How Does Minchyn’s Architecture Support Scalability?

Frontend Architecture: #

Minchyn’s mobile application is built with React Native 0.74.5 and Expo SDK 51, providing a native experience on iOS and Android from a single codebase. The app uses Expo Router for file-based navigation, making development intuitive and maintainable. MobX handles reactive state management, ensuring efficient data flow and UI updates. The custom component library includes reusable UI elements like buttons, cards, badges, and modals–all theme-aware and TypeScript-typed. For media handling, Expo AV provides robust video playback with gesture controls and performance optimization.

Backend Microservices: #

The backend consists of multiple specialized microservices built with Django and FastAPI. The main API service handles user authentication, content management, and social features using Django REST Framework. ML services run on FastAPI for high-performance inference, powering recommendation algorithms and content analysis. Analytics services process millions of events daily using Celery for background tasks. The video processor service handles media encoding and optimization. All services communicate through a unified API gateway with load balancing and rate limiting. PostgreSQL serves as the primary database with Redis for caching and real-time features.

AI/ML Infrastructure: #

Minchyn’s intelligence layer includes three core systems working in concert. The Adaptive Engagement Transformer (AET) uses transformer encoders and graph neural networks to recommend content based on user behavior patterns. The Social Graph Meta-Learner (SGML) employs graph attention networks to match creators with their ideal audiences. The Supreme Engagement Engine analyzes psychological patterns to optimize content delivery timing and presentation. All models train continuously on user interactions, improving recommendations over time. The ML pipeline runs on PyTorch with GPU acceleration for real-time inference at scale.