This part of my work grew after I returned to school and began studying technology, AI, and app development.
Learning opened a new world of ideas for me — ideas rooted in my real life: the farm, the animals, memory, care, the things I struggle with, and the tools I wish existed.
This space is less about finished products and more about curiosity, process, and growth.
Selected projects
Spring in a Box Farm Game
Educational game prototype · SwiftUI
A gentle gardening game inspired by my real farm, animals, and seasonal rhythms.
The project explores how learning can feel calm, playful, and rooted in care rather than instructions.
What I built
- Concept and game structure
- UX flow and screen hierarchy
- Visual system for calm, readable gameplay
- A working SwiftUI prototype
Tools: SwiftUI · iOS
Status: Working prototype
FIFA FanID+ App
Mobile app prototype · SwiftUI
A mobile app prototype designed to improve navigation, access, and the overall fan experience at large-scale international events like the FIFA World Cup.
The project focuses on reducing friction for international visitors through clearer flows, centralized access, and real-time information.
What I built
- Multi-screen iOS app prototype
- User flows focused on entry, navigation, and discovery
- Visual hierarchy and navigation patterns for high-density events
- A scalable structure designed for temporary event infrastructure
Tools: SwiftUI · iOS
Status: Working prototype
Other technical explorations
Best Planting Month Predictor for South Florida Herbs
Applied machine learning project
A machine learning model built to estimate the best month to plant specific herbs in South Florida, where traditional planting calendars often fail due to the subtropical climate.
The project was inspired by my experience as a grower and focuses on translating data into practical guidance.
What I built
- Curated and cleaned a custom herb dataset based on plant characteristics and environmental needs
- Trained and evaluated a Random Forest Regression model
- Measured model performance using R² and mean squared error (MSE)
- Exported the trained model for reuse and future tools
Tools: Python · Jupyter Notebook · pandas · scikit-learn
Status: Completed academic project
More tools and experiments are slowly growing here.
Catyobi 