madeline-underwood/arm-learning-paths
Nov 2024 – Apr 2026
3 Months active
Languages Used
BashCGradleKotlinMarkdown
Technical Skills
Android DevelopmentEmbedded SystemsMachine LearningMobile DevelopmentPerformance ProfilingBuild Configuration
Ben Clark
PROFILE
Ben Clark
Ben Clark developed and documented advanced profiling and AI integration workflows in the madeline-underwood/arm-learning-paths repository over three months. He created comprehensive learning paths for Android developers, including step-by-step guides for integrating Arm’s AI Chat library and profiling machine learning models on Arm devices. His work involved enhancing Gradle build compatibility using both Groovy and Kotlin DSL, clarifying onboarding prerequisites, and improving documentation for profiling with ArmNN and ExecuTorch. Using Kotlin, Bash, and Markdown, Ben focused on reducing setup friction, streamlining performance analysis, and enabling robust model integration, demonstrating depth in Android development, embedded systems, and educational content creation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
5
Lines of code
792
Activity Months3
Your Network
650 peopleSame Organization
@arm.com521
Work History
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.
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
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.
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.
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.
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
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Quality Metrics
Correctness94.4%
Maintainability91.4%
Architecture94.4%
Performance88.6%
AI Usage25.8%
Skills & Technologies
Programming Languages
BashCGradleKotlinMarkdown
Technical Skills
AI integrationAndroid DevelopmentAndroid developmentBuild ConfigurationDocumentationEmbedded SystemsFull Stack DevelopmentGradleKotlin DSLMachine LearningMobile DevelopmentPerformance ProfilingUI Designcontent writingdocumentation
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