Monash-FIT3170/2025W1-FindingNibbles
Apr 2025 – Sep 2025
4 Months active
Languages Used
CSSJSXJavaScriptHTMLJSONTypeScript
Technical Skills
CSSFront End DevelopmentFrontend DevelopmentReactUI/UX DesignAI Integration
koharuyanen
PROFILE
Koharuyanen
Over four months, Kevin Num built and enhanced AI-powered discovery features for the Monash-FIT3170/2025W1-FindingNibbles repository, focusing on both frontend and backend development. He implemented Vertex AI-driven dish recommendations, integrated Gemini AI for image generation, and modernized the UI using React and Tailwind CSS. Kevin addressed authentication and security by refining Google Cloud service account handling and removing sensitive files, improving reliability and data protection. His work included scalable UI scaffolding, state management, and data visualization, resulting in a robust, user-centric application. The depth of his contributions reflects a strong grasp of full stack development and cloud integration.
Overall Statistics
Feature vs Bugs
79%Features
Repository Contributions
26Total
Bugs
3
Commits
26
Features
11
Lines of code
2,620
Activity Months4
Your Network
131 peopleSame Organization
@student.monash.edu117
Work History
September 2025
1 Commits
Sep 1, 2025September 2025 focused on stabilizing Google Cloud AI Suggestions by fixing the service account authentication flow. Implemented a refactor to properly handle service account credentials provided as either a file path or a JSON object, ensuring reliable connections to Google Cloud services and reducing AI-suggestions connectivity failures. The work tightened security handling of credentials and improved system resilience, directly supporting business-critical AI features used by end users.
1 Commits
Sep 1, 2025September 2025 focused on stabilizing Google Cloud AI Suggestions by fixing the service account authentication flow. Implemented a refactor to properly handle service account credentials provided as either a file path or a JSON object, ensuring reliable connections to Google Cloud services and reducing AI-suggestions connectivity failures. The work tightened security handling of credentials and improved system resilience, directly supporting business-critical AI features used by end users.
September 2025
August 2025
11 Commits • 2 Features
Aug 1, 2025August 2025 monthly summary for Monash-FIT3170/2025W1-FindingNibbles. Focused on delivering AI-assisted discovery features, stabilizing UI state, and cleaning up deprecated AI paths to optimize maintenance and business value. Achievements span backend AI image generation, frontend integration, feature deprecation, and UX refinements.
August 2025
11 Commits • 2 Features
Aug 1, 2025August 2025 monthly summary for Monash-FIT3170/2025W1-FindingNibbles. Focused on delivering AI-assisted discovery features, stabilizing UI state, and cleaning up deprecated AI paths to optimize maintenance and business value. Achievements span backend AI image generation, frontend integration, feature deprecation, and UX refinements.
May 2025
11 Commits • 6 Features
May 1, 2025May 2025 summary for Monash-FIT3170/2025W1-FindingNibbles: Delivered a set of user-centric features, security hardening, and UI modernization that collectively boost engagement, streamline onboarding, and protect data. Key deliverables include a Vertex AI-powered dish recommendations system with user preferences and occasion prompts; a Roll-a-Dice Cuisine Picker in the navbar; a secure authentication landing flow directing unauthenticated users to login; a Tailwind-based UI refresh for NavBar/Sidebar; and meal-planner calorie-goal visualization. Also removed a sensitive JSON file to mitigate data exposure. These efforts reduced onboarding friction, improved UX, and strengthened frontend reliability and data protection.
11 Commits • 6 Features
May 1, 2025May 2025 summary for Monash-FIT3170/2025W1-FindingNibbles: Delivered a set of user-centric features, security hardening, and UI modernization that collectively boost engagement, streamline onboarding, and protect data. Key deliverables include a Vertex AI-powered dish recommendations system with user preferences and occasion prompts; a Roll-a-Dice Cuisine Picker in the navbar; a secure authentication landing flow directing unauthenticated users to login; a Tailwind-based UI refresh for NavBar/Sidebar; and meal-planner calorie-goal visualization. Also removed a sensitive JSON file to mitigate data exposure. These efforts reduced onboarding friction, improved UX, and strengthened frontend reliability and data protection.
May 2025
April 2025
3 Commits • 3 Features
Apr 1, 2025April 2025 — Foundational frontend work for Monash-FIT3170/2025W1-FindingNibbles, including project reorganization, initial frontend scaffolding, and a new UI feature for cuisine selection with a radius filter. No production bug fixes this month; focus on structural improvements and groundwork for user interaction.
April 2025
3 Commits • 3 Features
Apr 1, 2025April 2025 — Foundational frontend work for Monash-FIT3170/2025W1-FindingNibbles, including project reorganization, initial frontend scaffolding, and a new UI feature for cuisine selection with a radius filter. No production bug fixes this month; focus on structural improvements and groundwork for user interaction.
Activity
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Quality Metrics
Correctness83.2%
Maintainability83.0%
Architecture79.6%
Performance77.8%
AI Usage41.6%
Skills & Technologies
Programming Languages
CSSHTMLJSONJSXJavaScriptTypeScript
Technical Skills
AI IntegrationAPI DevelopmentAPI IntegrationAuthenticationBackend DevelopmentCSSConfiguration ManagementData VisualizationFront End DevelopmentFrontend DevelopmentFull Stack DevelopmentGemini APIGoogle Cloud PlatformGoogle Cloud Vertex AIHugging Face API
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline