madeline-underwood/arm-learning-paths
May 2025 – Jan 2026
2 Months active
Languages Used
CMarkdown
Technical Skills
DocumentationNumerical AnalysisPerformance OptimizationTechnical WritingC programmingdocumentation
Joana Cruz
PROFILE
Joana Cruz
Joana Cruz developed targeted learning resources and documentation for the madeline-underwood/arm-learning-paths repository, focusing on numerical accuracy and reproducibility in Libamath. She designed and authored a learning path that explains vector accuracy modes, encoding schemes, and floating-point representation, using C and Markdown to illustrate practical applications and ULP error concepts. In a subsequent update, Joana delivered reproducibility pathways and onboarding materials for Libamath’s ArmPL 26.01 release, providing enablement guidance and cross-architecture usage examples. Her work emphasized technical writing, numerical analysis, and performance optimization, resulting in deeper onboarding materials that improved developer understanding and accelerated adoption of advanced numerical features.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,008
Activity Months2
Your Network
651 peopleSame Organization
@arm.com522
Work History
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026: Delivered Reproducibility learning pathways and developer documentation for Libamath (ArmPL 26.01) in madeline-underwood/arm-learning-paths. Provided enablement guidance, usage examples across architectures, and onboarding content to accelerate adoption.
1 Commits • 1 Features
Jan 1, 2026January 2026: Delivered Reproducibility learning pathways and developer documentation for Libamath (ArmPL 26.01) in madeline-underwood/arm-learning-paths. Provided enablement guidance, usage examples across architectures, and onboarding content to accelerate adoption.
January 2026
May 2025
1 Commits • 1 Features
May 1, 2025In May 2025, delivered a focused learning resource for vector accuracy modes in Libamath within madeline-underwood/arm-learning-paths. The learning path explains accuracy levels, their encoding, ULP error, and floating-point representation, and is backed by a commit that introduces the content. Impact: improved developer onboarding and practical understanding, enabling more reliable numerical modeling in ARM learning contexts. Technologies/skills demonstrated include content design, technical writing, Libamath knowledge, floating-point concepts (ULP) and version control.
May 2025
1 Commits • 1 Features
May 1, 2025In May 2025, delivered a focused learning resource for vector accuracy modes in Libamath within madeline-underwood/arm-learning-paths. The learning path explains accuracy levels, their encoding, ULP error, and floating-point representation, and is backed by a commit that introduces the content. Impact: improved developer onboarding and practical understanding, enabling more reliable numerical modeling in ARM learning contexts. Technologies/skills demonstrated include content design, technical writing, Libamath knowledge, floating-point concepts (ULP) and version control.
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CMarkdown
Technical Skills
C programmingDocumentationNumerical AnalysisPerformance OptimizationTechnical Writingdocumentationnumerical methodsperformance optimization
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