulkan SDK from [Getting Started with the Windows Vulkan SDK](https://vulkan.lunarg.com/sdk/home). -{{% notice Note %}} +1. Under **Windows**, select **x64 / x86**. +2. Download the **SDK Installer** for version 1.4.321 or later. Do not select the Runtime Installer. +3. Run the installer and accept the default installation directory. +4. Keep the default component selections and accept the license agreement. +5. Complete the installation. +6. On the final screen, select **Run the Vulkan Configurator and Hardware Capabilities Viewer**, then click **Finish**. -Use Vulkan SDK version 1.4.321 or later. - -{{% /notice %}} +Verify that Vulkan Configurator opens successfully. You will use it in the next section to enable the ML Emulation Layers for Vulkan. ## Enable the emulation layers in Vulkan Configurator -Open **Vulkan Configurator** to enable the layers that emulate the `VK_ARM_data_graph` and `VK_ARM_tensors` extensions. +If Vulkan Configurator is not already open, launch it from the Windows Start menu. -On the **Vulkan Layers Available** tab, browse to the emulation layer folder in your virtual environment's `site-packages` directory: +In **Vulkan Configurator**, configure the emulation layers for the `VK_ARM_data_graph` and `VK_ARM_tensors` extensions. -```output -.venv/Lib/site-packages/emulation_layer/deploy/bin -``` +1. In the **Vulkan Layers Available** tab, add the directory containing the emulation layer manifests from your Python virtual environment: -![VL available](images/vl-available.png) + ```text + .venv\Lib\site-packages\emulation_layer\deploy\bin + ``` -On the **Vulkan Layers Configuration** tab, set **Vulkan Loader Configuration Scope** to **Any Running Vulkan Executable**. Select **New Configuration**, then enable the **Graph Emulation** and **Tensor Emulation** layers. + For example, if you created the virtual environment in your Windows user home directory, the path is: -Ensure that the **Graph** layer is listed above the **Tensor** layer. + ```text + C:\Users\\.venv\Lib\site-packages\emulation_layer\deploy\bin + ``` -![VL Config](images/vl-layers.png) +2. Verify that the following layers appear in the available layers list: + - `VK_LAYER_ML_Graph_Emulation` + - `VK_LAYER_ML_Tensor_Emulation` -{{% notice Important %}} +![Vulkan Layers Available](images/vl-available.png) -Keep Vulkan Configurator running while you run the Vulkan samples. +3. In the **Vulkan Layers Configuration** tab, enable system Vulkan layer configuration and ensure both emulation layers are set to **Enable**. + +4. Verify that the **Graph Emulation** layer is listed above the **Tensor Emulation** layer. +![Vulkan Layers Configuration](images/vl-layers.png) + +{{% notice Important %}} +Keep Vulkan Configurator running while you run the Vulkan samples. {{% /notice %}} -With the emulation layers configured, you're ready to build the Vulkan Samples. +With the emulation layers configured, you're ready to build the Vulkan Samples. Continue to the next section to get started. diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md index 3aa090de86..a342392eb6 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md +++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md @@ -22,6 +22,7 @@ The SDK provides tools and runtime components that help you integrate neural net | **VGF Library** | Lightweight runtime decoder for `.vgf` files containing graphs, constants, and shaders. | Load and use graphs in a game engine | https://github.com/arm/ai-ml-sdk-vgf-library | | **Scenario Runner** | Executes ML workloads declaratively using JSON-based scenario descriptions. | Prototype and validate workloads | https://github.com/arm/ai-ml-sdk-scenario-runner | | **Emulation Layer** | Vulkan layer that emulates data graph and tensor extensions using compute shaders. | Test on devices without native support for ML Extensions for Vulkan | https://github.com/arm/ai-ml-emulation-layer-for-vulkan | +| **ML Workload Library for Vulkan** | C++ library for creating and executing ML workloads using Vulkan. | Integrate ML workloads into Vulkan applications | https://github.com/arm/ai-ml-workload-library-for-vulkan | ## About the Hugging Face release diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/5-renderdoc.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/5-renderdoc.md index fb9e148b0d..9c05be8516 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/5-renderdoc.md +++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/5-renderdoc.md @@ -8,11 +8,11 @@ layout: learningpathall ## Debug and profile workloads with RenderDoc -Integrating machine learning into real-time rendering makes frame-level inspection and performance analysis critical. RenderDoc helps you visualize and debug the workloads by letting you step through frames, examine tensors, and inspect Vulkan API calls. +Integrating machine learning into real-time rendering makes frame-level inspection and performance analysis critical. RenderDoc is a GPU frame capture tool that helps developers debug and analyze Vulkan workloads. -RenderDoc is a powerful GPU frame capture tool that lets you: +With RenderDoc, you can: -- Step through a frame’s rendering process +- Step through a frame's rendering process - Inspect Vulkan API calls - View shader inputs and outputs - Examine GPU resource states and memory usage diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md index c453eca4bd..29884a3ab5 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md +++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/_index.md @@ -1,9 +1,9 @@ --- -title: Enable neural graphics using ML Extensions for Vulkan +title: Enable Neural Graphics using ML Extensions for Vulkan® description: Learn how to set up ML Emulation Layers for Vulkan, run sample applications with ML Extensions for Vulkan, and debug the workflow with RenderDoc. minutes_to_complete: 30 -who_is_this_for: This advanced Learning Path is for engine developers who want to create neural graphics with ML Extensions for Vulkan. +who_is_this_for: This advanced Learning Path is for developers who want to create neural graphics and AI/ML workloads using ML Extensions for Vulkan. learning_objectives: - Explain the purpose of neural graphics and the role of ML Extensions for Vulkan @@ -12,11 +12,11 @@ learning_objectives: - Debug the flow using RenderDoc prerequisites: - - Windows 11 development machine - - Visual Studio 2022 - - Visual Studio workload - Desktop development with C++ - - Visual Studio workload - .NET desktop build tools - - Visual Studio Code + - Windows 11 x86-64 computer (physical machine or virtual machine) + - Visual Studio 2022 with Desktop development with C++ and .NET desktop build tools workloads + - Visual Studio Code with C/C++ and CMake Tools extensions (Microsoft) + - Python 3.10 or later + - Git # START generated_summary_faq generated_summary_faq: From 8f3fac708f174fad74e309e7285e881ae46cd14a Mon Sep 17 00:00:00 2001 From: "BarbaraC@Arm" Date: Tue, 20 Oct 2026 09:25:19 -0700 Subject: [PATCH 008/109] Clarify Vulkan ML architecture and improve Learning Path setup 1-introduction.md: - Introduce Vulkan extensions and explain the purpose of Arm ML Emulation Layers - Clarify how emulation layers expose ML extensions on hardware without native support - Add an architecture diagram showing the application, emulation layers, driver, and GPU - Reduce repetition between the technical introduction and benefits section - Simplify the transition to the setup instructions 2-ml-ext-for-vulkan.md: - Align setup overview with the three installation and configuration steps - Simplify package descriptions and use consistent Python commands - Clarify the roles of Arm ML SDK for Vulkan and LunarG Vulkan SDK - Explain why emulation layers must be enabled after installation 3-first-sample.md: - Use PowerShell syntax highlighting consistently for Windows commands - Clarify the sample summary to reflect what developers observe when running the application - Remove wording that implies developers manually performed the underlying Vulkan API operations 4-scenario-runner.md: - Remove unnecessary blank line before the Hugging Face section images/ml-emulation-layers.png: - Add architecture diagram illustrating how the ML Emulation Layers fit into the Vulkan runtime stack --- .../vulkan-ml-sample/1-introduction.md | 12 ++++++--- .../vulkan-ml-sample/2-ml-ext-for-vulkan.md | 23 ++++++++++++------ .../vulkan-ml-sample/3-first-sample.md | 10 ++++---- .../vulkan-ml-sample/4-scenario-runner.md | 1 - .../images/ml-emulation-layers.png | Bin 0 -> 1212641 bytes 5 files changed, 29 insertions(+), 17 deletions(-) create mode 100644 content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/images/ml-emulation-layers.png diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/1-introduction.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/1-introduction.md index da2f464a38..f5883f98cf 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/1-introduction.md +++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/1-introduction.md @@ -12,8 +12,14 @@ Neural graphics combines real-time rendering with machine learning to improve vi ## How do ML Extensions for Vulkan support neural graphics workloads? +Vulkan extensions add functionality to the Vulkan API. Arm's ML Extensions for Vulkan introduce support for tensor processing and machine learning data graphs. + +To make these extensions available on Vulkan-capable hardware that does not support them natively, Arm provides **ML Emulation Layers for Vulkan**, part of the [ML SDK for Vulkan](https://arm.github.io/ai-ml-sdk-for-vulkan/). A Vulkan layer is a software component that sits between a Vulkan application and the Vulkan driver. The ML Emulation Layers expose Arm's ML extensions and emulate their functionality using existing Vulkan compute capabilities. + The `VK_ARM_tensors` extension introduces support for multidimensional tensor data in Vulkan, while `VK_ARM_data_graph` enables the execution of ML computational graphs using Vulkan data graph pipelines. These pipelines use SPIR-V, Vulkan's intermediate representation for GPU programs, to define and execute ML computational graphs. +![Arm ML Emulation Layers architecture#center](images/ml-emulation-layers.png) + To help developers adopt these features, the Tensor and Data Graph Vulkan Samples offer hands-on demonstrations. These samples address key challenges in ML integration, such as: @@ -29,14 +35,12 @@ This Learning Path walks you through setting up and running the first sample, `s As a game developer, you've probably noticed the rising demand for smarter, more immersive graphics — but also the increasing strain on GPU resources, especially on mobile. Vulkan's traditional pipelines give you fine-grained control, but finding the right tooling to integrate machine learning has been a challenge. That’s where the new ML extensions for Vulkan come in. -Arm’s `VK_ARM_tensors` and `VK_ARM_data_graph` extensions give you native Vulkan support for executing neural networks on the GPU — using structured tensors and data graph pipelines. Instead of chaining compute shaders to simulate ML models, you can now express them as dataflow graphs in SPIR-V and run them more efficiently. This opens the door to using AI techniques right alongside the graphics pipeline. +Instead of coordinating individual compute shaders to implement ML workloads, developers can express computations as dataflow graphs in SPIR-V. This provides a more structured approach to integrating machine learning alongside traditional Vulkan graphics workloads and creates opportunities for graph-level optimization. Although ML is widely used for image classification and large language models (LLMs), these extensions are particularly useful for game and graphics workloads. They prioritize predictable execution, GPU compatibility, and memory efficiency. Built-in support for tensor formats and pipeline sessions helps you combine traditional Vulkan rendering with machine learning. ## What's next? -To help developers explore these capabilities, Arm provides development tools and hands-on examples. The ML Emulation Layers for Vulkan allow developers to experiment with the extensions on Vulkan-capable hardware without native support. - For an example of neural graphics in practice, see the Learning Path [**Neural Super Sampling with Unreal Engine**](/learning-paths/mobile-graphics-and-gaming/nss-unreal/). -In this Learning Path, you'll start with the Vulkan Samples to explore the ML Extensions directly. Continue to the next section to set up your development environment. \ No newline at end of file +Continue to the next section to install and configure the ML Emulation Layers for Vulkan, then build and run the Vulkan Samples. diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md index 0361d2e473..16c3805b84 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md +++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md @@ -10,10 +10,11 @@ layout: learningpathall To run the Vulkan Samples, you first need to set up your development environment. -This setup involves two main steps: +This setup involves three main steps: - Create a Python virtual environment and install the ML Emulation Layers for Vulkan. -- Install the Vulkan SDK and configure the emulation layers for Vulkan, which simulate the `VK_ARM_data_graph` and `VK_ARM_tensors` extensions. +- Install the LunarG Vulkan SDK. +- Enable the ML Emulation Layers using Vulkan Configurator. ## Set up the development environment @@ -24,13 +25,13 @@ python --version cd $HOME ``` -The following steps create a Python virtual environment and install two packages: +Create a Python virtual environment and install the following packages: -- **ML Emulation Layers for Vulkan:** Part of Arm's open-source **ML SDK for Vulkan**, a toolkit that helps developers build and test neural graphics and AI/ML workloads using the ML Extensions for Vulkan. The emulation layers provide support for these extensions on Vulkan-capable hardware. -- **CMake:** Provides the build tools needed to compile the Vulkan Samples. +- **ML Emulation Layers for Vulkan:** Provides the emulation layers introduced in the previous section. +- **CMake:** A build tool used to configure and build the Vulkan Samples. ```powershell -py -m venv .venv +python -m venv .venv .\.venv\Scripts\Activate.ps1 python -m pip install --upgrade pip python -m pip install ai-ml-emulation-layer-for-vulkan cmake @@ -47,8 +48,15 @@ python -m pip show ai-ml-emulation-layer-for-vulkan Each command prints the installed version of the tool or shows where the appropriate files are located. + ### Install the Vulkan Software Development Kit +Arm's ML SDK for Vulkan and the LunarG Vulkan SDK serve different purposes. + +Arm's ML SDK for Vulkan provides the ML Emulation Layers installed earlier in this Learning Path. The LunarG Vulkan SDK provides Vulkan development tools, including Vulkan Configurator, which you'll use to enable those layers. + +The two SDKs work together to provide a development environment where you can build and run Vulkan applications using the emulated ML extensions. + Download and install the Vulkan SDK from [Getting Started with the Windows Vulkan SDK](https://vulkan.lunarg.com/sdk/home). 1. Under **Windows**, select **x64 / x86**. @@ -60,9 +68,10 @@ Download and install the Vulkan SDK from [Getting Started with the Windows Vulka Verify that Vulkan Configurator opens successfully. You will use it in the next section to enable the ML Emulation Layers for Vulkan. - ## Enable the emulation layers in Vulkan Configurator +Installing the ML Emulation Layers makes them available on your system, but they must also be enabled so that Vulkan applications can discover and use the emulated ML extensions. + If Vulkan Configurator is not already open, launch it from the Windows Start menu. In **Vulkan Configurator**, configure the emulation layers for the `VK_ARM_data_graph` and `VK_ARM_tensors` extensions. diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/3-first-sample.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/3-first-sample.md index 527214db76..1bd3d8fe43 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/3-first-sample.md +++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/3-first-sample.md @@ -14,7 +14,7 @@ The **Simple Tensor and Data Graph** sample is a starting point for working with With the environment set up, clone the sample code from the Khronos Group repository: -```bash +```powershell git clone --recurse-submodules https://github.com/KhronosGroup/Vulkan-Samples.git cd Vulkan-Samples ``` @@ -31,13 +31,13 @@ Enable [Developer Mode](https://learn.microsoft.com/en-us/windows/apps/get-start Generate Visual Studio project files using CMake: -```bash +```powershell cmake -G "Visual Studio 17 2022" -A x64 -S . -Bbuild/windows ``` Compile the `vulkan_samples` target in Release mode: -```bash +```powershell cmake --build build/windows --config Release --target vulkan_samples ``` @@ -45,7 +45,7 @@ cmake --build build/windows --config Release --target vulkan_samples Run the built executable using the following command: -```bash +```powershell build\windows\app\bin\Release\AMD64\vulkan_samples.exe sample simple_tensor_and_data_graph ``` @@ -55,7 +55,7 @@ The sample creates input and output tensors, binds them with descriptor sets and ## Summary and next steps -By running this sample, you've stepped through a complete Vulkan data graph pipeline powered by ML Extensions for Vulkan. You've created tensors, set up descriptors, built a SPIR-V-encoded ML graph, and dispatched inference without custom shaders. This workflow provides a foundation for neural graphics and extends to more complex graphics scenarios. +By running this sample, you've seen how a Vulkan application creates tensors, configures a data graph pipeline, and dispatches an ML workload using the ML Extensions for Vulkan. This provides a foundation for exploring more complex ML and graphics workloads. You can also explore the remaining data graph pipeline samples. Each sample's documentation is in its directory under `samples/extensions/tensor_and_data_graph/`. diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md index a342392eb6..d9fee657a1 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md +++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md @@ -24,7 +24,6 @@ The SDK provides tools and runtime components that help you integrate neural net | **Emulation Layer** | Vulkan layer that emulates data graph and tensor extensions using compute shaders. | Test on devices without native support for ML Extensions for Vulkan | https://github.com/arm/ai-ml-emulation-layer-for-vulkan | | **ML Workload Library for Vulkan** | C++ library for creating and executing ML workloads using Vulkan. | Integrate ML workloads into Vulkan applications | https://github.com/arm/ai-ml-workload-library-for-vulkan | - ## About the Hugging Face release The [NSS model page on Hugging Face](https://huggingface.co/Arm/neural-super-sampling) provides a minimal example, called a *scenario*, for running NSS on a sample frame. It includes a Windows-compatible Scenario Runner binary, the VGF model, and one frame of input and expected output data. 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installation to a main section heading 4-scenario-runner.md: - Introduce Scenario Runner and its role in the ML SDK for Vulkan - Explain VGF and its use for packaging ML workloads - Add instructions to download and run the Neural Super Sampling example - Document Scenario Runner installation and output verification - Connect the exercise to RenderDoc image inspection 5-renderdoc.md: - Streamline the introduction and focus on Vulkan inspection and debugging - Add instructions for viewing Neural Super Sampling DDS output - Clarify the transition to capturing the Simple Tensor and Data Graph sample - Consolidate the Vulkan capture instructions images/scenario-runner-architecture-400.png: - Add architecture diagram showing Scenario Runner, VGF Library, ML Emulation Layers, and Vulkan API --- .../vulkan-ml-sample/2-ml-ext-for-vulkan.md | 2 +- .../vulkan-ml-sample/4-scenario-runner.md | 116 ++++++++++++++++-- .../vulkan-ml-sample/5-renderdoc.md | 33 ++--- .../scenario-runner-architecture-400.png | Bin 0 -> 154937 bytes 4 files changed, 126 insertions(+), 25 deletions(-) create mode 100644 content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/images/scenario-runner-architecture-400.png diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md index 16c3805b84..c6730b8e46 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md +++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md @@ -49,7 +49,7 @@ python -m pip show ai-ml-emulation-layer-for-vulkan Each command prints the installed version of the tool or shows where the appropriate files are located. -### Install the Vulkan Software Development Kit +## Install the Vulkan Software Development Kit Arm's ML SDK for Vulkan and the LunarG Vulkan SDK serve different purposes. diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md index d9fee657a1..10216faf89 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md +++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md @@ -1,5 +1,5 @@ --- -title: Running a test with the Scenario Runner +title: Run an ML workload with Scenario Runner weight: 5 ### FIXED, DO NOT MODIFY @@ -8,13 +8,16 @@ layout: learningpathall ## Overview -You'll learn how to run a complete inference test with the **Scenario Runner** from Arm's ML SDK for Vulkan. You'll also explore the downloadable binaries and assets on Arm's Hugging Face page that demonstrate ML Extensions for Vulkan. +You've already configured your Vulkan development environment and run the Simple Tensor and Data Graph sample using the ML Emulation Layers. -## About the ML SDK for Vulkan +In this section, you'll use **Scenario Runner**, a tool from Arm's ML SDK for Vulkan, to execute a prebuilt Neural Super Sampling (NSS) workload and verify its output. -The SDK provides tools and runtime components that help you integrate neural networks into Vulkan-based applications. ML Extensions for Vulkan (`VK_ARM_data_graph` and `VK_ARM_tensors`) define the runtime interface. The SDK provides a workflow for converting, packaging, and deploying ML models in real-time applications such as games. +## Scenario Runner in the ML SDK for Vulkan + +Arm's ML SDK for Vulkan provides tools and libraries for preparing and executing ML workloads. You've already installed and configured the ML Emulation Layers, one of the SDK components. + +The following table summarizes the main SDK components and their roles. -### SDK component summary | Component | Description | Usage context | GitHub link | |------------------|------------------------------------------------------------------------------------------------------|-------------------------------------|--------------| @@ -24,10 +27,107 @@ The SDK provides tools and runtime components that help you integrate neural net | **Emulation Layer** | Vulkan layer that emulates data graph and tensor extensions using compute shaders. | Test on devices without native support for ML Extensions for Vulkan | https://github.com/arm/ai-ml-emulation-layer-for-vulkan | | **ML Workload Library for Vulkan** | C++ library for creating and executing ML workloads using Vulkan. | Integrate ML workloads into Vulkan applications | https://github.com/arm/ai-ml-workload-library-for-vulkan | -## About the Hugging Face release +Vulkan Graph Format (VGF) is a format for packaging machine learning graphs and their associated data for execution with Vulkan. It brings together the graph definition, shaders, and model constants in a single file, making it easier to distribute and load prebuilt ML workloads. + +For this exercise, Scenario Runner uses the VGF Library to load a prebuilt `.vgf` model and executes the workload described in a JSON scenario file through the configured ML Emulation Layers. + +The following diagram shows how these components fit together during execution. + +![Scenario Runner architecture#center](images/scenario-runner-architecture-400.png) + +## Run a Neural Super Sampling scenario + +You'll use the [Neural Super Sampling example on Hugging Face](https://huggingface.co/Arm/neural-super-sampling) to run an inference workload with Scenario Runner. The repository provides the model, sample data, and additional documentation. + +### Download the example + +The repository uses Git Large File Storage (Git LFS) to manage large model and data files. + +Open Windows PowerShell and navigate to your home directory: + +```powershell +cd $HOME +``` + +Verify that Git LFS is installed. If it is not installed, download it from the [Git LFS website](https://git-lfs.com/) before continuing. + +Verify and initialize Git LFS: + +```powershell +git lfs version +git lfs install +``` + +Clone the Neural Super Sampling repository: + +```powershell +git clone https://huggingface.co/Arm/neural-super-sampling +cd neural-super-sampling +git lfs pull +``` + +### Install Scenario Runner + +Use the Python virtual environment you created earlier when setting up the ML Emulation Layers for Vulkan. + +If the virtual environment is not already active, activate it: + +```powershell +& "$HOME\.venv\Scripts\Activate.ps1" +``` -The [NSS model page on Hugging Face](https://huggingface.co/Arm/neural-super-sampling) provides a minimal example, called a *scenario*, for running NSS on a sample frame. It includes a Windows-compatible Scenario Runner binary, the VGF model, and one frame of input and expected output data. You can use these assets to run an end-to-end workflow and explore the VGF model in more detail. +Install Scenario Runner using pip: + +```powershell +python -m pip install ai-ml-sdk-scenario-runner +``` + +Verify that Scenario Runner is available: + +```powershell +scenario-runner --help +``` + +### Run the Neural Super Sampling scenario + +Navigate to the `scenario` directory in the cloned repository: + +```powershell +cd "$HOME\neural-super-sampling\scenario" +``` + +Run Scenario Runner using the supplied JSON configuration: + +```powershell +scenario-runner --scenario configs/960x540_1920x1080_high_fragment.json --output out_high +``` + +The configuration specifies a Neural Super Sampling workload that upscales an input frame from 960 × 540 to 1920 × 1080. + +Scenario Runner loads the prebuilt VGF model, executes the workload through the configured ML Emulation Layers, and writes the results to the `out_high` directory. + +### Examine the results + +When execution completes, Scenario Runner displays: + +```text +[Scenario-Runner] INFO: Results stored +``` + +The generated files are saved in the `out_high` directory. + +Verify the output files using PowerShell: + +```powershell +Get-ChildItem .\out_high +``` + +The output includes DDS image files (`.dds`) and NumPy tensor files (`.npy`). + +You'll use RenderDoc in the next section to view the generated images. ## Next steps -Next, you'll use RenderDoc to debug and inspect the workloads in this Learning Path. +You've successfully executed a Neural Super Sampling workload using Scenario Runner. + +Next, you'll install RenderDoc for Arm GPUs to view the generated images and explore how to capture and inspect Vulkan workloads. diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/5-renderdoc.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/5-renderdoc.md index 9c05be8516..752698d62d 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/5-renderdoc.md +++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/5-renderdoc.md @@ -6,9 +6,9 @@ weight: 6 layout: learningpathall --- -## Debug and profile workloads with RenderDoc +## Inspect and debug Vulkan workloads with RenderDoc -Integrating machine learning into real-time rendering makes frame-level inspection and performance analysis critical. RenderDoc is a GPU frame capture tool that helps developers debug and analyze Vulkan workloads. +Integrating machine learning into real-time rendering requires tools to inspect and debug Vulkan workloads. RenderDoc is a GPU frame capture tool that lets developers examine Vulkan API calls, shaders, and GPU resources. With RenderDoc, you can: @@ -17,15 +17,6 @@ With RenderDoc, you can: - View shader inputs and outputs - Examine GPU resource states and memory usage -## When to use RenderDoc with the samples - -RenderDoc can help in scenarios such as: - -- Diagnosing unexpected visual output by stepping through draw calls -- Analyzing the order and behavior of Vulkan API calls -- Investigating memory consumption or GPU resource states -- Validating data graph pipeline execution or identifying synchronization issues - ## Install Arm Performance Studio To use RenderDoc with ML Extensions for Vulkan, install the Arm-customized version included with Arm Performance Studio: @@ -35,15 +26,25 @@ To use RenderDoc with ML Extensions for Vulkan, install the Arm-customized versi `Arm_Performance_Studio__windows_x86-64.exe` 3. Follow the installation instructions. -After installation, launch RenderDoc for Arm GPUs from the Windows Start menu. +## View the Neural Super Sampling output -## Capture Vulkan frames with RenderDoc +You can use RenderDoc for Arm GPUs to view the DDS images generated by Scenario Runner in the previous section. -You can capture and inspect Vulkan Samples that use ML Extensions for Vulkan, including API calls such as `vkCreateTensorARM` and structures such as `VK_STRUCTURE_TYPE_TENSOR_DESCRIPTION_ARM`. +The `out_colour.dds` image uses the `B10G11R11_UFLOAT` format, which is not widely supported by standard image viewers. + +1. Launch **RenderDoc for Arm GPUs** from the Windows Start menu. +2. Select **File → Open Capture**. +3. Set the file filter to **All Files**. +4. Navigate to the `neural-super-sampling\scenario\out_high` directory and open `out_colour.dds`. +5. Select the **Texture Viewer** tab to inspect the image. + +The output image has a resolution of 1920 × 1080. + +## Capture Vulkan frames with RenderDoc -RenderDoc is especially useful for visualizing tensor operations, inspecting resource bindings, and verifying correct data graph pipeline execution. +So far, you've used RenderDoc to view the output of the Neural Super Sampling workload. You can also use RenderDoc to capture and inspect Vulkan API calls during application execution. -## Capture with RenderDoc +To explore this functionality, return to the Simple Tensor and Data Graph sample you ran earlier in this Learning Path. By capturing a frame, you can inspect Vulkan operations, resource bindings, and data graph pipeline execution. 1. **Open RenderDoc**, and in the main window, go to the **Launch Application** section. 2. 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g);q&zo77MJKVlZ0%vOoV6aWAK07*qoM6N<$f^M!-a{vGU literal 0 HcmV?d00001 From e711e714501275d20b93ec5b6c9e2d1d12ec3476 Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Thu, 29 Oct 2026 16:49:03 -0500 Subject: [PATCH 010/109] Add browser search trial with WebMCP agent tools --- assets/search-lab/app.js | 133 ++++++++++++++++++ assets/search-lab/engine.mjs | 59 ++++++++ assets/search-lab/style.css | 89 ++++++++++++ assets/search-lab/worker.js | 20 +++ content/search-lab/_index.md | 11 ++ layouts/search-lab/list.html | 104 ++++++++++++++ .../layouts/_default/baseof.html | 1 + .../partials/footer/script-includes.html | 2 +- tools/search-lab/README.md | 36 +++++ tools/search-lab/WEBMCP_DEMO.md | 73 ++++++++++ tools/search-lab/check-index.mjs | 43 ++++++ tools/search-lab/engine.test.mjs | 73 ++++++++++ 12 files changed, 643 insertions(+), 1 deletion(-) create mode 100644 assets/search-lab/app.js create mode 100644 assets/search-lab/engine.mjs create mode 100644 assets/search-lab/style.css create mode 100644 assets/search-lab/worker.js create mode 100644 content/search-lab/_index.md create mode 100644 layouts/search-lab/list.html create mode 100644 tools/search-lab/README.md create mode 100644 tools/search-lab/WEBMCP_DEMO.md create mode 100644 tools/search-lab/check-index.mjs create mode 100644 tools/search-lab/engine.test.mjs diff --git a/assets/search-lab/app.js b/assets/search-lab/app.js new file mode 100644 index 0000000000..82ebaef01d --- /dev/null +++ b/assets/search-lab/app.js @@ -0,0 +1,133 @@ +const $ = id => document.getElementById(id); +const main = document.querySelector('main[data-index]'); +const worker = new Worker(main.dataset.worker, { type: 'module' }); +let nextId = 0, generation = 0, limit = 10; +const pending = new Map(); +worker.onmessage = ({ data }) => { + const request = pending.get(data.id); + if (!request) return; + pending.delete(data.id); + data.error ? request.reject(Error(data.error)) : request.resolve(data.result); +}; +worker.onerror = () => { + for (const request of pending.values()) request.reject(Error('Search worker failed. Reload the page to retry.')); + pending.clear(); + $('status').textContent = 'Search could not start. Reload the page to retry.'; +}; +function call(action, input) { + return new Promise((resolve, reject) => { + const id = ++nextId; + pending.set(id, { resolve, reject }); + worker.postMessage({ id, action, input, url: main.dataset.index }); + }); +} +const ready = call('init'); +const tool = { + name: 'search_arm_content', + description: 'Find Arm Learning Paths and install guides by keyword or author name. Returns source URLs and metadata excerpts; use these sources to read the full instructions. Scores indicate keyword relevance, not confidence.', + inputSchema: { type: 'object', properties: { + query: { type: 'string', minLength: 1, maxLength: 300 }, + type: { type: 'string', enum: ['', 'learning-path', 'install-guide'], default: '' }, + category: { type: 'string', description: 'Learning Path category slug, such as servers-and-cloud-computing.' }, + sort: { type: 'string', enum: ['updated', 'relevance'], default: 'updated' }, + limit: { type: 'integer', minimum: 1, maximum: 100, default: 10 } + }, required: ['query'], additionalProperties: false }, + annotations: { readOnlyHint: true }, + async execute(input, { signal } = {}) { + try { + signal?.throwIfAborted(); + await ready; + signal?.throwIfAborted(); + const result = await call('search', input); + signal?.throwIfAborted(); + return { content: [{ type: 'text', text: JSON.stringify(result) }], structuredContent: result }; + } catch (error) { + if (signal?.aborted) throw error; + return { isError: true, content: [{ type: 'text', text: error.message }] }; + } + } +}; +// The tester and the registered tool invoke precisely the same callback. +$('run-tool').onclick = async () => { + try { $('tool-output').textContent = JSON.stringify(await tool.execute(JSON.parse($('tool-input').value)), null, 2); } + catch (error) { $('tool-output').textContent = error.message; } +}; +async function register() { + const context = document.modelContext || navigator.modelContext; + if (!context?.registerTool) { + $('webmcp').textContent = 'Native WebMCP is unavailable in this browser. Use the tool tester below.'; + return; + } + try { + await context.registerTool(tool); + $('webmcp').textContent = 'WebMCP registered: search_arm_content'; + } catch (error) { $('webmcp').textContent = `WebMCP registration failed: ${error.message}. The tester still works.`; } +} +register(); +async function render() { + const current = ++generation; + const query = $('query').value.trim(); + const params = new URLSearchParams(); + if (query) params.set('q', query); + if ($('type').value) params.set('type', $('type').value); + if ($('category').value) params.set('category', $('category').value); + params.set('sort', $('sort').value); + history.replaceState(null, '', `${location.pathname}${params.size ? '?' + params : ''}${location.hash}`); + $('results').replaceChildren(); + $('more').hidden = true; + $('search-diagnostics').hidden = true; + if (!query) { $('status').textContent = ''; return; } + $('status').textContent = 'Searching…'; + const start = performance.now(); + try { + await ready; + const result = await call('search', { query, type: $('type').value, category: $('category').value, sort: $('sort').value, limit }); + if (current !== generation) return; + $('status').textContent = `${result.total} result${result.total === 1 ? '' : 's'} for “${query}”`; + $('performance').textContent = `Last search: ${result.searchMs.toFixed(1)} ms engine · ${(performance.now() - start).toFixed(1)} ms response`; + $('search-diagnostics').hidden = false; + if (!result.total) $('status').textContent += '. Try fewer words or remove a filter.'; + for (const record of result.results) { + const li = document.createElement('li'); li.className = 'search-result-card'; + const a = document.createElement('a'); a.href = record.url; a.textContent = record.title; + const h = document.createElement('h3'); h.className = 'result-title'; h.append(a); + const meta = document.createElement('p'); meta.className = 'result-meta'; + const badge = document.createElement('span'); badge.className = 'result-type'; + badge.textContent = record.type === 'learning-path' ? 'Learning Path' : 'Install guide'; meta.append(badge); + if (record.category) { const category = document.createElement('span'); category.textContent = categoryLabel(record.category); meta.append(category); } + const p = document.createElement('p'); p.className = 'result-description'; p.textContent = record.description || record.summary; + const footer = document.createElement('div'); footer.className = 'result-footer'; + if (record.authors?.length) { const authors = document.createElement('p'); authors.textContent = 'By ' + record.authors.join(', '); footer.append(authors); } + const updated = document.createElement('p'); + if (record.lastUpdated) { + const time = document.createElement('time'); time.dateTime = record.lastUpdated; + time.textContent = new Date(record.lastUpdated + 'T00:00:00Z').toLocaleDateString('en-GB', { day: 'numeric', month: 'short', year: 'numeric', timeZone: 'UTC' }); + updated.append('Last updated: ', time); + } else { updated.textContent = 'Last updated: unavailable'; } + footer.append(updated); li.append(meta, h, p, footer); $('results').append(li); + } + $('more').hidden = result.total <= limit || limit >= 100; + } catch (error) { if (current === generation) $('status').textContent = error.message; } +} +function categoryLabel(slug) { + return slug.split('-').map((word, index) => index && ['and'].includes(word) ? word : word[0].toUpperCase() + word.slice(1)).join(' '); +} +for (const button of main.querySelectorAll('[data-query]')) button.onclick = () => { + $('query').value = button.dataset.query; $('type').value = ''; $('category').value = ''; limit = 10; render(); +}; +let timer; +$('query').oninput = () => { clearTimeout(timer); timer = setTimeout(() => { limit = 10; render(); }, 120); }; +$('search-form').onsubmit = event => { event.preventDefault(); clearTimeout(timer); limit = 10; render(); }; +for (const id of ['type', 'category', 'sort']) $(id).onchange = () => { limit = 10; render(); }; +$('more').onclick = () => { limit = Math.min(100, limit + 10); render(); }; +const params = new URLSearchParams(location.search); +$('query').value = (params.get('q') || '').slice(0, 300); +if (['updated', 'relevance'].includes(params.get('sort'))) $('sort').value = params.get('sort'); +if (['learning-path', 'install-guide'].includes(params.get('type'))) $('type').value = params.get('type'); +try { + const info = await ready; + for (const category of info.categories) { const option = document.createElement('option'); option.value = category; option.textContent = categoryLabel(category); $('category').append(option); } + if (info.categories.includes(params.get('category'))) $('category').value = params.get('category'); + $('metrics').textContent = `${info.count} documents · ${(info.bytes / 1024).toFixed(0)} KB index (uncompressed) · ${info.loadMs.toFixed(0)} ms to download and prepare`; + render(); +} catch (error) { $('status').textContent = `Unable to load search: ${error.message} Reload to retry.`; } diff --git a/assets/search-lab/engine.mjs b/assets/search-lab/engine.mjs new file mode 100644 index 0000000000..486f939c0a --- /dev/null +++ b/assets/search-lab/engine.mjs @@ -0,0 +1,59 @@ +// Dependency-free BM25 baseline. Exact identifiers stay searchable alongside words. +const stop = new Set('a an and are at be can do for from how i in is it me my of on or the to using what with'.split(' ')); +export function tokenize(text) { + return String(text).toLowerCase().match(/[\p{L}\p{N}]+(?:[.+#-][\p{L}\p{N}+#]+)*/gu) || []; +} +export function createEngine(records) { + const fields = [['title', 5], ['description', 3], ['authors', 3], ['metadata', 2], ['prerequisites', 2], ['summary', 1], ['faq', 1]]; + const docs = records.map(record => ({ record, fields: fields.map(([key, boost]) => { + const value = record[key] || ''; + const tokens = tokenize(Array.isArray(value) ? value.join(' ') : value); + const counts = new Map(); + for (const token of tokens) counts.set(token, (counts.get(token) || 0) + 1); + return { counts, length: tokens.length, boost }; + }) })); + const averages = fields.map((_, i) => docs.reduce((n, d) => n + d.fields[i].length, 0) / (docs.length || 1) || 1); + const df = new Map(); + for (const doc of docs) { + const terms = new Set(doc.fields.flatMap(f => [...f.counts.keys()])); + for (const term of terms) df.set(term, (df.get(term) || 0) + 1); + } + return function search(input = {}) { + if (!input || typeof input !== 'object' || Array.isArray(input)) throw Error('Arguments must be an object.'); + if (Object.keys(input).some(key => !['query', 'type', 'category', 'limit', 'sort'].includes(key))) throw Error('Unknown search argument.'); + const { query, type = '', category = '', limit = 10, sort = 'updated' } = input; + if (typeof query !== 'string' || !query.trim() || query.length > 300) throw Error('Query must contain 1–300 characters.'); + if (!['', 'learning-path', 'install-guide'].includes(type)) throw Error('Unknown content type.'); + if (typeof category !== 'string' || (category && !records.some(r => r.category === category))) throw Error('Unknown category.'); + if (!Number.isInteger(limit) || limit < 1 || limit > 100) throw Error('Limit must be an integer from 1 to 100.'); + if (!['updated', 'relevance'].includes(sort)) throw Error('Unknown sort order.'); + const terms = [...new Set(tokenize(query).filter(t => !stop.has(t)))]; + if (!terms.length) return { total: 0, results: [] }; + const ranked = []; + for (const doc of docs) { + if ((type && doc.record.type !== type) || (category && doc.record.category !== category)) continue; + let score = 0, matched = 0; + for (const term of terms) { + let termScore = 0; + const idf = Math.log(1 + (docs.length - (df.get(term) || 0) + 0.5) / ((df.get(term) || 0) + 0.5)); + doc.fields.forEach((field, i) => { + const tf = field.counts.get(term) || 0; + termScore += field.boost * idf * tf * 2.2 / (tf + 1.2 * (0.25 + 0.75 * field.length / averages[i])); + }); + if (termScore) matched++; + score += termScore; + } + // Reward coverage so long natural-language queries can still find partial matches. + if (matched) ranked.push({ ...doc.record, score: score * (matched / terms.length) ** 2 }); + } + ranked.sort((a, b) => { + // ISO date strings sort chronologically; missing dates follow dated results. + const dateOrder = sort === 'updated' ? (b.lastUpdated || '').localeCompare(a.lastUpdated || '') : 0; + const typeOrder = sort === 'relevance' + ? Number(b.type === 'install-guide') - Number(a.type === 'install-guide') + : 0; + return dateOrder || typeOrder || b.score - a.score || a.id.localeCompare(b.id); + }); + return { total: ranked.length, results: ranked.slice(0, limit) }; + }; +} diff --git a/assets/search-lab/style.css b/assets/search-lab/style.css new file mode 100644 index 0000000000..51ea9550e7 --- /dev/null +++ b/assets/search-lab/style.css @@ -0,0 +1,89 @@ +/* Keep all experiment styles inside the shared site's content area. */ +.browser-search { + --search-surface: #fff; + --search-field: #fff; + --search-border: #c4c1cc; + --search-muted: #5c586b; + --search-link: #6023de; + --search-chip: #f0e9ff; + max-width: 1080px; + margin: 0 auto; + padding: 40px 16px 56px; + color: inherit; +} +html[theme="dark"] .browser-search { + --search-surface: #151237; + --search-field: var(--lp-background-color, #08052e); + --search-border: #52496d; + --search-muted: #c1bbd0; + --search-link: var(--lp-accent-secondary, #d1bafc); + --search-chip: #29204a; + color-scheme: dark; +} +.browser-search *, .browser-search *::before, .browser-search *::after { box-sizing: border-box; } +.browser-search .search-intro { margin-bottom: 28px; } +.browser-search .search-eyebrow { color: var(--search-link); font-weight: 700; letter-spacing: .12em; margin: 0 0 12px; } +.browser-search h2 { margin: 0 0 12px; font-size: clamp(1.7rem, 4vw, 2.25rem); line-height: 1.25; } +.browser-search p { line-height: 1.6; } +.browser-search .search-intro > p:last-child { margin-bottom: 0; } +.browser-search .search-panel { background: var(--search-surface); border: 1px solid var(--search-border); border-radius: 8px; padding: 28px; } +.browser-search label { display: block; font-weight: 700; margin-bottom: 8px; } +.browser-search .query-row { display: flex; gap: 12px; } +.browser-search input, .browser-search select, .browser-search textarea { + font: inherit; color: inherit; border: 1px solid var(--search-border); + border-radius: 4px; background: var(--search-field); padding: 12px; min-height: 48px; +} +.browser-search input { flex: 1; width: 100%; min-width: 0; } +.browser-search input::placeholder { color: var(--search-muted); opacity: 1; } +.browser-search select, .browser-search textarea { width: 100%; } +.browser-search .search-button { + display: inline-flex; align-items: center; justify-content: center; min-height: 48px; + border: 1px solid var(--lp-accent-primary, #6023de); border-radius: 4px; + background: var(--lp-accent-primary, #6023de); color: white; padding: 10px 26px; + font: inherit; font-weight: 700; cursor: pointer; +} +.browser-search .search-button:hover { background: #4c18b8; border-color: #4c18b8; } +.browser-search .search-button-secondary { background: transparent; color: var(--search-link); border-color: var(--search-link); } +.browser-search .search-button-secondary:hover { background: var(--search-chip); border-color: var(--search-link); } +.browser-search :focus-visible { outline: 3px solid var(--search-link); outline-offset: 4px; } +.browser-search .search-examples { display: flex; flex-wrap: wrap; align-items: center; gap: 8px 12px; margin-top: 14px; color: var(--search-muted); } +.browser-search .search-examples button { border: 0; background: transparent; color: var(--search-link); text-decoration: underline; text-underline-offset: 3px; font: inherit; cursor: pointer; padding: 4px 0; min-height: 32px; } +.browser-search .filters { display: grid; grid-template-columns: 1fr 1.4fr 1.2fr; gap: 20px; margin-top: 24px; padding-top: 24px; border-top: 1px solid var(--search-border); } +.browser-search .filters > div { min-width: 0; } +.browser-search .search-results { margin-top: 28px; } +.browser-search #status { font-weight: 700; margin-bottom: 20px; } +.browser-search .search-diagnostics { margin: -8px 0 20px; font-size: .9rem; } +.browser-search .search-diagnostics p { margin: 4px 0; } +.browser-search #results { list-style: none; padding: 0; margin: 0; display: grid; gap: 16px; } +.browser-search .search-result-card { background: var(--search-surface); border: 1px solid var(--search-border); border-radius: 8px; padding: 24px 28px; } +.browser-search .search-result-card:hover { border-color: var(--search-link); } +.browser-search .result-meta { display: flex; flex-wrap: wrap; gap: 8px 14px; align-items: center; margin: 0 0 12px; color: var(--search-muted); } +.browser-search .result-type { background: var(--search-chip); color: var(--search-link); border-radius: 4px; padding: 2px 10px; font-weight: 700; } +.browser-search .result-title { font-size: 1.35rem; margin: 0 0 10px; line-height: 1.35; } +.browser-search a { color: var(--search-link); text-underline-offset: 3px; } +.browser-search .result-title a { text-decoration: none; } +.browser-search .result-title a:hover { text-decoration: underline; } +.browser-search .result-description { margin: 0 0 16px; } +.browser-search .result-footer { display: flex; flex-wrap: wrap; gap: 8px 24px; color: var(--search-muted); border-top: 1px solid var(--search-border); padding-top: 14px; } +.browser-search .result-footer p { margin: 0; } +.browser-search #more { margin: 24px 0 0; } +.browser-search [hidden] { display: none !important; } +.browser-search .muted { color: var(--search-muted); } +.browser-search .agent-section { margin-top: 48px; padding-top: 36px; border-top: 1px solid var(--search-border); scroll-margin-top: 100px; } +.browser-search #search-form { scroll-margin-top: 100px; } +.browser-search .agent-section .search-intro { margin-bottom: 16px; } +.browser-search .search-tools { margin-top: 20px; } +.browser-search summary { cursor: pointer; font-weight: 700; } +.browser-search .search-tools h3 { font-size: 1.25rem; margin-top: 24px; } +.browser-search .search-tools textarea { font-family: ui-monospace, monospace; margin-bottom: 12px; } +.browser-search pre { white-space: pre-wrap; overflow-wrap: anywhere; max-height: 450px; overflow: auto; background: var(--search-field); color: inherit; border-radius: 4px; } +.browser-search pre:not(:empty) { border: 1px solid var(--search-border); padding: 16px; } +@media (max-width: 760px) { + .browser-search { padding: 28px 0 40px; } + .browser-search .filters { grid-template-columns: 1fr; gap: 16px; } + .browser-search .search-panel, .browser-search .search-result-card { padding: 20px; } +} +@media (max-width: 440px) { + .browser-search .query-row { flex-direction: column; } + .browser-search .query-row .search-button { width: 100%; } +} diff --git a/assets/search-lab/worker.js b/assets/search-lab/worker.js new file mode 100644 index 0000000000..300349124a --- /dev/null +++ b/assets/search-lab/worker.js @@ -0,0 +1,20 @@ +import { createEngine } from './engine.mjs'; +let search; +self.onmessage = async ({ data }) => { + const { id, action, input, url } = data; + try { + if (action === 'init') { + const start = performance.now(); + const response = await fetch(url); + if (!response.ok) throw Error(`Index request failed (${response.status}).`); + const text = await response.text(); + const index = JSON.parse(text); + search = createEngine(index.records); + self.postMessage({ id, result: { count: index.records.length, bytes: new TextEncoder().encode(text).length, loadMs: performance.now() - start, categories: [...new Set(index.records.map(r => r.category).filter(Boolean))].sort() } }); + } else { + if (!search) throw Error('Search index is not ready.'); + const start = performance.now(); + self.postMessage({ id, result: { ...search(input), searchMs: performance.now() - start } }); + } + } catch (error) { self.postMessage({ id, error: error.message }); } +}; diff --git a/content/search-lab/_index.md b/content/search-lab/_index.md new file mode 100644 index 0000000000..6a7f0aa735 --- /dev/null +++ b/content/search-lab/_index.md @@ -0,0 +1,11 @@ +--- +title: Search +description: Find Arm Learning Paths and install guides by topic, tool, platform, or author. +browser_search: true +url: /search/ +layout: list +sitemap: + disable: true +build: + list: never +--- diff --git a/layouts/search-lab/list.html b/layouts/search-lab/list.html new file mode 100644 index 0000000000..eaeb8746ae --- /dev/null +++ b/layouts/search-lab/list.html @@ -0,0 +1,104 @@ +{{ define "page-head" }} + +{{ $css := resources.Get "search-lab/style.css" | fingerprint }} + +{{ end }} + +{{ define "main" }} +{{- $records := slice -}} +{{- range site.Pages -}} + {{- $path := "" -}} + {{- with .File }}{{ $path = .Path }}{{ end -}} + {{- $isLP := and (eq .Section "learning-paths") (eq (len (split $path "/")) 4) (strings.HasSuffix $path "/_index.md") -}} + {{- $isGuide := and (eq .Section "install-guides") .Params.tool_install (not .Params.multitool_install_part) -}} + {{/* Explicit checks keep drafts out even when previewing with --buildDrafts. */}} + {{- $visible := not .Draft -}} + {{- if and (not .PublishDate.IsZero) (.PublishDate.After now) }}{{ $visible = false }}{{ end -}} + {{- if and (not .ExpiryDate.IsZero) (.ExpiryDate.Before now) }}{{ $visible = false }}{{ end -}} + {{- range .Ancestors -}} + {{- if or .Draft (and (not .PublishDate.IsZero) (.PublishDate.After now)) (and (not .ExpiryDate.IsZero) (.ExpiryDate.Before now)) }}{{ $visible = false }}{{ end -}} + {{- end -}} + {{- if and $visible (or $isLP $isGuide) -}} + {{- $kind := cond $isLP "learning-path" "install-guide" -}} + {{- $category := "" -}} + {{- if $isLP }}{{ $category = index (split $path "/") 1 }}{{ end -}} + {{- $summary := "" -}} + {{- $faq := slice -}} + {{- with .Params.generated_summary_faq -}} + {{- $summary = .summary | default "" -}} + {{- range .faqs -}} + {{- with .question }}{{ $faq = $faq | append (. | markdownify | plainify | htmlUnescape) }}{{ end -}} + {{- with .answer }}{{ $faq = $faq | append (. | markdownify | plainify | htmlUnescape) }}{{ end -}} + {{- end -}} + {{- end -}} + {{- $prerequisites := "" -}} + {{- with .Params.prerequisites -}} + {{- if reflect.IsSlice . -}} + {{- $prerequisites = delimit . " " -}} + {{- else -}} + {{- $prerequisites = . -}} + {{- end -}} + {{- $prerequisites = $prerequisites | markdownify | plainify | htmlUnescape -}} + {{- end -}} + {{- $lastUpdated := .Lastmod.UTC -}} + {{- if $isLP }}{{ $lastUpdated = partial "server-side-processing/calculate-last-update.html" . }}{{ end -}} + {{- $updated := "" -}} + {{- if not $lastUpdated.IsZero }}{{ $updated = $lastUpdated.Format "2006-01-02" }}{{ end -}} + {{- $authors := slice -}} + {{- with .Params.author -}} + {{- if reflect.IsSlice . -}} + {{- range . }}{{ $authors = $authors | append . }}{{ end -}} + {{- else -}} + {{- $authors = $authors | append . -}} + {{- end -}} + {{- end -}} + {{- $metadata := slice -}} + {{- range slice .Params.learning_objectives .Params.who_is_this_for .Params.additional_search_terms .Params.subjects .Params.platforms .Params.armips .Params.operatingsystems .Params.tools_software_languages .Params.skilllevels -}} + {{- with . }}{{ $metadata = $metadata | append (printf "%v" .) }}{{ end -}} + {{- end -}} + {{- $records = $records | append (dict "id" .RelPermalink "url" .Permalink "title" .Title "description" (.Description | default "") "summary" $summary "prerequisites" $prerequisites "faq" (delimit $faq " ") "authors" $authors "lastUpdated" $updated "metadata" (delimit $metadata " ") "type" $kind "category" $category) -}} + {{- end -}} +{{- end -}} +{{- $index := resources.FromString "search-lab/index.json" (dict "version" 1 "records" $records | jsonify) | fingerprint -}} +{{- $engine := resources.Get "search-lab/engine.mjs" | fingerprint -}} +{{- $workerSource := resources.Get "search-lab/worker.js" -}} +{{- $workerCode := replace $workerSource.Content "'./engine.mjs'" (printf "'%s'" $engine.RelPermalink) -}} +{{- $worker := resources.FromString "search-lab/worker.js" $workerCode | fingerprint -}} +{{- $app := resources.Get "search-lab/app.js" | fingerprint -}} +{{/* Publish worker dependencies without adding them to the main site's bundles. */}} +{{- $engineURL := $engine.RelPermalink -}} +

    +
    +

    EARLY ACCESS FEATURE

    +

    Try an alternative search

    +

    Explore Learning Paths and install guides by topic, tool, platform, or author.

    +
    + +
    +

    Loading search index…

    + +
      +
      +
      +
      +

      Search with an agent

      +

      Browser agents can search the same Learning Paths and install guides through the WebMCP tool. Use the tester below to explore how it works.

      +
      +

      If you don’t see “WebMCP registered” below, open chrome://flags/#enable-webmcp-testing in Chrome, set WebMCP for testing to Enabled, then relaunch Chrome and reopen this page.

      +

      Checking WebMCP support…

      +
      Try the agent search tool +

      Try the agent search tool

      +

      This tester calls the same search tool available to browser agents. You can use it even if your browser does not support WebMCP.

      + +
      
+
      
+      
+
      
+{{ end }}
diff --git a/themes/arm-design-system-hugo-theme/layouts/_default/baseof.html b/themes/arm-design-system-hugo-theme/layouts/_default/baseof.html
index d1561dd3b1..dc402be204 100644
--- a/themes/arm-design-system-hugo-theme/layouts/_default/baseof.html
+++ b/themes/arm-design-system-hugo-theme/layouts/_default/baseof.html
@@ -9,6 +9,7 @@
         {{partial "head/analytics.html" .}}
         {{partial "head/conditional-redirect.html" .}}
         {{partial "head/hotjar-surveys.html" .}}
+        {{block "page-head" .}}{{end}}
 
         {{ if .Param "math" }}
            {{ partialCached "math.html" . }}
diff --git a/themes/arm-design-system-hugo-theme/layouts/partials/footer/script-includes.html b/themes/arm-design-system-hugo-theme/layouts/partials/footer/script-includes.html
index 47ed61df98..cfc37c6277 100644
--- a/themes/arm-design-system-hugo-theme/layouts/partials/footer/script-includes.html
+++ b/themes/arm-design-system-hugo-theme/layouts/partials/footer/script-includes.html
@@ -66,7 +66,7 @@
 {{end}}
 
 
-{{if $is_list}}
+{{if and $is_list (not .Params.browser_search)}}
     
     
 {{end}}
diff --git a/tools/search-lab/README.md b/tools/search-lab/README.md
new file mode 100644
index 0000000000..f3684e1292
--- /dev/null
+++ b/tools/search-lab/README.md
@@ -0,0 +1,36 @@
+# Search lab
+
+An isolated `/search/` Hugo page using the shared site layout, navigation, masthead, footer, typography, and light/dark theme. Coveo and existing search entry points are unchanged. A page-head block in the shared base layout loads scoped styles, and the browser_search front-matter flag excludes legacy list-filter scripts on this page. The page excludes itself from the sitemap and requests `noindex`; this is discoverability control, not access control. Nothing has been deployed.
+
+## Try it
+
+From the repository root:
+
+```sh
+hugo server --bind 127.0.0.1 --port 1313
+```
+
+Open http://localhost:1313/search/ and try `GCC`, `Docker`, `llama.cpp Graviton`, and `SVE2`. Author names such as `Jason Andrews` are searchable too, including combinations such as `Jason GCC`; author names appear on each result. Filter by install guide when you want setup instructions. Results default to Last updated (newest first), with a Relevance option. Relevance lists matching install guides before Learning Paths, with each group ordered by keyword relevance. Dates match the content metadata table: the latest page date across a Learning Path, or the guide entry page date for install guides. Missing dates appear last; equal dates use relevance. Sorting applies before pagination. Queries, filters, and sorting are bookmarkable. Timings distinguish worker execution from the response time observed by the page; index loading is reported separately. Queries are not sent to a search service, although they are stored in the page URL and browser history.
+
+After a search completes, its execution and response times appear below the result count, along with the document count, index size, and initial index preparation time. These statistics stay hidden until a search completes, including searches with no matches.
+
+The **Search with an agent** section below the search results shows native registration status; you can also open `/search/#agent-tools` directly. Expand **Try the agent search tool**, edit the JSON, and run it. This invokes the exact callback registered with WebMCP. Native WebMCP requires a supporting browser/configuration; the tool tester is a fallback, not proof of an agent connection. Feature detection checks `document.modelContext` first and the older `navigator.modelContext` second. No polyfill or origin-trial token is bundled.
+
+For an actual native tool invocation from Codex through the Chrome DevTools CLI, follow the [CLI WebMCP demo](WEBMCP_DEMO.md). This bridge was verified against the local page without the Inspector extension or a Gemini API key.
+
+## Implementation
+
+- `layouts/search-lab/list.html`: Hugo generates the JSON index and publishes assets on every build. The index, scripts, stylesheet, and worker dependency use content fingerprints so refreshed pages load matching versions rather than stale cached assets. Learning Path landing pages and install-guide entry points only; excludes drafts, future and expired content, including ancestors, even in draft previews. URLs come from Hugo. Multi-part guide children are represented by their entry point.
+- `assets/search-lab/engine.mjs`: weighted BM25 keyword baseline. Title > description/authors > taxonomy/objectives/extra search terms/prerequisites > summary/FAQ questions and answers. Prerequisites accept lists or a single string; FAQ text comes from `generated_summary_faq.faqs`, when present. Markdown links in these added fields contribute their visible text, not their URLs. Generation metadata is excluded. Full tutorial steps and guide body text are not indexed. Coverage boosts multi-term matches; punctuation in identifiers such as `llama.cpp` survives tokenization. Scores are ranking values, not probabilities. No stemming, typo tolerance, synonym expansion, or embeddings yet.
+- `assets/search-lab/worker.js`: fetches and prepares the index once per page load and searches off the main thread.
+- `assets/search-lab/app.js`: accessible form, filters, results, timing, WebMCP adapter, and tool tester. Tool execution returns results without navigating or changing the human's current query.
+
+## Verify
+
+```sh
+node --test tools/search-lab/engine.test.mjs
+hugo --destination /private/tmp/arm-search-lab-build
+node tools/search-lab/check-index.mjs /private/tmp/arm-search-lab-build
+```
+
+For a draft-leak check, also build with `--buildDrafts` into a second directory and compare the record IDs. Review the demo at mobile width, keyboard-only, a slow network, no WebMCP, and a supported WebMCP browser before deploying. Desktop dark-theme rendering, example searches, native WebMCP registration, and native tool invocation through the Chrome DevTools CLI have been verified in Chrome. Mobile and light-theme visual checks remain outstanding.
diff --git a/tools/search-lab/WEBMCP_DEMO.md b/tools/search-lab/WEBMCP_DEMO.md
new file mode 100644
index 0000000000..40bbc0b599
--- /dev/null
+++ b/tools/search-lab/WEBMCP_DEMO.md
@@ -0,0 +1,73 @@
+# Codex CLI WebMCP demo
+
+Verified on October 29, 2026 with Chrome 156.0.8078.74 and `chrome-devtools-mcp` 1.12.0.
+
+The connection is:
+
+```text
+Codex → shell → Chrome DevTools CLI → Chrome → search_arm_content → browser search worker
+```
+
+Chrome DevTools provides the bridge to the page's native WebMCP registration. The page's tool tester and the WebMCP Inspector extension are not involved. This demonstration uses the current Codex session to run CLI commands; it does not launch a second Codex agent or require a Gemini API key.
+
+## Repeat the demo
+
+Start the site in one terminal from the repository root:
+
+```sh
+hugo server --bind 127.0.0.1 --port 1313
+```
+
+In another terminal, define a shortcut for the pinned Chrome DevTools CLI. The first invocation may download the package through npm. Chrome must already be installed.
+
+```sh
+devtools() {
+  npx --yes --package=chrome-devtools-mcp@1.12.0 chrome-devtools "$@"
+}
+
+devtools start --isolated --headless \
+  --categoryExperimentalWebmcp \
+  --chromeArg=--enable-features=WebMCP \
+  --no-usage-statistics --no-performance-crux
+
+devtools new_page http://localhost:1313/search/
+```
+
+Use the page ID printed by `new_page` in the next commands. The verified session used ID `2`; a new session may use another ID.
+
+```sh
+devtools list_webmcp_tools 2
+
+devtools execute_webmcp_tool 2 search_arm_content \
+  --input '{"query":"Jason Andrews","type":"learning-path","sort":"updated","limit":5}'
+```
+
+Discovery should list `search_arm_content` and its input schema. Execution should report `Completed` and return five results. For a live Codex demonstration, ask:
+
+> Use the Chrome DevTools CLI to open my local search page, discover its native WebMCP tools, and call search_arm_content for the five most recently updated Learning Paths by Jason Andrews. Show their titles and last-updated dates.
+
+Finish the isolated browser session with:
+
+```sh
+devtools stop
+```
+
+The browser feature flag is supplied to this isolated Chrome instance. This does not change the flags in your everyday Chrome profile or modify Codex configuration. If discovery returns no tools, check the page URL, wait for page initialization, and verify that Chrome was launched with both experimental options above.
+
+## Observed result
+
+The actual test used the existing preview at `http://localhost:8765/search/` and the cached package's CLI binary. Discovery returned the registered tool, and native execution reported `Completed`, 58 total matches, five returned results, and approximately **0.6 ms** of worker search time. That timing excludes CLI/browser communication and index loading.
+
+| Last updated | Learning Path |
+| --- | --- |
+| 2026-10-27 | Get started with Windows Subsystem for Linux on Arm |
+| 2026-10-27 | Deploy Arm Instances on AWS using Terraform |
+| 2026-10-27 | Deploy Arm virtual machines on Google Cloud Platform using Terraform |
+| 2026-10-22 | Run headless browser tests with Puppeteer on Arm Linux |
+| 2026-10-20 | Run agent-generated Python in GKE Agent Sandbox on Google Axion |
+
+These are the most recently **updated** matching Learning Paths, not necessarily the most recently published. The query searches indexed author names and text; it is not an exact author-only filter.
+
+The result demonstrates a local agent calling a page's structured tool through a browser bridge. It does not establish support in Chrome's Ask Gemini sidebar or other agents without such a bridge.
+
+References: [Chrome WebMCP automation and agents](https://developer.chrome.com/docs/devtools/application/webmcp#automation-and-agents), [Chrome DevTools CLI documentation](https://github.com/ChromeDevTools/chrome-devtools-mcp/blob/main/docs/cli.md).
diff --git a/tools/search-lab/check-index.mjs b/tools/search-lab/check-index.mjs
new file mode 100644
index 0000000000..2c796d42c2
--- /dev/null
+++ b/tools/search-lab/check-index.mjs
@@ -0,0 +1,43 @@
+import fs from 'node:fs';
+import path from 'node:path';
+import assert from 'node:assert/strict';
+import { gzipSync } from 'node:zlib';
+import { createEngine } from '../../assets/search-lab/engine.mjs';
+const root = process.argv[2];
+if (!root) throw Error('Pass the Hugo output directory.');
+const html = fs.readFileSync(path.join(root, 'search/index.html'), 'utf8');
+const assetPath = url => path.join(root, decodeURI(new URL(url, 'https://preview.invalid').pathname));
+const indexURL = html.match(/data-index="([^"]+)"/)[1];
+const workerURL = html.match(/data-worker="([^"]+)"/)[1];
+const bytes = fs.readFileSync(assetPath(indexURL));
+const { records } = JSON.parse(bytes);
+assert(records.length > 0);
+assert.equal(new Set(records.map(r => r.id)).size, records.length, 'Duplicate entries');
+for (const record of records) {
+  assert(record.title && record.description !== undefined);
+  assert.equal(typeof record.prerequisites, 'string');
+  assert.equal(typeof record.faq, 'string');
+  assert(record.id.startsWith('/learning-paths/') || record.id.startsWith('/install-guides/'));
+  assert(fs.existsSync(path.join(root, decodeURI(record.id), 'index.html')), `Missing page: ${record.id}`);
+}
+for (const url of [indexURL, workerURL, ...[...html.matchAll(/(?:src|href)="([^"]*\/search-lab\/[^"]+\.(?:js|css))"/g)].map(match => match[1])]) {
+  assert(fs.existsSync(assetPath(url)), `Missing asset: ${url}`);
+  assert(/\.[a-f0-9]{64}\./.test(url), `Asset lacks content fingerprint: ${url}`);
+}
+const worker = fs.readFileSync(assetPath(workerURL), 'utf8');
+const engineURL = worker.match(/import .* from '([^']+)'/)[1];
+assert(/\.[a-f0-9]{64}\.mjs$/.test(engineURL));
+assert(fs.existsSync(assetPath(engineURL)), 'Missing fingerprinted worker dependency');
+const search = createEngine(records);
+const performix = records.find(r => r.id === '/learning-paths/servers-and-cloud-computing/performix-get-started/');
+assert(performix?.prerequisites.includes('Performance Monitor Unit (PMU)'), 'Missing Learning Path prerequisites');
+assert(performix?.faq.includes('Which path should I use for my binary'), 'Missing FAQ question');
+assert(performix?.faq.includes('performix-analysis/dot_scalar'), 'Missing FAQ answer');
+assert(records.find(r => r.id === '/install-guides/arduino-pico/')?.prerequisites.includes('Arduino IDE'), 'Missing install guide prerequisites');
+console.log(JSON.stringify({ documents: records.length, withPrerequisites: records.filter(r => r.prerequisites).length, withFaq: records.filter(r => r.faq).length, bytes: bytes.length, gzipBytes: gzipSync(bytes).length }));
+for (const query of ['gcc', 'docker', 'llama.cpp Graviton', 'SVE2', 'Kubernetes', 'Jason Andrews']) {
+  const start = performance.now();
+  const result = search({ query, limit: 3 });
+  assert(result.total > 0, `No results: ${query}`);
+  console.log(JSON.stringify({ query, ms: +(performance.now() - start).toFixed(2), top: result.results.map(r => r.title) }));
+}
diff --git a/tools/search-lab/engine.test.mjs b/tools/search-lab/engine.test.mjs
new file mode 100644
index 0000000000..2aea2dfe8d
--- /dev/null
+++ b/tools/search-lab/engine.test.mjs
@@ -0,0 +1,73 @@
+import test from 'node:test';
+import assert from 'node:assert/strict';
+import { createEngine, tokenize } from '../../assets/search-lab/engine.mjs';
+const records = [
+  { id: '/gcc/', title: 'GNU Compiler', authors: ['Jason Andrews'], description: 'Install GCC on Linux', type: 'install-guide', category: '' },
+  { id: '/llama/', title: 'Deploy llama.cpp', authors: ['Pareena Verma', 'Jason Andrews'], description: 'Deploy a chatbot on AWS Graviton', type: 'learning-path', category: 'servers-and-cloud-computing' },
+  { id: '/other/', title: 'Python chatbot', summary: 'Example mentions llama.cpp', type: 'learning-path', category: 'cross-platform' }
+];
+const search = createEngine(records);
+test('technical punctuation survives tokenization', () => {
+  assert.deepEqual(tokenize('llama.cpp SVE2 C++'), ['llama.cpp', 'sve2', 'c++']);
+});
+test('prerequisites and FAQ questions and answers can independently find content', () => {
+  const expandedSearch = createEngine([
+    { id: '/path/', title: 'Application tutorial', type: 'learning-path', prerequisites: 'SSH access with PMU counters', faq: 'Which binary path should I use? Use performix-analysis/dot_scalar.' },
+    { id: '/guide/', title: 'Setup guide', type: 'install-guide', prerequisites: 'Arduino IDE' },
+    { id: '/empty/', title: 'Another tutorial', type: 'learning-path' }
+  ]);
+  for (const query of ['PMU', 'binary path', 'dot_scalar']) {
+    assert.equal(expandedSearch({ query }).results[0].id, '/path/');
+  }
+  assert.equal(expandedSearch({ query: 'Arduino' }).results[0].id, '/guide/');
+  assert.equal(expandedSearch({ query: 'Arduino', type: 'learning-path' }).total, 0);
+});
+test('exact title and query coverage win', () => {
+  assert.equal(search({ query: 'How do I deploy llama.cpp on Graviton?' }).results[0].id, '/llama/');
+});
+test('filters apply before limiting and total is independent of limit', () => {
+  assert.equal(search({ query: 'chatbot', limit: 1 }).total, 2);
+  assert.equal(search({ query: 'chatbot', category: 'cross-platform' }).results[0].id, '/other/');
+  assert.equal(search({ query: 'GCC', type: 'learning-path' }).total, 0);
+});
+test('reject invalid tool arguments', () => {
+  for (const input of [null, [], {}, { query: ' ' }, { query: 'gcc', limit: 101 }, { query: 'gcc', limit: 1.5 }, { query: 'gcc', category: 'fake' }, { query: 'gcc', type: 'fake' }]) assert.throws(() => search(input));
+});
+test('empty or irrelevant queries produce no matches', () => {
+  assert.equal(search({ query: 'the and' }).total, 0);
+  assert.equal(search({ query: 'unfindable' }).total, 0);
+});
+
+test('author names are searchable alongside topic terms and returned in results', () => {
+  assert.equal(search({ query: 'Jason Andrews' }).total, 2);
+  assert.equal(search({ query: 'pArEeNa VeRmA' }).results[0].id, '/llama/');
+  assert.equal(search({ query: 'Jason GCC' }).results[0].id, '/gcc/');
+  assert.deepEqual(search({ query: 'Pareena' }).results[0].authors, ['Pareena Verma', 'Jason Andrews']);
+});
+
+test('newest sorting applies to all matches before limiting, with relevance as an alternative', () => {
+  const datedSearch = createEngine([
+    { id: '/old/', title: 'Docker Docker Docker', lastUpdated: '2025-01-01', type: 'install-guide' },
+    { id: '/new/', title: 'Containers', description: 'Docker', lastUpdated: '2026-10-29', type: 'install-guide' },
+    { id: '/missing/', title: 'Docker', type: 'learning-path' }
+  ]);
+  assert.deepEqual(datedSearch({ query: 'docker' }).results.map(r => r.id), ['/new/', '/old/', '/missing/']);
+  assert.equal(datedSearch({ query: 'docker', limit: 1 }).results[0].id, '/new/');
+  assert.equal(datedSearch({ query: 'docker', sort: 'relevance' }).results[0].id, '/old/');
+  assert.throws(() => datedSearch({ query: 'docker', sort: 'invalid' }));
+});
+
+test('relevance puts matching install guides first, then ranks each type by score', () => {
+  const prioritize = createEngine([
+    { id: '/path/', title: 'Docker', type: 'learning-path', lastUpdated: '2026-10-29' },
+    { id: '/weak-guide/', title: 'Containers', summary: 'Docker', type: 'install-guide', lastUpdated: '2025-01-01' },
+    { id: '/strong-guide/', title: 'Docker', type: 'install-guide', lastUpdated: '2025-01-01' },
+    { id: '/weak-path/', title: 'Containers', summary: 'Docker', type: 'learning-path', lastUpdated: '2026-10-28' },
+    { id: '/unmatched/', title: 'GCC', type: 'install-guide' }
+  ]);
+  assert.deepEqual(prioritize({ query: 'docker', sort: 'relevance' }).results.map(r => r.id),
+    ['/strong-guide/', '/weak-guide/', '/path/', '/weak-path/']);
+  assert.equal(prioritize({ query: 'docker', sort: 'relevance', limit: 1 }).results[0].id, '/strong-guide/');
+  assert.equal(prioritize({ query: 'docker', sort: 'updated' }).results[0].id, '/path/');
+  assert.deepEqual(prioritize({ query: 'docker', sort: 'relevance', type: 'learning-path' }).results.map(r => r.id), ['/path/', '/weak-path/']);
+});

From 2f1119689cf380ee89466a31137188194a6d0e6e Mon Sep 17 00:00:00 2001
From: Kieran Hejmadi 
Date: Wed, 14 Oct 2026 13:43:14 +0100
Subject: [PATCH 011/109] Squash commit - inital draft for review

---
 .../compute-flow-getting-started/_index.md    |  75 ++++
 .../_next-steps.md                            |   8 +
 .../compute-flow-getting-started/how-to-1.md  |  70 ++++
 .../compute-flow-getting-started/how-to-2.md  | 363 ++++++++++++++++++
 .../compute-flow-getting-started/how-to-3.md  |  67 ++++
 5 files changed, 583 insertions(+)
 create mode 100644 content/learning-paths/laptops-and-desktops/compute-flow-getting-started/_index.md
 create mode 100644 content/learning-paths/laptops-and-desktops/compute-flow-getting-started/_next-steps.md
 create mode 100644 content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-1.md
 create mode 100644 content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-2.md
 create mode 100644 content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-3.md

diff --git a/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/_index.md b/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/_index.md
new file mode 100644
index 0000000000..327d986b1c
--- /dev/null
+++ b/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/_index.md
@@ -0,0 +1,75 @@
+---
+title: Getting Started with Compute Flow
+description: Connect speech recognition, translation, and speech synthesis with Compute Flow on your Apple silicon Mac, then replace a model without changing your Python application.
+
+minutes_to_complete: 30
+
+who_is_this_for: This is an introductory topic for developers who want to combine multiple AI models into a performant end-to-end AI applicaton running on an Arm CPU.
+
+learning_objectives:
+    - Explain how Compute Flow bricks and manifests separate application code from model implementations
+    - Run a local Python pipeline that translates English speech into French speech
+    - Replace the speech recognition model and verify the pipeline using the same application and recording
+
+prerequisites:
+    - An Apple silicon Mac running macOS
+    - Basic Python knowledge and familiarity with terminal commands
+    - Internet access and at least 15 GB of available storage
+
+author: Kieran Hejmadi
+
+# New Learning Paths are opted in for the next manual generated summary/FAQ run.
+# The generator resets this to false after a successful write.
+generate_summary_faq: true
+
+# Optional one-shot controls: set either field to true to regenerate just that
+# generated section the next time the summary/FAQ tool runs. The tool resets
+# them to false after a successful write.
+rerun_summary: false
+rerun_faqs: false
+
+### Tags
+skilllevels: Introductory
+subjects: ML
+armips: []
+tools_software_languages:
+    - Compute Flow
+    - Python
+    - uv
+operatingsystems:
+    - macOS
+
+further_reading:
+    - resource:
+        title: Compute Flow source repository (public URL placeholder)
+        link: https://github.com/ARM-software/ComputeFlow
+        type: documentation
+    - resource:
+        title: Compute Flow application development guide (public URL placeholder)
+        link: https://github.com/ARM-software/ComputeFlow/blob/main/docs/howto/developing_apps_with_compute_flow.md
+        type: documentation
+    - resource:
+        title: Compute Flow speech translation example (public URL placeholder)
+        link: https://github.com/ARM-software/ComputeFlow/blob/main/docs/examples/python/audio_to_audio_translation.md
+        type: documentation
+    - resource:
+        title: Install uv
+        link: https://docs.astral.sh/uv/getting-started/installation/
+        type: documentation
+
+# Publication checks: confirm the public repository and documentation URLs,
+# and verify native dependency bundles are accessible from the public checkout.
+# Source reference: Compute Flow 885a3466e33eb5e843cd55d1d0180e0bc292520a.
+# Validated on macOS 26.6.2 arm64 with Python 3.12.6 and AppleClang 21.0.0.
+# Both pipelines completed with the same script and input recording. WAV checks
+# passed at 24 kHz: Parakeet output 9.36 s, Moonshine output 7.76 s.
+# afplay exited successfully for both WAV files.
+# Local validation used the certifi CA bundle and disabled discovery of an
+# incompatible pre-installed llama.cpp package.
+
+### FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 1                       # _index.md always has weight of 1 to order correctly
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
+---
diff --git a/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/_next-steps.md b/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/_next-steps.md
new file mode 100644
index 0000000000..727b395ddd
--- /dev/null
+++ b/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/_next-steps.md
@@ -0,0 +1,8 @@
+---
+# ================================================================================
+#       FIXED, DO NOT MODIFY THIS FILE
+# ================================================================================
+weight: 21                  # The weight controls the order of the pages. _index.md always has weight 1.
+title: "Next Steps"         # Always the same, html page title.
+layout: "learningpathall"   # All files under learning paths have this same wrapper for Hugo processing.
+---
diff --git a/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-1.md b/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-1.md
new file mode 100644
index 0000000000..75ee88ed46
--- /dev/null
+++ b/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-1.md
@@ -0,0 +1,70 @@
+---
+title: Understand Compute Flow
+description: Understand how you can combine local AI models through Compute Flow bricks, manifests, and shared interfaces.
+weight: 2
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Connect models into a local application
+
+Compute Flow is an SDK for building AI applications from reusable components called *bricks*. Each brick exposes an AI capability through a shared interface for that capability. You connect those interfaces in Python or C++ while the brick handles model-specific inference. Compute Flow brings together runtimes such as LiteRT, ONNX Runtime, ExecuTorch, and llama.cpp for Arm devices.
+
+We'll use the example of an application that translates speech needs several models. One recognizes speech, another translates text, and a third generates audio. These models can use different inference runtimes, tensor formats, tokenizers, and configuration files. Connecting them directly means learning each runtime's API and writing code for each model. Replacing a model can mean rewriting that integration.
+
+You'll use Python to translate a short English recording into French speech on your Apple Silicon Mac. The application processes the recording through three stages:
+
+```text
+English WAV
+    |
+    v
+Speech-to-text: Parakeet TDT
+    | English text
+    v
+Text translation: Hy-MT2
+    | French text
+    v
+Text-to-speech: Qwen3-TTS
+    |
+    v
+French WAV
+```
+
+Python coordinates the stages: it passes the transcription to the translation brick, then passes the translation to the speech synthesis brick. Compute Flow doesn't automatically assemble this pipeline for you.
+
+## Separate the application from the model
+
+A *brick type* defines an input and output contract. A *brick variant* implements that contract using a particular model family. For example, Parakeet TDT and Moonshine are different speech-to-text variants. Both accept audio and its sample rate, and return text.
+
+Your pipeline uses these contracts:
+
+| Stage | Brick type | Input | Output |
+| --- | --- | --- | --- |
+| Recognize speech | `stt` | `raw_wav` and `sample_rate` | `text` |
+| Translate text | `lm_text` | `text_prompt` | `text` |
+| Synthesize speech | `tts` | `text_prompt` | `left_channel`, `right_channel`, and `sample_rate` |
+
+### Understand the model manifest
+
+A *model package* contains the weights and supporting assets that a brick needs. A *model manifest* is a JSON configuration file in that package. It tells Compute Flow which brick implementation to use and how to load and run the models.
+
+The manifest describes model filenames, inference runtimes, model-specific tensor formats, and supporting assets, such as tokenizers. It also provides configuration values and constants needed by the brick. The weights live in separate files referenced by the manifest.
+
+For example, the Parakeet package includes `parakeet_tdt_0_6b_v3_manifest.json`. Your Python application reads this file with `cf.load_manifest()`, selects the implementation with `cf.load_brick()`, and prepares the models with `brick.setup()`. This keeps model-specific file paths and runtime configuration out of your application code.
+
+## Use versioned brick APIs
+
+From Python or C++, you use a brick's versioned input/output contract and shared execution methods such as `setup()`, `run()`, and `reset_states()`. Parakeet and Moonshine both implement `stt.v1`, so your script keeps the same input and output handling when switching between them. Compute Flow's compatibility policy requires newer releases to support bricks using earlier API versions.
+
+For details, see the [Brick API guide](https://github.com/ARM-software/ComputeFlow/blob/main/docs/design/brick_api.md), [Brick Runtime Interface V1](https://github.com/ARM-software/ComputeFlow/blob/main/docs/design/brick_runtime_interface_v1.md), and [documentation README](https://github.com/ARM-software/ComputeFlow/blob/main/docs/README.md). These links use the public repository URL placeholder.
+
+## Prepare once, then run locally
+
+*Brick Manager*, the `compute-flow` command, discovers bricks, installs their source and model packages, and builds the native libraries and Python bindings. You'll update its registry, install the three models, and build for your Mac with `compute-flow build native`.
+
+Setup needs network access to download dependencies and model packages. After setup, the example reads the installed assets and performs inference on your Mac. It writes a WAV file locally and doesn't call a hosted inference service. You don't need a microphone because the input recording is included in a model package.
+
+## What you've learned
+
+Compute Flow separates your application from model-specific inference through versioned brick APIs and manifests. Brick Manager prepares the implementations and model assets, while your Python code connects their inputs and outputs. Next, you'll build and run the speech translation pipeline on your Mac.
diff --git a/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-2.md b/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-2.md
new file mode 100644
index 0000000000..2ade17de74
--- /dev/null
+++ b/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-2.md
@@ -0,0 +1,363 @@
+---
+title: Run a local speech translation pipeline
+description: Build Compute Flow on your Mac and connect three Python bricks to translate an English recording into French speech.
+weight: 3
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Prepare your Mac
+
+You'll install three model packages, build Compute Flow for your Mac, and create a Python application.
+
+First, check your  CPU architecture:
+
+```bash
+uname -m
+```
+
+You should see `arm64`.
+
+If `uv` isn't installed, use the [uv standalone installer](https://docs.astral.sh/uv/getting-started/installation/) with the following command.
+
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+```
+
+
+Next, if Apple's developer tools aren't installed, follow [Apple's instructions for installing the Xcode Command Line Tools](https://developer.apple.com/documentation/xcode/installing-the-command-line-tools/). The package includes Git, Clang, and the macOS SDK. If you already have Xcode or the Command Line Tools installed, use those tools.
+
+Finish with a quick check that the tools are available:
+
+```bash
+uv --version
+git --version
+command -v cc
+command -v c++
+```
+
+## Set up Compute Flow
+
+{{% notice Note %}}
+The public repository URL in this draft is a placeholder. Until it is available, use the Compute Flow checkout supplied through your project access. You also need access to its native dependency bundles. Run the remaining commands from that checkout's root directory.
+{{% /notice %}}
+
+When the public repository is available, obtain it with:
+
+```bash
+git clone https://github.com/ARM-software/ComputeFlow.git
+cd ComputeFlow
+```
+
+Create and activate the Python environment, then check the required system dependencies:
+
+```bash
+uv sync && source .venv/bin/activate
+compute-flow check
+```
+
+```output
+╭───────────────────────────────────────── OK Environment Ready ─────────────────────────────────────────╮
+│  All required system dependencies are available.                                                       │
+│  ─────────────────────────────────────────── Dependencies ───────────────────────────────────────────  │
+│                                                                                                        │
+│    Dependency      Executable     Status     Path                                                      │
+│   ──────────────────────────────────────────────────────────────────────────────────────────────────   │
+│    Git             git            found      /Library/Developer/CommandLineTools/usr/bin/git           │
+│                                                                                                        │
+│  ───────────────────────────────────────── Native Toolchain ─────────────────────────────────────────  │
+│                                                                                                        │
+│    Compiler       Toolchain     Version   Status   Path                                                │
+│   ──────────────────────────────────────────────────────────────────────────────────────────────────   │
+│    C compiler     Clang (cc)    21.0.0    found    /Library/Developer/CommandLineTools/usr/bin/cc      │
+│    C++ compiler   Clang (c++)   21.0.0    found    /Library/Developer/CommandLineTools/usr/bin/c++     │
+│                                                                                                        │
+│  ────────────────────────────────────────────── Tools ───────────────────────────────────────────────  │
+│                                                                                                        │
+│    Name              Version   Provided by       Status            Path                                │
+│   ──────────────────────────────────────────────────────────────────────────────────────────────────   │
+│    CMake             4.0.2     Brick Manager     will be           -                                   │
+│                                                  downloaded when                                       │
+│                                                  needed                                                │
+│    Git LFS           3.6.1     Brick Manager     will be           -                                   │
+│                                                  downloaded when                                       │
+│                                                  needed                                                │
+│                                                                                                        │
+│  ──────────────────────────────────────────── Toolchains ────────────────────────────────────────────  │
+│                                                                                                        │
+│    Name              Version     Provided by       Status            Path                              │
+│   ──────────────────────────────────────────────────────────────────────────────────────────────────   │
+│    Android™ NDK      r27c        Brick Manager     will be           -                                 │
+│                                                    downloaded when                                     │
+│                                                    needed                                              │
+│    Linux® Arm®       14.2.rel1   Brick Manager     will be           -                                 │
+│    64-bit                                          downloaded when                                     │
+│    toolchain                                       needed                                              │
+│                                                                                                        │
+╰────────────────────────────────────────────────────────────────────────────────────────────────────────╯
+
+```
+
+Compute Flow requires Python 3.11 or later. `uv sync` installs the project's Python dependencies into the virtual environment. With its default settings, [uv also downloads a compatible Python interpreter](https://docs.astral.sh/uv/concepts/python-versions/) if it can't find one locally.
+
+`compute-flow check` reports missing system dependencies and suggests installation commands. It doesn't install them or test whether the compiler can compile and link a program. If it reports a missing native compiler or Git installation, follow the suggested action and run the check again.
+
+Brick Manager automatically downloads managed tools, such as CMake and Git LFS, when a command needs them. The check reports these as `will be downloaded when needed`; you don't need to install them manually. For example, `compute-flow build native` downloads its managed CMake version if it isn't already available.
+
+
+## Install the three model packages
+
+Update Brick Manager's registry cache and install the models used by your pipeline:
+
+{{% notice Tip %}}
+For higher rate limits and faster downloads from Hugging Face, set the optional `HF_TOKEN` environment variable to your Hugging Face access token before installing the models. You can [create a Hugging Face access token](https://huggingface.co/settings/tokens) in account settings.
+{{% /notice %}}
+
+```bash
+compute-flow update
+compute-flow model install stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic
+compute-flow model install lm_text:lm-text-generic hy-mt2-1-8b-2bit-translation
+compute-flow model install tts:qwen3-tts qwen3-tts-0-6b-custom-voice
+```
+
+Each `model install` command installs the brick source, if needed, and its model package. The identifiers select these implementations:
+
+| Brick | Model package | Pipeline role |
+| --- | --- | --- |
+| `stt:parakeet-tdt` | Parakeet TDT 0.6B v3, dynamic INT8 | Recognize the English recording |
+| `lm_text:lm-text-generic` | Hy-MT2 1.8B, 2-bit | Translate the transcription into French |
+| `tts:qwen3-tts` | Qwen3-TTS 0.6B Custom Voice | Generate French speech |
+
+Models are downloaded to the `~/.compute-flow` directory by default.
+
+```bash
+compute-flow model info stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic
+compute-flow model info lm_text:lm-text-generic hy-mt2-1-8b-2bit-translation
+compute-flow model info tts:qwen3-tts qwen3-tts-0-6b-custom-voice
+```
+
+## Build for your Mac
+
+Build the installed bricks and the native Python bindings:
+
+```bash
+compute-flow build native
+```
+
+If successful, you should see output similar to the following. 
+
+```output
+╭────────────────────────────── OK Build Complete ──────────────────────────────╮
+│  Build directory  /Users/kiehej01/compute_flow/compute_flow/build_native      │
+│  Application SDK  /Users/kiehej01/compute_flow/compute_flow/build_native/sdk  │
+╰───────────────────────────────────────────────────────────────────────────────╯
+```
+
+The build downloads optimized macOS Arm64 runtimes such as LiteRT, ExecuTorch, and ONNX Runtime with support for KleidiAI, packages them with your installed brick implementations in an application SDK for C++ applications, and installs Python bindings so you can use the same native functionality from Python.
+
+```output
+├── CMakeLists.txt
+├── force_load_libraries.txt
+├── include
+│   └── compute_flow
+│       ├── accelerator.h
+│       ├── bindings
+│       ├── bricks
+│       ├── check.h
+│       ├── compute_flow.h
+│       ├── lm_params.h
+│       ├── lm_runners
+│       ├── manifest.h
+│       ├── memory_planner.h
+│       ├── model_data.h
+│       ├── profiler
+│       ├── runners
+│       ├── runtime.h
+│       └── tensor_dict.h
+├── lib
+│   ├── libcompute_flow_brick_factory.a
+│   ├── libcompute_flow_core.a
+│   ├── libcompute_flow.a
+│   ├── libexecutorch.a
+│   ├── liblm_text_core.a
+│   ├── liboptimized_native_cpu_ops_lib.a
+│   ├── libstt_core.a
+│   ├── libtts_core.a
+│   └── libxnnpack_backend.a
+├── README.md
+└── share
+    └── compute_flow
+        └── bricks
+```
+
+The second command confirms that your active environment can load the native module. If the import fails, check that the build completed successfully and that the same virtual environment is active. A dependency download error needs to be resolved before the bindings can be built.
+
+{{% notice Troubleshooting %}}
+If the build fails with `ld: tapi error`, your compiler and macOS SDK may be incompatible. From the checkout root, with your virtual environment active, select matching Apple tools:
+
+```bash
+export CC="$(xcrun --find clang)"
+export CXX="$(xcrun --find clang++)"
+export SDKROOT="$(xcrun --sdk macosx --show-sdk-path)"
+```
+
+Delete the generated `build_native` directory to clear the saved compiler settings, then rebuild in the same terminal:
+
+```bash
+rm -rf build_native
+compute-flow build native
+```
+{{% /notice %}}
+
+## Create the Python application
+
+Create a directory for your application:
+
+```bash
+mkdir -p audio_translation_example
+```
+
+Save the following code as `audio_translation_example/audio_translation.py`. It loads each manifest, configures the languages, and connects the three bricks. The sample-rate check prevents passing incompatible audio to the speech recognition model.
+
+```python
+# SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates 
+# SPDX-License-Identifier: Apache-2.0
+
+"""Translate an English recording with three local Compute Flow bricks."""
+
+import argparse
+from pathlib import Path
+
+import compute_flow_py as cf
+import numpy as np
+import soundfile as sf
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--stt-manifest", required=True)
+    parser.add_argument("--translation-manifest", required=True)
+    parser.add_argument("--tts-manifest", required=True)
+    parser.add_argument("--input", required=True, dest="input_audio")
+    parser.add_argument("--source-language", required=True)
+    parser.add_argument("--destination-language", required=True)
+    parser.add_argument("--output", default="translated_french.wav")
+    args = parser.parse_args()
+
+    # Both speech recognition variants in this tutorial expect 16 kHz audio.
+    audio, input_rate = sf.read(args.input_audio, dtype="float32", always_2d=True)
+    if input_rate != 16000 or audio.shape[0] == 0:
+        raise ValueError("Use a nonempty 16 kHz recording, such as beckett.wav")
+    mono = np.mean(audio, axis=1, dtype=np.float32)
+    raw_wav = np.ascontiguousarray(mono.reshape(1, -1), dtype=np.float32)
+    sample_rate = np.asarray([input_rate], dtype=np.int64)
+
+    stt_manifest = cf.load_manifest(args.stt_manifest)
+    translation_manifest = cf.load_manifest(args.translation_manifest)
+    tts_manifest = cf.load_manifest(args.tts_manifest)
+    translation_manifest.set_config("src_lang", args.source_language)
+    translation_manifest.set_config("dst_lang", args.destination_language)
+    tts_manifest.set_config("language", args.destination_language.lower())
+
+    options = cf.BrickOptions()
+    options.runtime.enable_warmup = True
+    stt = cf.load_brick(stt_manifest)
+    translation = cf.load_brick(translation_manifest)
+    tts = cf.load_brick(tts_manifest)
+    stt.setup(stt_manifest, options)
+    translation.setup(translation_manifest, options)
+    tts.setup(tts_manifest, options)
+
+    # Keep the waveform and sample-rate tensors alive throughout recognition.
+    stt.reset_states()
+    stt_input = {"raw_wav": raw_wav, "sample_rate": sample_rate}
+    transcription_chunks = []
+    while not stt.is_generation_complete():
+        result = stt.run(stt_input)
+        transcription_chunks.append(str(np.asarray(result["text"]).item()))
+    transcription = "".join(transcription_chunks)
+    if not transcription.strip():
+        raise RuntimeError("Speech recognition returned no text")
+    print(f"Transcription: {transcription}", flush=True)
+
+    # Pass the complete transcription to the translation brick.
+    translation.reset_states()
+    translation_input = {"text_prompt": transcription}
+    translation_chunks = []
+    while not translation.is_generation_complete():
+        result = translation.run(translation_input)
+        translation_chunks.append(str(np.asarray(result["text"]).item()))
+    translated_text = "".join(translation_chunks)
+    if not translated_text.strip():
+        raise RuntimeError("Translation returned no text")
+    print(f"Translation: {translated_text}", flush=True)
+
+    # Copy each audio chunk before the brick reuses its output buffers.
+    tts.reset_states()
+    tts_input = {"text_prompt": translated_text}
+    audio_chunks = []
+    output_rate = 0
+    while not tts.is_generation_complete():
+        result = tts.run(tts_input)
+        left = np.asarray(result["left_channel"], dtype=np.float32).reshape(-1)
+        right = np.asarray(result["right_channel"], dtype=np.float32).reshape(-1)
+        output_rate = int(np.asarray(result["sample_rate"]).reshape(-1)[0])
+        if left.size:
+            audio_chunks.append(np.column_stack((left, right)))
+
+    stt.reset_states()
+    translation.reset_states()
+    tts.reset_states()
+    if not audio_chunks or output_rate <= 0:
+        raise RuntimeError("Speech synthesis returned no audio")
+    output_audio = np.concatenate(audio_chunks, axis=0)
+    sf.write(args.output, output_audio, output_rate, subtype="FLOAT")
+    print(f"Saved: {Path(args.output).resolve()}")
+
+
+if __name__ == "__main__":
+    main()
+```
+
+The input waveform has shape `(1, number_of_samples)` and uses `float32`; its sample rate uses `int64`. Each input dictionary remains alive until its generation loop completes. `np.column_stack()` copies the left and right audio channels into a stereo chunk before another call can reuse the brick's buffers.
+
+`load_manifest()` reads the model configuration, `load_brick()` selects the implementation, and `setup()` prepares it for inference. Warmup happens during setup, so the first printed transcription can take time to appear. Each `run()` call returns the next output from a brick; the loop continues until `is_generation_complete()` returns `True`. `reset_states()` clears the request state while retaining the loaded models.
+
+All three bricks share these execution methods, documented in [Brick Runtime Interface V1](https://github.com/ARM-software/ComputeFlow/blob/main/docs/design/brick_runtime_interface_v1.md).
+
+The Qwen3-TTS manifest filename includes `en`, but the script sets its `language` configuration to `french` before setup. The selected Custom Voice model supports French synthesis. The translation configuration similarly sets English as the source language and French as the destination.
+
+## Translate the packaged recording
+
+Resolve the package locations through Brick Manager instead of hard-coding cache paths. Then run your application using the sample supplied with Parakeet:
+
+```bash
+PARAKEET_PACKAGE="$(compute-flow model path stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic)"
+HY_MT2_PACKAGE="$(compute-flow model path lm_text:lm-text-generic hy-mt2-1-8b-2bit-translation)"
+QWEN3_TTS_PACKAGE="$(compute-flow model path tts:qwen3-tts qwen3-tts-0-6b-custom-voice)"
+
+python audio_translation_example/audio_translation.py \
+  --stt-manifest "$PARAKEET_PACKAGE/parakeet_tdt_0_6b_v3_manifest.json" \
+  --translation-manifest "$HY_MT2_PACKAGE/hy_mt2_1_8b_manifest.json" \
+  --tts-manifest "$QWEN3_TTS_PACKAGE/qwen3_tts_custom_voice_en_manifest.json" \
+  --input "$PARAKEET_PACKAGE/samples/beckett.wav" \
+  --source-language English \
+  --destination-language French \
+  --output translated_french.wav
+```
+
+The application prints `Transcription:`, `Translation:`, and `Saved:` lines alongside the runtime logs. The output from one run is shown here, with the checkout path shortened:
+
+```output
+Transcription: Ever tried, ever failed. No matter. Try again. Fail again. Fail better.
+Translation: Avez-vous déjà essayé, avez-vous déjà échoué. Peu importe. Essayez à nouveau. Écheclez encore. Échouez mieux.
+Saved: /translated_french.wav
+```
+
+Check that the transcription matches the English recording and that the translation preserves its meaning. Generated wording and punctuation can vary and can contain errors: this run produced the incorrect word "Écheclez". Completing the pipeline verifies the integration; review the generated text and speech separately for translation quality.
+
+
+## What you've accomplished
+
+You've installed three model packages, built Compute Flow, and connected their brick interfaces in a Python application. Your application now turns an English recording into a local French WAV file. Next, you'll replace the speech recognition model and run the same application again.
diff --git a/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-3.md b/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-3.md
new file mode 100644
index 0000000000..434a2c906a
--- /dev/null
+++ b/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-3.md
@@ -0,0 +1,67 @@
+---
+title: Replace the speech recognition model
+description: Replace Parakeet with Moonshine in your speech translation pipeline and verify both runs without changing your Python application.
+weight: 4
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Understand what changes when you replace a model
+
+Speech-to-text (`stt`) is a brick type: audio and a sample rate go in, and text comes out. A brick implementation, such as `stt:parakeet-tdt`, provides the code that runs supported models. A model package supplies the trained weights and assets; its manifest selects the implementation and configures how those files are used.
+
+You'll replace both the Parakeet implementation and its model package with `stt:moonshine` and `moonshine-base-en`. Both implementations follow `stt.v1`, even though Parakeet uses LiteRT and Moonshine uses ONNX Runtime.
+
+Your Python script stays unchanged: `cf.load_manifest(args.stt_manifest)` reads the manifest supplied as an argument, `cf.load_brick(stt_manifest)` selects its implementation, and `stt.setup(stt_manifest, options)` prepares the models. No model name, weights path, or inference backend is hardcoded into these calls. The same `stt.run(stt_input)` loop returns text for the translation stage.
+
+Moonshine Base English recognizes English only and expects 16 kHz audio, so keep using the same `beckett.wav` recording.
+
+## Install Moonshine and rebuild Compute Flow
+
+Use the same Compute Flow checkout and activated virtual environment as before. List Moonshine's models, install the English package, and inspect its manifest:
+
+```bash
+compute-flow model list stt:moonshine
+compute-flow model install stt:moonshine moonshine-base-en
+compute-flow model info stt:moonshine moonshine-base-en
+```
+
+The package contains `moonshine_manifest_base_en.json`. Build Compute Flow again to include the newly installed implementation:
+
+```bash
+compute-flow build native
+```
+
+This build adds the Moonshine implementation to the available bricks. You don't edit your Python application or replace the other models. Selecting a different manifest works only when its brick implementation has been built and its model assets are installed.
+
+## Run the same application with Moonshine
+
+Resolve the package paths again so this command also works in a fresh terminal with the environment activated. Keep using the Parakeet package's recording to hold the input constant:
+
+```bash
+MOONSHINE_PACKAGE="$(compute-flow model path stt:moonshine moonshine-base-en)"
+PARAKEET_PACKAGE="$(compute-flow model path stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic)"
+HY_MT2_PACKAGE="$(compute-flow model path lm_text:lm-text-generic hy-mt2-1-8b-2bit-translation)"
+QWEN3_TTS_PACKAGE="$(compute-flow model path tts:qwen3-tts qwen3-tts-0-6b-custom-voice)"
+
+python audio_translation_example/audio_translation.py \
+  --stt-manifest "$MOONSHINE_PACKAGE/moonshine_manifest_base_en.json" \
+  --translation-manifest "$HY_MT2_PACKAGE/hy_mt2_1_8b_manifest.json" \
+  --tts-manifest "$QWEN3_TTS_PACKAGE/qwen3_tts_custom_voice_en_manifest.json" \
+  --input "$PARAKEET_PACKAGE/samples/beckett.wav" \
+  --source-language English \
+  --destination-language French \
+  --output translated_french_moonshine.wav
+```
+
+Only the `--stt-manifest` and output filename differ from your first run. The new manifest selects Moonshine while the same generation loop consumes its text output. The other two stages continue to receive text through the same interfaces.
+
+If the manifest can't be loaded, check the path reported by `model info`. If the brick implementation is unavailable, confirm that the native build succeeded after installing Moonshine and that you're using the same virtual environment.
+
+
+## What you've accomplished
+
+You've learned how brick types, implementations, model packages, and manifests fit together. You used Brick Manager to install models and build Compute Flow on your Apple silicon Mac, then connected speech recognition, translation, and speech synthesis in Python to turn an English recording into a French WAV file locally.
+
+Finally, you replaced Parakeet with Moonshine by installing its brick and model package, rebuilding, and selecting a different manifest. The shared speech-to-text interface kept your Python script unchanged. You can use this approach to combine other bricks and select compatible models for your own applications.

From efe11b22e73225678c45106ceb7c5232e8421542 Mon Sep 17 00:00:00 2001
From: Kieran Hejmadi 
Date: Mon, 26 Oct 2026 10:43:36 +0000
Subject: [PATCH 012/109] fix - moved compute-flow to cross-platform category
 discoverable through laptops-and-desktops

---
 .../compute-flow-getting-started/_index.md                   | 5 +++++
 .../compute-flow-getting-started/_next-steps.md              | 0
 .../compute-flow-getting-started/how-to-1.md                 | 0
 .../compute-flow-getting-started/how-to-2.md                 | 0
 .../compute-flow-getting-started/how-to-3.md                 | 0
 5 files changed, 5 insertions(+)
 rename content/learning-paths/{laptops-and-desktops => cross-platform}/compute-flow-getting-started/_index.md (97%)
 rename content/learning-paths/{laptops-and-desktops => cross-platform}/compute-flow-getting-started/_next-steps.md (100%)
 rename content/learning-paths/{laptops-and-desktops => cross-platform}/compute-flow-getting-started/how-to-1.md (100%)
 rename content/learning-paths/{laptops-and-desktops => cross-platform}/compute-flow-getting-started/how-to-2.md (100%)
 rename content/learning-paths/{laptops-and-desktops => cross-platform}/compute-flow-getting-started/how-to-3.md (100%)

diff --git a/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/_index.md b/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
similarity index 97%
rename from content/learning-paths/laptops-and-desktops/compute-flow-getting-started/_index.md
rename to content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
index 327d986b1c..7aa34701a5 100644
--- a/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/_index.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
@@ -39,6 +39,11 @@ tools_software_languages:
 operatingsystems:
     - macOS
 
+### Cross-platform metadata only
+shared_path: true
+shared_between:
+    - laptops-and-desktops
+
 further_reading:
     - resource:
         title: Compute Flow source repository (public URL placeholder)
diff --git a/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/_next-steps.md b/content/learning-paths/cross-platform/compute-flow-getting-started/_next-steps.md
similarity index 100%
rename from content/learning-paths/laptops-and-desktops/compute-flow-getting-started/_next-steps.md
rename to content/learning-paths/cross-platform/compute-flow-getting-started/_next-steps.md
diff --git a/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-1.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
similarity index 100%
rename from content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-1.md
rename to content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
diff --git a/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-2.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
similarity index 100%
rename from content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-2.md
rename to content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
diff --git a/content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-3.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
similarity index 100%
rename from content/learning-paths/laptops-and-desktops/compute-flow-getting-started/how-to-3.md
rename to content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md

From a04982b211f526cf8a06b9603750489a46041988 Mon Sep 17 00:00:00 2001
From: Kieran Hejmadi 
Date: Mon, 26 Oct 2026 11:47:02 +0000
Subject: [PATCH 013/109] improvement - add troubleshooting guidance for
 dependency compatibility issues

---
 .../compute-flow-getting-started/how-to-2.md  | 31 +++++++++----------
 1 file changed, 14 insertions(+), 17 deletions(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
index 2ade17de74..bb5e3a6378 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
@@ -154,6 +154,20 @@ If successful, you should see output similar to the following.
 ╰───────────────────────────────────────────────────────────────────────────────╯
 ```
 
+{{% notice Troubleshooting Tip %}}
+
+Compute Flow uses prebuilt, optimized dependency binaries where available. Some dependencies, such as llama.cpp, are first checked on your local machine. A preinstalled version may be incompatible with Compute Flow and cause build errors.
+
+You can build the supported runtime dependencies from their pinned source revisions instead with the following command, although this takes longer:
+
+```bash
+compute-flow build native --from-source
+```
+
+Alternatively, either update the dependency causing the error to a compatible version or uninstall the local version to allow Compute Flow to build just the missing depencencies from source.
+
+{{% /notice %}}
+
 The build downloads optimized macOS Arm64 runtimes such as LiteRT, ExecuTorch, and ONNX Runtime with support for KleidiAI, packages them with your installed brick implementations in an application SDK for C++ applications, and installs Python bindings so you can use the same native functionality from Python.
 
 ```output
@@ -193,23 +207,6 @@ The build downloads optimized macOS Arm64 runtimes such as LiteRT, ExecuTorch, a
 
 The second command confirms that your active environment can load the native module. If the import fails, check that the build completed successfully and that the same virtual environment is active. A dependency download error needs to be resolved before the bindings can be built.
 
-{{% notice Troubleshooting %}}
-If the build fails with `ld: tapi error`, your compiler and macOS SDK may be incompatible. From the checkout root, with your virtual environment active, select matching Apple tools:
-
-```bash
-export CC="$(xcrun --find clang)"
-export CXX="$(xcrun --find clang++)"
-export SDKROOT="$(xcrun --sdk macosx --show-sdk-path)"
-```
-
-Delete the generated `build_native` directory to clear the saved compiler settings, then rebuild in the same terminal:
-
-```bash
-rm -rf build_native
-compute-flow build native
-```
-{{% /notice %}}
-
 ## Create the Python application
 
 Create a directory for your application:

From da0bdaddd7e86920246e130b8dbc149afada4232 Mon Sep 17 00:00:00 2001
From: Kieran Hejmadi 
Date: Mon, 26 Oct 2026 12:58:43 +0000
Subject: [PATCH 014/109] reorder to use unquantized moonshine-base-en first

---
 .../compute-flow-getting-started/how-to-1.md  |  4 +-
 .../compute-flow-getting-started/how-to-2.md  | 26 +++++++------
 .../compute-flow-getting-started/how-to-3.md  | 38 +++++++++----------
 3 files changed, 35 insertions(+), 33 deletions(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
index 75ee88ed46..44c18e4ba2 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
@@ -19,7 +19,7 @@ You'll use Python to translate a short English recording into French speech on y
 English WAV
     |
     v
-Speech-to-text: Parakeet TDT
+Speech-to-text: Moonshine Base English (FP32)
     | English text
     v
 Text translation: Hy-MT2
@@ -51,7 +51,7 @@ A *model package* contains the weights and supporting assets that a brick needs.
 
 The manifest describes model filenames, inference runtimes, model-specific tensor formats, and supporting assets, such as tokenizers. It also provides configuration values and constants needed by the brick. The weights live in separate files referenced by the manifest.
 
-For example, the Parakeet package includes `parakeet_tdt_0_6b_v3_manifest.json`. Your Python application reads this file with `cf.load_manifest()`, selects the implementation with `cf.load_brick()`, and prepares the models with `brick.setup()`. This keeps model-specific file paths and runtime configuration out of your application code.
+For example, the Moonshine Base English package includes `moonshine_manifest_base_en.json`. Your Python application reads this file with `cf.load_manifest()`, selects the implementation with `cf.load_brick()`, and prepares the models with `brick.setup()`. This keeps model-specific file paths and runtime configuration out of your application code.
 
 ## Use versioned brick APIs
 
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
index bb5e3a6378..f4bcdbc099 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
@@ -116,7 +116,7 @@ For higher rate limits and faster downloads from Hugging Face, set the optional
 
 ```bash
 compute-flow update
-compute-flow model install stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic
+compute-flow model install stt:moonshine moonshine-base-en
 compute-flow model install lm_text:lm-text-generic hy-mt2-1-8b-2bit-translation
 compute-flow model install tts:qwen3-tts qwen3-tts-0-6b-custom-voice
 ```
@@ -125,14 +125,16 @@ Each `model install` command installs the brick source, if needed, and its model
 
 | Brick | Model package | Pipeline role |
 | --- | --- | --- |
-| `stt:parakeet-tdt` | Parakeet TDT 0.6B v3, dynamic INT8 | Recognize the English recording |
+| `stt:moonshine` | Moonshine Base English, FP32 | Recognize the English recording |
 | `lm_text:lm-text-generic` | Hy-MT2 1.8B, 2-bit | Translate the transcription into French |
 | `tts:qwen3-tts` | Qwen3-TTS 0.6B Custom Voice | Generate French speech |
 
+Moonshine Base English recognizes English only and uses ONNX Runtime with 32-bit floating-point (FP32) models. It expects 16 kHz audio.
+
 Models are downloaded to the `~/.compute-flow` directory by default.
 
 ```bash
-compute-flow model info stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic
+compute-flow model info stt:moonshine moonshine-base-en
 compute-flow model info lm_text:lm-text-generic hy-mt2-1-8b-2bit-translation
 compute-flow model info tts:qwen3-tts qwen3-tts-0-6b-custom-voice
 ```
@@ -158,14 +160,14 @@ If successful, you should see output similar to the following.
 
 Compute Flow uses prebuilt, optimized dependency binaries where available. Some dependencies, such as llama.cpp, are first checked on your local machine. A preinstalled version may be incompatible with Compute Flow and cause build errors.
 
-You can build the supported runtime dependencies from their pinned source revisions instead with the following command, although this takes longer:
+In the first instance, **either update the dependency causing the error to a compatible version or uninstall the local version** to allow Compute Flow to build just the missing depencencies from source.
+
+As a fallback, you can build the supported runtime dependencies from their pinned source revisions with the following command, although this will take up to 1 hour so we strongly recommend attempting the steps above before building from source:
 
 ```bash
 compute-flow build native --from-source
 ```
 
-Alternatively, either update the dependency causing the error to a compatible version or uninstall the local version to allow Compute Flow to build just the missing depencencies from source.
-
 {{% /notice %}}
 
 The build downloads optimized macOS Arm64 runtimes such as LiteRT, ExecuTorch, and ONNX Runtime with support for KleidiAI, packages them with your installed brick implementations in an application SDK for C++ applications, and installs Python bindings so you can use the same native functionality from Python.
@@ -327,24 +329,24 @@ The Qwen3-TTS manifest filename includes `en`, but the script sets its `language
 
 ## Translate the packaged recording
 
-Resolve the package locations through Brick Manager instead of hard-coding cache paths. Then run your application using the sample supplied with Parakeet:
+Resolve the package locations through Brick Manager instead of hard-coding cache paths. Then run your application using the sample supplied with Moonshine:
 
 ```bash
-PARAKEET_PACKAGE="$(compute-flow model path stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic)"
+MOONSHINE_PACKAGE="$(compute-flow model path stt:moonshine moonshine-base-en)"
 HY_MT2_PACKAGE="$(compute-flow model path lm_text:lm-text-generic hy-mt2-1-8b-2bit-translation)"
 QWEN3_TTS_PACKAGE="$(compute-flow model path tts:qwen3-tts qwen3-tts-0-6b-custom-voice)"
 
 python audio_translation_example/audio_translation.py \
-  --stt-manifest "$PARAKEET_PACKAGE/parakeet_tdt_0_6b_v3_manifest.json" \
+  --stt-manifest "$MOONSHINE_PACKAGE/moonshine_manifest_base_en.json" \
   --translation-manifest "$HY_MT2_PACKAGE/hy_mt2_1_8b_manifest.json" \
   --tts-manifest "$QWEN3_TTS_PACKAGE/qwen3_tts_custom_voice_en_manifest.json" \
-  --input "$PARAKEET_PACKAGE/samples/beckett.wav" \
+  --input "$MOONSHINE_PACKAGE/samples/beckett.wav" \
   --source-language English \
   --destination-language French \
   --output translated_french.wav
 ```
 
-The application prints `Transcription:`, `Translation:`, and `Saved:` lines alongside the runtime logs. The output from one run is shown here, with the checkout path shortened:
+The application prints `Transcription:`, `Translation:`, and `Saved:` lines alongside the runtime logs. The following illustrative output shows these lines, with the checkout path shortened. Your transcription and translation can differ:
 
 ```output
 Transcription: Ever tried, ever failed. No matter. Try again. Fail again. Fail better.
@@ -352,7 +354,7 @@ Translation: Avez-vous déjà essayé, avez-vous déjà échoué. Peu importe. E
 Saved: /translated_french.wav
 ```
 
-Check that the transcription matches the English recording and that the translation preserves its meaning. Generated wording and punctuation can vary and can contain errors: this run produced the incorrect word "Écheclez". Completing the pipeline verifies the integration; review the generated text and speech separately for translation quality.
+Check that the transcription matches the English recording and that the translation preserves its meaning. Generated wording and punctuation can vary and can contain errors: the illustrative output contains the incorrect word "Écheclez". Completing the pipeline verifies the integration; review the generated text and speech separately for translation quality.
 
 
 ## What you've accomplished
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
index 434a2c906a..2c0f771c50 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
@@ -1,6 +1,6 @@
 ---
 title: Replace the speech recognition model
-description: Replace Parakeet with Moonshine in your speech translation pipeline and verify both runs without changing your Python application.
+description: Replace Moonshine with Parakeet in your speech translation pipeline and verify both runs without changing your Python application.
 weight: 4
 
 ### FIXED, DO NOT MODIFY
@@ -9,35 +9,35 @@ layout: learningpathall
 
 ## Understand what changes when you replace a model
 
-Speech-to-text (`stt`) is a brick type: audio and a sample rate go in, and text comes out. A brick implementation, such as `stt:parakeet-tdt`, provides the code that runs supported models. A model package supplies the trained weights and assets; its manifest selects the implementation and configures how those files are used.
+Speech-to-text (`stt`) is a brick type: audio and a sample rate go in, and text comes out. A brick implementation, such as `stt:moonshine`, provides the code that runs supported models. A model package supplies the trained weights and assets; its manifest selects the implementation and configures how those files are used.
 
-You'll replace both the Parakeet implementation and its model package with `stt:moonshine` and `moonshine-base-en`. Both implementations follow `stt.v1`, even though Parakeet uses LiteRT and Moonshine uses ONNX Runtime.
+You'll replace Moonshine Base English FP32 with `stt:parakeet-tdt` and `parakeet-tdt-0-6b-v3-int8-dynamic`. Moonshine uses FP32 models with ONNX Runtime. The Parakeet TDT 0.6B v3 package uses dynamic INT8 quantization with LiteRT. Both implementations follow `stt.v1`.
 
 Your Python script stays unchanged: `cf.load_manifest(args.stt_manifest)` reads the manifest supplied as an argument, `cf.load_brick(stt_manifest)` selects its implementation, and `stt.setup(stt_manifest, options)` prepares the models. No model name, weights path, or inference backend is hardcoded into these calls. The same `stt.run(stt_input)` loop returns text for the translation stage.
 
-Moonshine Base English recognizes English only and expects 16 kHz audio, so keep using the same `beckett.wav` recording.
+Parakeet also expects 16 kHz audio, so keep using the same English `beckett.wav` recording.
 
-## Install Moonshine and rebuild Compute Flow
+## Install Parakeet and rebuild Compute Flow
 
-Use the same Compute Flow checkout and activated virtual environment as before. List Moonshine's models, install the English package, and inspect its manifest:
+Use the same Compute Flow checkout and activated virtual environment as before. List Parakeet's models, install the dynamic INT8 package, and inspect its manifest:
 
 ```bash
-compute-flow model list stt:moonshine
-compute-flow model install stt:moonshine moonshine-base-en
-compute-flow model info stt:moonshine moonshine-base-en
+compute-flow model list stt:parakeet-tdt
+compute-flow model install stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic
+compute-flow model info stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic
 ```
 
-The package contains `moonshine_manifest_base_en.json`. Build Compute Flow again to include the newly installed implementation:
+The package contains `parakeet_tdt_0_6b_v3_manifest.json`. Build Compute Flow again to include the newly installed implementation:
 
 ```bash
 compute-flow build native
 ```
 
-This build adds the Moonshine implementation to the available bricks. You don't edit your Python application or replace the other models. Selecting a different manifest works only when its brick implementation has been built and its model assets are installed.
+This build adds the Parakeet implementation to the available bricks. You don't edit your Python application or replace the other models. Selecting a different manifest works only when its brick implementation has been built and its model assets are installed.
 
-## Run the same application with Moonshine
+## Run the same application with Parakeet
 
-Resolve the package paths again so this command also works in a fresh terminal with the environment activated. Keep using the Parakeet package's recording to hold the input constant:
+Resolve the package paths again so this command also works in a fresh terminal with the environment activated. Keep using the Moonshine package's recording to hold the input constant:
 
 ```bash
 MOONSHINE_PACKAGE="$(compute-flow model path stt:moonshine moonshine-base-en)"
@@ -46,22 +46,22 @@ HY_MT2_PACKAGE="$(compute-flow model path lm_text:lm-text-generic hy-mt2-1-8b-2b
 QWEN3_TTS_PACKAGE="$(compute-flow model path tts:qwen3-tts qwen3-tts-0-6b-custom-voice)"
 
 python audio_translation_example/audio_translation.py \
-  --stt-manifest "$MOONSHINE_PACKAGE/moonshine_manifest_base_en.json" \
+  --stt-manifest "$PARAKEET_PACKAGE/parakeet_tdt_0_6b_v3_manifest.json" \
   --translation-manifest "$HY_MT2_PACKAGE/hy_mt2_1_8b_manifest.json" \
   --tts-manifest "$QWEN3_TTS_PACKAGE/qwen3_tts_custom_voice_en_manifest.json" \
-  --input "$PARAKEET_PACKAGE/samples/beckett.wav" \
+  --input "$MOONSHINE_PACKAGE/samples/beckett.wav" \
   --source-language English \
   --destination-language French \
-  --output translated_french_moonshine.wav
+  --output translated_french_parakeet.wav
 ```
 
-Only the `--stt-manifest` and output filename differ from your first run. The new manifest selects Moonshine while the same generation loop consumes its text output. The other two stages continue to receive text through the same interfaces.
+Only the `--stt-manifest` and output filename differ from your first run. The new manifest selects Parakeet while the same generation loop consumes its text output. The other two stages continue to receive text through the same interfaces.
 
-If the manifest can't be loaded, check the path reported by `model info`. If the brick implementation is unavailable, confirm that the native build succeeded after installing Moonshine and that you're using the same virtual environment.
+If the manifest can't be loaded, check the path reported by `model info`. If the brick implementation is unavailable, confirm that the native build succeeded after installing Parakeet and that you're using the same virtual environment.
 
 
 ## What you've accomplished
 
 You've learned how brick types, implementations, model packages, and manifests fit together. You used Brick Manager to install models and build Compute Flow on your Apple silicon Mac, then connected speech recognition, translation, and speech synthesis in Python to turn an English recording into a French WAV file locally.
 
-Finally, you replaced Parakeet with Moonshine by installing its brick and model package, rebuilding, and selecting a different manifest. The shared speech-to-text interface kept your Python script unchanged. You can use this approach to combine other bricks and select compatible models for your own applications.
+Finally, you replaced Moonshine with Parakeet by installing its brick and model package, rebuilding, and selecting a different manifest. The shared speech-to-text interface kept your Python script unchanged. You can use this approach to combine other bricks and select compatible models for your own applications.

From 324c8497395f28945021479eeca261a4126c5821 Mon Sep 17 00:00:00 2001
From: Kieran Hejmadi 
Date: Mon, 26 Oct 2026 13:11:45 +0000
Subject: [PATCH 015/109] Revert "reorder to use unquantized moonshine-base-en
 first"

This reverts commit 7f9873bba6b986781a13a4a09a44e865bbe61654.
---
 .../compute-flow-getting-started/how-to-1.md  |  4 +-
 .../compute-flow-getting-started/how-to-2.md  | 26 ++++++-------
 .../compute-flow-getting-started/how-to-3.md  | 38 +++++++++----------
 3 files changed, 33 insertions(+), 35 deletions(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
index 44c18e4ba2..75ee88ed46 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
@@ -19,7 +19,7 @@ You'll use Python to translate a short English recording into French speech on y
 English WAV
     |
     v
-Speech-to-text: Moonshine Base English (FP32)
+Speech-to-text: Parakeet TDT
     | English text
     v
 Text translation: Hy-MT2
@@ -51,7 +51,7 @@ A *model package* contains the weights and supporting assets that a brick needs.
 
 The manifest describes model filenames, inference runtimes, model-specific tensor formats, and supporting assets, such as tokenizers. It also provides configuration values and constants needed by the brick. The weights live in separate files referenced by the manifest.
 
-For example, the Moonshine Base English package includes `moonshine_manifest_base_en.json`. Your Python application reads this file with `cf.load_manifest()`, selects the implementation with `cf.load_brick()`, and prepares the models with `brick.setup()`. This keeps model-specific file paths and runtime configuration out of your application code.
+For example, the Parakeet package includes `parakeet_tdt_0_6b_v3_manifest.json`. Your Python application reads this file with `cf.load_manifest()`, selects the implementation with `cf.load_brick()`, and prepares the models with `brick.setup()`. This keeps model-specific file paths and runtime configuration out of your application code.
 
 ## Use versioned brick APIs
 
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
index f4bcdbc099..bb5e3a6378 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
@@ -116,7 +116,7 @@ For higher rate limits and faster downloads from Hugging Face, set the optional
 
 ```bash
 compute-flow update
-compute-flow model install stt:moonshine moonshine-base-en
+compute-flow model install stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic
 compute-flow model install lm_text:lm-text-generic hy-mt2-1-8b-2bit-translation
 compute-flow model install tts:qwen3-tts qwen3-tts-0-6b-custom-voice
 ```
@@ -125,16 +125,14 @@ Each `model install` command installs the brick source, if needed, and its model
 
 | Brick | Model package | Pipeline role |
 | --- | --- | --- |
-| `stt:moonshine` | Moonshine Base English, FP32 | Recognize the English recording |
+| `stt:parakeet-tdt` | Parakeet TDT 0.6B v3, dynamic INT8 | Recognize the English recording |
 | `lm_text:lm-text-generic` | Hy-MT2 1.8B, 2-bit | Translate the transcription into French |
 | `tts:qwen3-tts` | Qwen3-TTS 0.6B Custom Voice | Generate French speech |
 
-Moonshine Base English recognizes English only and uses ONNX Runtime with 32-bit floating-point (FP32) models. It expects 16 kHz audio.
-
 Models are downloaded to the `~/.compute-flow` directory by default.
 
 ```bash
-compute-flow model info stt:moonshine moonshine-base-en
+compute-flow model info stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic
 compute-flow model info lm_text:lm-text-generic hy-mt2-1-8b-2bit-translation
 compute-flow model info tts:qwen3-tts qwen3-tts-0-6b-custom-voice
 ```
@@ -160,14 +158,14 @@ If successful, you should see output similar to the following.
 
 Compute Flow uses prebuilt, optimized dependency binaries where available. Some dependencies, such as llama.cpp, are first checked on your local machine. A preinstalled version may be incompatible with Compute Flow and cause build errors.
 
-In the first instance, **either update the dependency causing the error to a compatible version or uninstall the local version** to allow Compute Flow to build just the missing depencencies from source.
-
-As a fallback, you can build the supported runtime dependencies from their pinned source revisions with the following command, although this will take up to 1 hour so we strongly recommend attempting the steps above before building from source:
+You can build the supported runtime dependencies from their pinned source revisions instead with the following command, although this takes longer:
 
 ```bash
 compute-flow build native --from-source
 ```
 
+Alternatively, either update the dependency causing the error to a compatible version or uninstall the local version to allow Compute Flow to build just the missing depencencies from source.
+
 {{% /notice %}}
 
 The build downloads optimized macOS Arm64 runtimes such as LiteRT, ExecuTorch, and ONNX Runtime with support for KleidiAI, packages them with your installed brick implementations in an application SDK for C++ applications, and installs Python bindings so you can use the same native functionality from Python.
@@ -329,24 +327,24 @@ The Qwen3-TTS manifest filename includes `en`, but the script sets its `language
 
 ## Translate the packaged recording
 
-Resolve the package locations through Brick Manager instead of hard-coding cache paths. Then run your application using the sample supplied with Moonshine:
+Resolve the package locations through Brick Manager instead of hard-coding cache paths. Then run your application using the sample supplied with Parakeet:
 
 ```bash
-MOONSHINE_PACKAGE="$(compute-flow model path stt:moonshine moonshine-base-en)"
+PARAKEET_PACKAGE="$(compute-flow model path stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic)"
 HY_MT2_PACKAGE="$(compute-flow model path lm_text:lm-text-generic hy-mt2-1-8b-2bit-translation)"
 QWEN3_TTS_PACKAGE="$(compute-flow model path tts:qwen3-tts qwen3-tts-0-6b-custom-voice)"
 
 python audio_translation_example/audio_translation.py \
-  --stt-manifest "$MOONSHINE_PACKAGE/moonshine_manifest_base_en.json" \
+  --stt-manifest "$PARAKEET_PACKAGE/parakeet_tdt_0_6b_v3_manifest.json" \
   --translation-manifest "$HY_MT2_PACKAGE/hy_mt2_1_8b_manifest.json" \
   --tts-manifest "$QWEN3_TTS_PACKAGE/qwen3_tts_custom_voice_en_manifest.json" \
-  --input "$MOONSHINE_PACKAGE/samples/beckett.wav" \
+  --input "$PARAKEET_PACKAGE/samples/beckett.wav" \
   --source-language English \
   --destination-language French \
   --output translated_french.wav
 ```
 
-The application prints `Transcription:`, `Translation:`, and `Saved:` lines alongside the runtime logs. The following illustrative output shows these lines, with the checkout path shortened. Your transcription and translation can differ:
+The application prints `Transcription:`, `Translation:`, and `Saved:` lines alongside the runtime logs. The output from one run is shown here, with the checkout path shortened:
 
 ```output
 Transcription: Ever tried, ever failed. No matter. Try again. Fail again. Fail better.
@@ -354,7 +352,7 @@ Translation: Avez-vous déjà essayé, avez-vous déjà échoué. Peu importe. E
 Saved: /translated_french.wav
 ```
 
-Check that the transcription matches the English recording and that the translation preserves its meaning. Generated wording and punctuation can vary and can contain errors: the illustrative output contains the incorrect word "Écheclez". Completing the pipeline verifies the integration; review the generated text and speech separately for translation quality.
+Check that the transcription matches the English recording and that the translation preserves its meaning. Generated wording and punctuation can vary and can contain errors: this run produced the incorrect word "Écheclez". Completing the pipeline verifies the integration; review the generated text and speech separately for translation quality.
 
 
 ## What you've accomplished
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
index 2c0f771c50..434a2c906a 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
@@ -1,6 +1,6 @@
 ---
 title: Replace the speech recognition model
-description: Replace Moonshine with Parakeet in your speech translation pipeline and verify both runs without changing your Python application.
+description: Replace Parakeet with Moonshine in your speech translation pipeline and verify both runs without changing your Python application.
 weight: 4
 
 ### FIXED, DO NOT MODIFY
@@ -9,35 +9,35 @@ layout: learningpathall
 
 ## Understand what changes when you replace a model
 
-Speech-to-text (`stt`) is a brick type: audio and a sample rate go in, and text comes out. A brick implementation, such as `stt:moonshine`, provides the code that runs supported models. A model package supplies the trained weights and assets; its manifest selects the implementation and configures how those files are used.
+Speech-to-text (`stt`) is a brick type: audio and a sample rate go in, and text comes out. A brick implementation, such as `stt:parakeet-tdt`, provides the code that runs supported models. A model package supplies the trained weights and assets; its manifest selects the implementation and configures how those files are used.
 
-You'll replace Moonshine Base English FP32 with `stt:parakeet-tdt` and `parakeet-tdt-0-6b-v3-int8-dynamic`. Moonshine uses FP32 models with ONNX Runtime. The Parakeet TDT 0.6B v3 package uses dynamic INT8 quantization with LiteRT. Both implementations follow `stt.v1`.
+You'll replace both the Parakeet implementation and its model package with `stt:moonshine` and `moonshine-base-en`. Both implementations follow `stt.v1`, even though Parakeet uses LiteRT and Moonshine uses ONNX Runtime.
 
 Your Python script stays unchanged: `cf.load_manifest(args.stt_manifest)` reads the manifest supplied as an argument, `cf.load_brick(stt_manifest)` selects its implementation, and `stt.setup(stt_manifest, options)` prepares the models. No model name, weights path, or inference backend is hardcoded into these calls. The same `stt.run(stt_input)` loop returns text for the translation stage.
 
-Parakeet also expects 16 kHz audio, so keep using the same English `beckett.wav` recording.
+Moonshine Base English recognizes English only and expects 16 kHz audio, so keep using the same `beckett.wav` recording.
 
-## Install Parakeet and rebuild Compute Flow
+## Install Moonshine and rebuild Compute Flow
 
-Use the same Compute Flow checkout and activated virtual environment as before. List Parakeet's models, install the dynamic INT8 package, and inspect its manifest:
+Use the same Compute Flow checkout and activated virtual environment as before. List Moonshine's models, install the English package, and inspect its manifest:
 
 ```bash
-compute-flow model list stt:parakeet-tdt
-compute-flow model install stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic
-compute-flow model info stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic
+compute-flow model list stt:moonshine
+compute-flow model install stt:moonshine moonshine-base-en
+compute-flow model info stt:moonshine moonshine-base-en
 ```
 
-The package contains `parakeet_tdt_0_6b_v3_manifest.json`. Build Compute Flow again to include the newly installed implementation:
+The package contains `moonshine_manifest_base_en.json`. Build Compute Flow again to include the newly installed implementation:
 
 ```bash
 compute-flow build native
 ```
 
-This build adds the Parakeet implementation to the available bricks. You don't edit your Python application or replace the other models. Selecting a different manifest works only when its brick implementation has been built and its model assets are installed.
+This build adds the Moonshine implementation to the available bricks. You don't edit your Python application or replace the other models. Selecting a different manifest works only when its brick implementation has been built and its model assets are installed.
 
-## Run the same application with Parakeet
+## Run the same application with Moonshine
 
-Resolve the package paths again so this command also works in a fresh terminal with the environment activated. Keep using the Moonshine package's recording to hold the input constant:
+Resolve the package paths again so this command also works in a fresh terminal with the environment activated. Keep using the Parakeet package's recording to hold the input constant:
 
 ```bash
 MOONSHINE_PACKAGE="$(compute-flow model path stt:moonshine moonshine-base-en)"
@@ -46,22 +46,22 @@ HY_MT2_PACKAGE="$(compute-flow model path lm_text:lm-text-generic hy-mt2-1-8b-2b
 QWEN3_TTS_PACKAGE="$(compute-flow model path tts:qwen3-tts qwen3-tts-0-6b-custom-voice)"
 
 python audio_translation_example/audio_translation.py \
-  --stt-manifest "$PARAKEET_PACKAGE/parakeet_tdt_0_6b_v3_manifest.json" \
+  --stt-manifest "$MOONSHINE_PACKAGE/moonshine_manifest_base_en.json" \
   --translation-manifest "$HY_MT2_PACKAGE/hy_mt2_1_8b_manifest.json" \
   --tts-manifest "$QWEN3_TTS_PACKAGE/qwen3_tts_custom_voice_en_manifest.json" \
-  --input "$MOONSHINE_PACKAGE/samples/beckett.wav" \
+  --input "$PARAKEET_PACKAGE/samples/beckett.wav" \
   --source-language English \
   --destination-language French \
-  --output translated_french_parakeet.wav
+  --output translated_french_moonshine.wav
 ```
 
-Only the `--stt-manifest` and output filename differ from your first run. The new manifest selects Parakeet while the same generation loop consumes its text output. The other two stages continue to receive text through the same interfaces.
+Only the `--stt-manifest` and output filename differ from your first run. The new manifest selects Moonshine while the same generation loop consumes its text output. The other two stages continue to receive text through the same interfaces.
 
-If the manifest can't be loaded, check the path reported by `model info`. If the brick implementation is unavailable, confirm that the native build succeeded after installing Parakeet and that you're using the same virtual environment.
+If the manifest can't be loaded, check the path reported by `model info`. If the brick implementation is unavailable, confirm that the native build succeeded after installing Moonshine and that you're using the same virtual environment.
 
 
 ## What you've accomplished
 
 You've learned how brick types, implementations, model packages, and manifests fit together. You used Brick Manager to install models and build Compute Flow on your Apple silicon Mac, then connected speech recognition, translation, and speech synthesis in Python to turn an English recording into a French WAV file locally.
 
-Finally, you replaced Moonshine with Parakeet by installing its brick and model package, rebuilding, and selecting a different manifest. The shared speech-to-text interface kept your Python script unchanged. You can use this approach to combine other bricks and select compatible models for your own applications.
+Finally, you replaced Parakeet with Moonshine by installing its brick and model package, rebuilding, and selecting a different manifest. The shared speech-to-text interface kept your Python script unchanged. You can use this approach to combine other bricks and select compatible models for your own applications.

From 06e73fb82713517d8c13319d19bd827eb3355c0e Mon Sep 17 00:00:00 2001
From: Kieran Hejmadi 
Date: Mon, 26 Oct 2026 13:27:30 +0000
Subject: [PATCH 016/109] add subheading section around using cli to profile
 and experiment quickly

---
 .../compute-flow-getting-started/how-to-3.md  | 90 +++++++++++++++++++
 1 file changed, 90 insertions(+)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
index 434a2c906a..130b059dff 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
@@ -60,6 +60,96 @@ Only the `--stt-manifest` and output filename differ from your first run. The ne
 If the manifest can't be loaded, check the path reported by `model info`. If the brick implementation is unavailable, confirm that the native build succeeded after installing Moonshine and that you're using the same virtual environment.
 
 
+## Easily Compare speech recognition performance
+
+Use the built STT CLI to compare the speech recognition stage. Changing the manifest lets you experiment quickly with another model.
+
+Parakeet's model files total approximately 683 MB, with dynamic INT8 quantization for fully connected weights and FP32 external interfaces. Moonshine's FP32 model files total approximately 247 MB. These are different model families using LiteRT and ONNX Runtime respectively.
+
+Use the package variables from the previous commands. Run both alternatives on the same mono 16 kHz recording, with one CPU thread and profiling enabled:
+
+```bash
+INPUT_AUDIO="$PARAKEET_PACKAGE/samples/beckett.wav"
+```
+
+{{< tabpane-normal >}}
+  {{< tab header="Parakeet dynamic INT8" >}}
+Run the Parakeet manifest:
+
+```bash
+./build_native/stt_cli \
+  -m "$PARAKEET_PACKAGE/parakeet_tdt_0_6b_v3_manifest.json" \
+  -a "$INPUT_AUDIO" \
+  -t 1 --enable-op-profiling
+```
+
+The output is similar to this captured excerpt, showing the featurizer's operator breakdown and the complete scope summary. The first-call report and other operator tables are omitted:
+
+```output
+[TRANSCRIPTION]
+Ever tried, ever failed. No matter. Try again. Fail again. Fail better.
+...
+All Calls
+Scope: run() [total ms: 985.12]
+Scope stats | calls: 30 | avg ms: 32.84 | min ms: 0.34 | max ms: 487.10
+Model + Op Breakdown
+runner          contributor                                              calls                                         %      total ms      avg ms      min ms      max ms
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+litert          featurizer
+                  GATHER_ND                                                  4 [██████████████░░░░░░░░░░░░░░░░]  45.01%          7.86        1.96        1.94        1.98
+                  Fully Connected (NC, QP8, F32, QC8W) GEMM                 12 [████████░░░░░░░░░░░░░░░░░░░░░░]  25.58%          4.46        0.37        0.18        0.49
+                  ADD                                                        4 [██████░░░░░░░░░░░░░░░░░░░░░░░░]  19.29%          3.37        0.84        0.81        0.87
+                  Multiply (ND)                                             24 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.27%          0.22        0.01        0.00        0.03
+                  Log (NC)                                                   4 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   0.85%          0.15        0.04        0.03        0.05
+                  Extra Ops                                                    [█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   2.37%          0.41
+                  Overhead                                                     [██░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   5.64%          0.98
+...
+Scope Summary
+runner          contributor                                              calls                                         %      total ms      avg ms      min ms      max ms
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+litert          encoder                                                      2 [█████████████████████████████░]  95.60%        941.76      470.88      469.83      471.93
+litert          featurizer                                                   4 [█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.77%         17.45        4.36        4.28        4.44
+litert          joint_step                                                  42 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.43%         14.06        0.33        0.31        0.36
+litert          decoder_step                                                30 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.12%         11.06        0.37        0.34        0.65
+                Overhead                                                    30 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   0.08%          0.79        0.03
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+                Scope Total                                                                                                     985.12
+```
+  {{< /tab >}}
+  {{< tab header="Moonshine FP32" >}}
+Run the Moonshine manifest:
+
+```bash
+./build_native/stt_cli \
+  -m "$MOONSHINE_PACKAGE/moonshine_manifest_base_en.json" \
+  -a "$INPUT_AUDIO" \
+  -t 1 --enable-op-profiling
+```
+
+The output is similar to this captured report. This ONNX Runtime runner reports model timings without operator details:
+
+```output
+[TRANSCRIPTION]
+Ever tried ever failed, no matter try again fail again fail better.
+----------------
+Scope: run() [total ms: 290.30]
+Scope stats | calls: 1 | avg ms: 290.30 | min ms: 290.30 | max ms: 290.30
+Scope Summary
+runner          contributor                                              calls                                         %      total ms      avg ms      min ms      max ms
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+onnxrt          encoder                                                      1 [██████████████████████░░░░░░░░]  74.69%        216.82      216.82      216.82      216.82
+onnxrt          decoder                                                     15 [████████░░░░░░░░░░░░░░░░░░░░░░]  25.21%         73.19        4.88        3.23       25.37
+                Overhead                                                     1 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   0.10%          0.29        0.29
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+                Scope Total                                                                                                     290.30
+```
+  {{< /tab >}}
+{{< /tabpane-normal >}}
+
+These timings exclude setup and warmup, include profiling overhead, and use a single 9.963-second audio sample. We haven't assessed changes in transcription accuracy. On an Apple M3 Pro, Moonshine takes 290.30 ms versus Parakeet's 985.12 ms. Compare total `run()` time: Parakeet's 30 streaming calls complete one transcription, not 30 benchmark repetitions.
+
+Pass your own representative audio samples in a JSON file with `-j runs.json` to evaluate more inputs. The shared Python API and CLI let you quickly profile models across different runtimes and find the most suitable model for your application by changing manifests.
+
 ## What you've accomplished
 
 You've learned how brick types, implementations, model packages, and manifests fit together. You used Brick Manager to install models and build Compute Flow on your Apple silicon Mac, then connected speech recognition, translation, and speech synthesis in Python to turn an English recording into a French WAV file locally.

From e6097fa37eae80234b9c1fd53633fc83344a72df Mon Sep 17 00:00:00 2001
From: Kieran Hejmadi 
Date: Mon, 26 Oct 2026 13:29:03 +0000
Subject: [PATCH 017/109] prioritize fixing potential dependency issues on
 system before building from source

---
 .../cross-platform/compute-flow-getting-started/how-to-2.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
index bb5e3a6378..da1bd12c67 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
@@ -158,14 +158,14 @@ If successful, you should see output similar to the following.
 
 Compute Flow uses prebuilt, optimized dependency binaries where available. Some dependencies, such as llama.cpp, are first checked on your local machine. A preinstalled version may be incompatible with Compute Flow and cause build errors.
 
-You can build the supported runtime dependencies from their pinned source revisions instead with the following command, although this takes longer:
+In the first instance, **either update the dependency causing the error to a compatible version or uninstall the local version** to allow Compute Flow to build just the missing depencencies from source.
+
+As a fallback, you can build the supported runtime dependencies from their pinned source revisions with the following command, although this will take up to 1 hour so we strongly recommend attempting the steps above before building from source:
 
 ```bash
 compute-flow build native --from-source
 ```
 
-Alternatively, either update the dependency causing the error to a compatible version or uninstall the local version to allow Compute Flow to build just the missing depencencies from source.
-
 {{% /notice %}}
 
 The build downloads optimized macOS Arm64 runtimes such as LiteRT, ExecuTorch, and ONNX Runtime with support for KleidiAI, packages them with your installed brick implementations in an application SDK for C++ applications, and installs Python bindings so you can use the same native functionality from Python.

From 3334efa33e5efde4240359012a39821b27c5e97e Mon Sep 17 00:00:00 2001
From: Kieran Hejmadi 
Date: Tue, 27 Oct 2026 14:04:27 +0000
Subject: [PATCH 018/109] replace with actual public URL

---
 .../compute-flow-getting-started/_index.md             | 10 +---------
 .../compute-flow-getting-started/how-to-2.md           |  4 ++--
 2 files changed, 3 insertions(+), 11 deletions(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md b/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
index 7aa34701a5..aa5f39edde 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
@@ -47,15 +47,7 @@ shared_between:
 further_reading:
     - resource:
         title: Compute Flow source repository (public URL placeholder)
-        link: https://github.com/ARM-software/ComputeFlow
-        type: documentation
-    - resource:
-        title: Compute Flow application development guide (public URL placeholder)
-        link: https://github.com/ARM-software/ComputeFlow/blob/main/docs/howto/developing_apps_with_compute_flow.md
-        type: documentation
-    - resource:
-        title: Compute Flow speech translation example (public URL placeholder)
-        link: https://github.com/ARM-software/ComputeFlow/blob/main/docs/examples/python/audio_to_audio_translation.md
+        link: https://github.com/ARM-software/compute-flow
         type: documentation
     - resource:
         title: Install uv
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
index da1bd12c67..d33f04d1ff 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
@@ -46,8 +46,8 @@ The public repository URL in this draft is a placeholder. Until it is available,
 When the public repository is available, obtain it with:
 
 ```bash
-git clone https://github.com/ARM-software/ComputeFlow.git
-cd ComputeFlow
+git clone https://github.com/ARM-software/compute-flow.git
+cd compute-flow
 ```
 
 Create and activate the Python environment, then check the required system dependencies:

From 2a4d8b5f303e25a0a8d4eb0b277b24fe4a09b390 Mon Sep 17 00:00:00 2001
From: Kieran Hejmadi 
Date: Thu, 29 Oct 2026 17:10:59 +0000
Subject: [PATCH 019/109] incorporate feedback on _index.md

---
 .../compute-flow-getting-started/_index.md         | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md b/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
index aa5f39edde..abdf646ca2 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
@@ -1,18 +1,18 @@
 ---
-title: Getting Started with Compute Flow
-description: Connect speech recognition, translation, and speech synthesis with Compute Flow on your Apple silicon Mac, then replace a model without changing your Python application.
+title: Get Started with Compute Flow
+description: Build and run an audio-to-audio pipeline as a Python application on your Arm-based device, combining Compute Flow bricks for speech recognition, translation, and speech synthesis
 
-minutes_to_complete: 30
+minutes_to_complete: 15
 
-who_is_this_for: This is an introductory topic for developers who want to combine multiple AI models into a performant end-to-end AI applicaton running on an Arm CPU.
+who_is_this_for: This is an introductory topic for developers who want to combine multiple AI models into a performant end-to-end AI applicaton running on an Arm-based CPU. This learning path has been validated on an Apple® Silicon Mac but is expected to work on other Arm-based CPUs.
 
 learning_objectives:
     - Explain how Compute Flow bricks and manifests separate application code from model implementations
-    - Run a local Python pipeline that translates English speech into French speech
-    - Replace the speech recognition model and verify the pipeline using the same application and recording
+    - Deploy an AI audio-to-audio translation pipline in Python with Compute Flow. 
+    - Select the speech recognition model and verify the pipeline using the same application and recording
 
 prerequisites:
-    - An Apple silicon Mac running macOS
+    - An Arm-based system such as an Apple® silicon Mac.
     - Basic Python knowledge and familiarity with terminal commands
     - Internet access and at least 15 GB of available storage
 

From b81376bd40331353efa28912a4f267de84f606cb Mon Sep 17 00:00:00 2001
From: Kieran Hejmadi 
Date: Thu, 29 Oct 2026 17:11:24 +0000
Subject: [PATCH 020/109] incorporate feedback on how-to-1.md

---
 .../compute-flow-getting-started/how-to-1.md  | 43 +++++++++----------
 1 file changed, 20 insertions(+), 23 deletions(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
index 75ee88ed46..a9c84490c5 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
@@ -1,49 +1,51 @@
 ---
-title: Understand Compute Flow
-description: Understand how you can combine local AI models through Compute Flow bricks, manifests, and shared interfaces.
+title: What is Compute Flow
+description: Learn about Compute Flow bricks, manifests, and shared interfaces to combine multiple AI models and run them locally on-device.
 weight: 2
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## Connect models into a local application
+## Combine Compute Flow bricks to connect multiple AI models into a local application.
 
-Compute Flow is an SDK for building AI applications from reusable components called *bricks*. Each brick exposes an AI capability through a shared interface for that capability. You connect those interfaces in Python or C++ while the brick handles model-specific inference. Compute Flow brings together runtimes such as LiteRT, ONNX Runtime, ExecuTorch, and llama.cpp for Arm devices.
+Compute Flow is an SDK for building AI applications from reusable AI building blocks called *bricks*. Each brick exposes an AI capability through a shared interface for that capability. You connect those interfaces in Python or C++ while the brick handles model-specific inference.
 
-We'll use the example of an application that translates speech needs several models. One recognizes speech, another translates text, and a third generates audio. These models can use different inference runtimes, tensor formats, tokenizers, and configuration files. Connecting them directly means learning each runtime's API and writing code for each model. Replacing a model can mean rewriting that integration.
+A *brick type* defines an input and output contract. A *brick variant* implements that contract using a particular model family. For example, Parakeet TDT and Moonshine are different speech-to-text variants. Both accept audio and its sample rate, and return text.
+
+In this Learning Path, we'll use three different bricks to build the audio translation application. The first recognises human speech, the second translates speech into text, the third generate audio. Each of these bricks use different models to get to our aims of translating English into French.
+
+The figure below illustrate the three stages of our audio translation application, pointing out the AI models that are used at each stage.
 
 You'll use Python to translate a short English recording into French speech on your Apple Silicon Mac. The application processes the recording through three stages:
 
 ```text
-English WAV
+English WAV (Input)
     |
     v
-Speech-to-text: Parakeet TDT
+Stage 1. Speech-to-text: Parakeet TDT
     | English text
     v
-Text translation: Hy-MT2
+Stage 2. Text translation: Hy-MT2
     | French text
     v
-Text-to-speech: Qwen3-TTS
+Stage 3. Text-to-speech: Qwen3-TTS
     |
     v
-French WAV
+French WAV (Output)
 ```
 
-Python coordinates the stages: it passes the transcription to the translation brick, then passes the translation to the speech synthesis brick. Compute Flow doesn't automatically assemble this pipeline for you.
+Python coordinates the stages: it passes the speech-to-text transcription to the translation brick, then passes the translation to the speech synthesis brick. Compute Flow doesn't automatically assemble this pipeline for you.
 
 ## Separate the application from the model
 
-A *brick type* defines an input and output contract. A *brick variant* implements that contract using a particular model family. For example, Parakeet TDT and Moonshine are different speech-to-text variants. Both accept audio and its sample rate, and return text.
-
 Your pipeline uses these contracts:
 
 | Stage | Brick type | Input | Output |
 | --- | --- | --- | --- |
-| Recognize speech | `stt` | `raw_wav` and `sample_rate` | `text` |
+| Audio transcription | `speech-to-text (stt)` | `raw_wav` and `sample_rate` | `text` |
 | Translate text | `lm_text` | `text_prompt` | `text` |
-| Synthesize speech | `tts` | `text_prompt` | `left_channel`, `right_channel`, and `sample_rate` |
+| Synthesize speech | `text-to-speech (tts)` | `text_prompt` | `left_channel`, `right_channel`, and `sample_rate` |
 
 ### Understand the model manifest
 
@@ -53,18 +55,13 @@ The manifest describes model filenames, inference runtimes, model-specific tenso
 
 For example, the Parakeet package includes `parakeet_tdt_0_6b_v3_manifest.json`. Your Python application reads this file with `cf.load_manifest()`, selects the implementation with `cf.load_brick()`, and prepares the models with `brick.setup()`. This keeps model-specific file paths and runtime configuration out of your application code.
 
-## Use versioned brick APIs
-
-From Python or C++, you use a brick's versioned input/output contract and shared execution methods such as `setup()`, `run()`, and `reset_states()`. Parakeet and Moonshine both implement `stt.v1`, so your script keeps the same input and output handling when switching between them. Compute Flow's compatibility policy requires newer releases to support bricks using earlier API versions.
-
-For details, see the [Brick API guide](https://github.com/ARM-software/ComputeFlow/blob/main/docs/design/brick_api.md), [Brick Runtime Interface V1](https://github.com/ARM-software/ComputeFlow/blob/main/docs/design/brick_runtime_interface_v1.md), and [documentation README](https://github.com/ARM-software/ComputeFlow/blob/main/docs/README.md). These links use the public repository URL placeholder.
-
 ## Prepare once, then run locally
 
-*Brick Manager*, the `compute-flow` command, discovers bricks, installs their source and model packages, and builds the native libraries and Python bindings. You'll update its registry, install the three models, and build for your Mac with `compute-flow build native`.
+To discover and install the required Compute Flow Bricks, alongside with the selected AI models for our audio-to-audio translation pipeline, we will use Compute Flow Brick Manager.
+The Brick Manager is responsible for installing the bricks type and brick variant based on users' selected AI model, and builds the user's developed application for the selected runtime platforms.
 
 Setup needs network access to download dependencies and model packages. After setup, the example reads the installed assets and performs inference on your Mac. It writes a WAV file locally and doesn't call a hosted inference service. You don't need a microphone because the input recording is included in a model package.
 
 ## What you've learned
 
-Compute Flow separates your application from model-specific inference through versioned brick APIs and manifests. Brick Manager prepares the implementations and model assets, while your Python code connects their inputs and outputs. Next, you'll build and run the speech translation pipeline on your Mac.
+Compute Flow provides reusable AI building blocks that you can combine together to build AI application to deploy locally on your device. Compute Flows Bricks allow users to focus on the AI models actions, inputs and outputs, rather than the AI models inference, easing out the combination and replacement of different AI models.

From f3c1b36bd9ee2b4f7047c7ff8a6f03cf3117f88a Mon Sep 17 00:00:00 2001
From: Kieran Hejmadi 
Date: Thu, 29 Oct 2026 17:11:48 +0000
Subject: [PATCH 021/109] incorporate feedback in how-to-2.md

---
 .../compute-flow-getting-started/how-to-2.md  | 250 ++++++++----------
 1 file changed, 111 insertions(+), 139 deletions(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
index d33f04d1ff..21f8a8d85b 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
@@ -1,6 +1,6 @@
 ---
 title: Run a local speech translation pipeline
-description: Build Compute Flow on your Mac and connect three Python bricks to translate an English recording into French speech.
+description: Build Compute Flow on your Mac and connect three Compute Flow bricks to translate an English recording into French speech.
 weight: 3
 
 ### FIXED, DO NOT MODIFY
@@ -37,13 +37,15 @@ command -v cc
 command -v c++
 ```
 
-## Set up Compute Flow
+Finally, remove any previously installed `llama.cpp` versions. For example if installed with the `brew` package manager, run the following command:
 
-{{% notice Note %}}
-The public repository URL in this draft is a placeholder. Until it is available, use the Compute Flow checkout supplied through your project access. You also need access to its native dependency bundles. Run the remaining commands from that checkout's root directory.
-{{% /notice %}}
+```bash
+brew uninstall llama.cpp
+```
 
-When the public repository is available, obtain it with:
+## Set up Compute Flow
+
+Run the following command.
 
 ```bash
 git clone https://github.com/ARM-software/compute-flow.git
@@ -125,11 +127,11 @@ Each `model install` command installs the brick source, if needed, and its model
 
 | Brick | Model package | Pipeline role |
 | --- | --- | --- |
-| `stt:parakeet-tdt` | Parakeet TDT 0.6B v3, dynamic INT8 | Recognize the English recording |
-| `lm_text:lm-text-generic` | Hy-MT2 1.8B, 2-bit | Translate the transcription into French |
-| `tts:qwen3-tts` | Qwen3-TTS 0.6B Custom Voice | Generate French speech |
+| `stt:parakeet-tdt` | Parakeet TDT 0.6B v3, dynamic INT8 | Transcribe the English recording |
+| `lm_text:lm-text-generic` | Hy-MT2 1.8B, 2-bit | Translate English text into French |
+| `tts:qwen3-tts` | Qwen3-TTS 0.6B Custom Voice | Synthesize French speech |
 
-Models are downloaded to the `~/.compute-flow` directory by default.
+Models are downloaded to the `~/.compute-flow` directory by default.The `compute-flow model info` command displays details about a specified model
 
 ```bash
 compute-flow model info stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic
@@ -139,7 +141,7 @@ compute-flow model info tts:qwen3-tts qwen3-tts-0-6b-custom-voice
 
 ## Build for your Mac
 
-Build the installed bricks and the native Python bindings:
+Build the installed bricks:
 
 ```bash
 compute-flow build native
@@ -149,63 +151,12 @@ If successful, you should see output similar to the following.
 
 ```output
 ╭────────────────────────────── OK Build Complete ──────────────────────────────╮
-│  Build directory  /Users/kiehej01/compute_flow/compute_flow/build_native      │
-│  Application SDK  /Users/kiehej01/compute_flow/compute_flow/build_native/sdk  │
+│  Build directory  /Users/user/compute_flow/compute_flow/build_native      │
+│  Application SDK  /Users/user/compute_flow/compute_flow/build_native/sdk  │
 ╰───────────────────────────────────────────────────────────────────────────────╯
 ```
 
-{{% notice Troubleshooting Tip %}}
-
-Compute Flow uses prebuilt, optimized dependency binaries where available. Some dependencies, such as llama.cpp, are first checked on your local machine. A preinstalled version may be incompatible with Compute Flow and cause build errors.
-
-In the first instance, **either update the dependency causing the error to a compatible version or uninstall the local version** to allow Compute Flow to build just the missing depencencies from source.
-
-As a fallback, you can build the supported runtime dependencies from their pinned source revisions with the following command, although this will take up to 1 hour so we strongly recommend attempting the steps above before building from source:
-
-```bash
-compute-flow build native --from-source
-```
-
-{{% /notice %}}
-
-The build downloads optimized macOS Arm64 runtimes such as LiteRT, ExecuTorch, and ONNX Runtime with support for KleidiAI, packages them with your installed brick implementations in an application SDK for C++ applications, and installs Python bindings so you can use the same native functionality from Python.
-
-```output
-├── CMakeLists.txt
-├── force_load_libraries.txt
-├── include
-│   └── compute_flow
-│       ├── accelerator.h
-│       ├── bindings
-│       ├── bricks
-│       ├── check.h
-│       ├── compute_flow.h
-│       ├── lm_params.h
-│       ├── lm_runners
-│       ├── manifest.h
-│       ├── memory_planner.h
-│       ├── model_data.h
-│       ├── profiler
-│       ├── runners
-│       ├── runtime.h
-│       └── tensor_dict.h
-├── lib
-│   ├── libcompute_flow_brick_factory.a
-│   ├── libcompute_flow_core.a
-│   ├── libcompute_flow.a
-│   ├── libexecutorch.a
-│   ├── liblm_text_core.a
-│   ├── liboptimized_native_cpu_ops_lib.a
-│   ├── libstt_core.a
-│   ├── libtts_core.a
-│   └── libxnnpack_backend.a
-├── README.md
-└── share
-    └── compute_flow
-        └── bricks
-```
-
-The second command confirms that your active environment can load the native module. If the import fails, check that the build completed successfully and that the same virtual environment is active. A dependency download error needs to be resolved before the bindings can be built.
+The build command downloads inference runtimes optimized for your deployment platform and packages them with your installed brick implementations into an application SDK available at `build_native/sdk`. 
 
 ## Create the Python application
 
@@ -215,13 +166,12 @@ Create a directory for your application:
 mkdir -p audio_translation_example
 ```
 
-Save the following code as `audio_translation_example/audio_translation.py`. It loads each manifest, configures the languages, and connects the three bricks. The sample-rate check prevents passing incompatible audio to the speech recognition model.
+Save the following code as `audio_translation_example/audio_translation.py`. The below code provides everything you need to run the audio-to-audio translation pipeline. You will see that the code first load and configure each of the three bricks required for our application, then execute each brick, with the output from the previous brick being pass to the next one, and finally, save the output of our pipeline.
 
 ```python
-# SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates 
-# SPDX-License-Identifier: Apache-2.0
+"""Translate speech between languages with three Compute Flow bricks."""
 
-"""Translate an English recording with three local Compute Flow bricks."""
+from __future__ import annotations
 
 import argparse
 from pathlib import Path
@@ -231,7 +181,7 @@ import numpy as np
 import soundfile as sf
 
 
-def main() -> None:
+def main(argv: list[str] | None = None) -> int:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("--stt-manifest", required=True)
     parser.add_argument("--translation-manifest", required=True)
@@ -239,93 +189,116 @@ def main() -> None:
     parser.add_argument("--input", required=True, dest="input_audio")
     parser.add_argument("--source-language", required=True)
     parser.add_argument("--destination-language", required=True)
-    parser.add_argument("--output", default="translated_french.wav")
-    args = parser.parse_args()
+    parser.add_argument(
+        "--output", default="translated_french.wav", dest="output_audio"
+    )
+    args = parser.parse_args(argv)
+
+    # Load the input as a batch containing one mono float32 waveform.
+    input_audio, input_sample_rate = sf.read(
+        args.input_audio, dtype="float32", always_2d=True
+    )
+    mono_audio = np.mean(input_audio, axis=1, dtype=np.float32)
+    raw_wav = np.ascontiguousarray(mono_audio.reshape(1, -1), dtype=np.float32)
+    sample_rate = np.asarray([input_sample_rate], dtype=np.int64)
 
-    # Both speech recognition variants in this tutorial expect 16 kHz audio.
-    audio, input_rate = sf.read(args.input_audio, dtype="float32", always_2d=True)
-    if input_rate != 16000 or audio.shape[0] == 0:
-        raise ValueError("Use a nonempty 16 kHz recording, such as beckett.wav")
-    mono = np.mean(audio, axis=1, dtype=np.float32)
-    raw_wav = np.ascontiguousarray(mono.reshape(1, -1), dtype=np.float32)
-    sample_rate = np.asarray([input_rate], dtype=np.int64)
+    options = cf.BrickOptions()
+    options.runtime.enable_warmup = True
 
+    # Load the manifests.
     stt_manifest = cf.load_manifest(args.stt_manifest)
     translation_manifest = cf.load_manifest(args.translation_manifest)
     tts_manifest = cf.load_manifest(args.tts_manifest)
+
+    # Configure the requested translation and text-to-speech languages.
     translation_manifest.set_config("src_lang", args.source_language)
     translation_manifest.set_config("dst_lang", args.destination_language)
     tts_manifest.set_config("language", args.destination_language.lower())
 
-    options = cf.BrickOptions()
-    options.runtime.enable_warmup = True
-    stt = cf.load_brick(stt_manifest)
-    translation = cf.load_brick(translation_manifest)
-    tts = cf.load_brick(tts_manifest)
-    stt.setup(stt_manifest, options)
-    translation.setup(translation_manifest, options)
-    tts.setup(tts_manifest, options)
-
-    # Keep the waveform and sample-rate tensors alive throughout recognition.
-    stt.reset_states()
+    # Load the bricks.
+    stt_brick = cf.load_brick(stt_manifest)
+    translation_brick = cf.load_brick(translation_manifest)
+    tts_brick = cf.load_brick(tts_manifest)
+
+    # Set up the bricks.
+    stt_brick.setup(stt_manifest, options)
+    translation_brick.setup(translation_manifest, options)
+    tts_brick.setup(tts_manifest, options)
+
+    #################################
+    # Run speech-to-text brick
+    #################################
+    stt_brick.reset_states()
+
     stt_input = {"raw_wav": raw_wav, "sample_rate": sample_rate}
     transcription_chunks = []
-    while not stt.is_generation_complete():
-        result = stt.run(stt_input)
-        transcription_chunks.append(str(np.asarray(result["text"]).item()))
-    transcription = "".join(transcription_chunks)
-    if not transcription.strip():
-        raise RuntimeError("Speech recognition returned no text")
-    print(f"Transcription: {transcription}", flush=True)
-
-    # Pass the complete transcription to the translation brick.
-    translation.reset_states()
-    translation_input = {"text_prompt": transcription}
+    while not stt_brick.is_generation_complete():
+        stt_output = stt_brick.run(stt_input)
+        transcription_chunks.append(str(np.asarray(stt_output["text"]).item()))
+
+    #################################
+    # Run translation brick
+    #################################
+    translation_brick.reset_states()
+
+    translation_input = {"text_prompt": "".join(transcription_chunks)}
     translation_chunks = []
-    while not translation.is_generation_complete():
-        result = translation.run(translation_input)
-        translation_chunks.append(str(np.asarray(result["text"]).item()))
-    translated_text = "".join(translation_chunks)
-    if not translated_text.strip():
-        raise RuntimeError("Translation returned no text")
-    print(f"Translation: {translated_text}", flush=True)
-
-    # Copy each audio chunk before the brick reuses its output buffers.
-    tts.reset_states()
-    tts_input = {"text_prompt": translated_text}
-    audio_chunks = []
-    output_rate = 0
-    while not tts.is_generation_complete():
-        result = tts.run(tts_input)
-        left = np.asarray(result["left_channel"], dtype=np.float32).reshape(-1)
-        right = np.asarray(result["right_channel"], dtype=np.float32).reshape(-1)
-        output_rate = int(np.asarray(result["sample_rate"]).reshape(-1)[0])
+    while not translation_brick.is_generation_complete():
+        translation_output = translation_brick.run(translation_input)
+        translation_text = np.asarray(translation_output["text"]).item()
+        translation_chunks.append(str(translation_text))
+
+    tts_input = {"text_prompt": "".join(translation_chunks)}
+    left_chunks = []
+    right_chunks = []
+    output_sample_rate = 0
+
+    #################################
+    # Run text-to-speech brick
+    #################################
+    tts_brick.reset_states()
+
+    while not tts_brick.is_generation_complete():
+        tts_output = tts_brick.run(tts_input)
+        left = np.asarray(tts_output["left_channel"], dtype=np.float32).reshape(-1)
+        right = np.asarray(tts_output["right_channel"], dtype=np.float32).reshape(-1)
+        sample_rate_tensor = np.asarray(tts_output["sample_rate"])
+        output_sample_rate = int(sample_rate_tensor.reshape(-1)[0])
+
         if left.size:
-            audio_chunks.append(np.column_stack((left, right)))
+            left_chunks.append(left.copy())
+            right_chunks.append(right.copy())
 
-    stt.reset_states()
-    translation.reset_states()
-    tts.reset_states()
-    if not audio_chunks or output_rate <= 0:
-        raise RuntimeError("Speech synthesis returned no audio")
-    output_audio = np.concatenate(audio_chunks, axis=0)
-    sf.write(args.output, output_audio, output_rate, subtype="FLOAT")
-    print(f"Saved: {Path(args.output).resolve()}")
+    tts_brick.reset_states()
 
+    if not left_chunks or output_sample_rate == 0:
+        raise RuntimeError("Qwen3-TTS did not generate audio")
 
-if __name__ == "__main__":
-    main()
-```
+    left_audio = np.concatenate(left_chunks)
+    right_audio = np.concatenate(right_chunks)
+    output_audio = np.column_stack((left_audio, right_audio))
 
-The input waveform has shape `(1, number_of_samples)` and uses `float32`; its sample rate uses `int64`. Each input dictionary remains alive until its generation loop completes. `np.column_stack()` copies the left and right audio channels into a stereo chunk before another call can reuse the brick's buffers.
+    sf.write(
+        args.output_audio,
+        output_audio,
+        output_sample_rate,
+        subtype="FLOAT",
+    )
 
-`load_manifest()` reads the model configuration, `load_brick()` selects the implementation, and `setup()` prepares it for inference. Warmup happens during setup, so the first printed transcription can take time to appear. Each `run()` call returns the next output from a brick; the loop continues until `is_generation_complete()` returns `True`. `reset_states()` clears the request state while retaining the loaded models.
+    print(f"Transcription: {''.join(transcription_chunks)}")
+    print(f"Translation: {''.join(translation_chunks)}")
+    print(f"Saved: {Path(args.output_audio).resolve()}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
+```
 
 All three bricks share these execution methods, documented in [Brick Runtime Interface V1](https://github.com/ARM-software/ComputeFlow/blob/main/docs/design/brick_runtime_interface_v1.md).
 
-The Qwen3-TTS manifest filename includes `en`, but the script sets its `language` configuration to `french` before setup. The selected Custom Voice model supports French synthesis. The translation configuration similarly sets English as the source language and French as the destination.
 
-## Translate the packaged recording
+## Translate a pre-recorded sample
 
 Resolve the package locations through Brick Manager instead of hard-coding cache paths. Then run your application using the sample supplied with Parakeet:
 
@@ -344,17 +317,16 @@ python audio_translation_example/audio_translation.py \
   --output translated_french.wav
 ```
 
-The application prints `Transcription:`, `Translation:`, and `Saved:` lines alongside the runtime logs. The output from one run is shown here, with the checkout path shortened:
+The expected output is:
 
 ```output
 Transcription: Ever tried, ever failed. No matter. Try again. Fail again. Fail better.
 Translation: Avez-vous déjà essayé, avez-vous déjà échoué. Peu importe. Essayez à nouveau. Écheclez encore. Échouez mieux.
-Saved: /translated_french.wav
 ```
 
-Check that the transcription matches the English recording and that the translation preserves its meaning. Generated wording and punctuation can vary and can contain errors: this run produced the incorrect word "Écheclez". Completing the pipeline verifies the integration; review the generated text and speech separately for translation quality.
+Check that the transcription matches the English recording and that the translation preserves its meaning. An accurate translation is, ""Déjà essayé, déjà échoué. Peu importe. Essaie encore. Échoue encore. Échoue mieux.". We will later demonstrate how to select alternative models with different performance and accuracy tradeoffs. 
 
 
 ## What you've accomplished
 
-You've installed three model packages, built Compute Flow, and connected their brick interfaces in a Python application. Your application now turns an English recording into a local French WAV file. Next, you'll replace the speech recognition model and run the same application again.
+You've installed three Compute Flow brick types, using three of their model variants to build and audio-to-audio pipeline. Your application now turns an English recording into a local French WAV file. Next, you'll replace the speech recognition model and run the same application again.

From e5f804c4e2dfe4336e4b8dcb72f1d38ed50aa466 Mon Sep 17 00:00:00 2001
From: Kieran Hejmadi 
Date: Thu, 29 Oct 2026 17:12:24 +0000
Subject: [PATCH 022/109] breakout final section into 2 pages and incorporate
 feedback

---
 .../compute-flow-getting-started/how-to-3.md  | 25 +++---
 .../compute-flow-getting-started/how-to-4.md  | 76 +++++++++++++++++++
 2 files changed, 85 insertions(+), 16 deletions(-)
 create mode 100644 content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
index 130b059dff..ffa259d899 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
@@ -1,6 +1,6 @@
 ---
-title: Replace the speech recognition model
-description: Replace Parakeet with Moonshine in your speech translation pipeline and verify both runs without changing your Python application.
+title: Replace the speech-to-text brick
+description: Replace the Parakeet AI model with Moonshine in your speech-to-text brick
 weight: 4
 
 ### FIXED, DO NOT MODIFY
@@ -9,17 +9,15 @@ layout: learningpathall
 
 ## Understand what changes when you replace a model
 
-Speech-to-text (`stt`) is a brick type: audio and a sample rate go in, and text comes out. A brick implementation, such as `stt:parakeet-tdt`, provides the code that runs supported models. A model package supplies the trained weights and assets; its manifest selects the implementation and configures how those files are used.
+Speech-to-text is a brick type: audio and a sample rate go in, and text comes out.
 
 You'll replace both the Parakeet implementation and its model package with `stt:moonshine` and `moonshine-base-en`. Both implementations follow `stt.v1`, even though Parakeet uses LiteRT and Moonshine uses ONNX Runtime.
 
 Your Python script stays unchanged: `cf.load_manifest(args.stt_manifest)` reads the manifest supplied as an argument, `cf.load_brick(stt_manifest)` selects its implementation, and `stt.setup(stt_manifest, options)` prepares the models. No model name, weights path, or inference backend is hardcoded into these calls. The same `stt.run(stt_input)` loop returns text for the translation stage.
 
-Moonshine Base English recognizes English only and expects 16 kHz audio, so keep using the same `beckett.wav` recording.
-
 ## Install Moonshine and rebuild Compute Flow
 
-Use the same Compute Flow checkout and activated virtual environment as before. List Moonshine's models, install the English package, and inspect its manifest:
+Using the same virtual environment as before, list Moonshine's models, install the English package, and inspect its manifest:
 
 ```bash
 compute-flow model list stt:moonshine
@@ -37,11 +35,10 @@ This build adds the Moonshine implementation to the available bricks. You don't
 
 ## Run the same application with Moonshine
 
-Resolve the package paths again so this command also works in a fresh terminal with the environment activated. Keep using the Parakeet package's recording to hold the input constant:
+Resolve the package paths again so this command also works in a fresh terminal with the environment activated. Moonshine's package includes the same `beckett.wav` recording used in the first run:
 
 ```bash
 MOONSHINE_PACKAGE="$(compute-flow model path stt:moonshine moonshine-base-en)"
-PARAKEET_PACKAGE="$(compute-flow model path stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic)"
 HY_MT2_PACKAGE="$(compute-flow model path lm_text:lm-text-generic hy-mt2-1-8b-2bit-translation)"
 QWEN3_TTS_PACKAGE="$(compute-flow model path tts:qwen3-tts qwen3-tts-0-6b-custom-voice)"
 
@@ -49,27 +46,23 @@ python audio_translation_example/audio_translation.py \
   --stt-manifest "$MOONSHINE_PACKAGE/moonshine_manifest_base_en.json" \
   --translation-manifest "$HY_MT2_PACKAGE/hy_mt2_1_8b_manifest.json" \
   --tts-manifest "$QWEN3_TTS_PACKAGE/qwen3_tts_custom_voice_en_manifest.json" \
-  --input "$PARAKEET_PACKAGE/samples/beckett.wav" \
+  --input "$MOONSHINE_PACKAGE/samples/beckett.wav" \
   --source-language English \
   --destination-language French \
   --output translated_french_moonshine.wav
 ```
 
-Only the `--stt-manifest` and output filename differ from your first run. The new manifest selects Moonshine while the same generation loop consumes its text output. The other two stages continue to receive text through the same interfaces.
-
-If the manifest can't be loaded, check the path reported by `model info`. If the brick implementation is unavailable, confirm that the native build succeeded after installing Moonshine and that you're using the same virtual environment.
-
-
 ## Easily Compare speech recognition performance
 
 Use the built STT CLI to compare the speech recognition stage. Changing the manifest lets you experiment quickly with another model.
 
 Parakeet's model files total approximately 683 MB, with dynamic INT8 quantization for fully connected weights and FP32 external interfaces. Moonshine's FP32 model files total approximately 247 MB. These are different model families using LiteRT and ONNX Runtime respectively.
 
-Use the package variables from the previous commands. Run both alternatives on the same mono 16 kHz recording, with one CPU thread and profiling enabled:
+Resolve the Parakeet package path for its manifest. Run both alternatives on the same mono 16 kHz recording, with one CPU thread and profiling enabled:
 
 ```bash
-INPUT_AUDIO="$PARAKEET_PACKAGE/samples/beckett.wav"
+PARAKEET_PACKAGE="$(compute-flow model path stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic)"
+INPUT_AUDIO="$MOONSHINE_PACKAGE/samples/beckett.wav"
 ```
 
 {{< tabpane-normal >}}
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
new file mode 100644
index 0000000000..c533d90bc3
--- /dev/null
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
@@ -0,0 +1,76 @@
+---
+title: Compare Speech-to-text performance
+description: Replace the Parakeet AI model with Moonshine in your speech-to-text brick
+weight: 5
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+##  Compare Speech-to-text performance
+
+Use the built STT CLI to compare the speech recognition stage. Changing the manifest lets you experiment quickly with another model.
+
+Parakeet's model files total approximately 683 MB, with dynamic INT8 quantization for fully connected weights and FP32 external interfaces. Moonshine's FP32 model files total approximately 247 MB. These are different model families using LiteRT and ONNX Runtime respectively.
+
+Run the following command:
+
+```bash
+PARAKEET_PACKAGE="$(compute-flow model path stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic)"
+INPUT_AUDIO="$PARAKEET_PACKAGE/samples/beckett.wav"
+```
+
+{{< tabpane-normal >}}
+  {{< tab header="Parakeet dynamic INT8" >}}
+Run the Parakeet manifest:
+
+```bash
+./build_native/stt_cli \
+  -m "$PARAKEET_PACKAGE/parakeet_tdt_0_6b_v3_manifest.json" \
+  -a "$INPUT_AUDIO" \
+  -t 1 --enable-op-profiling
+```
+
+The output should include timing information like the excerpt below, along with statistics for individual operators. The operator statistics are omitted here for brevity.
+
+```output
+[TRANSCRIPTION]
+Ever tried, ever failed. No matter. Try again. Fail again. Fail better.
+----------------
+Scope: run() [total ms: 985.12]
+Scope stats | calls: 30 | avg ms: 32.84 | min ms: 0.34 | max ms: 487.10
+```
+  {{< /tab >}}
+  {{< tab header="Moonshine FP32" >}}
+Run the Moonshine manifest:
+
+```bash
+./build_native/stt_cli \
+  -m "$MOONSHINE_PACKAGE/moonshine_manifest_base_en.json" \
+  -a "$INPUT_AUDIO" \
+  -t 1 --enable-op-profiling
+```
+
+The output should include timing information like the excerpt below, along with statistics for individual operators. The operator statistics are omitted here for brevity.
+
+```output
+[TRANSCRIPTION]
+Ever tried ever failed, no matter try again fail again fail better.
+----------------
+Scope: run() [total ms: 290.30]
+Scope stats | calls: 1 | avg ms: 290.30 | min ms: 290.30 | max ms: 290.30
+```
+  {{< /tab >}}
+{{< /tabpane-normal >}}
+
+Compare the total `run()` time. Parakeet's 30 streaming calls complete one transcription compared with a single call for Moonshine.
+ 
+These results are one example of how to compare the models; your timings will vary with your hardware and setup. On an Apple M3 Pro, Moonshine took 290.30 ms and Parakeet took 985.12 ms to process a single 9.963-second audio sample. The timings exclude setup and warmup, include profiling overhead, and do not assess transcription accuracy.
+
+Pass your own representative audio samples in a JSON file with `-j runs.json` to evaluate more inputs. The shared Python API and CLI let you quickly profile models across different runtimes and find the most suitable model for your application by changing manifests.
+
+## What you've accomplished
+
+You've learned how brick types, implementations, model packages, and manifests fit together. You used Brick Manager to install models and build Compute Flow on your Apple silicon Mac, then connected speech recognition, translation, and speech synthesis in Python to turn an English recording into a French WAV file locally.
+
+Finally, you replaced Parakeet with Moonshine by installing its brick and model package, rebuilding, and selecting a different manifest. The shared speech-to-text interface kept your Python script unchanged. You can use this approach to combine other bricks and select compatible models for your own applications.

From 50b489f8ec4a65c4910a0da892286c342652080b Mon Sep 17 00:00:00 2001
From: anupras-mohapatra-arm 
Date: Fri, 30 Oct 2026 14:06:51 -0500
Subject: [PATCH 023/109] compute flow first pass

---
 .../compute-flow-getting-started/_index.md    |  10 +-
 .../compute-flow-getting-started/how-to-1.md  |  37 +++---
 .../compute-flow-getting-started/how-to-2.md  |  50 ++++----
 .../compute-flow-getting-started/how-to-3.md  | 114 +++---------------
 .../compute-flow-getting-started/how-to-4.md  |  61 +++++++---
 5 files changed, 113 insertions(+), 159 deletions(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md b/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
index abdf646ca2..5e7cbe9757 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
@@ -1,18 +1,18 @@
 ---
-title: Get Started with Compute Flow
+title: Build an audio-to-audio pipeline on Arm with Compute Flow
 description: Build and run an audio-to-audio pipeline as a Python application on your Arm-based device, combining Compute Flow bricks for speech recognition, translation, and speech synthesis
 
 minutes_to_complete: 15
 
-who_is_this_for: This is an introductory topic for developers who want to combine multiple AI models into a performant end-to-end AI applicaton running on an Arm-based CPU. This learning path has been validated on an Apple® Silicon Mac but is expected to work on other Arm-based CPUs.
+who_is_this_for: This is an introductory topic for developers who want to combine multiple AI models into a performant end-to-end AI applicaton running on an Arm-based CPU. 
 
 learning_objectives:
-    - Explain how Compute Flow bricks and manifests separate application code from model implementations
+    - Identify how Compute Flow bricks and manifests separate application code from model implementations.
     - Deploy an AI audio-to-audio translation pipline in Python with Compute Flow. 
-    - Select the speech recognition model and verify the pipeline using the same application and recording
+    - Select the speech recognition model and verify the pipeline using the same application and recording.
 
 prerequisites:
-    - An Arm-based system such as an Apple® silicon Mac.
+    - An Arm-based system such as an Apple® silicon Mac
     - Basic Python knowledge and familiarity with terminal commands
     - Internet access and at least 15 GB of available storage
 
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
index a9c84490c5..1ad2fd3eb2 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
@@ -1,5 +1,5 @@
 ---
-title: What is Compute Flow
+title: Understand the Compute Flow SDK
 description: Learn about Compute Flow bricks, manifests, and shared interfaces to combine multiple AI models and run them locally on-device.
 weight: 2
 
@@ -7,17 +7,15 @@ weight: 2
 layout: learningpathall
 ---
 
-## Combine Compute Flow bricks to connect multiple AI models into a local application.
+## Compute Flow bricks
 
-Compute Flow is an SDK for building AI applications from reusable AI building blocks called *bricks*. Each brick exposes an AI capability through a shared interface for that capability. You connect those interfaces in Python or C++ while the brick handles model-specific inference.
+Compute Flow is an SDK for building AI applications from reusable AI building blocks called bricks. Each brick exposes an AI capability through a shared interface for that capability. You connect the interfaces in Python or C++ while the brick handles model-specific inference.
 
-A *brick type* defines an input and output contract. A *brick variant* implements that contract using a particular model family. For example, Parakeet TDT and Moonshine are different speech-to-text variants. Both accept audio and its sample rate, and return text.
+A brick type defines an input and output contract. A brick variant implements that contract using a particular model family. For example, Parakeet TDT and Moonshine are different speech-to-text variants. Both accept audio and its sample rate, and return text.
 
-In this Learning Path, we'll use three different bricks to build the audio translation application. The first recognises human speech, the second translates speech into text, the third generate audio. Each of these bricks use different models to get to our aims of translating English into French.
+You'll use three different bricks to build an audio translation application. The first recognises human speech, the second translates speech into text, and the third generates audio. Each brick uses different models to accomplish the goal of translating English into French.
 
-The figure below illustrate the three stages of our audio translation application, pointing out the AI models that are used at each stage.
-
-You'll use Python to translate a short English recording into French speech on your Apple Silicon Mac. The application processes the recording through three stages:
+You'll use Python to translate a short English recording into French speech on your Apple silicon Mac. The application processes the recording through three stages:
 
 ```text
 English WAV (Input)
@@ -35,11 +33,11 @@ Stage 3. Text-to-speech: Qwen3-TTS
 French WAV (Output)
 ```
 
-Python coordinates the stages: it passes the speech-to-text transcription to the translation brick, then passes the translation to the speech synthesis brick. Compute Flow doesn't automatically assemble this pipeline for you.
+Python coordinates the stages. It passes the speech-to-text transcription to the translation brick, then passes the translation to the speech synthesis brick. Compute Flow doesn't automatically assemble this pipeline for you.
 
 ## Separate the application from the model
 
-Your pipeline uses these contracts:
+Your pipeline uses the following contracts:
 
 | Stage | Brick type | Input | Output |
 | --- | --- | --- | --- |
@@ -49,19 +47,22 @@ Your pipeline uses these contracts:
 
 ### Understand the model manifest
 
-A *model package* contains the weights and supporting assets that a brick needs. A *model manifest* is a JSON configuration file in that package. It tells Compute Flow which brick implementation to use and how to load and run the models.
+A model package contains the weights and supporting assets that a brick needs. A model manifest is a JSON configuration file in that package. It tells Compute Flow which brick implementation to use and how to load and run the models.
 
-The manifest describes model filenames, inference runtimes, model-specific tensor formats, and supporting assets, such as tokenizers. It also provides configuration values and constants needed by the brick. The weights live in separate files referenced by the manifest.
+The manifest describes model filenames, inference runtimes, and model-specific tensor formats. It also describes supporting assets such as tokenizers. The manifest provides configuration values and constants needed by the brick. The weights live in separate files that are referenced by the manifest.
 
 For example, the Parakeet package includes `parakeet_tdt_0_6b_v3_manifest.json`. Your Python application reads this file with `cf.load_manifest()`, selects the implementation with `cf.load_brick()`, and prepares the models with `brick.setup()`. This keeps model-specific file paths and runtime configuration out of your application code.
 
-## Prepare once, then run locally
+## Compute Flow Brick Manager
+
+To discover and install the required Compute Flow Bricks, you'll use Compute Flow Brick Manager alongside the selected AI models.
+
+The Brick Manager is responsible for installing the bricks type and brick variant based on the selected AI model. It builds the developed application for the selected runtime platforms.
 
-To discover and install the required Compute Flow Bricks, alongside with the selected AI models for our audio-to-audio translation pipeline, we will use Compute Flow Brick Manager.
-The Brick Manager is responsible for installing the bricks type and brick variant based on users' selected AI model, and builds the user's developed application for the selected runtime platforms.
+To complete setup, you need network access to download dependencies and model packages. After setup, the example application reads the installed assets and performs inference on your Mac. It writes a WAV file locally and doesn't call a hosted inference service. You don't need a microphone because the input recording is included in a model package.
 
-Setup needs network access to download dependencies and model packages. After setup, the example reads the installed assets and performs inference on your Mac. It writes a WAV file locally and doesn't call a hosted inference service. You don't need a microphone because the input recording is included in a model package.
+## What you've learned and what's next
 
-## What you've learned
+You've learned that Compute Flow provides reusable AI building blocks that you can combine to build locally deployable AI applications. You've also learned about Compute Flows Bricks.
 
-Compute Flow provides reusable AI building blocks that you can combine together to build AI application to deploy locally on your device. Compute Flows Bricks allow users to focus on the AI models actions, inputs and outputs, rather than the AI models inference, easing out the combination and replacement of different AI models.
+Next, you'll build and run the example audio translation application with Compute Flow. 
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
index 21f8a8d85b..4684523a05 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
@@ -11,24 +11,23 @@ layout: learningpathall
 
 You'll install three model packages, build Compute Flow for your Mac, and create a Python application.
 
-First, check your  CPU architecture:
+First, check your CPU architecture:
 
 ```bash
 uname -m
 ```
 
-You should see `arm64`.
+The expected output is `arm64`.
 
-If `uv` isn't installed, use the [uv standalone installer](https://docs.astral.sh/uv/getting-started/installation/) with the following command.
+If `uv` isn't installed, use the [uv standalone installer](https://docs.astral.sh/uv/getting-started/installation/) with the following command:
 
 ```bash
 curl -LsSf https://astral.sh/uv/install.sh | sh
 ```
 
+Next, if Apple developer tools aren't installed, [install Xcode Command Line Tools](https://developer.apple.com/documentation/xcode/installing-the-command-line-tools/). The package includes Git, Clang, and the macOS SDK. If you already have Xcode or the Command Line Tools installed, use those tools.
 
-Next, if Apple's developer tools aren't installed, follow [Apple's instructions for installing the Xcode Command Line Tools](https://developer.apple.com/documentation/xcode/installing-the-command-line-tools/). The package includes Git, Clang, and the macOS SDK. If you already have Xcode or the Command Line Tools installed, use those tools.
-
-Finish with a quick check that the tools are available:
+Check that the tools are available:
 
 ```bash
 uv --version
@@ -37,7 +36,7 @@ command -v cc
 command -v c++
 ```
 
-Finally, remove any previously installed `llama.cpp` versions. For example if installed with the `brew` package manager, run the following command:
+Finally, remove any previously installed `llama.cpp` versions. For example, if installed with the `brew` package manager, run the following command:
 
 ```bash
 brew uninstall llama.cpp
@@ -45,7 +44,7 @@ brew uninstall llama.cpp
 
 ## Set up Compute Flow
 
-Run the following command.
+Clone the Compute Flow repository:
 
 ```bash
 git clone https://github.com/ARM-software/compute-flow.git
@@ -101,18 +100,17 @@ compute-flow check
 
 ```
 
-Compute Flow requires Python 3.11 or later. `uv sync` installs the project's Python dependencies into the virtual environment. With its default settings, [uv also downloads a compatible Python interpreter](https://docs.astral.sh/uv/concepts/python-versions/) if it can't find one locally.
+Compute Flow requires Python 3.11 or later. `uv sync` installs the project Python dependencies into the virtual environment. With its default settings, [uv also downloads a compatible Python interpreter](https://docs.astral.sh/uv/concepts/python-versions/) if it can't find one locally.
 
 `compute-flow check` reports missing system dependencies and suggests installation commands. It doesn't install them or test whether the compiler can compile and link a program. If it reports a missing native compiler or Git installation, follow the suggested action and run the check again.
 
-Brick Manager automatically downloads managed tools, such as CMake and Git LFS, when a command needs them. The check reports these as `will be downloaded when needed`; you don't need to install them manually. For example, `compute-flow build native` downloads its managed CMake version if it isn't already available.
-
+Brick Manager automatically downloads managed tools, such as CMake and Git LFS, when a command needs them. The check reports these as `will be downloaded when needed`, so you don't need to install them manually. For example, `compute-flow build native` downloads its managed CMake version if it isn't already available.
 
 ## Install the three model packages
 
-Update Brick Manager's registry cache and install the models used by your pipeline:
+Update the Brick Manager registry cache and install the models that will be used by your pipeline:
 
-{{% notice Tip %}}
+{{% notice Note %}}
 For higher rate limits and faster downloads from Hugging Face, set the optional `HF_TOKEN` environment variable to your Hugging Face access token before installing the models. You can [create a Hugging Face access token](https://huggingface.co/settings/tokens) in account settings.
 {{% /notice %}}
 
@@ -123,7 +121,7 @@ compute-flow model install lm_text:lm-text-generic hy-mt2-1-8b-2bit-translation
 compute-flow model install tts:qwen3-tts qwen3-tts-0-6b-custom-voice
 ```
 
-Each `model install` command installs the brick source, if needed, and its model package. The identifiers select these implementations:
+Each `model install` command installs the brick source, if needed, and its model package. The identifiers select the following implementations:
 
 | Brick | Model package | Pipeline role |
 | --- | --- | --- |
@@ -131,7 +129,7 @@ Each `model install` command installs the brick source, if needed, and its model
 | `lm_text:lm-text-generic` | Hy-MT2 1.8B, 2-bit | Translate English text into French |
 | `tts:qwen3-tts` | Qwen3-TTS 0.6B Custom Voice | Synthesize French speech |
 
-Models are downloaded to the `~/.compute-flow` directory by default.The `compute-flow model info` command displays details about a specified model
+Models are downloaded to the `~/.compute-flow` directory by default. The `compute-flow model info` command displays details about a specified model
 
 ```bash
 compute-flow model info stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic
@@ -147,7 +145,7 @@ Build the installed bricks:
 compute-flow build native
 ```
 
-If successful, you should see output similar to the following. 
+If successful, the output is similar to: 
 
 ```output
 ╭────────────────────────────── OK Build Complete ──────────────────────────────╮
@@ -156,7 +154,7 @@ If successful, you should see output similar to the following.
 ╰───────────────────────────────────────────────────────────────────────────────╯
 ```
 
-The build command downloads inference runtimes optimized for your deployment platform and packages them with your installed brick implementations into an application SDK available at `build_native/sdk`. 
+The build command downloads inference runtimes optimized for your deployment platform. It packages them with your installed brick implementations into an application SDK available at `build_native/sdk`. 
 
 ## Create the Python application
 
@@ -166,7 +164,7 @@ Create a directory for your application:
 mkdir -p audio_translation_example
 ```
 
-Save the following code as `audio_translation_example/audio_translation.py`. The below code provides everything you need to run the audio-to-audio translation pipeline. You will see that the code first load and configure each of the three bricks required for our application, then execute each brick, with the output from the previous brick being pass to the next one, and finally, save the output of our pipeline.
+Save the following code as `audio_translation_example/audio_translation.py`:
 
 ```python
 """Translate speech between languages with three Compute Flow bricks."""
@@ -294,13 +292,14 @@ def main(argv: list[str] | None = None) -> int:
 if __name__ == "__main__":
     raise SystemExit(main())
 ```
+The code provides everything you need to run the audio-to-audio translation pipeline. The code first loads and configures each of the three bricks required for the application. It then runs each brick, with the output from the previous brick being passed to the next one. Finally, it saves the output of our pipeline.
 
 All three bricks share these execution methods, documented in [Brick Runtime Interface V1](https://github.com/ARM-software/ComputeFlow/blob/main/docs/design/brick_runtime_interface_v1.md).
 
 
 ## Translate a pre-recorded sample
 
-Resolve the package locations through Brick Manager instead of hard-coding cache paths. Then run your application using the sample supplied with Parakeet:
+Resolve the package locations through Brick Manager instead of hard-coding cache paths. Then, run your application using the sample supplied with Parakeet:
 
 ```bash
 PARAKEET_PACKAGE="$(compute-flow model path stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic)"
@@ -324,9 +323,16 @@ Transcription: Ever tried, ever failed. No matter. Try again. Fail again. Fail b
 Translation: Avez-vous déjà essayé, avez-vous déjà échoué. Peu importe. Essayez à nouveau. Écheclez encore. Échouez mieux.
 ```
 
-Check that the transcription matches the English recording and that the translation preserves its meaning. An accurate translation is, ""Déjà essayé, déjà échoué. Peu importe. Essaie encore. Échoue encore. Échoue mieux.". We will later demonstrate how to select alternative models with different performance and accuracy tradeoffs. 
+Check that the transcription matches the English recording and that the translation preserves its meaning. An accurate translation is as follows:
+
+```text
+ Déjà essayé, déjà échoué. Peu importe. Essaie encore. Échoue encore. Échoue mieux.
+```
+
+You'll later learn how to select alternative models with different performance and accuracy tradeoffs. 
 
+## What you've accomplished and what's next
 
-## What you've accomplished
+You've installed three Compute Flow brick types and used three of their model variants to build an audio-to-audio pipeline. Your application now turns an English recording into a local French WAV file. 
 
-You've installed three Compute Flow brick types, using three of their model variants to build and audio-to-audio pipeline. Your application now turns an English recording into a local French WAV file. Next, you'll replace the speech recognition model and run the same application again.
+Next, you'll replace the speech recognition model and run the same application again.
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
index ffa259d899..e1085aa62c 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
@@ -9,15 +9,17 @@ layout: learningpathall
 
 ## Understand what changes when you replace a model
 
+Now that you've built the audio-to-audio pipeline, you can make changes to it.
+
 Speech-to-text is a brick type: audio and a sample rate go in, and text comes out.
 
 You'll replace both the Parakeet implementation and its model package with `stt:moonshine` and `moonshine-base-en`. Both implementations follow `stt.v1`, even though Parakeet uses LiteRT and Moonshine uses ONNX Runtime.
 
-Your Python script stays unchanged: `cf.load_manifest(args.stt_manifest)` reads the manifest supplied as an argument, `cf.load_brick(stt_manifest)` selects its implementation, and `stt.setup(stt_manifest, options)` prepares the models. No model name, weights path, or inference backend is hardcoded into these calls. The same `stt.run(stt_input)` loop returns text for the translation stage.
+The Python script remains unchanged. `cf.load_manifest(args.stt_manifest)` reads the manifest supplied as an argument. `cf.load_brick(stt_manifest)` selects its implementation, and `stt.setup(stt_manifest, options)` prepares the models. No model name, weights path, or inference backend is hardcoded into these calls. The same `stt.run(stt_input)` loop returns text for the translation stage.
 
 ## Install Moonshine and rebuild Compute Flow
 
-Using the same virtual environment as before, list Moonshine's models, install the English package, and inspect its manifest:
+Using the same virtual environment as earlier, list Moonshine models, install the English package, and inspect its manifest:
 
 ```bash
 compute-flow model list stt:moonshine
@@ -25,17 +27,21 @@ compute-flow model install stt:moonshine moonshine-base-en
 compute-flow model info stt:moonshine moonshine-base-en
 ```
 
-The package contains `moonshine_manifest_base_en.json`. Build Compute Flow again to include the newly installed implementation:
+The package contains `moonshine_manifest_base_en.json`. 
+
+Build Compute Flow again to include the newly installed implementation:
 
 ```bash
 compute-flow build native
 ```
 
-This build adds the Moonshine implementation to the available bricks. You don't edit your Python application or replace the other models. Selecting a different manifest works only when its brick implementation has been built and its model assets are installed.
+This build adds the Moonshine implementation to the available bricks. Don't edit your Python application or replace the other models. 
+
+Selecting a different manifest works only when its brick implementation has been built and its model assets are installed.
 
 ## Run the same application with Moonshine
 
-Resolve the package paths again so this command also works in a fresh terminal with the environment activated. Moonshine's package includes the same `beckett.wav` recording used in the first run:
+Resolve the package paths again so that the command also works in a fresh terminal with the environment activated:
 
 ```bash
 MOONSHINE_PACKAGE="$(compute-flow model path stt:moonshine moonshine-base-en)"
@@ -51,100 +57,10 @@ python audio_translation_example/audio_translation.py \
   --destination-language French \
   --output translated_french_moonshine.wav
 ```
+The Moonshine package includes the same `beckett.wav` recording that was used in the first run.
 
-## Easily Compare speech recognition performance
-
-Use the built STT CLI to compare the speech recognition stage. Changing the manifest lets you experiment quickly with another model.
-
-Parakeet's model files total approximately 683 MB, with dynamic INT8 quantization for fully connected weights and FP32 external interfaces. Moonshine's FP32 model files total approximately 247 MB. These are different model families using LiteRT and ONNX Runtime respectively.
-
-Resolve the Parakeet package path for its manifest. Run both alternatives on the same mono 16 kHz recording, with one CPU thread and profiling enabled:
-
-```bash
-PARAKEET_PACKAGE="$(compute-flow model path stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic)"
-INPUT_AUDIO="$MOONSHINE_PACKAGE/samples/beckett.wav"
-```
-
-{{< tabpane-normal >}}
-  {{< tab header="Parakeet dynamic INT8" >}}
-Run the Parakeet manifest:
-
-```bash
-./build_native/stt_cli \
-  -m "$PARAKEET_PACKAGE/parakeet_tdt_0_6b_v3_manifest.json" \
-  -a "$INPUT_AUDIO" \
-  -t 1 --enable-op-profiling
-```
-
-The output is similar to this captured excerpt, showing the featurizer's operator breakdown and the complete scope summary. The first-call report and other operator tables are omitted:
-
-```output
-[TRANSCRIPTION]
-Ever tried, ever failed. No matter. Try again. Fail again. Fail better.
-...
-All Calls
-Scope: run() [total ms: 985.12]
-Scope stats | calls: 30 | avg ms: 32.84 | min ms: 0.34 | max ms: 487.10
-Model + Op Breakdown
-runner          contributor                                              calls                                         %      total ms      avg ms      min ms      max ms
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-litert          featurizer
-                  GATHER_ND                                                  4 [██████████████░░░░░░░░░░░░░░░░]  45.01%          7.86        1.96        1.94        1.98
-                  Fully Connected (NC, QP8, F32, QC8W) GEMM                 12 [████████░░░░░░░░░░░░░░░░░░░░░░]  25.58%          4.46        0.37        0.18        0.49
-                  ADD                                                        4 [██████░░░░░░░░░░░░░░░░░░░░░░░░]  19.29%          3.37        0.84        0.81        0.87
-                  Multiply (ND)                                             24 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.27%          0.22        0.01        0.00        0.03
-                  Log (NC)                                                   4 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   0.85%          0.15        0.04        0.03        0.05
-                  Extra Ops                                                    [█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   2.37%          0.41
-                  Overhead                                                     [██░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   5.64%          0.98
-...
-Scope Summary
-runner          contributor                                              calls                                         %      total ms      avg ms      min ms      max ms
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-litert          encoder                                                      2 [█████████████████████████████░]  95.60%        941.76      470.88      469.83      471.93
-litert          featurizer                                                   4 [█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.77%         17.45        4.36        4.28        4.44
-litert          joint_step                                                  42 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.43%         14.06        0.33        0.31        0.36
-litert          decoder_step                                                30 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.12%         11.06        0.37        0.34        0.65
-                Overhead                                                    30 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   0.08%          0.79        0.03
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-                Scope Total                                                                                                     985.12
-```
-  {{< /tab >}}
-  {{< tab header="Moonshine FP32" >}}
-Run the Moonshine manifest:
-
-```bash
-./build_native/stt_cli \
-  -m "$MOONSHINE_PACKAGE/moonshine_manifest_base_en.json" \
-  -a "$INPUT_AUDIO" \
-  -t 1 --enable-op-profiling
-```
-
-The output is similar to this captured report. This ONNX Runtime runner reports model timings without operator details:
-
-```output
-[TRANSCRIPTION]
-Ever tried ever failed, no matter try again fail again fail better.
-----------------
-Scope: run() [total ms: 290.30]
-Scope stats | calls: 1 | avg ms: 290.30 | min ms: 290.30 | max ms: 290.30
-Scope Summary
-runner          contributor                                              calls                                         %      total ms      avg ms      min ms      max ms
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-onnxrt          encoder                                                      1 [██████████████████████░░░░░░░░]  74.69%        216.82      216.82      216.82      216.82
-onnxrt          decoder                                                     15 [████████░░░░░░░░░░░░░░░░░░░░░░]  25.21%         73.19        4.88        3.23       25.37
-                Overhead                                                     1 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   0.10%          0.29        0.29
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-                Scope Total                                                                                                     290.30
-```
-  {{< /tab >}}
-{{< /tabpane-normal >}}
-
-These timings exclude setup and warmup, include profiling overhead, and use a single 9.963-second audio sample. We haven't assessed changes in transcription accuracy. On an Apple M3 Pro, Moonshine takes 290.30 ms versus Parakeet's 985.12 ms. Compare total `run()` time: Parakeet's 30 streaming calls complete one transcription, not 30 benchmark repetitions.
-
-Pass your own representative audio samples in a JSON file with `-j runs.json` to evaluate more inputs. The shared Python API and CLI let you quickly profile models across different runtimes and find the most suitable model for your application by changing manifests.
-
-## What you've accomplished
+## What you've accomplished and what's mext
 
-You've learned how brick types, implementations, model packages, and manifests fit together. You used Brick Manager to install models and build Compute Flow on your Apple silicon Mac, then connected speech recognition, translation, and speech synthesis in Python to turn an English recording into a French WAV file locally.
+You've replaced Parakeet with Moonshine by installing its brick and model package, rebuilding, and selecting a different manifest. The shared speech-to-text interface kept your Python script unchanged. 
 
-Finally, you replaced Parakeet with Moonshine by installing its brick and model package, rebuilding, and selecting a different manifest. The shared speech-to-text interface kept your Python script unchanged. You can use this approach to combine other bricks and select compatible models for your own applications.
+Next, you'll compare performance between Parakeet and Moonshine. 
\ No newline at end of file
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
index c533d90bc3..c71ba10180 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
@@ -1,5 +1,5 @@
 ---
-title: Compare Speech-to-text performance
+title: Compare speech-to-text performance
 description: Replace the Parakeet AI model with Moonshine in your speech-to-text brick
 weight: 5
 
@@ -7,17 +7,17 @@ weight: 5
 layout: learningpathall
 ---
 
-##  Compare Speech-to-text performance
+## Compare speech recognition performance
 
-Use the built STT CLI to compare the speech recognition stage. Changing the manifest lets you experiment quickly with another model.
+Use the built speech-to-text CLI to compare the speech recognition stage. Changing the manifest lets you experiment quickly with another model.
 
-Parakeet's model files total approximately 683 MB, with dynamic INT8 quantization for fully connected weights and FP32 external interfaces. Moonshine's FP32 model files total approximately 247 MB. These are different model families using LiteRT and ONNX Runtime respectively.
+The Parakeet model files total approximately 683 MB, with dynamic INT8 quantization for fully connected weights and FP32 external interfaces. The Moonshine FP32 model files total approximately 247 MB. These are different model families using LiteRT and ONNX Runtime respectively.
 
-Run the following command:
+Resolve the Parakeet package path for its manifest. Run both alternatives on the same mono 16 kHz recording, with one CPU thread and profiling enabled:
 
 ```bash
 PARAKEET_PACKAGE="$(compute-flow model path stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic)"
-INPUT_AUDIO="$PARAKEET_PACKAGE/samples/beckett.wav"
+INPUT_AUDIO="$MOONSHINE_PACKAGE/samples/beckett.wav"
 ```
 
 {{< tabpane-normal >}}
@@ -31,14 +31,37 @@ Run the Parakeet manifest:
   -t 1 --enable-op-profiling
 ```
 
-The output should include timing information like the excerpt below, along with statistics for individual operators. The operator statistics are omitted here for brevity.
+The output is similar to this captured excerpt, showing the featurizer operator breakdown and the complete scope summary. The first-call report and other operator tables are omitted:
 
 ```output
 [TRANSCRIPTION]
 Ever tried, ever failed. No matter. Try again. Fail again. Fail better.
-----------------
+...
+All Calls
 Scope: run() [total ms: 985.12]
 Scope stats | calls: 30 | avg ms: 32.84 | min ms: 0.34 | max ms: 487.10
+Model + Op Breakdown
+runner          contributor                                              calls                                         %      total ms      avg ms      min ms      max ms
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+litert          featurizer
+                  GATHER_ND                                                  4 [██████████████░░░░░░░░░░░░░░░░]  45.01%          7.86        1.96        1.94        1.98
+                  Fully Connected (NC, QP8, F32, QC8W) GEMM                 12 [████████░░░░░░░░░░░░░░░░░░░░░░]  25.58%          4.46        0.37        0.18        0.49
+                  ADD                                                        4 [██████░░░░░░░░░░░░░░░░░░░░░░░░]  19.29%          3.37        0.84        0.81        0.87
+                  Multiply (ND)                                             24 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.27%          0.22        0.01        0.00        0.03
+                  Log (NC)                                                   4 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   0.85%          0.15        0.04        0.03        0.05
+                  Extra Ops                                                    [█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   2.37%          0.41
+                  Overhead                                                     [██░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   5.64%          0.98
+...
+Scope Summary
+runner          contributor                                              calls                                         %      total ms      avg ms      min ms      max ms
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+litert          encoder                                                      2 [█████████████████████████████░]  95.60%        941.76      470.88      469.83      471.93
+litert          featurizer                                                   4 [█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.77%         17.45        4.36        4.28        4.44
+litert          joint_step                                                  42 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.43%         14.06        0.33        0.31        0.36
+litert          decoder_step                                                30 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.12%         11.06        0.37        0.34        0.65
+                Overhead                                                    30 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   0.08%          0.79        0.03
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+                Scope Total                                                                                                     985.12
 ```
   {{< /tab >}}
   {{< tab header="Moonshine FP32" >}}
@@ -51,7 +74,7 @@ Run the Moonshine manifest:
   -t 1 --enable-op-profiling
 ```
 
-The output should include timing information like the excerpt below, along with statistics for individual operators. The operator statistics are omitted here for brevity.
+The output is similar to the following captured report. This ONNX Runtime runner reports model timings without operator details:
 
 ```output
 [TRANSCRIPTION]
@@ -59,18 +82,26 @@ Ever tried ever failed, no matter try again fail again fail better.
 ----------------
 Scope: run() [total ms: 290.30]
 Scope stats | calls: 1 | avg ms: 290.30 | min ms: 290.30 | max ms: 290.30
+Scope Summary
+runner          contributor                                              calls                                         %      total ms      avg ms      min ms      max ms
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+onnxrt          encoder                                                      1 [██████████████████████░░░░░░░░]  74.69%        216.82      216.82      216.82      216.82
+onnxrt          decoder                                                     15 [████████░░░░░░░░░░░░░░░░░░░░░░]  25.21%         73.19        4.88        3.23       25.37
+                Overhead                                                     1 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   0.10%          0.29        0.29
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+                Scope Total                                                                                                     290.30
 ```
   {{< /tab >}}
 {{< /tabpane-normal >}}
 
-Compare the total `run()` time. Parakeet's 30 streaming calls complete one transcription compared with a single call for Moonshine.
- 
-These results are one example of how to compare the models; your timings will vary with your hardware and setup. On an Apple M3 Pro, Moonshine took 290.30 ms and Parakeet took 985.12 ms to process a single 9.963-second audio sample. The timings exclude setup and warmup, include profiling overhead, and do not assess transcription accuracy.
+These timings are based on a single 9.963-second audio sample. The timings exclude setup and warmup and include profiling overhead. This isn't an assessment of changes to transcription accuracy. 
+
+On an Apple M3 Pro, Moonshine takes 290.30 ms, while Parakeet takes 985.12 ms to process the audio sample. Compare total `run()` time: 30 Parakeet streaming calls complete one transcription, not 30 benchmark repetitions.
 
-Pass your own representative audio samples in a JSON file with `-j runs.json` to evaluate more inputs. The shared Python API and CLI let you quickly profile models across different runtimes and find the most suitable model for your application by changing manifests.
+Pass your own representative audio samples in a JSON file with `-j runs.json` to evaluate more inputs. The shared Python API and CLI let you quickly profile models across different runtimes. You can find the most suitable model for your application by changing manifests.
 
 ## What you've accomplished
 
-You've learned how brick types, implementations, model packages, and manifests fit together. You used Brick Manager to install models and build Compute Flow on your Apple silicon Mac, then connected speech recognition, translation, and speech synthesis in Python to turn an English recording into a French WAV file locally.
+You've compared speech recognition performance between Parakeet and Moonshine. 
 
-Finally, you replaced Parakeet with Moonshine by installing its brick and model package, rebuilding, and selecting a different manifest. The shared speech-to-text interface kept your Python script unchanged. You can use this approach to combine other bricks and select compatible models for your own applications.
+You can use the approach described in the Learning Path to combine other bricks and select compatible models for your own applications.

From 5eb2fb9036603696c22a432ea8a6650598c21332 Mon Sep 17 00:00:00 2001
From: anupras-mohapatra-arm 
Date: Fri, 30 Oct 2026 14:17:51 -0500
Subject: [PATCH 024/109] assisted pass

---
 .../cross-platform/compute-flow-getting-started/_index.md   | 6 +++---
 .../cross-platform/compute-flow-getting-started/how-to-2.md | 5 ++++-
 .../cross-platform/compute-flow-getting-started/how-to-3.md | 4 ++--
 .../cross-platform/compute-flow-getting-started/how-to-4.md | 2 +-
 4 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md b/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
index 5e7cbe9757..86f6f37ecc 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
@@ -4,12 +4,12 @@ description: Build and run an audio-to-audio pipeline as a Python application on
 
 minutes_to_complete: 15
 
-who_is_this_for: This is an introductory topic for developers who want to combine multiple AI models into a performant end-to-end AI applicaton running on an Arm-based CPU. 
+who_is_this_for: This is an introductory topic for developers who want to combine multiple AI models into a performant end-to-end AI application running on an Arm-based CPU.
 
 learning_objectives:
     - Identify how Compute Flow bricks and manifests separate application code from model implementations.
-    - Deploy an AI audio-to-audio translation pipline in Python with Compute Flow. 
-    - Select the speech recognition model and verify the pipeline using the same application and recording.
+    - Deploy an AI audio-to-audio translation pipeline in Python with Compute Flow.
+    - Select a speech recognition model and verify the pipeline using the same application and recording.
 
 prerequisites:
     - An Arm-based system such as an Apple® silicon Mac
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
index 4684523a05..4b66801458 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
@@ -58,6 +58,8 @@ uv sync && source .venv/bin/activate
 compute-flow check
 ```
 
+The environment check reports the available dependencies and native toolchain:
+
 ```output
 ╭───────────────────────────────────────── OK Environment Ready ─────────────────────────────────────────╮
 │  All required system dependencies are available.                                                       │
@@ -129,7 +131,7 @@ Each `model install` command installs the brick source, if needed, and its model
 | `lm_text:lm-text-generic` | Hy-MT2 1.8B, 2-bit | Translate English text into French |
 | `tts:qwen3-tts` | Qwen3-TTS 0.6B Custom Voice | Synthesize French speech |
 
-Models are downloaded to the `~/.compute-flow` directory by default. The `compute-flow model info` command displays details about a specified model
+Models are downloaded to the `~/.compute-flow` directory by default. The `compute-flow model info` command displays details about a specified model:
 
 ```bash
 compute-flow model info stt:parakeet-tdt parakeet-tdt-0-6b-v3-int8-dynamic
@@ -292,6 +294,7 @@ def main(argv: list[str] | None = None) -> int:
 if __name__ == "__main__":
     raise SystemExit(main())
 ```
+
 The code provides everything you need to run the audio-to-audio translation pipeline. The code first loads and configures each of the three bricks required for the application. It then runs each brick, with the output from the previous brick being passed to the next one. Finally, it saves the output of our pipeline.
 
 All three bricks share these execution methods, documented in [Brick Runtime Interface V1](https://github.com/ARM-software/ComputeFlow/blob/main/docs/design/brick_runtime_interface_v1.md).
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
index e1085aa62c..392934ba5a 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
@@ -59,8 +59,8 @@ python audio_translation_example/audio_translation.py \
 ```
 The Moonshine package includes the same `beckett.wav` recording that was used in the first run.
 
-## What you've accomplished and what's mext
+## What you've accomplished and what's next
 
 You've replaced Parakeet with Moonshine by installing its brick and model package, rebuilding, and selecting a different manifest. The shared speech-to-text interface kept your Python script unchanged. 
 
-Next, you'll compare performance between Parakeet and Moonshine. 
\ No newline at end of file
+Next, you'll compare performance between Parakeet and Moonshine.
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
index c71ba10180..96a7172c87 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
@@ -1,6 +1,6 @@
 ---
 title: Compare speech-to-text performance
-description: Replace the Parakeet AI model with Moonshine in your speech-to-text brick
+description: Compare Parakeet and Moonshine speech-to-text performance using the same audio sample.
 weight: 5
 
 ### FIXED, DO NOT MODIFY

From 402dabb45deec652c78ec8867947892b6b7bf025 Mon Sep 17 00:00:00 2001
From: anupras-mohapatra-arm 
Date: Fri, 30 Oct 2026 14:44:50 -0500
Subject: [PATCH 025/109] adding summary

---
 .../compute-flow-getting-started/_index.md    | 46 ++++++++++++++++++-
 1 file changed, 45 insertions(+), 1 deletion(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md b/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
index 86f6f37ecc..79888fd484 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/_index.md
@@ -16,11 +16,55 @@ prerequisites:
     - Basic Python knowledge and familiarity with terminal commands
     - Internet access and at least 15 GB of available storage
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-10-30T19:19:38Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 4d713ef29d940b9043e743e78543b697ed42036d7557c4840126ee434003ac9d
+  summary_generated_at: '2026-10-30T19:19:38Z'
+  summary_source_hash: 4d713ef29d940b9043e743e78543b697ed42036d7557c4840126ee434003ac9d
+  faq_generated_at: '2026-10-30T19:19:38Z'
+  faq_source_hash: 4d713ef29d940b9043e743e78543b697ed42036d7557c4840126ee434003ac9d
+  summary: >-
+    You'll set up Compute Flow and use it to build an audio-to-audio pipeline on an Arm-based Mac. First, you'll learn about Compute Flow and bricks. Then, you'll create a Python application that connects speech-to-text, translation,
+    and text-to-speech bricks. You'll start with the Parakeet model for speech-to-text. Then, you'll switch to Moonshine by installing its implementation and model, rebuilding, and selecting its
+    manifest. Finally, you'll compare speech-to-text performance between the two models.
+  # faqs:
+  # - question: How do I know my Mac is using the Arm architecture?
+  #   answer: >-
+  #     Run `uname -m` in your terminal and confirm that the output is `arm64`.
+  # - question: What should I do if Compute Flow reports missing dependencies?
+  #   answer: >-
+  #     Run `compute-flow check`, follow the suggested action for each missing dependency, and rerun
+  #     `compute-flow check` to confirm the result. The check does not install dependencies or test
+  #     whether a compiler can compile and link a program.
+  # - question: Which option should I use to switch from Parakeet to Moonshine for speech recognition?
+  #   answer: >-
+  #     Install the Moonshine speech-to-text implementation and model, rebuild with
+  #     `compute-flow build native`, then pass `moonshine_manifest_base_en.json` to your application
+  #     or CLI. You can keep your Python script unchanged because both variants implement `stt.v1`.
+  # - question: What result should I expect when the pipeline runs successfully?
+  #   answer: >-
+  #     You should see the transcription and translation in your terminal. The application saves
+  #     synthesized audio to `translated_french.wav` by default, or to the path you provide with
+  #     `--output`.
+  # - question: How do I compare the two speech-to-text variants correctly?
+  #   answer: >-
+  #     Run the built speech-to-text CLI for both models on the same 9.963-second mono 16 kHz sample,
+  #     changing only the manifest, and compare their total `run()` times. These sample timings do not
+  #     assess transcription accuracy.
+# END generated_summary_faq
+
 author: Kieran Hejmadi
 
 # New Learning Paths are opted in for the next manual generated summary/FAQ run.
 # The generator resets this to false after a successful write.
-generate_summary_faq: true
+generate_summary_faq: false
 
 # Optional one-shot controls: set either field to true to regenerate just that
 # generated section the next time the summary/FAQ tool runs. The tool resets

From e77e12ec48093b82e95b93a5be04681367495ad9 Mon Sep 17 00:00:00 2001
From: anupras-mohapatra-arm 
Date: Fri, 30 Oct 2026 15:42:55 -0500
Subject: [PATCH 026/109] edits

---
 .../compute-flow-getting-started/how-to-1.md  | 16 +++++++--------
 .../compute-flow-getting-started/how-to-2.md  | 20 +++++++++++--------
 .../compute-flow-getting-started/how-to-3.md  |  2 +-
 .../compute-flow-getting-started/how-to-4.md  |  2 +-
 4 files changed, 22 insertions(+), 18 deletions(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
index 1ad2fd3eb2..506c798199 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-1.md
@@ -15,7 +15,7 @@ A brick type defines an input and output contract. A brick variant implements th
 
 You'll use three different bricks to build an audio translation application. The first recognises human speech, the second translates speech into text, and the third generates audio. Each brick uses different models to accomplish the goal of translating English into French.
 
-You'll use Python to translate a short English recording into French speech on your Apple silicon Mac. The application processes the recording through three stages:
+You'll use a Python application to translate a short English recording into French speech on your Apple silicon Mac. The application processes the recording through three stages:
 
 ```text
 English WAV (Input)
@@ -37,7 +37,7 @@ Python coordinates the stages. It passes the speech-to-text transcription to the
 
 ## Separate the application from the model
 
-Your pipeline uses the following contracts:
+You'll use the following contracts:
 
 | Stage | Brick type | Input | Output |
 | --- | --- | --- | --- |
@@ -49,20 +49,20 @@ Your pipeline uses the following contracts:
 
 A model package contains the weights and supporting assets that a brick needs. A model manifest is a JSON configuration file in that package. It tells Compute Flow which brick implementation to use and how to load and run the models.
 
-The manifest describes model filenames, inference runtimes, and model-specific tensor formats. It also describes supporting assets such as tokenizers. The manifest provides configuration values and constants needed by the brick. The weights live in separate files that are referenced by the manifest.
+The manifest describes model filenames, inference runtimes, and model-specific tensor formats. It also describes supporting assets such as tokenizers. The manifest provides configuration values and constants that are needed by the brick. The weights live in separate files that are referenced by the manifest.
 
-For example, the Parakeet package includes `parakeet_tdt_0_6b_v3_manifest.json`. Your Python application reads this file with `cf.load_manifest()`, selects the implementation with `cf.load_brick()`, and prepares the models with `brick.setup()`. This keeps model-specific file paths and runtime configuration out of your application code.
+For example, the Parakeet package includes `parakeet_tdt_0_6b_v3_manifest.json`. The Python application reads this file with `cf.load_manifest()`. It selects the implementation with `cf.load_brick()`, and prepares the models with `brick.setup()`. This keeps model-specific file paths and runtime configuration out of your application code.
 
 ## Compute Flow Brick Manager
 
-To discover and install the required Compute Flow Bricks, you'll use Compute Flow Brick Manager alongside the selected AI models.
+To discover and install the required Compute Flow bricks, you'll use Compute Flow Brick Manager alongside the selected AI models.
 
-The Brick Manager is responsible for installing the bricks type and brick variant based on the selected AI model. It builds the developed application for the selected runtime platforms.
+The Brick Manager is responsible for installing the brick type and variant based on the selected AI model. It builds the developed application for the selected runtime platforms.
 
-To complete setup, you need network access to download dependencies and model packages. After setup, the example application reads the installed assets and performs inference on your Mac. It writes a WAV file locally and doesn't call a hosted inference service. You don't need a microphone because the input recording is included in a model package.
+To complete setup, you need network access to download dependencies and model packages. After setup, the application reads the installed assets and performs inference on your Mac. It writes a WAV file locally and doesn't call a hosted inference service. You don't need a microphone because the input recording is included in a model package.
 
 ## What you've learned and what's next
 
-You've learned that Compute Flow provides reusable AI building blocks that you can combine to build locally deployable AI applications. You've also learned about Compute Flows Bricks.
+You've learned that Compute Flow provides reusable AI building blocks that you can combine to build locally deployable AI applications. You've also learned about Compute Flows Brick Manager.
 
 Next, you'll build and run the example audio translation application with Compute Flow. 
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
index 4b66801458..de83961e10 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
@@ -36,7 +36,9 @@ command -v cc
 command -v c++
 ```
 
-Finally, remove any previously installed `llama.cpp` versions. For example, if installed with the `brew` package manager, run the following command:
+Finally, remove any previously installed `llama.cpp` versions.
+
+For example, if installed with the `brew` package manager, run the following command:
 
 ```bash
 brew uninstall llama.cpp
@@ -104,16 +106,16 @@ The environment check reports the available dependencies and native toolchain:
 
 Compute Flow requires Python 3.11 or later. `uv sync` installs the project Python dependencies into the virtual environment. With its default settings, [uv also downloads a compatible Python interpreter](https://docs.astral.sh/uv/concepts/python-versions/) if it can't find one locally.
 
-`compute-flow check` reports missing system dependencies and suggests installation commands. It doesn't install them or test whether the compiler can compile and link a program. If it reports a missing native compiler or Git installation, follow the suggested action and run the check again.
+`compute-flow check` reports missing system dependencies and suggests installation commands. It doesn't install dependencies or test whether the compiler can compile and link a program. If it reports a missing native compiler or Git installation, follow the suggested action and run the check again.
 
-Brick Manager automatically downloads managed tools, such as CMake and Git LFS, when a command needs them. The check reports these as `will be downloaded when needed`, so you don't need to install them manually. For example, `compute-flow build native` downloads its managed CMake version if it isn't already available.
+Brick Manager automatically downloads managed tools, such as CMake and Git Large File Storage (LFS), when a command needs them. The check reports these as `will be downloaded when needed`, so you don't need to install them manually. For example, `compute-flow build native` downloads its managed CMake version if it isn't already available.
 
 ## Install the three model packages
 
 Update the Brick Manager registry cache and install the models that will be used by your pipeline:
 
 {{% notice Note %}}
-For higher rate limits and faster downloads from Hugging Face, set the optional `HF_TOKEN` environment variable to your Hugging Face access token before installing the models. You can [create a Hugging Face access token](https://huggingface.co/settings/tokens) in account settings.
+For faster Hugging Face downloads and higher rate limits, set the optional `HF_TOKEN` environment variable before installing the models. You can [create a Hugging Face access token](https://huggingface.co/settings/tokens) in account settings.
 {{% /notice %}}
 
 ```bash
@@ -123,7 +125,9 @@ compute-flow model install lm_text:lm-text-generic hy-mt2-1-8b-2bit-translation
 compute-flow model install tts:qwen3-tts qwen3-tts-0-6b-custom-voice
 ```
 
-Each `model install` command installs the brick source, if needed, and its model package. The identifiers select the following implementations:
+Each `model install` command installs the brick source, if needed, and its model package. 
+
+The identifiers select the following implementations:
 
 | Brick | Model package | Pipeline role |
 | --- | --- | --- |
@@ -295,9 +299,9 @@ if __name__ == "__main__":
     raise SystemExit(main())
 ```
 
-The code provides everything you need to run the audio-to-audio translation pipeline. The code first loads and configures each of the three bricks required for the application. It then runs each brick, with the output from the previous brick being passed to the next one. Finally, it saves the output of our pipeline.
+The code provides everything that you need to run the audio-to-audio translation pipeline. The code first loads and configures each of the three bricks required for the application. It then runs each brick, with the output from the previous brick being passed to the next one. Finally, it saves the pipeline output.
 
-All three bricks share these execution methods, documented in [Brick Runtime Interface V1](https://github.com/ARM-software/ComputeFlow/blob/main/docs/design/brick_runtime_interface_v1.md).
+All three bricks share these deployment methods, documented in [Brick Runtime Interface V1](https://github.com/ARM-software/ComputeFlow/blob/main/docs/design/brick_runtime_interface_v1.md).
 
 
 ## Translate a pre-recorded sample
@@ -336,6 +340,6 @@ You'll later learn how to select alternative models with different performance a
 
 ## What you've accomplished and what's next
 
-You've installed three Compute Flow brick types and used three of their model variants to build an audio-to-audio pipeline. Your application now turns an English recording into a local French WAV file. 
+You've installed three Compute Flow brick types and used three of their model variants to build an audio-to-audio pipeline. Your application now turns an English recording into a local French `.wav` file. 
 
 Next, you'll replace the speech recognition model and run the same application again.
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
index 392934ba5a..31b3dd6649 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-3.md
@@ -9,7 +9,7 @@ layout: learningpathall
 
 ## Understand what changes when you replace a model
 
-Now that you've built the audio-to-audio pipeline, you can make changes to it.
+Now that you've built the audio-to-audio pipeline, you can make changes to the speech-to-text component.
 
 Speech-to-text is a brick type: audio and a sample rate go in, and text comes out.
 
diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
index 96a7172c87..815598d67f 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
@@ -31,7 +31,7 @@ Run the Parakeet manifest:
   -t 1 --enable-op-profiling
 ```
 
-The output is similar to this captured excerpt, showing the featurizer operator breakdown and the complete scope summary. The first-call report and other operator tables are omitted:
+The output is similar to the following captured excerpt showing the featurizer operator breakdown and the complete scope summary. The first-call report and other operator tables are omitted:
 
 ```output
 [TRANSCRIPTION]

From bc29e58bcd3f64f69f14edc767047f5670cd3222 Mon Sep 17 00:00:00 2001
From: anupras-mohapatra-arm 
Date: Fri, 30 Oct 2026 16:05:06 -0500
Subject: [PATCH 027/109] edits

---
 .../cross-platform/compute-flow-getting-started/how-to-4.md     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
index 815598d67f..a3875dd2d8 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
@@ -104,4 +104,4 @@ Pass your own representative audio samples in a JSON file with `-j runs.json` to
 
 You've compared speech recognition performance between Parakeet and Moonshine. 
 
-You can use the approach described in the Learning Path to combine other bricks and select compatible models for your own applications.
+You can use the Learning Path steps to combine other bricks and select compatible models for your own applications.

From 8cd6db00cf10d9a44b119ab3d956c0c056d0d229 Mon Sep 17 00:00:00 2001
From: anupras-mohapatra-arm 
Date: Fri, 30 Oct 2026 16:08:51 -0500
Subject: [PATCH 028/109] edits

---
 .../cross-platform/compute-flow-getting-started/how-to-4.md     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
index a3875dd2d8..ea45279a2e 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
@@ -104,4 +104,4 @@ Pass your own representative audio samples in a JSON file with `-j runs.json` to
 
 You've compared speech recognition performance between Parakeet and Moonshine. 
 
-You can use the Learning Path steps to combine other bricks and select compatible models for your own applications.
+You can extend the Learning Path to combine other bricks and select compatible models for your own applications.

From d594ac5e89dda89c0ba0986dff9f9130eab5a4c3 Mon Sep 17 00:00:00 2001
From: Usamah Zaheer 
Date: Mon, 2 Nov 2026 14:22:27 +0000
Subject: [PATCH 029/109] Align Silero VAD Learning Path with the E2E example

Pin the updated example, match its released package setup and default
input/calibration/build flow, and refresh the threshold, state explanation,
reference output, and diagrams.

Validate a fresh Linux AArch64 export, runtime/application builds, and FVP
run: 79 frames agree at threshold 0.5. Documentation and Hugo checks pass.

This change was authored with assistance from OpenAI Codex.
---
 .../1-prepare-environment.md                  | 128 +++++++-----------
 .../silero-vad-ethos-u/2-run-example.md       |  56 ++------
 .../silero-vad-ethos-u/3-run-on-fvp.md        |  64 ++++-----
 .../silero-vad-ethos-u/4-validate-output.md   |  37 +++--
 .../silero-vad-ethos-u/_index.md              |   6 +-
 .../silero-vad-deployment-lanes.svg           |   6 +-
 .../silero-vad-validation-result.svg          |  20 +--
 7 files changed, 120 insertions(+), 197 deletions(-)

diff --git a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/1-prepare-environment.md b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/1-prepare-environment.md
index be504cf463..94c5e2f4e3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/1-prepare-environment.md
+++ b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/1-prepare-environment.md
@@ -56,12 +56,13 @@ python3.12 --version
 
 ## Create an isolated ExecuTorch environment
 
-Use ExecuTorch `v1.5.1` for the Python package, example, and native runtime. Clone the matching source:
+Use the source revision from the Silero VAD end-to-end example with the released ExecuTorch `1.5.1` Python package:
 
 ```bash
 git clone https://github.com/pytorch/executorch.git
 cd executorch
-git checkout 3b60683923245cf472b7323426920e15623ba361
+git fetch origin d78bc1b0ba1c33a90d2d93662de118577f37efd4
+git checkout d78bc1b0ba1c33a90d2d93662de118577f37efd4
 git submodule sync --recursive
 git submodule update --init --recursive
 ```
@@ -75,7 +76,7 @@ git rev-parse --short=12 HEAD
 The expected output is:
 
 ```output
-3b6068392324
+d78bc1b0ba1c
 ```
 
 Create and activate a Python virtual environment:
@@ -88,68 +89,46 @@ python -m pip install --upgrade pip
 
 ## Install the Arm backend tools
 
-The Arm setup script downloads the Arm GNU Toolchain, Ethos-U Vela compiler, and supporting Python packages. On Linux, the script also installs the Corstone FVPs. Review the license terms presented by the script before using its EULA acceptance option.
+The Arm setup script installs the Arm GNU Toolchain and, on Linux, the Corstone FVPs. Review the license terms presented by the script before using its EULA acceptance option.
 
 {{% notice macOS %}}
-Before you run the Arm setup command on macOS, install [Docker Desktop](/install-guides/docker/docker-desktop/) and follow the [AVH FVPs on macOS install guide](/install-guides/fvps-on-macos/). Add the FVPs-on-Mac `bin` directory to `PATH`. Confirm that Docker is running and that `FVP_Corstone_SSE-320` resolves to the wrapper:
+Before running Arm setup, install [Docker Desktop](/install-guides/docker/docker-desktop/) and start Docker. Install the [FVPs-on-Mac wrappers](https://github.com/Arm-Examples/FVPs-on-Mac) at the revision for FVP `11.31.28`:
 
 ```bash
-docker info >/dev/null
-command -v FVP_Corstone_SSE-320
+mkdir -p examples/arm/arm-scratch "$HOME/.armlm"
+git clone https://github.com/Arm-Examples/FVPs-on-Mac.git \
+  examples/arm/arm-scratch/FVPs-on-Mac
+git -C examples/arm/arm-scratch/FVPs-on-Mac checkout --detach \
+  cc7a4be5456db085619647811fac29e9795f806e
+bash examples/arm/arm-scratch/FVPs-on-Mac/build.sh
+export PATH="$PWD/examples/arm/arm-scratch/FVPs-on-Mac/bin:$PATH"
+export FVP_MOUNT_DIR="$PWD"
+FVP_Corstone_SSE-320 --version
 ```
 {{% /notice %}}
 
-Run the setup script from the ExecuTorch repository root:
+Install released PyTorch and run Arm setup from the ExecuTorch repository root. The setup options select the cross-compiler and FVPs; the next section installs the Python export tools:
 
 ```bash
-./examples/arm/setup.sh --i-agree-to-the-contained-eula
-source examples/arm/arm-scratch/setup_path.sh
+python -m pip install "torch==2.14.0" --index-url https://download.pytorch.org/whl/cpu
+./examples/arm/setup.sh --i-agree-to-the-contained-eula \
+  --disable-ethos-u-deps --enable-baremetal-toolchain --enable-fvps
 ```
 
-The second command adds the downloaded cross-compiler and, on Linux, FVP binaries to the current shell environment. If you start a new shell, you'll need to run the command again.
-
-Check that the two target tools are available:
-
-```bash
-command -v arm-none-eabi-g++
-command -v FVP_Corstone_SSE-320
-```
-
-The compiler resolves under `examples/arm/arm-scratch/`. On Linux, the FVP also resolves under this directory. On macOS, the FVP resolves under the FVPs-on-Mac wrapper directory. If the compiler is missing, source `examples/arm/arm-scratch/setup_path.sh` again. If the FVP is missing on macOS, add the wrapper directory to `PATH`.
-
 ## Install ExecuTorch
 
-Remove the unused model-viewing and test packages installed by Arm setup:
+Install the released ExecuTorch package and its Ethos-U dependencies:
 
 ```bash
-python -m pip uninstall --yes \
-  tosa-adapter-model-explorer ai-edge-model-explorer \
-  pytest-timeout pte-adapter-model-explorer
+python -m pip install "executorch[ethos_u]==1.5.1" "torchao==0.18.0" "cmake<4" pytest
+source examples/arm/arm-scratch/setup_path.sh
 ```
 
-Install the released packages for your host. Linux uses CPU wheels; macOS uses Apple silicon wheels. Keep this step after Arm setup to resolve its older FlatBuffers dependency:
-
-{{< tabpane code=true >}}
-  {{< tab header="Linux" language="bash" >}}
-python -m pip install \
-  --index-url https://pypi.org/simple \
-  --extra-index-url https://download.pytorch.org/whl/cpu \
-  "executorch[ethos-u]==1.5.1" \
-  "torch==2.14.0+cpu" "torchvision==0.29.0+cpu" "torchao==0.18.0+cpu" \
-  "cmake==3.31.10" "zstd==1.5.7.2"
-  {{< /tab >}}
-  {{< tab header="macOS" language="bash" >}}
-python -m pip install \
-  --index-url https://pypi.org/simple \
-  "executorch[ethos-u]==1.5.1" \
-  "torch==2.14.0" "torchvision==0.29.0" "torchao==0.18.0" \
-  "cmake==3.31.10" "zstd==1.5.7.2"
-  {{< /tab >}}
-{{< /tabpane >}}
-
-Check dependency consistency and the imports used by export and CMake code generation:
+The path script adds the downloaded cross-compiler and, on Linux, FVP binaries to the current shell. Check the target tools and Python installation:
 
 ```bash
+command -v arm-none-eabi-g++
+command -v FVP_Corstone_SSE-320
 python -m pip check
 python - <<'PY'
 import executorch.codegen.tools.selective_build
@@ -160,63 +139,50 @@ print("ExecuTorch installation verified")
 PY
 ```
 
-The checks should report `No broken requirements found.` and `ExecuTorch installation verified`. These commands replace `install_executorch.sh`, which uses nightly and test indexes. If you rerun Arm setup, repeat this section before continuing.
+The Python checks should report `No broken requirements found.` and `ExecuTorch installation verified`.
+
+In each new shell, return to the ExecuTorch repository root and restore the environment and tool paths:
+
+```bash
+source .venv/bin/activate
+source examples/arm/arm-scratch/setup_path.sh
+if [ "$(uname -s)" = Darwin ]; then
+  export PATH="$PWD/examples/arm/arm-scratch/FVPs-on-Mac/bin:$PATH"
+  export FVP_MOUNT_DIR="$PWD"
+fi
+```
 
 ## Download the model and sample audio
 
-Create one workspace for the files generated in this Learning Path:
+Create a directory for the model and audio inputs:
 
 ```bash
-mkdir -p silero-vad-work/{assets,export}
+mkdir -p silero-vad
 ```
 
 Download the model and sample audio from the tested Silero VAD revision:
 
 ```bash
 curl --fail --location \
-  --output silero-vad-work/assets/silero_vad.jit \
+  --output silero-vad/silero_vad.jit \
   https://raw.githubusercontent.com/snakers4/silero-vad/dbacf536adadf42210f37ae50fbaf75f6235b3cf/src/silero_vad/data/silero_vad.jit
 
 curl --fail --location \
-  --output silero-vad-work/assets/test.wav \
+  --output silero-vad/test.wav \
   https://raw.githubusercontent.com/snakers4/silero-vad/dbacf536adadf42210f37ae50fbaf75f6235b3cf/tests/data/test.wav
 ```
 
-## Create two audio clips
+## Create the validation clip
 
-The target processes 2.5 seconds of audio. Copy the following snippet once to create a calibration clip and a separate validation clip:
+Use the example's helper to take the first 40,000 samples of `test.wav`. This creates a 2.5-second validation clip that fits the runtime's audio limit and produces 79 frames:
 
 ```bash
-python3 - <<'PY'
-import wave
-from pathlib import Path
-
-source_path = Path("silero-vad-work/assets/test.wav")
-clips = (("calibration.wav", 0.0), ("validation.wav", 2.5))
-
-with wave.open(str(source_path), "rb") as source:
-    parameters = source.getparams()
-    if (
-        parameters.nchannels,
-        parameters.sampwidth,
-        parameters.framerate,
-        parameters.comptype,
-    ) != (1, 2, 16000, "NONE"):
-        raise SystemExit("test.wav must be mono, 16 kHz, 16-bit PCM")
-    if source.getnframes() < 5 * parameters.framerate:
-        raise SystemExit("test.wav does not contain five seconds of audio")
-    for name, start_seconds in clips:
-        source.setpos(int(start_seconds * parameters.framerate))
-        frames = source.readframes(int(2.5 * parameters.framerate))
-        if len(frames) != 40000 * parameters.sampwidth:
-            raise SystemExit(f"Could not create a 2.5-second {name} clip")
-        with wave.open(str(source_path.with_name(name)), "wb") as target:
-            target.setparams(parameters)
-            target.writeframes(frames)
-PY
+python examples/arm/silero_vad_example_ethos_u/model_export/generate_smoke_audio.py \
+  --source-wav silero-vad/test.wav \
+  --output silero-vad/validation.wav
 ```
 
-Use `calibration.wav` to calibrate quantization. The FVP will process the separate `validation.wav` clip.
+Use `test.wav` for calibration and `validation.wav` for the host reference and FVP. These recordings overlap to check deployment consistency. Use representative calibration audio and separate validation recordings when measuring model quality.
 
 ## What you've accomplished and what's next
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/2-run-example.md b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/2-run-example.md
index a08911d488..e7f5462eb8 100644
--- a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/2-run-example.md
+++ b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/2-run-example.md
@@ -9,7 +9,7 @@ layout: learningpathall
 
 ## Prepare to export the model
 
-You prepared the model and two audio clips earlier. Now, use the calibration clip to quantize Silero VAD and the validation clip to create a host reference.
+You prepared the model and validation clip earlier. Now, use the source recording to calibrate Silero VAD and the validation clip to create a host reference.
 
 From the ExecuTorch repository root, activate the environment:
 
@@ -18,29 +18,6 @@ source .venv/bin/activate
 source examples/arm/arm-scratch/setup_path.sh
 ```
 
-## Build the host quantized operators
-
-The exporter requires the host shared library that registers the quantized operator out variants. Build the operator before exporting the model:
-
-```bash
-cmake \
-  -S . \
-  -B silero-vad-work/quantized_ops_aot \
-  -DCMAKE_BUILD_TYPE=Release \
-  -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON \
-  -DEXECUTORCH_BUILD_KERNELS_QUANTIZED_AOT=ON \
-  -DEXECUTORCH_BUILD_XNNPACK=OFF \
-  -DPYTHON_EXECUTABLE="$(command -v python)"
-
-cmake --build silero-vad-work/quantized_ops_aot \
-  --target quantized_ops_aot_lib --parallel
-
-export EXECUTORCH_QUANTIZED_OPS_AOT_LIBRARY="$(find \
-  silero-vad-work/quantized_ops_aot/kernels/quantized \
-  -name 'libquantized_ops_aot_lib.*' -type f -print -quit)"
-test -s "$EXECUTORCH_QUANTIZED_OPS_AOT_LIBRARY"
-```
-
 ## Export the model
 
 Run the exporter and save its output for the delegation check:
@@ -48,44 +25,41 @@ Run the exporter and save its output for the delegation check:
 ```bash
 set -o pipefail
 
-python3 examples/arm/silero_vad_example_ethos_u/model_export/export_silero_vad_ethos_u.py \
-  --jit-model silero-vad-work/assets/silero_vad.jit \
-  --calibration-audio silero-vad-work/assets/calibration.wav \
-  --validation-audio silero-vad-work/assets/validation.wav \
-  --output-path silero-vad-work/export/silero_vad_ethos_u.pte \
-  --expected-output-path silero-vad-work/export/expected_probs.bin \
-  --num-calibration-frames 32 \
-  --num-validation-frames 0 2>&1 | \
-  tee silero-vad-work/export/export.log
+python examples/arm/silero_vad_example_ethos_u/model_export/export_silero_vad_ethos_u.py \
+  --jit-model silero-vad/silero_vad.jit \
+  --calibration-audio silero-vad/test.wav \
+  --validation-audio silero-vad/validation.wav 2>&1 | tee export.log
 ```
 
 The output is similar to:
 
 ```output
-Wrote expected probabilities to silero-vad-work/export/expected_probs.bin
+Wrote expected probabilities to ./expected_probs.bin
 Lowering to Ethos-U85...
-Exported model saved to silero-vad-work/export/silero_vad_ethos_u.pte
+Exported model saved to ./silero_vad_ethos_u.pte
 ```
 
+The default export uses up to 256 calibration frames and all 79 validation frames. It writes the PTE and reference probabilities in the repository root.
+
 Verify the generated outputs and the number of reference probabilities:
 
 ```bash
 for artifact in \
-  silero-vad-work/export/silero_vad_ethos_u.pte \
-  silero-vad-work/export/expected_probs.bin \
-  silero-vad-work/export/export.log; do
+  silero_vad_ethos_u.pte \
+  expected_probs.bin \
+  export.log; do
   test -s "$artifact" || {
     echo "Missing export artifact: $artifact" >&2
     exit 1
   }
 done
 
-test "$(wc -c < silero-vad-work/export/expected_probs.bin)" -eq $((79 * 4)) || {
+test "$(wc -c < expected_probs.bin)" -eq $((79 * 4)) || {
   echo "Expected 79 float32 reference probabilities" >&2
   exit 1
 }
 
-test "$(grep -c 'CPU operators = 0' silero-vad-work/export/export.log)" -eq 2 || {
+test "$(grep -c 'CPU operators = 0' export.log)" -eq 2 || {
   echo "Expected both Vela subgraphs to contain no CPU operators" >&2
   exit 1
 }
@@ -99,7 +73,7 @@ The application supplies one 512-sample audio frame at a time. The exported prog
 
 ![Runtime diagram showing 64 context samples and a 512-sample frame entering the ExecuTorch program, model operations delegated to Ethos-U85, an internal int8 hidden and cell state reused between calls, and one speech probability emitted every 32 ms.#center](silero-vad-streaming-delegation.svg "Silero VAD streaming state and Ethos-U delegation boundary")
 
-The Vela output contains two subgraphs with no CPU operators: the main VAD network and a smaller state-quantization subgraph. Only small boundary conversions and the state update remain as portable ExecuTorch operations.
+The Vela output contains two subgraphs with no CPU operators: the main VAD network and a smaller state-quantization subgraph. Quantization, layout copies, and recurrent-state writes between delegates run on the CPU.
 
 ## What you've accomplished and what's next
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/3-run-on-fvp.md b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/3-run-on-fvp.md
index c768c17135..9f65ac6f42 100644
--- a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/3-run-on-fvp.md
+++ b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/3-run-on-fvp.md
@@ -21,7 +21,8 @@ source examples/arm/arm-scratch/setup_path.sh
 
 cmake --preset arm-baremetal \
   -DCMAKE_BUILD_TYPE=Release \
-  -B cmake-out-arm
+  -DTARGET_CPU=cortex-m85 \
+  -B cmake-out-arm -S .
 cmake --build cmake-out-arm --target install --parallel
 ```
 
@@ -29,67 +30,52 @@ The installed libraries provide the ExecuTorch runtime, portable operators, and
 
 ## Build the Silero VAD application
 
-Configure the application with the exported model, validation audio, and a speech threshold of `0.55`. 
+Configure the application with the exported model, validation audio, and the default speech threshold of `0.5`. The command selects the UART address required by FVPs-on-Mac:
 
-Select your host operating system:
+```bash
+runtime_cmake_args=()
+if [ "$(uname -s)" = Darwin ]; then
+  runtime_cmake_args+=(-DUART0_BASE=0x49303000)
+fi
 
-{{< tabpane code=true >}}
-  {{< tab header="Linux" language="bash" >}}
-cmake \
-  -S examples/arm/silero_vad_example_ethos_u/runtime \
-  -B silero-vad-work/app \
-  -DCMAKE_TOOLCHAIN_FILE="$PWD/examples/arm/ethos-u-setup/arm-none-eabi-gcc.cmake" \
-  -DET_BUILD_DIR_PATH="$PWD/cmake-out-arm" \
-  -DTARGET_CPU=cortex-m85 \
-  -DET_PTE_FILE_PATH="$PWD/silero-vad-work/export/silero_vad_ethos_u.pte" \
-  -DAUDIO_PATH="$PWD/silero-vad-work/assets/validation.wav" \
-  -DVAD_THRESHOLD=0.55 \
-  -DPYTHON_EXECUTABLE="$(command -v python)"
-  {{< /tab >}}
-  {{< tab header="macOS" language="bash" >}}
 cmake \
-  -S examples/arm/silero_vad_example_ethos_u/runtime \
-  -B silero-vad-work/app \
+  "${runtime_cmake_args[@]}" \
   -DCMAKE_TOOLCHAIN_FILE="$PWD/examples/arm/ethos-u-setup/arm-none-eabi-gcc.cmake" \
-  -DET_BUILD_DIR_PATH="$PWD/cmake-out-arm" \
+  -DCMAKE_BUILD_TYPE=Release \
   -DTARGET_CPU=cortex-m85 \
-  -DET_PTE_FILE_PATH="$PWD/silero-vad-work/export/silero_vad_ethos_u.pte" \
-  -DAUDIO_PATH="$PWD/silero-vad-work/assets/validation.wav" \
-  -DVAD_THRESHOLD=0.55 \
-  -DUART0_BASE=0x49303000 \
-  -DPYTHON_EXECUTABLE="$(command -v python)"
-  {{< /tab >}}
-{{< /tabpane >}}
+  -DET_PTE_FILE_PATH="$PWD/silero_vad_ethos_u.pte" \
+  -DAUDIO_PATH="$PWD/silero-vad/validation.wav" \
+  -B examples/arm/silero_vad_ethos_u \
+  -S examples/arm/silero_vad_example_ethos_u/runtime
+```
 
 Build the configured application:
 
 ```bash
-cmake --build silero-vad-work/app \
+cmake --build examples/arm/silero_vad_ethos_u \
   --target silero_vad_ethos_u --parallel
 ```
 
-The build creates `silero-vad-work/app/silero_vad_ethos_u`. This ELF image contains both the `.pte` model and the 2.5-second validation clip.
+The build creates `examples/arm/silero_vad_ethos_u/silero_vad_ethos_u`. This ELF image contains both the `.pte` model and the 2.5-second validation clip.
 
 ## Run Silero VAD on the FVP
 
 Run the application and save its simulated UART output to `fvp.log`:
 
 ```bash
-mkdir -p silero-vad-work/fvp
 set -o pipefail
 bash backends/arm/scripts/run_fvp.sh \
-  --elf=silero-vad-work/app/silero_vad_ethos_u \
+  --elf=examples/arm/silero_vad_ethos_u/silero_vad_ethos_u \
   --target=ethos-u85-256 \
-  --timeout=300 2>&1 | tee silero-vad-work/fvp/fvp.log
+  --timeout=300 2>&1 | tee fvp.log
 ```
 
-The application prints one speech probability every 32 ms. Near the end, it reports a summary and stops the simulation:
-
-The output is similar to:
+The application processes one audio frame every 32 ms of recording time, then prints the probabilities and a summary. The output is similar to:
 
 ```output
-1 segments, 79 frames, 2.5s
-Speech: 57/79 frames (72.2%)
+2 segments, 79 frames, 2.5s
+Speech: 56/79 frames (70.9%)
+Model executed successfully.
 Simulation complete, 0
 No problems found!
 ```
@@ -100,8 +86,8 @@ Confirm that the application and its serial log exist:
 
 ```bash
 for artifact in \
-  silero-vad-work/app/silero_vad_ethos_u \
-  silero-vad-work/fvp/fvp.log; do
+  examples/arm/silero_vad_ethos_u/silero_vad_ethos_u \
+  fvp.log; do
   test -s "$artifact" || {
     echo "Missing target artifact: $artifact" >&2
     exit 1
diff --git a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/4-validate-output.md b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/4-validate-output.md
index 92caeaf99c..77f004824f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/4-validate-output.md
+++ b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/4-validate-output.md
@@ -15,27 +15,28 @@ Inspect the first probability records and the detected speech segments:
 
 ```bash
 grep -Eo 'PROB .*$' \
-  silero-vad-work/fvp/fvp.log | head -n 10
+  fvp.log | head -n 10
 grep -Eo 'SEGMENT .*$' \
-  silero-vad-work/fvp/fvp.log
+  fvp.log
 ```
 
 The output is similar to:
 
 ```output
-PROB 0.000 0.211681 silence
-PROB 0.032 0.211681 silence
+PROB 0.000 0.062716 silence
+PROB 0.032 0.105834 silence
 ...
-PROB 0.224 0.995684 speech
+PROB 0.096 0.936824 speech
 ...
-SEGMENT 0.224 2.048 speech
+SEGMENT 0.096 0.224 speech
+SEGMENT 0.288 1.952 speech
 ```
 
-Each `PROB` line contains the frame timestamp in seconds, the probability of speech, and the decision produced with a threshold of `0.55`. Your probability values and speech segments depend on the validation audio.
+Each `PROB` line contains the frame timestamp in seconds, the probability of speech, and the decision produced with a threshold of `0.5`. Your probability values and speech segments depend on the validation audio.
 
-The validation question is: Did the host and FVP label every frame the same way? In this run, both the host and the FVP found one speech segment from `0.224` to `2.048` seconds, with no decision mismatches.
+The validation question is: Did the host and FVP label every frame the same way? In the reference run, both the host and FVP found speech from `0.096` to `0.224` seconds and from `0.288` to `1.952` seconds, with no decision mismatches.
 
-![Two matching timelines for the same 2.5-second audio clip. The host reference and FVP output both show silence, speech from 0.224 to 2.048 seconds, then silence. All 79 frame decisions match.#center](silero-vad-validation-result.svg "Host and FVP produce the same speech decisions")
+![Two matching timelines for the same 2.5-second audio clip. The host reference and FVP output show speech from 0.096 to 0.224 seconds and from 0.288 to 1.952 seconds. All 79 frame decisions match.#center](silero-vad-validation-result.svg "Host and FVP produce the same speech decisions")
 
 ## Compare the host and FVP results
 
@@ -43,26 +44,22 @@ Compare the saved host reference with the probabilities in the FVP serial log:
 
 ```bash
 source .venv/bin/activate
-python3 examples/arm/silero_vad_example_ethos_u/runtime/compare_vad_probs.py \
-  --expected silero-vad-work/export/expected_probs.bin \
-  --actual-log silero-vad-work/fvp/fvp.log \
-  --threshold 0.55 \
+python examples/arm/silero_vad_example_ethos_u/runtime/compare_vad_probs.py \
+  --expected expected_probs.bin \
+  --actual-log fvp.log \
   --atol 0.25 \
   --mean-atol 0.02
 ```
 
-The output is similar to:
+The comparison must report 79 probability values and zero threshold mismatches at `0.500`. The expected final output is:
 
 ```output
-Compared 79 probability values
-Max abs error: 0.156800 at frame 22
-Mean abs error: 0.018459
-Threshold mismatches at 0.550: 0
+Validation passed
 ```
 
-The comparison checks that the host and FVP produced the same number of finite probabilities, that the numerical differences stay within the stated tolerances, and that every frame has the same speech or silence decision.
+It checks that both outputs contain the same number of finite probabilities, that maximum and mean absolute errors stay within `0.25` and `0.02`, and that every frame has the same speech or silence decision.
 
-The pinned exporter retains the recurrent state left after generating the host reference, so the FVP starts from a different state. Treat this as a functional check of the example clip's speech decisions; the probability errors include that state difference.
+The exporter restores the recurrent state after generating the reference, so the host and FVP start from the same state. This checks deployment consistency against the quantized host model. Measuring speech-detection accuracy requires labelled audio.
 
 ## What you've accomplished
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/_index.md b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/_index.md
index 69424cbf83..2d1e5ae872 100644
--- a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/_index.md
@@ -34,8 +34,8 @@ generated_summary_faq:
   faq_source_hash: f56e6a716c01c38d2eb7f19f379804a0bf8d1f3faac81c75121ddb3470cfb799
   summary: >-
     You'll deploy the stateful Silero VAD model on an Arm Ethos-U85 virtual target.
-    First, you'll prepare ExecuTorch, the model, and audio clips. Then, you'll build host quantized operators,
-    export a `.pte` model, and create a host reference. Finally, you'll package the model and validation clip
+    First, you'll prepare ExecuTorch, the model, and validation audio. Then, you'll
+    export a `.pte` model and create a host reference. Finally, you'll package the model and validation clip
     into a Cortex-M85 application, run it on the Corstone-320 FVP, and compare its speech decisions with
     the host reference.
   faqs:
@@ -97,7 +97,7 @@ further_reading:
         type: documentation
     - resource:
         title: Silero VAD Ethos-U example source
-        link: https://github.com/pytorch/executorch/tree/3b60683923245cf472b7323426920e15623ba361/examples/arm/silero_vad_example_ethos_u
+        link: https://github.com/pytorch/executorch/tree/d78bc1b0ba1c33a90d2d93662de118577f37efd4/examples/arm/silero_vad_example_ethos_u
         type: repository
     - resource:
         title: Silero VAD project
diff --git a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/silero-vad-deployment-lanes.svg b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/silero-vad-deployment-lanes.svg
index 7843bc0b55..18a950b0d7 100644
--- a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/silero-vad-deployment-lanes.svg
+++ b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/silero-vad-deployment-lanes.svg
@@ -29,9 +29,9 @@
     
   
   
-    Split audio
-    calibration |
-    validation
+    Prepare audio
+    test.wav +
+    validation clip
   
   
     
diff --git a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/silero-vad-validation-result.svg b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/silero-vad-validation-result.svg
index b633a569c4..95d0752c0f 100644
--- a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/silero-vad-validation-result.svg
+++ b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/silero-vad-validation-result.svg
@@ -1,29 +1,29 @@
 
 
   Host and FVP produce identical Silero VAD speech decisions
-  Two matching timelines show silence, then speech from 0.224 to 2.048 seconds, then silence. The host reference and FVP output agree on all 79 frames.
+  Two matching timelines show speech from 0.096 to 0.224 seconds and from 0.288 to 1.952 seconds. The host reference and FVP output agree on all 79 frames.
   
   
 
   Host and FVP found the same speech
   Both processed the same 2.5-second audio clip
-  Gray = silence   •   Green = speech
+  Green = speech: 0.096–0.224 s and 0.288–1.952 s
 
   Host reference
   FVP output
 
   0 s
-  0.224 s
-  2.048 s
+  1 s
+  2 s
   2.5 s
 
   
-    
-    
-    
-    
-    
-    
+    
+    
+    
+    
+    
+    
   
 
   SPEECH

From c780110dffad1b3ece50f8497975f326ba118381 Mon Sep 17 00:00:00 2001
From: Usamah Zaheer 
Date: Mon, 2 Nov 2026 14:31:00 +0000
Subject: [PATCH 030/109] Align MobileSAM Learning Path with the E2E example

Pin the updated example, use its stock source installer before Arm setup,
and align host requirements, timed mode, and unavailable FVP measurements.
Remove obsolete dependency workarounds and old reference measurements.

Validate a fresh Linux AArch64 source install, export, runner build, and
FVP run: host IoU 0.9547 and target IoU 0.9737. Documentation and Hugo
checks pass.

This change was authored with assistance from OpenAI Codex.
---
 .../deploy-mobilesam-on-ethos-u/_index.md     |  4 +-
 .../prepare-the-environment.md                | 88 ++++++-------------
 .../run-the-example.md                        |  2 +-
 .../validate-the-results.md                   | 15 +---
 4 files changed, 33 insertions(+), 76 deletions(-)

diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/_index.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/_index.md
index 6973fb6bcb..566279fbb0 100644
--- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/_index.md
+++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/_index.md
@@ -42,7 +42,7 @@ generated_summary_faq:
   - question: How do I know my host is supported before I start?
     answer: >-
       Run the preflight check to verify macOS 15 or later on Apple silicon, or glibc 2.28 or later on Linux.
-      You'll also check the required tools, including `python3.12`, `git`, `cmake`, `c++`, and either `Ninja` or `Make`.
+      You'll also check `python3.12`, `git`, `c++`, `ninja`, `curl`, `pkg-config`, and `xz`.
   - question: Which directory should I run commands from during export and build?
     answer: >-
       Run commands from the `ExecuTorch` repository root. Activate your Python environment and source
@@ -93,7 +93,7 @@ operatingsystems:
 further_reading:
     - resource:
         title: ExecuTorch MobileSAM prompt segmentation example
-        link: https://github.com/pytorch/executorch/tree/5c4d2c4a0150a0809bc77be9a67093f80f60a60f/examples/arm/mobilesam_prompt_segmentation_example_ethos_u
+        link: https://github.com/pytorch/executorch/tree/fe93a8b0fc8d363fbb3bfa2aa18e253507fd13f8/examples/arm/mobilesam_prompt_segmentation_example_ethos_u
         type: repository
     - resource:
         title: ExecuTorch Arm Ethos-U backend documentation
diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md
index 3d9f17e9b5..8e9e8ea459 100644
--- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md
+++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md
@@ -8,7 +8,7 @@ layout: "learningpathall"
 
 ## Check your development machine
 
-Use Python 3.12 with development headers and virtual-environment support. Run the following preflight check before downloading the source:
+Use Python 3.12 with development headers and virtual-environment support. On Linux, also install the GLib and OpenMP runtime packages (`libglib2.0-0` and `libgomp1` on Ubuntu). Run the following preflight check before downloading the source:
 
 ```bash
 case "$(uname -s)/$(uname -m)" in
@@ -21,7 +21,7 @@ case "$(uname -s)/$(uname -m)" in
     ;;
 esac
 
-for tool in python3.12 git cmake c++; do
+for tool in python3.12 git c++ ninja curl pkg-config xz; do
   command -v "$tool" >/dev/null || {
     echo "Missing required tool: $tool" >&2
     exit 1
@@ -44,14 +44,8 @@ elif sys.platform.startswith("linux"):
         )
 PY
 
-if ! command -v ninja >/dev/null && ! command -v make >/dev/null; then
-  echo "Install Ninja or Make before continuing." >&2
-  exit 1
-fi
-
 python3.12 --version
 git --version
-cmake --version | head -n 1
 c++ --version | head -n 1
 ```
 
@@ -59,9 +53,9 @@ The check confirms that you are using a supported Linux host or Apple silicon Ma
 
 - Python 3.12
 - Git
-- CMake
 - A host C++ compiler
-- Ninja or Make
+- Ninja
+- curl, pkg-config, and xz
 
 The Python export dependencies require macOS 15 or later on Apple silicon, or glibc 2.28 or later on Linux. The pinned [TOSA tools](https://pypi.org/project/tosa-tools/2026.5.0/#files) set the macOS minimum, and the [PyTorch wheels](https://pypi.org/project/torch/2.14.0/#files) require the Linux glibc version. The preflight check verifies these requirements before you install the dependencies.
 
@@ -74,12 +68,13 @@ Clone ExecuTorch, select the revision used by this Learning Path, and initialize
 ```bash
 git clone https://github.com/pytorch/executorch.git
 cd executorch
-git checkout 5c4d2c4a0150a0809bc77be9a67093f80f60a60f
+git fetch origin fe93a8b0fc8d363fbb3bfa2aa18e253507fd13f8
+git checkout fe93a8b0fc8d363fbb3bfa2aa18e253507fd13f8
 git submodule sync
 git submodule update --init --recursive
 ```
 
-Run the remaining commands from the ExecuTorch repository root. The pinned revision keeps the commands aligned with the [MobileSAM example source](https://github.com/pytorch/executorch/tree/5c4d2c4a0150a0809bc77be9a67093f80f60a60f/examples/arm/mobilesam_prompt_segmentation_example_ethos_u).
+Run the remaining commands from the ExecuTorch repository root. The pinned revision keeps the commands aligned with the [MobileSAM example source](https://github.com/pytorch/executorch/tree/fe93a8b0fc8d363fbb3bfa2aa18e253507fd13f8/examples/arm/mobilesam_prompt_segmentation_example_ethos_u).
 
 ## Create a Python environment
 
@@ -90,6 +85,16 @@ python3.12 -m venv .venv
 source .venv/bin/activate
 ```
 
+## Install ExecuTorch
+
+Build the Python package from the same checkout used by the example and native runner:
+
+```bash
+CMAKE_GENERATOR=Ninja ./install_executorch.sh --optional-dependency ethos_u
+```
+
+The installer supplies CMake, the model export dependencies, and the example requirements. It selects released PyTorch and TorchAO packages for the CPU workflow.
+
 ## Install the Arm development tools
 
 {{% notice Note %}}
@@ -108,60 +113,15 @@ The Arm setup script installs the pinned GNU bare-metal toolchain, Ethos-U Vela
 source examples/arm/arm-scratch/setup_path.sh
 ```
 
-## Install ExecuTorch
-
-Remove unused developer packages installed by Arm setup:
-
-```bash
-python -m pip uninstall -y \
-  tosa-adapter-model-explorer ai-edge-model-explorer \
-  pte-adapter-model-explorer pytest-timeout
-```
-
-Install released PyTorch, Torchvision, and TorchAO packages. Linux uses the stable CPU wheel index; macOS uses the Apple silicon wheels from PyPI:
-
-```bash
-case "$(uname -s)" in
-  Linux)
-    python -m pip install \
-      --index-url https://pypi.org/simple \
-      --extra-index-url https://download.pytorch.org/whl/cpu \
-      'torch==2.14.0+cpu' 'torchvision==0.29.0+cpu' 'torchao==0.18.0+cpu'
-    ;;
-  Darwin)
-    python -m pip install --index-url https://pypi.org/simple \
-      'torch==2.14.0' 'torchvision==0.29.0' 'torchao==0.18.0'
-    ;;
-  *)
-    echo "Use a supported Linux host or Apple silicon Mac." >&2
-    exit 1
-    ;;
-esac
-```
-
-Install the source build dependencies and the `timm` version used by the pinned MobileSAM example:
-
-```bash
-python -m pip install --index-url https://pypi.org/simple \
-  'cmake==3.31.10' 'packaging==26.3' 'setuptools==84.0.0' \
-  'wheel==0.48.0' 'pyyaml==6.0.3' 'zstd==1.5.7.2' \
-  'certifi==2026.7.22' 'patchelf==0.19.1.0; sys_platform == "linux"' \
-  'timm==1.0.7'
-```
+## Verify the environment
 
-Build and install ExecuTorch from the same checkout used by the example and native runner:
+Check that the Python dependencies are consistent after Arm setup:
 
 ```bash
-CMAKE_ARGS='-DEXECUTORCH_BUILD_MLX=OFF' env -u DEBUG \
-  python -m pip install --no-build-isolation '.[ethos_u]'
 python -m pip check
 ```
 
-Installing the Ethos-U extra after Arm setup resolves its older FlatBuffers dependency. Before exporting, confirm that `pip check` reports `No broken requirements found.`
-
-These commands replace `install_executorch.sh` and its TorchAO nightly with released dependencies. If you rerun Arm setup, repeat the package removal and Python installation steps.
-
-## Verify the environment
+Confirm that the output is `No broken requirements found.`
 
 Check the ExecuTorch and Ethos-U imports used by the exporter:
 
@@ -212,6 +172,14 @@ MobileSAM example: PASS
 
 The comparison image, `fvp_comparison.png`, is saved under `arm_test/mobilesam/result/`. After the script completes, continue to [Validate the MobileSAM segmentation result](../validate-the-results/) to inspect the artifacts.
 
+To run the same workflow with simulated NPU counters, use timing mode:
+
+```bash
+./examples/arm/mobilesam_prompt_segmentation_example_ethos_u/run.sh --timed
+```
+
+Timing mode uses a 7,200-second timeout; the default fast mode uses 300 seconds. CPU timing and CPU/NPU ratios remain unavailable on FVP.
+
 To work through each stage manually, skip this script and continue to the next page. The following pages explain the same preparation, export, build, and validation steps. Continue only after each command succeeds; files from an earlier run can remain after a failed command.
 
 ## What you've accomplished and what's next
diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/run-the-example.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/run-the-example.md
index 4a4ada8fc3..85f10c3008 100644
--- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/run-the-example.md
+++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/run-the-example.md
@@ -69,7 +69,7 @@ backends/arm/scripts/run_fvp.sh \
 
 The runner reads the tensor specified by `-i input.bin` and writes its first output tensor to `output-0.bin` under `arm_test/mobilesam/io/`. The console transcript is saved to `arm_test/mobilesam/fvp.log`; validation reads the tensor file.
 
-`--fast` selects fast Ethos-U simulation for functional validation. Don't use its cycle counts or performance monitoring unit (PMU) counters to benchmark the model.
+`--fast` selects functional validation and reports CPU timing, CPU/NPU ratios, and NPU performance monitoring unit (PMU) counters as `N/A`. To collect simulated NPU counters, omit `--fast` and set `--timeout=7200`. CPU timing and CPU/NPU ratios remain unavailable; the simulated NPU counters are not measurements of real-device latency.
 
 Confirm that the run created the output file before continuing:
 
diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md
index 647cc27b88..d4ee4e20e3 100644
--- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md
+++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/validate-the-results.md
@@ -36,15 +36,6 @@ python3 -m json.tool arm_test/mobilesam/export/metrics.json
 
 The `fp32_quantized_iou` value must also be at least `0.9`.
 
-A reference run at the pinned commit produced these scores:
-
-| Comparison | IoU |
-| --- | --- |
-| Floating-point and quantized host masks | 0.9436 |
-| FVP and quantized host masks | 0.9764 |
-
-This reference run used Ubuntu 24.04.4 on AArch64 with Python 3.12.3 and the dependencies from [environment setup](../prepare-the-environment/). Treat these scores as examples; use the two `0.9` thresholds to check your own run.
-
 ## Confirm Ethos-U delegation
 
 Display the delegation report written during export:
@@ -53,12 +44,10 @@ Display the delegation report written during export:
 sed -n '1,120p' arm_test/mobilesam/export/delegation.txt
 ```
 
-Confirm that `Total delegated subgraphs` is `1`. The exporter checks this before writing the `.pte`. The reference run above produced:
+Confirm that `Total delegated subgraphs` is `1`. The exporter checks this before writing the `.pte`. The output includes:
 
 ```output
 Total delegated subgraphs: 1
-Number of delegated nodes: 5076
-Number of non-delegated nodes: 3
 ```
 
 Node counts can vary with dependency versions. Use the values from your export when reporting delegation.
@@ -66,7 +55,7 @@ Node counts can vary with dependency versions. Use the values from your export w
 In `arm_test/mobilesam/fvp.log`, also confirm `NPU delegations: 1`. This count records a completed NPU invocation at runtime; the export report shows how the model was partitioned.
 
 {{% notice Note %}}
-The FVP can report `Inference CPU ratio: 100.00 %` and `Inference NPU ratio: 0.00 %` even when the NPU executes the delegated subgraph. These ratios use CPU-side timing, not delegated operator counts. CPU timing isn't cycle accurate on this FVP. Fast mode can also zero PMU event counters and isn't suitable for benchmarking. Use the delegation count and mask comparison to validate this run.
+The FVP reports CPU timing and CPU/NPU ratios as `N/A`. Fast mode also marks NPU PMU counters as unavailable; timing mode reports simulated NPU counters. Use the delegation count and mask comparison to validate execution. Simulated counters are not measurements of real-device latency.
 {{% /notice %}}
 
 ## Inspect the visual result

From fba08974a4ab96ab359a87436966cb31fff62af2 Mon Sep 17 00:00:00 2001
From: Kieran Hejmadi 
Date: Mon, 2 Nov 2026 15:40:41 +0000
Subject: [PATCH 031/109] add verified french translation by native speaker

---
 .../cross-platform/compute-flow-getting-started/how-to-2.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
index de83961e10..87fcd7ef21 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-2.md
@@ -327,13 +327,13 @@ The expected output is:
 
 ```output
 Transcription: Ever tried, ever failed. No matter. Try again. Fail again. Fail better.
-Translation: Avez-vous déjà essayé, avez-vous déjà échoué. Peu importe. Essayez à nouveau. Écheclez encore. Échouez mieux.
+Translation: Avez-vous déjà essayé, avez-vous déjà échoué. Peu importe. Essayez à nouveau. Échouez encore. Échouez mieux.
 ```
 
-Check that the transcription matches the English recording and that the translation preserves its meaning. An accurate translation is as follows:
+Check that the transcription matches the English recording and that the translation preserves its meaning. An accurate translation in the polite form is as follows:
 
 ```text
- Déjà essayé, déjà échoué. Peu importe. Essaie encore. Échoue encore. Échoue mieux.
+Avez-vous déjà essayé, avez-vous déjà échoué. Peu importe. Essayez à nouveau. Échouez encore. Échouez mieux.
 ```
 
 You'll later learn how to select alternative models with different performance and accuracy tradeoffs. 

From 52945568f1fc0a39dcd505d02d83186f25ca841a Mon Sep 17 00:00:00 2001
From: Kieran Hejmadi 
Date: Mon, 2 Nov 2026 17:06:17 +0000
Subject: [PATCH 032/109] fix accidental revert during copy edit

---
 .../compute-flow-getting-started/how-to-4.md  | 41 +++----------------
 1 file changed, 6 insertions(+), 35 deletions(-)

diff --git a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
index ea45279a2e..deefb2185c 100644
--- a/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
+++ b/content/learning-paths/cross-platform/compute-flow-getting-started/how-to-4.md
@@ -31,7 +31,7 @@ Run the Parakeet manifest:
   -t 1 --enable-op-profiling
 ```
 
-The output is similar to the following captured excerpt showing the featurizer operator breakdown and the complete scope summary. The first-call report and other operator tables are omitted:
+The output is similar to the following. The operator breakdown has been omitted for brevity.
 
 ```output
 [TRANSCRIPTION]
@@ -40,28 +40,6 @@ Ever tried, ever failed. No matter. Try again. Fail again. Fail better.
 All Calls
 Scope: run() [total ms: 985.12]
 Scope stats | calls: 30 | avg ms: 32.84 | min ms: 0.34 | max ms: 487.10
-Model + Op Breakdown
-runner          contributor                                              calls                                         %      total ms      avg ms      min ms      max ms
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-litert          featurizer
-                  GATHER_ND                                                  4 [██████████████░░░░░░░░░░░░░░░░]  45.01%          7.86        1.96        1.94        1.98
-                  Fully Connected (NC, QP8, F32, QC8W) GEMM                 12 [████████░░░░░░░░░░░░░░░░░░░░░░]  25.58%          4.46        0.37        0.18        0.49
-                  ADD                                                        4 [██████░░░░░░░░░░░░░░░░░░░░░░░░]  19.29%          3.37        0.84        0.81        0.87
-                  Multiply (ND)                                             24 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.27%          0.22        0.01        0.00        0.03
-                  Log (NC)                                                   4 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   0.85%          0.15        0.04        0.03        0.05
-                  Extra Ops                                                    [█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   2.37%          0.41
-                  Overhead                                                     [██░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   5.64%          0.98
-...
-Scope Summary
-runner          contributor                                              calls                                         %      total ms      avg ms      min ms      max ms
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-litert          encoder                                                      2 [█████████████████████████████░]  95.60%        941.76      470.88      469.83      471.93
-litert          featurizer                                                   4 [█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.77%         17.45        4.36        4.28        4.44
-litert          joint_step                                                  42 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.43%         14.06        0.33        0.31        0.36
-litert          decoder_step                                                30 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   1.12%         11.06        0.37        0.34        0.65
-                Overhead                                                    30 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   0.08%          0.79        0.03
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-                Scope Total                                                                                                     985.12
 ```
   {{< /tab >}}
   {{< tab header="Moonshine FP32" >}}
@@ -74,7 +52,8 @@ Run the Moonshine manifest:
   -t 1 --enable-op-profiling
 ```
 
-The output is similar to the following captured report. This ONNX Runtime runner reports model timings without operator details:
+The output is similar to the following. The operator breakdown has been omitted for brevity.
+
 
 ```output
 [TRANSCRIPTION]
@@ -82,23 +61,15 @@ Ever tried ever failed, no matter try again fail again fail better.
 ----------------
 Scope: run() [total ms: 290.30]
 Scope stats | calls: 1 | avg ms: 290.30 | min ms: 290.30 | max ms: 290.30
-Scope Summary
-runner          contributor                                              calls                                         %      total ms      avg ms      min ms      max ms
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-onnxrt          encoder                                                      1 [██████████████████████░░░░░░░░]  74.69%        216.82      216.82      216.82      216.82
-onnxrt          decoder                                                     15 [████████░░░░░░░░░░░░░░░░░░░░░░]  25.21%         73.19        4.88        3.23       25.37
-                Overhead                                                     1 [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░]   0.10%          0.29        0.29
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-                Scope Total                                                                                                     290.30
 ```
   {{< /tab >}}
 {{< /tabpane-normal >}}
 
-These timings are based on a single 9.963-second audio sample. The timings exclude setup and warmup and include profiling overhead. This isn't an assessment of changes to transcription accuracy. 
+Compare the total `run()` time. Parakeet's 30 streaming calls complete one transcription compared with a single call for Moonshine.
 
-On an Apple M3 Pro, Moonshine takes 290.30 ms, while Parakeet takes 985.12 ms to process the audio sample. Compare total `run()` time: 30 Parakeet streaming calls complete one transcription, not 30 benchmark repetitions.
+These results are one example of how to compare the models; your timings will vary with your hardware and setup. On an Apple M3 Pro, Moonshine took 290.30 ms and Parakeet took 985.12 ms to process a single 9.963-second audio sample. The timings exclude setup and warmup, include profiling overhead, and do not assess transcription accuracy.
 
-Pass your own representative audio samples in a JSON file with `-j runs.json` to evaluate more inputs. The shared Python API and CLI let you quickly profile models across different runtimes. You can find the most suitable model for your application by changing manifests.
+Pass your own representative audio samples in a JSON file with `-j runs.json` to evaluate more inputs. The shared Python API and CLI let you quickly profile models across different runtimes and find the most suitable model for your application by changing manifests.
 
 ## What you've accomplished
 

From 911a15d8186dc43542c21e567e8b60828d268197 Mon Sep 17 00:00:00 2001
From: Usamah Zaheer 
Date: Mon, 2 Nov 2026 17:07:05 +0000
Subject: [PATCH 033/109] Fix Silero learner build limits and macOS FVP path

---
 .../silero-vad-ethos-u/3-run-on-fvp.md                    | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/3-run-on-fvp.md b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/3-run-on-fvp.md
index 9f65ac6f42..8f020a8b04 100644
--- a/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/3-run-on-fvp.md
+++ b/content/learning-paths/embedded-and-microcontrollers/silero-vad-ethos-u/3-run-on-fvp.md
@@ -18,12 +18,16 @@ Continue from the ExecuTorch repository root. Activate the environment and build
 ```bash
 source .venv/bin/activate
 source examples/arm/arm-scratch/setup_path.sh
+if [ "$(uname -s)" = Darwin ]; then
+  export PATH="$PWD/examples/arm/arm-scratch/FVPs-on-Mac/bin:$PATH"
+  export FVP_MOUNT_DIR="$PWD"
+fi
 
 cmake --preset arm-baremetal \
   -DCMAKE_BUILD_TYPE=Release \
   -DTARGET_CPU=cortex-m85 \
   -B cmake-out-arm -S .
-cmake --build cmake-out-arm --target install --parallel
+cmake --build cmake-out-arm --target install --parallel 2
 ```
 
 The installed libraries provide the ExecuTorch runtime, portable operators, and Ethos-U backend used by the application.
@@ -53,7 +57,7 @@ Build the configured application:
 
 ```bash
 cmake --build examples/arm/silero_vad_ethos_u \
-  --target silero_vad_ethos_u --parallel
+  --target silero_vad_ethos_u --parallel 2
 ```
 
 The build creates `examples/arm/silero_vad_ethos_u/silero_vad_ethos_u`. This ELF image contains both the `.pte` model and the 2.5-second validation clip.

From 88ba916e62a02595b6453dd348bf259722b68dd0 Mon Sep 17 00:00:00 2001
From: Usamah Zaheer 
Date: Mon, 2 Nov 2026 17:37:59 +0000
Subject: [PATCH 034/109] Pin the tested macOS FVP setup for MobileSAM

---
 .../prepare-the-environment.md                        | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md
index 8e9e8ea459..56b7056332 100644
--- a/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md
+++ b/content/learning-paths/embedded-and-microcontrollers/deploy-mobilesam-on-ethos-u/prepare-the-environment.md
@@ -98,10 +98,19 @@ The installer supplies CMake, the model export dependencies, and the example req
 ## Install the Arm development tools
 
 {{% notice Note %}}
-Before you run the Arm setup command on macOS, install [Docker Desktop](/install-guides/docker/docker-desktop/) and follow the [AVH FVPs on macOS install guide](/install-guides/fvps-on-macos/). Add the FVPs-on-Mac `bin` directory to `PATH`. The wrapper runs the Linux Corstone-320 FVP in a container. Confirm that Docker is running and that `FVP_Corstone_SSE-320` resolves to the wrapper:
+Before you run the Arm setup command on macOS, install [Docker Desktop](/install-guides/docker/docker-desktop/) and start Docker. Install the [FVPs-on-Mac wrappers](https://github.com/Arm-Examples/FVPs-on-Mac) at the revision for FVP `11.31.28`. The wrapper runs the Linux Corstone-320 FVP in a container. Run these commands from the ExecuTorch repository root:
 
 ```bash
 docker info >/dev/null
+mkdir -p examples/arm/arm-scratch "$HOME/.armlm"
+git clone https://github.com/Arm-Examples/FVPs-on-Mac.git \
+  examples/arm/arm-scratch/FVPs-on-Mac
+git -C examples/arm/arm-scratch/FVPs-on-Mac checkout --detach \
+  cc7a4be5456db085619647811fac29e9795f806e
+bash examples/arm/arm-scratch/FVPs-on-Mac/build.sh
+export PATH="$PWD/examples/arm/arm-scratch/FVPs-on-Mac/bin:$PATH"
+export FVP_MOUNT_DIR="$PWD"
+FVP_Corstone_SSE-320 --version
 command -v FVP_Corstone_SSE-320
 ```
 {{% /notice %}}

From 89fdcceb77044388ff6c546c48e09b0b00f4647b Mon Sep 17 00:00:00 2001
From: anupras-mohapatra-arm 
Date: Mon, 2 Nov 2026 12:29:14 -0600
Subject: [PATCH 035/109] c2patool first pass

---
 content/install-guides/c2patool.md | 26 ++++++++++++--------------
 1 file changed, 12 insertions(+), 14 deletions(-)

diff --git a/content/install-guides/c2patool.md b/content/install-guides/c2patool.md
index 6806971709..dee0e6c87d 100644
--- a/content/install-guides/c2patool.md
+++ b/content/install-guides/c2patool.md
@@ -5,8 +5,6 @@ minutes_to_complete: 20
 official_docs: https://github.com/contentauth/c2patool
 author: Kostiantyn Perehuda
 
-draft: true
-
 test_maintenance: false
 
 ### FIXED, DO NOT MODIFY
@@ -17,19 +15,13 @@ multitool_install_part: false
 layout: installtoolsall
 ---
 
-## Overview
+Content Credentials use the Coalition for Content Provenance and Authenticity (C2PA) standard to record media provenance. [C2PA Tool](https://github.com/contentauth/c2patool), invoked as `c2patool`, reads, creates, and validates Content Credentials in supported media files. 
 
-[C2PA Tool](https://github.com/contentauth/c2patool), invoked as `c2patool`, reads, creates, and
-validates Content Credentials in supported media files. Content Credentials use the
-Coalition for Content Provenance and Authenticity (C2PA) standard to record media provenance.
+You can install C2PA Tool on 64-bit Arm Linux, macOS on Apple silicon, or Windows on Arm. The Linux instructions in this guide cover Ubuntu and Debian.
 
-You can install C2PA Tool on 64-bit Arm Linux, macOS on Apple silicon, or Windows on Arm.
-The Linux instructions cover Ubuntu and Debian.
+On macOS, you'll install C2PA Tool with Homebrew. 
 
-On macOS, install C2PA Tool with Homebrew. On Arm Linux and Windows on Arm, the following
-steps use Cargo, Rust's package manager, to download the source code, compile it for Arm,
-and install the executable. Building from source produces an executable that runs natively
-on your Arm system.
+On Arm Linux and Windows on Arm, you'll use Cargo, the Rust package manager. With Cargo, you'll download the source code, compile it for Arm, and install the executable. Building from source produces an executable that runs natively on your Arm system.
 
 Homebrew and Cargo install the version available from their package source. For release
 information, see [C2PA Tool releases](https://github.com/contentauth/c2patool/releases).
@@ -76,7 +68,9 @@ Open a new PowerShell window and confirm that 7-Zip is available:
 7z -h
 ```
 
-The output displays the 7-Zip version and command usage. Then build and install C2PA Tool from source:
+The output displays the 7-Zip version and command usage. 
+
+Then, build and install C2PA Tool from source:
 
 ```console
 cargo install c2patool --locked
@@ -106,7 +100,7 @@ The expected output starts with:
 Tool for displaying and creating C2PA manifests.
 ```
 
-## Troubleshooting
+## Troubleshoot installation issues
 
 If your shell can't find `c2patool`, open a new terminal. For a Cargo installation, confirm that
 `$HOME/.cargo/bin` on Linux or `%USERPROFILE%\.cargo\bin` on Windows is in your `PATH`.
@@ -139,3 +133,7 @@ For a Cargo installation, run:
 ```console
 cargo uninstall c2patool
 ```
+
+## Next steps
+
+You can now use C2PA Tool to work with Content Credentials on Arm using a CLI. 
\ No newline at end of file

From e4c4c138c2b5f8cc8b5515cb542ba9062e600284 Mon Sep 17 00:00:00 2001
From: anupras-mohapatra-arm 
Date: Mon, 2 Nov 2026 12:37:51 -0600
Subject: [PATCH 036/109] assisted edits

---
 content/install-guides/c2patool.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/content/install-guides/c2patool.md b/content/install-guides/c2patool.md
index dee0e6c87d..160973d52d 100644
--- a/content/install-guides/c2patool.md
+++ b/content/install-guides/c2patool.md
@@ -21,7 +21,7 @@ You can install C2PA Tool on 64-bit Arm Linux, macOS on Apple silicon, or Window
 
 On macOS, you'll install C2PA Tool with Homebrew. 
 
-On Arm Linux and Windows on Arm, you'll use Cargo, the Rust package manager. With Cargo, you'll download the source code, compile it for Arm, and install the executable. Building from source produces an executable that runs natively on your Arm system.
+On Arm Linux and Windows on Arm, use Cargo, the Rust package manager, to install C2PA Tool. With Cargo, you'll download the source code and compile it for Arm. This produces an executable for your Arm platform.
 
 Homebrew and Cargo install the version available from their package source. For release
 information, see [C2PA Tool releases](https://github.com/contentauth/c2patool/releases).
@@ -136,4 +136,4 @@ cargo uninstall c2patool
 
 ## Next steps
 
-You can now use C2PA Tool to work with Content Credentials on Arm using a CLI. 
\ No newline at end of file
+You can now use the C2PA Tool to work with Content Credentials on Arm. 
\ No newline at end of file

From fc245d1fed2e1598dfdc81a39884b6ddb7db38cf Mon Sep 17 00:00:00 2001
From: "BarbaraC@Arm" 
Date: Mon, 2 Nov 2026 10:44:04 -0800
Subject: [PATCH 037/109] Update Scenario Runner compatibility and CLI commands

---
 .../vulkan-ml-sample/2-ml-ext-for-vulkan.md    |  2 +-
 .../vulkan-ml-sample/4-scenario-runner.md      | 18 +++++++++++++-----
 2 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md
index c6730b8e46..2d897573ea 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md
@@ -18,7 +18,7 @@ This setup involves three main steps:
 
 ## Set up the development environment
 
-Open **Windows PowerShell** from the Windows 11 Start menu. Verify that Python 3.10 or later is installed, then navigate to your user home directory:
+Open **Windows PowerShell** from the Windows 11 Start menu. Verify that Python 3.10 or later is installed. Use a standard CPython build rather than a free-threaded build, then navigate to your user home directory:
 
 ```powershell
 python --version
diff --git a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md
index 10216faf89..7cbf57019d 100644
--- a/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md
+++ b/content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/4-scenario-runner.md
@@ -27,7 +27,7 @@ The following table summarizes the main SDK components and their roles.
 | **Emulation Layer** | Vulkan layer that emulates data graph and tensor extensions using compute shaders.                      | Test on devices without native support for ML Extensions for Vulkan | https://github.com/arm/ai-ml-emulation-layer-for-vulkan |
 | **ML Workload Library for Vulkan** | C++ library for creating and executing ML workloads using Vulkan. | Integrate ML workloads into Vulkan applications | https://github.com/arm/ai-ml-workload-library-for-vulkan |
 
-Vulkan Graph Format (VGF) is a format for packaging machine learning graphs and their associated data for execution with Vulkan. It brings together the graph definition, shaders, and model constants in a single file, making it easier to distribute and load prebuilt ML workloads.
+VGF is a format for packaging machine learning graphs and their associated data for execution with Vulkan. It brings together the graph definition, shaders, and model constants in a single file, making it easier to distribute and load prebuilt ML workloads.
 
 For this exercise, Scenario Runner uses the VGF Library to load a prebuilt `.vgf` model and executes the workload described in a JSON scenario file through the configured ML Emulation Layers.
 
@@ -76,16 +76,24 @@ If the virtual environment is not already active, activate it:
 & "$HOME\.venv\Scripts\Activate.ps1"
 ```
 
-Install Scenario Runner using pip:
+Install Scenario Runner:
 
 ```powershell
-python -m pip install ai-ml-sdk-scenario-runner
+python -m pip install "ai-ml-sdk-scenario-runner>=0.11.0"
+```
+
+This installs the latest compatible version of Scenario Runner, starting from v0.11.0. If no compatible version is available for your Python environment, the installation will fail rather than selecting an older release.
+
+Verify the installed version:
+
+```powershell
+python -m pip show ai-ml-sdk-scenario-runner
 ```
 
 Verify that Scenario Runner is available:
 
 ```powershell
-scenario-runner --help
+scenario_runner --help
 ```
 
 ### Run the Neural Super Sampling scenario
@@ -99,7 +107,7 @@ cd "$HOME\neural-super-sampling\scenario"
 Run Scenario Runner using the supplied JSON configuration:
 
 ```powershell
-scenario-runner --scenario configs/960x540_1920x1080_high_fragment.json --output out_high
+scenario_runner --scenario configs/960x540_1920x1080_high_fragment.json --output out_high
 ```
 
 The configuration specifies a Neural Super Sampling workload that upscales an input frame from 960 × 540 to 1920 × 1080.

From 5a01fdac1a209b1db37a3ee84e65199d95ca7a91 Mon Sep 17 00:00:00 2001
From: anupras-mohapatra-arm 
Date: Mon, 2 Nov 2026 12:53:42 -0600
Subject: [PATCH 038/109] exiftool first pass

---
 content/install-guides/exiftool.md | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/content/install-guides/exiftool.md b/content/install-guides/exiftool.md
index 74c98b90d1..a2d0056ed1 100644
--- a/content/install-guides/exiftool.md
+++ b/content/install-guides/exiftool.md
@@ -5,8 +5,6 @@ minutes_to_complete: 10
 official_docs: https://exiftool.org/install.html
 author: Kostiantyn Perehuda
 
-draft: true
-
 test_maintenance: false
 
 ### FIXED, DO NOT MODIFY
@@ -17,14 +15,12 @@ multitool_install_part: false
 layout: installtoolsall
 ---
 
-## Overview
-
 [ExifTool](https://exiftool.org/) reads and writes metadata in image, audio, and video files.
 You can install it on macOS on Apple silicon, 64-bit Arm Linux, or Windows 11 on Arm.
-On Windows on Arm, you use the Windows x64 executable under emulation.
+On Windows on Arm, you'll use the Windows x64 executable under emulation.
 
-Package-manager commands install the version available from your package source. See the
-[ExifTool download page](https://exiftool.org/) for current releases.
+Package-manager commands install the version available from your package source. For current releases, see the
+[ExifTool download page](https://exiftool.org/).
 
 ## Install on macOS
 
@@ -62,8 +58,11 @@ Follow the [official Windows installation instructions](https://exiftool.org/ins
 2. Extract the archive and open the extracted folder containing `exiftool(-k).exe`.
 3. In File Explorer, select **View > Show > File name extensions**, then rename `exiftool(-k).exe` to `exiftool.exe`.
 4. Move `exiftool.exe` and its accompanying `exiftool_files` folder into a permanent folder, such as `C:\Users\\Tools\ExifTool`. Keep them together.
-5. Search the Start menu for **Edit environment variables for your account** and open it. Under **User variables**, select **Path**, then **Edit > New**. Add the full path to the folder directly containing `exiftool.exe`, such as `C:\Users\\Tools\ExifTool`, replacing `` with your Windows user folder name. Select **OK** to save and close the dialogs.
-6. Open a new PowerShell window so it picks up the change.
+5. Search the Start menu for **Edit environment variables for your account** and open it. 
+6. Under **User variables**, select **Path**, then **Edit > New**. 
+7. Add the full path to the folder directly containing `exiftool.exe`, such as `C:\Users\\Tools\ExifTool`. Replace `` with the name of your Windows user folder. 
+8. Select **OK** to save and close the dialogs.
+9. Open a new PowerShell window so it picks up the change.
 
 ## Verify the installation
 
@@ -79,7 +78,7 @@ The output is similar to:
 13.55
 ```
 
-Your version can differ. List the available metadata tags to confirm ExifTool runs:
+Your version can differ. List the available metadata tags to confirm that ExifTool runs:
 
 ```console
 exiftool -list
@@ -87,11 +86,15 @@ exiftool -list
 
 The output begins with `Available tags:` followed by tag names.
 
-## Troubleshooting
+## Troubleshoot installation issues
 
 If your shell can't find `exiftool`, open a new terminal and confirm that the directory containing
 `exiftool` is in your `PATH`. On Windows, also check that the executable is named `exiftool.exe`
 and that `exiftool_files` is still beside it.
 
-If Ubuntu cannot locate the package, check that the `universe` repository is enabled for your Ubuntu
-release, then run `sudo apt update` again.
+If Ubuntu can't locate the package, check that the `universe` repository is enabled for your Ubuntu
+release. After checking, run `sudo apt update` again.
+
+## Next steps
+
+You can now use ExifTool to to read and write media metadata on Arm.

From c82d48050de3e450860ced026cffcb0a1012573b Mon Sep 17 00:00:00 2001
From: anupras-mohapatra-arm 
Date: Mon, 2 Nov 2026 13:29:41 -0600
Subject: [PATCH 039/109] nit

---
 content/install-guides/exiftool.md | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/content/install-guides/exiftool.md b/content/install-guides/exiftool.md
index a2d0056ed1..8773f4c8f8 100644
--- a/content/install-guides/exiftool.md
+++ b/content/install-guides/exiftool.md
@@ -16,7 +16,7 @@ layout: installtoolsall
 ---
 
 [ExifTool](https://exiftool.org/) reads and writes metadata in image, audio, and video files.
-You can install it on macOS on Apple silicon, 64-bit Arm Linux, or Windows 11 on Arm.
+You can install ExifTool on macOS on Apple silicon, 64-bit Arm Linux, or Windows 11 on Arm.
 On Windows on Arm, you'll use the Windows x64 executable under emulation.
 
 Package-manager commands install the version available from your package source. For current releases, see the
@@ -88,9 +88,8 @@ The output begins with `Available tags:` followed by tag names.
 
 ## Troubleshoot installation issues
 
-If your shell can't find `exiftool`, open a new terminal and confirm that the directory containing
-`exiftool` is in your `PATH`. On Windows, also check that the executable is named `exiftool.exe`
-and that `exiftool_files` is still beside it.
+If your shell can't find `exiftool`, open a new terminal. After opening the terminal, confirm that the directory containing
+`exiftool` is in your `PATH`. On Windows, also check that the executable is named `exiftool.exe` and that `exiftool_files` is still beside it.
 
 If Ubuntu can't locate the package, check that the `universe` repository is enabled for your Ubuntu
 release. After checking, run `sudo apt update` again.

From 08700d2d711ef40f09eba2110c7761dbeb19ef86 Mon Sep 17 00:00:00 2001
From: anupras-mohapatra-arm 
Date: Mon, 2 Nov 2026 13:47:31 -0600
Subject: [PATCH 040/109] edits

---
 content/install-guides/exiftool.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/content/install-guides/exiftool.md b/content/install-guides/exiftool.md
index 8773f4c8f8..d2d189a019 100644
--- a/content/install-guides/exiftool.md
+++ b/content/install-guides/exiftool.md
@@ -58,11 +58,11 @@ Follow the [official Windows installation instructions](https://exiftool.org/ins
 2. Extract the archive and open the extracted folder containing `exiftool(-k).exe`.
 3. In File Explorer, select **View > Show > File name extensions**, then rename `exiftool(-k).exe` to `exiftool.exe`.
 4. Move `exiftool.exe` and its accompanying `exiftool_files` folder into a permanent folder, such as `C:\Users\\Tools\ExifTool`. Keep them together.
-5. Search the Start menu for **Edit environment variables for your account** and open it. 
-6. Under **User variables**, select **Path**, then **Edit > New**. 
+5. Search the **Start** menu for **Edit environment variables for your account** and open it. 
+6. Under **User variables**, select **Path**, then select **Edit > New**. 
 7. Add the full path to the folder directly containing `exiftool.exe`, such as `C:\Users\\Tools\ExifTool`. Replace `` with the name of your Windows user folder. 
 8. Select **OK** to save and close the dialogs.
-9. Open a new PowerShell window so it picks up the change.
+9. Open a new PowerShell window so that it picks up the change.
 
 ## Verify the installation
 
@@ -88,7 +88,7 @@ The output begins with `Available tags:` followed by tag names.
 
 ## Troubleshoot installation issues
 
-If your shell can't find `exiftool`, open a new terminal. After opening the terminal, confirm that the directory containing
+If your shell can't find `exiftool`, open a new terminal. Confirm that the directory containing
 `exiftool` is in your `PATH`. On Windows, also check that the executable is named `exiftool.exe` and that `exiftool_files` is still beside it.
 
 If Ubuntu can't locate the package, check that the `universe` repository is enabled for your Ubuntu
@@ -96,4 +96,4 @@ release. After checking, run `sudo apt update` again.
 
 ## Next steps
 
-You can now use ExifTool to to read and write media metadata on Arm.
+You can now use ExifTool to read and write media metadata on Arm.

From 4b3ccb76aa73c1c68e3d05440ce9c9450df10307 Mon Sep 17 00:00:00 2001
From: anupras-mohapatra-arm 
Date: Mon, 2 Nov 2026 13:54:43 -0600
Subject: [PATCH 041/109] edits

---
 content/install-guides/c2patool.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/content/install-guides/c2patool.md b/content/install-guides/c2patool.md
index 160973d52d..763a5bf3cb 100644
--- a/content/install-guides/c2patool.md
+++ b/content/install-guides/c2patool.md
@@ -59,7 +59,7 @@ Build C2PA Tool from source with the ARM64 Rust toolchain to install a native AR
 on Windows on Arm.
 
 Install the Microsoft C++ Build Tools and [Rust](https://www.rust-lang.org/tools/install) with the
-MSVC toolchain for ARM64. Install [7-Zip](https://www.7-zip.org/) and add its installation
+Microsoft Visual C++ (MSVC) toolchain for ARM64. Install [7-Zip](https://www.7-zip.org/) and add its installation
 directory (typically `C:\Program Files\7-Zip`) to your `PATH`.
 
 Open a new PowerShell window and confirm that 7-Zip is available:

From afd2d981dc0b24584117fcb9689a0f0787028d5b Mon Sep 17 00:00:00 2001
From: Jason Andrews 
Date: Mon, 2 Nov 2026 14:38:34 -0600
Subject: [PATCH 042/109] work on early access search feature

---
 assets/search-lab/app.js         |  42 ++++++++----
 assets/search-lab/engine.mjs     |  61 +++++++++++++----
 assets/search-lab/style.css      |   2 +-
 layouts/search-lab/list.html     |   2 +-
 tools/search-lab/README.md       |  31 ++++-----
 tools/search-lab/WEBMCP_DEMO.md  |  48 +++++---------
 tools/search-lab/app.test.mjs    | 109 +++++++++++++++++++++++++++++++
 tools/search-lab/engine.test.mjs |  60 +++++++++++++++--
 8 files changed, 271 insertions(+), 84 deletions(-)
 create mode 100644 tools/search-lab/app.test.mjs

diff --git a/assets/search-lab/app.js b/assets/search-lab/app.js
index 82ebaef01d..2be6fe4937 100644
--- a/assets/search-lab/app.js
+++ b/assets/search-lab/app.js
@@ -1,35 +1,47 @@
 const $ = id => document.getElementById(id);
 const main = document.querySelector('main[data-index]');
-const worker = new Worker(main.dataset.worker, { type: 'module' });
+let worker, workerFailure;
 let nextId = 0, generation = 0, limit = 10;
 const pending = new Map();
-worker.onmessage = ({ data }) => {
-  const request = pending.get(data.id);
-  if (!request) return;
-  pending.delete(data.id);
-  data.error ? request.reject(Error(data.error)) : request.resolve(data.result);
-};
-worker.onerror = () => {
-  for (const request of pending.values()) request.reject(Error('Search worker failed. Reload the page to retry.'));
+function failWorker() {
+  workerFailure = Error('Search is unavailable. Reload the page to retry.');
+  worker?.terminate();
+  ++generation;
+  for (const request of pending.values()) request.reject(workerFailure);
   pending.clear();
-  $('status').textContent = 'Search could not start. Reload the page to retry.';
-};
+  $('more').hidden = true;
+  $('search-diagnostics').hidden = true;
+  $('status').textContent = workerFailure.message;
+}
+try {
+  worker = new Worker(main.dataset.worker, { type: 'module' });
+  worker.onmessage = ({ data }) => {
+    const request = pending.get(data.id);
+    if (!request) return;
+    pending.delete(data.id);
+    data.error ? request.reject(Error(data.error)) : request.resolve(data.result);
+  };
+  worker.onerror = failWorker;
+  worker.onmessageerror = failWorker;
+} catch { failWorker(); }
 function call(action, input) {
+  if (workerFailure) return Promise.reject(workerFailure);
   return new Promise((resolve, reject) => {
     const id = ++nextId;
     pending.set(id, { resolve, reject });
-    worker.postMessage({ id, action, input, url: main.dataset.index });
+    try { worker.postMessage({ id, action, input, url: main.dataset.index }); }
+    catch { failWorker(); }
   });
 }
 const ready = call('init');
 const tool = {
   name: 'search_arm_content',
-  description: 'Find Arm Learning Paths and install guides by keyword or author name. Returns source URLs and metadata excerpts; use these sources to read the full instructions. Scores indicate keyword relevance, not confidence.',
+  description: 'Find Arm Learning Paths and install guides by keyword or author name, with word prefixes and limited typo tolerance. Relevance defaults to complete query matches first. Returns source URLs and metadata excerpts; use these sources to read the full instructions. Scores indicate keyword relevance, not confidence.',
   inputSchema: { type: 'object', properties: {
     query: { type: 'string', minLength: 1, maxLength: 300 },
     type: { type: 'string', enum: ['', 'learning-path', 'install-guide'], default: '' },
     category: { type: 'string', description: 'Learning Path category slug, such as servers-and-cloud-computing.' },
-    sort: { type: 'string', enum: ['updated', 'relevance'], default: 'updated' },
+    sort: { type: 'string', enum: ['relevance', 'updated'], default: 'relevance' },
     limit: { type: 'integer', minimum: 1, maximum: 100, default: 10 }
   }, required: ['query'], additionalProperties: false },
   annotations: { readOnlyHint: true },
@@ -76,6 +88,7 @@ async function render() {
   $('results').replaceChildren();
   $('more').hidden = true;
   $('search-diagnostics').hidden = true;
+  if (workerFailure) { $('status').textContent = workerFailure.message; return; }
   if (!query) { $('status').textContent = ''; return; }
   $('status').textContent = 'Searching…';
   const start = performance.now();
@@ -87,6 +100,7 @@ async function render() {
     $('performance').textContent = `Last search: ${result.searchMs.toFixed(1)} ms engine · ${(performance.now() - start).toFixed(1)} ms response`;
     $('search-diagnostics').hidden = false;
     if (!result.total) $('status').textContent += '. Try fewer words or remove a filter.';
+    if (result.total > 100) $('status').textContent += `. Showing ${result.results.length} of the first 100 results. Narrow your search to find other matches.`;
     for (const record of result.results) {
       const li = document.createElement('li'); li.className = 'search-result-card';
       const a = document.createElement('a'); a.href = record.url; a.textContent = record.title;
diff --git a/assets/search-lab/engine.mjs b/assets/search-lab/engine.mjs
index 486f939c0a..0bb326c5fd 100644
--- a/assets/search-lab/engine.mjs
+++ b/assets/search-lab/engine.mjs
@@ -3,6 +3,18 @@ const stop = new Set('a an and are at be can do for from how i in is it me my of
 export function tokenize(text) {
   return String(text).toLowerCase().match(/[\p{L}\p{N}]+(?:[.+#-][\p{L}\p{N}+#]+)*/gu) || [];
 }
+// One insertion, deletion, substitution, or adjacent transposition.
+function oneEditAway(a, b) {
+  if (Math.abs(a.length - b.length) > 1) return false;
+  let i = 0;
+  while (i < a.length && a[i] === b[i]) i++;
+  if (i === a.length) return b.length === a.length + 1;
+  if (a.length === b.length) {
+    return a.slice(i + 1) === b.slice(i + 1)
+      || (a[i] === b[i + 1] && a[i + 1] === b[i] && a.slice(i + 2) === b.slice(i + 2));
+  }
+  return a.length > b.length ? a.slice(i + 1) === b.slice(i) : a.slice(i) === b.slice(i + 1);
+}
 export function createEngine(records) {
   const fields = [['title', 5], ['description', 3], ['authors', 3], ['metadata', 2], ['prerequisites', 2], ['summary', 1], ['faq', 1]];
   const docs = records.map(record => ({ record, fields: fields.map(([key, boost]) => {
@@ -18,10 +30,11 @@ export function createEngine(records) {
     const terms = new Set(doc.fields.flatMap(f => [...f.counts.keys()]));
     for (const term of terms) df.set(term, (df.get(term) || 0) + 1);
   }
+  const vocabulary = [...df.keys()];
   return function search(input = {}) {
     if (!input || typeof input !== 'object' || Array.isArray(input)) throw Error('Arguments must be an object.');
     if (Object.keys(input).some(key => !['query', 'type', 'category', 'limit', 'sort'].includes(key))) throw Error('Unknown search argument.');
-    const { query, type = '', category = '', limit = 10, sort = 'updated' } = input;
+    const { query, type = '', category = '', limit = 10, sort = 'relevance' } = input;
     if (typeof query !== 'string' || !query.trim() || query.length > 300) throw Error('Query must contain 1–300 characters.');
     if (!['', 'learning-path', 'install-guide'].includes(type)) throw Error('Unknown content type.');
     if (typeof category !== 'string' || (category && !records.some(r => r.category === category))) throw Error('Unknown category.');
@@ -29,31 +42,53 @@ export function createEngine(records) {
     if (!['updated', 'relevance'].includes(sort)) throw Error('Unknown sort order.');
     const terms = [...new Set(tokenize(query).filter(t => !stop.has(t)))];
     if (!terms.length) return { total: 0, results: [] };
+    const expansions = terms.map(term => {
+      const candidates = [{ token: term, weight: 1 }];
+      if (/^\p{L}{3,}$/u.test(term)) {
+        for (const token of vocabulary) {
+          if (token !== term && token.startsWith(term)) candidates.push({ token, weight: 0.65 });
+        }
+        // Correct a typo only when there is no exact token or prefix completion.
+        if (!df.has(term) && candidates.length === 1 && /^\p{L}{5,}$/u.test(term)) {
+          for (const token of vocabulary) {
+            if (/^\p{L}{5,}$/u.test(token) && oneEditAway(term, token)) candidates.push({ token, weight: 0.4 });
+          }
+        }
+      }
+      return candidates.map(candidate => ({ ...candidate,
+        idf: Math.log(1 + (docs.length - (df.get(candidate.token) || 0) + 0.5) / ((df.get(candidate.token) || 0) + 0.5))
+      }));
+    });
     const ranked = [];
     for (const doc of docs) {
       if ((type && doc.record.type !== type) || (category && doc.record.category !== category)) continue;
-      let score = 0, matched = 0;
-      for (const term of terms) {
+      let score = 0, matched = 0, exact = 0;
+      for (const candidates of expansions) {
         let termScore = 0;
-        const idf = Math.log(1 + (docs.length - (df.get(term) || 0) + 0.5) / ((df.get(term) || 0) + 0.5));
+        let exactMatch = false;
         doc.fields.forEach((field, i) => {
-          const tf = field.counts.get(term) || 0;
-          termScore += field.boost * idf * tf * 2.2 / (tf + 1.2 * (0.25 + 0.75 * field.length / averages[i]));
+          let best = 0;
+          for (const { token, weight, idf } of candidates) {
+            const tf = field.counts.get(token) || 0;
+            if (weight === 1 && tf) exactMatch = true;
+            best = Math.max(best, weight * field.boost * idf * tf * 2.2 / (tf + 1.2 * (0.25 + 0.75 * field.length / averages[i])));
+          }
+          termScore += best;
         });
         if (termScore) matched++;
+        if (exactMatch) exact++;
         score += termScore;
       }
       // Reward coverage so long natural-language queries can still find partial matches.
-      if (matched) ranked.push({ ...doc.record, score: score * (matched / terms.length) ** 2 });
+      if (matched) ranked.push({ record: { ...doc.record, score: score * (matched / terms.length) ** 2 },
+        tier: exact === terms.length ? 2 : matched === terms.length ? 1 : 0 });
     }
     ranked.sort((a, b) => {
       // ISO date strings sort chronologically; missing dates follow dated results.
-      const dateOrder = sort === 'updated' ? (b.lastUpdated || '').localeCompare(a.lastUpdated || '') : 0;
-      const typeOrder = sort === 'relevance'
-        ? Number(b.type === 'install-guide') - Number(a.type === 'install-guide')
-        : 0;
-      return dateOrder || typeOrder || b.score - a.score || a.id.localeCompare(b.id);
+      const dateOrder = sort === 'updated' ? (b.record.lastUpdated || '').localeCompare(a.record.lastUpdated || '') : 0;
+      const coverageOrder = sort === 'relevance' ? b.tier - a.tier : 0;
+      return dateOrder || coverageOrder || b.record.score - a.record.score || a.record.id.localeCompare(b.record.id);
     });
-    return { total: ranked.length, results: ranked.slice(0, limit) };
+    return { total: ranked.length, results: ranked.slice(0, limit).map(item => item.record) };
   };
 }
diff --git a/assets/search-lab/style.css b/assets/search-lab/style.css
index 51ea9550e7..346e4d3f06 100644
--- a/assets/search-lab/style.css
+++ b/assets/search-lab/style.css
@@ -79,7 +79,7 @@ html[theme="dark"] .browser-search {
 .browser-search pre { white-space: pre-wrap; overflow-wrap: anywhere; max-height: 450px; overflow: auto; background: var(--search-field); color: inherit; border-radius: 4px; }
 .browser-search pre:not(:empty) { border: 1px solid var(--search-border); padding: 16px; }
 @media (max-width: 760px) {
-  .browser-search { padding: 28px 0 40px; }
+  .browser-search { padding: 28px 16px 40px; }
   .browser-search .filters { grid-template-columns: 1fr; gap: 16px; }
   .browser-search .search-panel, .browser-search .search-result-card { padding: 20px; }
 }
diff --git a/layouts/search-lab/list.html b/layouts/search-lab/list.html
index eaeb8746ae..c60069cb51 100644
--- a/layouts/search-lab/list.html
+++ b/layouts/search-lab/list.html
@@ -78,7 +78,7 @@ 
      Try an alternative search
      
 
      Try:
      
 
      
 
      
-
      
+
      
 
      
 
      Loading search index…
      
 
 
      
 
      Loading search index…