StyleSwipe

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Inspiration

Choosing what to wear is time-consuming and often frustrating. Most fashion apps either overwhelm users or treat outfits as static sets. We wanted to make styling fast, personalized, and intelligent—like having a stylist that actually learns you.

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What it does

StyleSwipe is an AI-powered fashion app where users swipe through outfits while autonomous agents work in the background to find clothing tailored to their style.

Unlike traditional systems, StyleSwipe: • 👕 Understands individual clothing items, not just full outfits • 🤖 Uses AI agents to continuously discover and refine recommendations • 🔄 Dynamically builds outfits by combining pieces that match your preferences

The result is a system that doesn’t just suggest outfits—it actively curates your style in real time.

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How we built it • Frontend: TypeScript/JavaScript swipe-based UI for rapid feedback • Backend: Python server handling user data, recommendation logic, and agent coordination • AI System: • Autonomous agents for item discovery and outfit assembly • Preference learning from swipe behavior • Item-level feature analysis for precise matching

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Challenges we ran into • Modeling fine-grained preferences at the clothing-item level • Designing agents that balance exploration vs. personalization • Ensuring compatibility between independently selected pieces • Keeping recommendations fast and responsive

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Accomplishments that we’re proud of • Built a working agent-driven recommendation system • Achieved item-level styling intelligence, not just outfit matching • Created a seamless loop where the app learns instantly from user input • Delivered a polished, interactive prototype under hackathon constraints

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What we learned • How to design real-world AI agent systems • Why UX is critical for making AI feel useful • The power of granular data (items vs. outfits) in recommendations

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What’s next for StyleSwipe • Closet integration + real wardrobe syncing • Direct shopping from recommendations • Smarter, specialized agents (e.g., seasonal, occasion-based) • Mobile app + social sharing

Built With

• auth
• fastapi
• gemini
• javascript
• postgresql
• python
• typescript
• vision