scrumdojo/quizmaster
Mar 2026 – Mar 2026
1 Month active
Languages Used
JavaMarkdownTypeScript
Technical Skills
API designAPI developmentJSON handlingTypeScriptbackend developmentfront end development
Daniel Michovský
PROFILE
Daniel Michovský
Worked on the scrumdojo/quizmaster repository to deliver explainable AI capabilities for AI-generated answers and questions, focusing on expanding the data model and enhancing the response structure. Leveraged TypeScript and JSON handling to add detailed explanations and correct answer indices to the API, improving the interpretability of AI-assisted Q&A. Refactored frontend components to optimize explanation count checks, resulting in faster load times and more maintainable UI interactions. This work increased user trust by making AI decisions more transparent and laid the groundwork for improved analytics and maintainability, demonstrating depth in both backend and frontend development within a single feature release.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
186
Activity Months1
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Work History
March 2026
3 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for scrumdojo/quizmaster: Delivered Explainable AI capabilities for AI answers and generated questions, expanding the data model and response structure, and improved frontend performance around explanation counts. This work increases user trust, improves interpretability of AI-generated content, and sets a foundation for better analytics and maintainability. Key outcomes include a robust explainability data model, richer question-generation responses, and faster, more maintainable UI interactions for AI-assisted Q&A.
3 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for scrumdojo/quizmaster: Delivered Explainable AI capabilities for AI answers and generated questions, expanding the data model and response structure, and improved frontend performance around explanation counts. This work increases user trust, improves interpretability of AI-generated content, and sets a foundation for better analytics and maintainability. Key outcomes include a robust explainability data model, richer question-generation responses, and faster, more maintainable UI interactions for AI-assisted Q&A.
March 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability86.6%
Architecture86.6%
Performance86.6%
AI Usage46.6%
Skills & Technologies
Programming Languages
JavaMarkdownTypeScript
Technical Skills
API designAPI developmentJSON handlingTypeScriptbackend developmentfront end developmentfrontend developmenttestingunit testing
Repositories Contributed To
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