Jeremiah worked on the Monash-FIT3170/2025W2-FindingNibbles repository, delivering nine features and two bug fixes over four months. He built core recipe and recommendation flows, implemented dietary-based dish suggestions, and enhanced menu analysis with robust API development and integration using TypeScript, Flutter, and NestJS. His approach emphasized clean repository initialization, data modeling with DTOs, and error handling to ensure reliability. Jeremiah improved user experience by refining UI elements, adding state management, and enabling safe cancellation of long-running processes. His work addressed both backend and frontend challenges, resulting in a cohesive, maintainable codebase that supports ongoing feature development.
October 2025 monthly summary for Monash-FIT3170/2025W2-FindingNibbles: focused on reliability, user control, and UI clarity. Delivered three key contributions: a crash-avoidance fix in recipe generation by defaulting to all appliances and requiring at least one selection; a safe cancellation flow for long-running recipe generation via a loading dialog cancel option and a cancellation flag; and a UI refinement adding cuisine tags (up to two) on restaurant cards to improve readability and layout. These changes reduce crashes, give users safer control over processes, and enhance discoverability, driving smoother user experience and product reliability.
October 2025 monthly summary for Monash-FIT3170/2025W2-FindingNibbles: focused on reliability, user control, and UI clarity. Delivered three key contributions: a crash-avoidance fix in recipe generation by defaulting to all appliances and requiring at least one selection; a safe cancellation flow for long-running recipe generation via a loading dialog cancel option and a cancellation flag; and a UI refinement adding cuisine tags (up to two) on restaurant cards to improve readability and layout. These changes reduce crashes, give users safer control over processes, and enhance discoverability, driving smoother user experience and product reliability.
September 2025 – Monash-FIT3170/2025W2-FindingNibbles: Delivered diet-aware dish recommendations and strengthened platform reliability. Key work included implementing the best-dish retrieval endpoint with DTOs and robust error handling, adding a dietary-based dish suggestion flow with API integration and a user-facing popup, and enhancing the menu-analysis UX to enable selecting the best dish after analysis. A temporary Random Dish feature was added and subsequently removed per strategic direction. Security and build hygiene improvements fixed the backend token verification secret, Android build configuration, and cleanup of unused plugins/imports, with improved route alignment and dietary-requirement handling.
September 2025 – Monash-FIT3170/2025W2-FindingNibbles: Delivered diet-aware dish recommendations and strengthened platform reliability. Key work included implementing the best-dish retrieval endpoint with DTOs and robust error handling, adding a dietary-based dish suggestion flow with API integration and a user-facing popup, and enhancing the menu-analysis UX to enable selecting the best dish after analysis. A temporary Random Dish feature was added and subsequently removed per strategic direction. Security and build hygiene improvements fixed the backend token verification secret, Android build configuration, and cleanup of unused plugins/imports, with improved route alignment and dietary-requirement handling.
May 2025 monthly summary for Monash-FIT3170/2025W2-FindingNibbles: Delivered end-to-end enhancements to the Recipe Page and Recipe Recommendations, enabling a cohesive user flow from discovery to execution. Key outcomes include core Recipe Page implementation (ingredients and instructions) with navigation, data modeling, and UI refinements; a new Recipe Recommendations flow with filters and a reload mechanism; and improvements to theming, layout, and user interactions (e.g., a favourite button). Critical fixes addressed data-generation and display consistency and resolved recipe page issues in the underlying service layer. Legacy data source simplifications were completed by removing recipes.json. These changes drive faster onboarding of new users to discover, save, and rate recipes, while establishing a solid foundation for LLMS-driven content generation.
May 2025 monthly summary for Monash-FIT3170/2025W2-FindingNibbles: Delivered end-to-end enhancements to the Recipe Page and Recipe Recommendations, enabling a cohesive user flow from discovery to execution. Key outcomes include core Recipe Page implementation (ingredients and instructions) with navigation, data modeling, and UI refinements; a new Recipe Recommendations flow with filters and a reload mechanism; and improvements to theming, layout, and user interactions (e.g., a favourite button). Critical fixes addressed data-generation and display consistency and resolved recipe page issues in the underlying service layer. Legacy data source simplifications were completed by removing recipes.json. These changes drive faster onboarding of new users to discover, save, and rate recipes, while establishing a solid foundation for LLMS-driven content generation.
In March 2025, the FindingNibbles project was initialized with a solid foundation. Key deliverable this month was establishing the repository, updating the README to reflect ownership, and creating project scaffolding to enable future development. No user-facing features were released this period; the focus was on setup, governance, and establishing a clean commit history to support ongoing work.
In March 2025, the FindingNibbles project was initialized with a solid foundation. Key deliverable this month was establishing the repository, updating the README to reflect ownership, and creating project scaffolding to enable future development. No user-facing features were released this period; the focus was on setup, governance, and establishing a clean commit history to support ongoing work.
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