SKATEBEATZ

Your skate. Your sound.

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Inspiration:

Beginners or professionals, young or old, no matter who you are, skateboarding can always be your go-to sport. It’s not just about the tricks, but also the vibe you bring to every ride. To elevate that experience and help skaters connect with music that matches their energy, we wanted to create a tool that syncs your skating style with your favorite beats. Skatebeatz is designed to capture every skater’s unique rhythm, whether you’re cruising solo or hitting the park with friends, and recommend songs that perfectly fit your skating vibe.

What it does:

Skatebeatz is a web app that predicts songs that match your mood and skating style. Based on your answers about how, when, and with whom you skate, it generates a playlist that feels just right for your session. Whether you’re skating in the early morning chill or under neon lights at night, alone or with your crew, Skatebeatz finds the tracks that keep you rolling.

How we built it:

We built Skatebeatz using a combination of technologies. The frontend is powered by Next.js, designed with a presentable, user-friendly interface so skaters can easily navigate and input their preferences. On the backend, we used Gemini API to make the experience intelligent and personal. The Gemini API analyzes the quiz results to generate personalized playlist recommendations, creating a seamless connection between your skating style and your sound.

Challenges we ran into:

Time was definitely our biggest challenge. Working with multiple APIs and integrating them smoothly into the code while managing to design a pretty and easy to use website under tight deadlines was tricky. Making sure everything connected properly between Gemini and our frontend took a lot of testing, assistance and adjustments.

Accomplishments that we're proud of:

We’re proud that our Gemini API integration actually works. Getting it to analyze quiz data and return results felt like a big win for our team. We also managed to build creative and visually appealing design, combining both functionality and style to make the user feel fun and unique.

What we learned:

We learned how to use prompts effectively in AI, manage our time efficiently to build something advanced in a short period, and connect different APIs and tools to create a complete full-stack application. This project taught us how to balance creativity, teamwork, and technical problem-solving all at once.

What's next for SKATEBEATZ

We plan to expand Skatebeatz by adding more advanced features, like live music recommendations that adapt to your skating speed and fitness data in real-time. We also want to refine our AI algorithm for even better playlist matching and introduce a share feature where users can share their skate vibes or playlists with friends and listen together across devices.

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