madeline-underwood/arm-learning-paths
Nov 2024 – Jun 2026
4 Months active
Languages Used
BashCGradleKotlinMarkdown
Technical Skills
Android DevelopmentEmbedded SystemsMachine LearningMobile DevelopmentPerformance ProfilingBuild Configuration
Ben Clark
PROFILE
Ben Clark
Contributed to the madeline-underwood/arm-learning-paths repository by designing and documenting learning paths that guide Android developers through integrating AI features such as on-device coaching, chatbots, and model profiling. Leveraged Kotlin, Gradle, and Docker to implement Android applications with AI capabilities, including CameraX and MediaPipe for pose detection, LLM integration, and text-to-speech workflows. Enhanced developer experience by automating build scripts, standardizing development environments, and clarifying onboarding documentation. Focused on performance profiling for ML models using ArmNN and ExecuTorch, providing concrete examples and step-by-step guides. Prioritized clear technical writing and educational content to accelerate adoption and streamline cross-team collaboration.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
7
Lines of code
4,509
Activity Months4
Your Network
712 peopleSame Organization
@arm.com573
Work History
June 2026
4 Commits • 2 Features
Jun 1, 2026June 2026 monthly summary highlighting key achievements in madeline-underwood/arm-learning-paths. Focused on delivering features, establishing a scalable developer experience, and setting up an on-device coaching pathway.
4 Commits • 2 Features
Jun 1, 2026June 2026 monthly summary highlighting key achievements in madeline-underwood/arm-learning-paths. Focused on delivering features, establishing a scalable developer experience, and setting up an on-device coaching pathway.
June 2026
April 2026
3 Commits • 1 Features
Apr 1, 2026Concise monthly summary for April 2026 focused on the Android Arm AI Chat Integration Learning Path in the madeline-underwood/arm-learning-paths repo. Delivered a comprehensive learning path that enables Android developers to integrate Arm's AI Chat library, covering project setup, library integration, UI/UX considerations, and model usage. Updated documentation to rename terminology from Beginner to Introductory and clarified AndroidManifest.xml modification steps to reduce onboarding time and common integration pitfalls. Documentation improvements were validated through peer review and committed alongside feature work.
April 2026
3 Commits • 1 Features
Apr 1, 2026Concise monthly summary for April 2026 focused on the Android Arm AI Chat Integration Learning Path in the madeline-underwood/arm-learning-paths repo. Delivered a comprehensive learning path that enables Android developers to integrate Arm's AI Chat library, covering project setup, library integration, UI/UX considerations, and model usage. Updated documentation to rename terminology from Beginner to Introductory and clarified AndroidManifest.xml modification steps to reduce onboarding time and common integration pitfalls. Documentation improvements were validated through peer review and committed alongside feature work.
January 2025
3 Commits • 3 Features
Jan 1, 2025January 2025 (2025-01) performance summary for madeline-underwood/arm-learning-paths. This month focused on delivering critical profiling capabilities, improving build tooling compatibility, and clarifying onboarding prerequisites to accelerate performance analysis and optimization workflows. Key accomplishments include the delivery of an Android ExecuTorch Profiling Guide to enable end-to-end profiling on Android devices, the addition of Gradle/Kotlin DSL Build Script Compatibility to allow builds with both Groovy and Kotlin DSLs (improving compatibility with newer Gradle versions), and an update to Learning Path Prerequisites to include ExecuTorch as an alternative profiling target to Arm NN. These items collectively reduce setup friction, expand supported environments, and shorten time-to-insight for model profiling. Bugs: No critical bugs fixed this month. Minor documentation text fixes were applied to improve clarity around profiling workflows and Arm NN descriptions. Impact: The changes strengthen our profiling workflow, streamline cross-DSL Gradle configurations, and improve developer onboarding. This supports faster performance tuning, better device-level visibility, and a more robust path for teams adopting ExecuTorch on Android and related ML workloads. Technologies/Skills demonstrated: Android profiling workflows (ExecuTorch, ETDump, ExecuTorch Inspector), cross-DSL Gradle configuration (Groovy and Kotlin DSLs), Gradle script compatibility, documentation authoring and iteration, ML inference profiling concepts.
3 Commits • 3 Features
Jan 1, 2025January 2025 (2025-01) performance summary for madeline-underwood/arm-learning-paths. This month focused on delivering critical profiling capabilities, improving build tooling compatibility, and clarifying onboarding prerequisites to accelerate performance analysis and optimization workflows. Key accomplishments include the delivery of an Android ExecuTorch Profiling Guide to enable end-to-end profiling on Android devices, the addition of Gradle/Kotlin DSL Build Script Compatibility to allow builds with both Groovy and Kotlin DSLs (improving compatibility with newer Gradle versions), and an update to Learning Path Prerequisites to include ExecuTorch as an alternative profiling target to Arm NN. These items collectively reduce setup friction, expand supported environments, and shorten time-to-insight for model profiling. Bugs: No critical bugs fixed this month. Minor documentation text fixes were applied to improve clarity around profiling workflows and Arm NN descriptions. Impact: The changes strengthen our profiling workflow, streamline cross-DSL Gradle configurations, and improve developer onboarding. This supports faster performance tuning, better device-level visibility, and a more robust path for teams adopting ExecuTorch on Android and related ML workloads. Technologies/Skills demonstrated: Android profiling workflows (ExecuTorch, ETDump, ExecuTorch Inspector), cross-DSL Gradle configuration (Groovy and Kotlin DSLs), Gradle script compatibility, documentation authoring and iteration, ML inference profiling concepts.
January 2025
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for madeline-underwood/arm-learning-paths focusing on profiling ML models on Arm devices. Delivered enhanced documentation and tooling guidance with a concrete example application, clarifying the workflow for profiling tflite models using ArmNN, and integrating custom annotations via Streamline. Improved learning-path clarity on how to execute, interpret, and reproduce profiling results, aimed at accelerating performance optimization for Arm-based deployments.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for madeline-underwood/arm-learning-paths focusing on profiling ML models on Arm devices. Delivered enhanced documentation and tooling guidance with a concrete example application, clarifying the workflow for profiling tflite models using ArmNN, and integrating custom annotations via Streamline. Improved learning-path clarity on how to execute, interpret, and reproduce profiling results, aimed at accelerating performance optimization for Arm-based deployments.
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Quality Metrics
Correctness96.4%
Maintainability94.6%
Architecture96.4%
Performance87.2%
AI Usage43.6%
Skills & Technologies
Programming Languages
BashCGradleKotlinMarkdown
Technical Skills
AI integrationAndroidAndroid DevelopmentAndroid developmentBuild ConfigurationCameraXDevOpsDockerDocumentationEmbedded SystemsFull Stack DevelopmentGitHub ActionsGradleHugoKotlin
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