TAP-GGC/tap-ws1-astro-agnosticui
Apr 2026 – Apr 2026
1 Month active
Languages Used
JavaScriptMarkdownPython
Technical Skills
Emotion DetectionGitGitHubInteractive SystemsPythonRaspberry Pi
nnguyen79pine
PROFILE
Nnguyen79pine
During April 2026, contributed to the TAP-GGC/tap-ws1-astro-agnosticui repository by delivering two core features focused on educational technology and data enrichment. Developed a hardware-enabled emotion detection demo, MoodyPi, integrating Raspberry Pi and Python to create an interactive system with public documentation and clear educational objectives. Enhanced the student directory by updating graduation data and enriching student profiles, improving data quality and discoverability. Demonstrated skills in data modeling, front end development, and Git-based collaboration, while emphasizing open-source readiness through comprehensive onboarding materials. The work addressed both technical integration and user experience, supporting better onboarding and collaborative potential for future contributors.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
238
Activity Months1
Your Network
37 peopleSame Organization
@ggc.edu7
Shared Repositories
30
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Work History
April 2026
3 Commits • 2 Features
Apr 1, 2026April 2026 Monthly Summary for TAP-GGC/tap-ws1-astro-agnosticui: - Key features delivered: two major enhancements to the student/education UI and to the project ecosystem. - Major bugs fixed: none reported this period. - Overall impact and accomplishments: improved data quality and discoverability; introduced a hardware-enabled education demo with public documentation; strengthened onboarding and collaboration potential. - Technologies/skills demonstrated: data modeling and enrichment, Git-based collaboration, hardware-software integration (Raspberry Pi), comprehensive project documentation, and open-source readiness.
3 Commits • 2 Features
Apr 1, 2026April 2026 Monthly Summary for TAP-GGC/tap-ws1-astro-agnosticui: - Key features delivered: two major enhancements to the student/education UI and to the project ecosystem. - Major bugs fixed: none reported this period. - Overall impact and accomplishments: improved data quality and discoverability; introduced a hardware-enabled education demo with public documentation; strengthened onboarding and collaboration potential. - Technologies/skills demonstrated: data modeling and enrichment, Git-based collaboration, hardware-software integration (Raspberry Pi), comprehensive project documentation, and open-source readiness.
April 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage33.4%
Skills & Technologies
Programming Languages
JavaScriptMarkdownPython
Technical Skills
Emotion DetectionGitGitHubInteractive SystemsPythonRaspberry PiSQLcomputer visiondata analysisdata visualizationfront end developmentmachine learning
Repositories Contributed To
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