FitAgent

FitAgent is an innovative, multi-chain nutrition tracking platform that combines AI-powered food analysis with Web3 incentives to encourage healthy eating habits. Here's a detailed breakdown:

Core Functionality:

AI-Powered Nutrition Analysis: Users simply take a photo of their meal, and our fine-tuned Vision Transformer (ViT) model instantly analyzes it to provide detailed nutritional information (calories, macros, micronutrients) with over 90% accuracy. Personalized AI Coaching: Our Venice AI integration (qwen3-235b) generates personalized, context-aware nutrition advice, meal suggestions, and health goals based on the user's dietary history, preferences, and health objectives. Web3 Incentive System: Base Protocol Integration: Users earn Verifiable Points (VP) on Base for meeting nutrition goals, with rewards that scale with their consistency streak. Dynamic Flow NFTs: As users progress, their NFT evolves through different visual stages (Seedling → Sprout → Plant → Tree → Forest Guardian), with each evolution featuring unique AI-generated artwork. Technical Architecture:

Frontend: Next.js 15 with a mobile-first design, featuring a camera-first interface for seamless photo capture. Backend: High-performance Rust (Actix-web) API handling image processing, AI integration, and blockchain interactions. AI/ML: Custom fine-tuned ViT model for food recognition and Venice AI for natural language coaching. Unique Value Proposition: FitAgent stands out by combining the accuracy of AI-powered nutrition tracking with the engagement of Web3 incentives, creating a gamified health experience that rewards users for making better food choices. The platform is designed to be accessible to both crypto-natives and mainstream users, with a focus on simplicity and user experience.

Core Architecture Frontend (Next.js 15 + TypeScript) Framework: Next.js 15 with App Router for optimal performance and SEO UI Components: Tailwind CSS + shadcn/ui for beautiful, accessible components Web3 Integration: OnchainKit for wallet connections and blockchain interactions State Management: React Query for server state + Zustand for client state Image Capture: Custom camera component with fallback to file upload Backend (Rust + Actix-web) API Layer: High-performance Rust server handling 10k+ concurrent users AI Services: Vision: Custom fine-tuned ViT-tiny model (optimized for <1s inference) Coaching: Venice AI's qwen3-235b with RAG (Retrieval-Augmented Generation) Database: PostgreSQL with Diesel ORM for structured data Caching: Redis for session management and rate limiting

1. AI/ML Pipeline Image Processing: On-device image compression (WebP) before upload Server-side preprocessing with Rust's image-rs Custom data augmentation pipeline for model training Model Serving: ONNX Runtime for optimized inference Dynamic batching for handling traffic spikes