Monash-FIT3170/2025W1-FindingNibbles
Mar 2025 – Oct 2025
7 Months active
Languages Used
MarkdownCSSHTMLJSXJavaScriptJSONTypeScriptGit
Technical Skills
DocumentationFull Stack DevelopmentMeteor.jsMongoDBNode.jsReact.js
oher0004
PROFILE
Oher0004
Omila Herath contributed to the Monash-FIT3170/2025W1-FindingNibbles repository by building full-stack features that enhanced restaurant discovery, user authentication, and AI-driven dish suggestions. She implemented interactive map filtering and optimized geospatial calculations using JavaScript and React, reducing API calls and improving performance. Herath integrated Vertex AI for personalized recommendations, designed robust MongoDB data models, and established secure API endpoints. She addressed deployment stability through configuration management and security best practices, while also refactoring backend logic for reliable data persistence. Her work demonstrated depth in both frontend and backend development, resulting in a scalable, maintainable, and user-focused application.
Overall Statistics
Feature vs Bugs
82%Features
Repository Contributions
63Total
Bugs
6
Commits
63
Features
27
Lines of code
344,002
Activity Months7
Your Network
130 peopleSame Organization
@student.monash.edu116
Work History
October 2025
11 Commits • 2 Features
Oct 1, 2025Month: 2025-10 — AI-driven personalization and stability improvements for Monash-FIT3170/2025W1-FindingNibbles. Delivered AI-Driven Dish Suggestions with Vertex AI integration plus user preference management, with data models and basic API endpoints enabling personalized recommendations and a mock data fallback for reliability. Fixed Travel Planning crashes by removing problematic sections, relocating useRef initialization, and temporarily disabling a brittle image existence check. Implemented code hygiene and stability improvements, including formatting, minor UI fixes, and reverting MealPlanner input handling. Impact: enhanced user engagement through personalized suggestions, reduced crash risk, and a cleaner, more maintainable codebase. Technologies demonstrated: Vertex AI, data modeling, API design, React patterns, debugging, and code quality practices.
11 Commits • 2 Features
Oct 1, 2025Month: 2025-10 — AI-driven personalization and stability improvements for Monash-FIT3170/2025W1-FindingNibbles. Delivered AI-Driven Dish Suggestions with Vertex AI integration plus user preference management, with data models and basic API endpoints enabling personalized recommendations and a mock data fallback for reliability. Fixed Travel Planning crashes by removing problematic sections, relocating useRef initialization, and temporarily disabling a brittle image existence check. Implemented code hygiene and stability improvements, including formatting, minor UI fixes, and reverting MealPlanner input handling. Impact: enhanced user engagement through personalized suggestions, reduced crash risk, and a cleaner, more maintainable codebase. Technologies demonstrated: Vertex AI, data modeling, API design, React patterns, debugging, and code quality practices.
October 2025
September 2025
4 Commits • 3 Features
Sep 1, 2025In September 2025, the FindingNibbles project delivered meaningful improvements to data reliability, feature readiness, and code quality. Key outcomes include robust restaurant data persistence using a SaveRestaurantInput payload with enhanced error feedback, the introduction of a Food Suggestion Generator component with adjusted data seeding, and a targeted cleanup eliminating noisy console logs to improve maintainability. These changes reduce save errors, enable cleaner data management, and provide a stronger base for future feature development across the Monash-FIT3170/2025W1-FindingNibbles repository.
September 2025
4 Commits • 3 Features
Sep 1, 2025In September 2025, the FindingNibbles project delivered meaningful improvements to data reliability, feature readiness, and code quality. Key outcomes include robust restaurant data persistence using a SaveRestaurantInput payload with enhanced error feedback, the introduction of a Food Suggestion Generator component with adjusted data seeding, and a targeted cleanup eliminating noisy console logs to improve maintainability. These changes reduce save errors, enable cleaner data management, and provide a stronger base for future feature development across the Monash-FIT3170/2025W1-FindingNibbles repository.
August 2025
10 Commits • 3 Features
Aug 1, 2025August 2025 — Monthly summary for Monash-FIT3170/2025W1-FindingNibbles. Focused on delivering data-driven AI readiness, stability, and security improvements. Key outcomes include a new food items data model with seed data to support prompt training, refactored indexing and persistence for search-related data, and hardening measures to prevent startup issues and data leakage. The changes reduced deployment risk, improved user experience with more robust AI suggestions, and showcased proficiency in database design, Meteor.js/Tracker UI work, error handling, and security hygiene.
10 Commits • 3 Features
Aug 1, 2025August 2025 — Monthly summary for Monash-FIT3170/2025W1-FindingNibbles. Focused on delivering data-driven AI readiness, stability, and security improvements. Key outcomes include a new food items data model with seed data to support prompt training, refactored indexing and persistence for search-related data, and hardening measures to prevent startup issues and data leakage. The changes reduced deployment risk, improved user experience with more robust AI suggestions, and showcased proficiency in database design, Meteor.js/Tracker UI work, error handling, and security hygiene.
August 2025
July 2025
1 Commits • 1 Features
Jul 1, 2025In 2025-07, delivered a targeted optimization for the map coordinate generation in Monash-FIT3170/2025W1-FindingNibbles. The change significantly reduced API calls and improved map performance, with a single focused commit. This work reduced API calls from 49 to 7, cutting server load and latency, and enabling a faster, more scalable user experience. No major bugs were reported this month; the focus was on performance engineering and code quality. Overall impact: improved responsiveness, cost efficiency, and maintainability. Technologies demonstrated: algorithm optimization, performance profiling, and clean code practices across the repository.
July 2025
1 Commits • 1 Features
Jul 1, 2025In 2025-07, delivered a targeted optimization for the map coordinate generation in Monash-FIT3170/2025W1-FindingNibbles. The change significantly reduced API calls and improved map performance, with a single focused commit. This work reduced API calls from 49 to 7, cutting server load and latency, and enabling a faster, more scalable user experience. No major bugs were reported this month; the focus was on performance engineering and code quality. Overall impact: improved responsiveness, cost efficiency, and maintainability. Technologies demonstrated: algorithm optimization, performance profiling, and clean code practices across the repository.
May 2025
35 Commits • 16 Features
May 1, 2025May 2025 performance highlights for Monash-FIT3170/2025W1-FindingNibbles. Delivered core map enhancements that improve restaurant discovery and UX, added robust cuisine filtering with accurate map updates, and implemented distance-based filtering to ensure relevant results. Strengthened release engineering and CI hygiene, including packaging, testing, and API key management to enable reliable production deployments. Fixed critical UX bugs to stabilize filters and interactions, setting a solid foundation for next features.
35 Commits • 16 Features
May 1, 2025May 2025 performance highlights for Monash-FIT3170/2025W1-FindingNibbles. Delivered core map enhancements that improve restaurant discovery and UX, added robust cuisine filtering with accurate map updates, and implemented distance-based filtering to ensure relevant results. Strengthened release engineering and CI hygiene, including packaging, testing, and API key management to enable reliable production deployments. Fixed critical UX bugs to stabilize filters and interactions, setting a solid foundation for next features.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for Monash-FIT3170/2025W1-FindingNibbles: Key feature delivered was a Task App with User Authentication, introducing a to-do list with per-user access. The release included project scaffolding, client-side React components, and server-side task management (create, delete, toggle completion). No major bugs fixed in this period (based on provided data). Overall impact: provides secured, end-to-end task management for users, enabling improved productivity and workflow efficiency. Technologies demonstrated: React frontend, backend task management endpoints, user authentication integration, and project scaffolding for scalable feature growth.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for Monash-FIT3170/2025W1-FindingNibbles: Key feature delivered was a Task App with User Authentication, introducing a to-do list with per-user access. The release included project scaffolding, client-side React components, and server-side task management (create, delete, toggle completion). No major bugs fixed in this period (based on provided data). Overall impact: provides secured, end-to-end task management for users, enabling improved productivity and workflow efficiency. Technologies demonstrated: React frontend, backend task management endpoints, user authentication integration, and project scaffolding for scalable feature growth.
March 2025
1 Commits • 1 Features
Mar 1, 2025Month: 2025-03. Focused on documenting contributor attribution for Monash-FIT3170/2025W1-FindingNibbles. Delivered a documentation feature that updates the README to include Omila Herath's name and email, improving attribution and compliance with contribution guidelines.
1 Commits • 1 Features
Mar 1, 2025Month: 2025-03. Focused on documenting contributor attribution for Monash-FIT3170/2025W1-FindingNibbles. Delivered a documentation feature that updates the README to include Omila Herath's name and email, improving attribution and compliance with contribution guidelines.
March 2025
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Quality Metrics
Correctness82.0%
Maintainability84.2%
Architecture75.8%
Performance77.6%
AI Usage22.4%
Skills & Technologies
Programming Languages
CSSGitHTMLJSONJSXJavaScriptMarkdownTypeScriptgitignore
Technical Skills
AI IntegrationAPI DevelopmentAPI IntegrationAsset ManagementBackend DevelopmentBuild ToolsCloud ServicesCode FormattingCode HygieneCode RefactoringConfiguration ManagementDatabase IndexingDatabase ManagementDebuggingDocumentation
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