
Contributed to the TDT4290-Gr6/math-mate repository by building an AI-driven math problem solver and dataset generation workflow, integrating OpenAI and Gemini APIs for step-by-step solution creation and structured validation. Enhanced the platform’s architecture with clean code practices, CI/CD automation using GitHub Actions, and comprehensive documentation to streamline onboarding and development. Delivered robust user flows, including authentication with NextAuth.js, country and subject selection, and persistent state management via Supabase and localStorage. Leveraged TypeScript, React, and Docker to ensure reproducible deployments, while implementing accessibility improvements, end-to-end Cypress testing, and codebase standardization to support maintainability, scalability, and reliable user experiences.
October 2025 (2025-10) monthly summary for math-mate (TDT4290-Gr6). This sprint focused on delivering UX enhancements, robust user flows, and reproducible deployment while strengthening the foundation for scale. Key features and business value were shipped, while critical bugs were fixed to stabilize onboarding and daily usage. Highlights below focus on business value and technical achievements.
October 2025 (2025-10) monthly summary for math-mate (TDT4290-Gr6). This sprint focused on delivering UX enhancements, robust user flows, and reproducible deployment while strengthening the foundation for scale. Key features and business value were shipped, while critical bugs were fixed to stabilize onboarding and daily usage. Highlights below focus on business value and technical achievements.
September 2025 focused on delivering automation, AI-assisted workflows, and architecture/documentation improvements in the math-mate project to accelerate problem dataset generation, improve code quality, and enhance onboarding. Key outcomes include an AI-driven workflow for generating step-by-step solutions and datasets with structured validation, CI/CD automation with linting and CodeRabbit integration, and updated architecture and contributor guidelines to streamline development. These efforts improved velocity, reproducibility, and reliability across the development lifecycle.
September 2025 focused on delivering automation, AI-assisted workflows, and architecture/documentation improvements in the math-mate project to accelerate problem dataset generation, improve code quality, and enhance onboarding. Key outcomes include an AI-driven workflow for generating step-by-step solutions and datasets with structured validation, CI/CD automation with linting and CodeRabbit integration, and updated architecture and contributor guidelines to streamline development. These efforts improved velocity, reproducibility, and reliability across the development lifecycle.

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