Ezekiel P. Villadolid's Builder Journey

My journey as a builder has been a mix of creativity, problem-solving, and continuous learning. From crafting digital designs to developing web and mobile applications, I’ve explored various technologies to bring ideas to life. My experience spans graphic design, UI/UX, and software development, blending artistry with functionality. Whether leading design initiatives or building tech solutions, I thrive on turning concepts into impactful projects. With every challenge, I refine my skills, pushing boundaries to create meaningful and innovative solutions.

GitHub Twitter

Shipped

Building

Month Streak

Open Source

2025 Progress

Current: April

Latest Updates

💡 Learning • Rent2Reuse Demo Model

Mar 11

🔹 Training & Fine-Tuning: The model is built using MobileNetV2 and trained to classify items into Prohibited and Accepted categories. It handles 180 classes, with 35 prohibited and 145 accepted. 🔹 Interactive Demo: I integrated the model with Gradio to create a simple web interface, now live on Hugging Face Spaces. 🔹 Key Learnings: ✅ Set up TensorFlow GPU on WSL for faster training. ✅ Used Git LFS to manage large model files during deployment. ✅ Added top-3 predictions to improve result insights.

💡 Learning • AiRa

Feb 3

Through this project, I deepened my understanding of TypeScript in React Native, improved my skills in Expo Router, Nativewind, and Appwrite, and explored the integration of expo-video for enhanced media playback.

Products Timeline

January

In Progress

AiRa

AiRa is a modern mobile application built with Expo Router, powered by a solid backend infrastructure provided by Appwrite, and styled using NativeWind (Tailwind CSS for React Native). With TypeScript for enhanced type safety, this app offers an engaging and visually appealing experience, allowing users to share AI-generated videos seamlessly within the community.

Expo NativeWind Appwrite TypeScript

Building

February

Completed

Rent2Reuse Demo Model

Fine-Tuned MobileNetV2 Image Classification Model for Prohibited and Accepted Items: This project showcases a fine-tuned MobileNetV2 image classification model trained to distinguish between Prohibited and Accepted items. The dataset includes 180 distinct classes, with 35 categorized as prohibited and 145 as accepted. The model was trained using TensorFlow on a GPU-accelerated environment with WSL. To enhance usability, I integrated Gradio for an interactive web interface, providing both top-1 and top-3 predictions with corresponding confidence scores.

TensorFlow MobileNetV2 Gradio Hugging Face Spaces Python WSL Jupyter Lab

View Details Demo

Lessons Learned

"Learned fine-tuning techniques using MobileNetV2 to achieve improved classification accuracy. Gained experience in GPU setup and troubleshooting on WSL for efficient TensorFlow model training. Learned to build and deploy interactive model interfaces using Gradio for enhanced user experience. Developed strategies to categorize predictions into distinct groups for better interpretation of results. Explored best practices in managing large model files and ensuring proper deployment workflows on Hugging Face Spaces."

2025 Progress

Latest Updates

Products Timeline

AiRa

Rent2Reuse Demo Model

Lessons Learned

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