What I Built
EcoMind is an AI-powered carbon footprint analyzer that helps individuals understand their environmental impact and receive personalized, actionable recommendations to reduce their carbon emissions.
Live Demo: https://ecomind-coral.vercel.app
GitHub: https://github.com/mzunain/ecomind
Demo
EcoMind provides:
- π Instant carbon footprint analysis based on your lifestyle
- π Sustainability score (1-10) with detailed breakdown
- π‘ Top 5 personalized reduction actions ranked by impact
- π± Region-specific advice (optimized for Nordic countries)
- β‘ Fast, beautiful dark theme UI
Journey
Why Google Gemini?
I chose Google Gemini for its structured JSON output capability, which is perfect for generating consistent, type-safe environmental data. The model excels at understanding complex lifestyle patterns and providing nuanced, regional advice.
Technical Implementation
Using Next.js 15 with the App Router and Google's @google/generative-ai SDK, I implemented:
- Structured Schema: Defined a TypeScript schema for carbon analysis
- Regional Context: Added location-aware recommendations
- Difficulty Scoring: Categorized actions by implementation difficulty
- Premium UI: Modern glassmorphism design with Tailwind CSS
Challenges Faced
- Model naming: Gemini API model versions required experimentation
- Structured output validation: Ensuring consistent JSON responses
- Regional accuracy: Balancing global averages with local context (e.g., Finland's clean energy grid)
Category Submission
Best Use of Google Gemini - EcoMind showcases Gemini's strength in structured data generation and contextual understanding for environmental impact analysis.
Additional Prize Categories
N/A




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