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
During March 2026, Daniel Michovsky developed explainable AI capabilities for the scrumdojo/quizmaster repository, focusing on enhancing the interpretability of AI-generated answers and questions. He expanded the data model and JSON response structure to include detailed explanations and correct answer indices, using TypeScript and Java across both backend and frontend components. Daniel refactored frontend logic to optimize explanation count checks, resulting in faster and more maintainable UI interactions. His work addressed the need for greater transparency in AI-assisted Q&A, improving user trust and data fidelity while laying a foundation for future analytics and reducing the volume of support inquiries.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
186
Activity Months1
Your Network
114 peopleWork 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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