Stir

Inspiration

Ever stared at your pantry wondering what to cook? We've all been there - having ingredients but no inspiration. Stir was born from the frustration of food waste and the desire to make cooking accessible to everyone, regardless of skill level. We wanted to create a platform that transforms your existing ingredients into culinary adventures, connecting food lovers worldwide while reducing waste.

What it does

Stir is an AI-powered cooking assistant that transforms your pantry into endless recipe possibilities. Simply add your ingredients, and our platform generates personalized recipe suggestions from global cuisines using TheMealDB API. Users can share their culinary creations with photos/videos, save favorites, and discover new dishes through our social feed. It's like having a personal chef who knows exactly what's in your kitchen!

How we built it

Built with React frontend and Node.js/Express backend, integrated with Supabase for database and authentication. We leveraged TheMealDB API for diverse global recipes and implemented AI-powered ingredient matching. The app features real-time social sharing, media uploads via Supabase Storage, and a responsive design with Tailwind CSS. Key technologies include React Query for state management, JWT authentication, and PostgreSQL with Row Level Security.

Challenges we ran into

Major challenges included API integration complexity - initially using Spoonacular but hitting rate limits, then pivoting to TheMealDB for unlimited access. Database schema mismatches between UUID and INTEGER types caused significant debugging time. Media upload implementation required switching from Cloudinary to Supabase Storage due to credit limits. Port conflicts and authentication flow issues also required extensive troubleshooting. The biggest challenge was ensuring seamless user experience across all features while maintaining data consistency.

Accomplishments that we're proud of

We're proud of creating a fully functional full-stack application with real-time features in a hackathon timeframe. Successfully integrated multiple APIs (TheMealDB, Supabase) and built a complete user authentication system. Implemented advanced features like media uploads, social sharing, and AI-powered recipe matching. Created a responsive, modern UI that works across devices. Most importantly, we built something that solves a real problem - reducing food waste while making cooking more accessible and social.

What we learned

We learned the importance of API selection and rate limiting considerations early in development. Database schema consistency is crucial - mixing UUID and INTEGER types caused major headaches. Supabase's Row Level Security requires careful policy design. Media handling is complex but essential for social features. Most importantly, we learned that user experience should drive technical decisions, not the other way around. The pivot from Spoonacular to TheMealDB taught us to always have backup plans.

What's next for Stir

Next steps include implementing advanced AI features like dietary restriction filtering and nutritional analysis. We plan to add meal planning capabilities, grocery list generation, and cooking timer integration. Social features will expand with user following, recipe ratings, and cooking challenges. Mobile app development is planned, along with integration with grocery delivery services. We're also exploring machine learning to improve recipe recommendations based on user preferences and cooking history. The goal is to become the ultimate cooking companion for home chefs worldwide.

Built With

• express.js
• javascript
• json
• lucide
• mealdb
• node.js
• openai
• react
• supabase
• tailwind
• vite