madeline-underwood/arm-learning-paths
Jul 2025 – Jan 2026
2 Months active
Languages Used
BashMarkdownPython
Technical Skills
Bash ScriptingDocumentationEdge AIEdge ComputingEmbedded SystemsGenerative AI
fidel-makatia
PROFILE
Fidel-makatia
Over two months, Fidel Makatia developed and documented privacy-first GenAI and TinyML learning paths in the madeline-underwood/arm-learning-paths repository. He delivered a local-processing smart home system for Raspberry Pi 5, emphasizing privacy and reducing cloud reliance through Bash and Python scripting, robust setup guides, and performance benchmarking. Fidel also created a comprehensive onboarding path for NXP Ethos-U on FRDM i.MX 93, extending ExecuTorch build instructions to support Ethos-U65 NPU. His work focused on embedded systems, model deployment, and documentation, improving discoverability and accelerating onboarding for ARM-based edge AI development with a clear, well-structured technical approach.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
3
Lines of code
2,418
Activity Months2
Your Network
129 peopleWork History
January 2026
2 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for madeline-underwood/arm-learning-paths: Delivered two key features to accelerate TinyML development on NXP Ethos-U hardware. Implemented a comprehensive NXP Ethos-U learning path for the FRDM i.MX 93 and extended ExecuTorch build instructions to include Ethos-U65 NPU support. No major bugs fixed this month. Overall impact: Improved onboarding and prototyping speed for Ethos-U enabled workflows; strengthened build reliability and tooling for Ethos-U65, enabling faster iteration and broader adoption. Technologies/skills demonstrated: NXP Ethos-U, FRDM i.MX 93, Ethos-U65, TinyML deployment, build automation, patching, documentation, cross-repo collaboration.
2 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for madeline-underwood/arm-learning-paths: Delivered two key features to accelerate TinyML development on NXP Ethos-U hardware. Implemented a comprehensive NXP Ethos-U learning path for the FRDM i.MX 93 and extended ExecuTorch build instructions to include Ethos-U65 NPU support. No major bugs fixed this month. Overall impact: Improved onboarding and prototyping speed for Ethos-U enabled workflows; strengthened build reliability and tooling for Ethos-U65, enabling faster iteration and broader adoption. Technologies/skills demonstrated: NXP Ethos-U, FRDM i.MX 93, Ethos-U65, TinyML deployment, build automation, patching, documentation, cross-repo collaboration.
January 2026
July 2025
3 Commits • 1 Features
Jul 1, 2025July 2025: Delivered Privacy-first GenAI Smart Home Learning Path for ARM-based devices (Raspberry Pi 5) with robust local processing. Consolidated and expanded setup guides (Ollama and Python), AWS CLI prerequisites, and refreshed prerequisites. Added tools and languages, conducted performance benchmarks, and implemented organizational reorganization to improve discoverability. This work enables privacy-preserving edge AI for smart-home scenarios, reduces cloud dependency, and accelerates onboarding for ARM-focused development.
July 2025
3 Commits • 1 Features
Jul 1, 2025July 2025: Delivered Privacy-first GenAI Smart Home Learning Path for ARM-based devices (Raspberry Pi 5) with robust local processing. Consolidated and expanded setup guides (Ollama and Python), AWS CLI prerequisites, and refreshed prerequisites. Added tools and languages, conducted performance benchmarks, and implemented organizational reorganization to improve discoverability. This work enables privacy-preserving edge AI for smart-home scenarios, reduces cloud dependency, and accelerates onboarding for ARM-focused development.
Activity
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Quality Metrics
Correctness94.0%
Maintainability92.0%
Architecture92.0%
Performance92.0%
AI Usage28.0%
Skills & Technologies
Programming Languages
BashMarkdownPython
Technical Skills
Bash ScriptingC/C++DocumentationEdge AIEdge ComputingEmbedded SystemsEmbedded Systems DevelopmentGenerative AILLM DeploymentLinuxMachine LearningModel DeploymentPythonPython DevelopmentPython Scripting
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