Architecture
Technical architecture and system design of the CodeNull platform
CodeNull Architecture
CodeNull follows a modern, scalable architecture designed to provide a seamless experience for creating web applications without coding. This document outlines the technical components and how they interact to power the platform.
System Overview
The CodeNull platform is built on a microservices architecture with a clear separation between frontend interfaces, backend services, AI processing, and data storage layers. This architecture ensures scalability, maintainability, and the ability to evolve individual components independently.
Architecture Components
User Interface Layer
Next.js-based frontend with dashboard interfaces
API Services Layer
FastAPI-powered backend services
AI Generative Core
AI models for code and content generation
Database Systems
MongoDB for flexible data storage
Storage Services
Asset management and file storage
DevOps Infrastructure
CI/CD pipelines and containerization
Technical Stack
Next.js
React framework for server-rendered applications
Tailwind CSS
Utility-first CSS framework
ShadCN
Accessible component library
Lucide
High-quality icon set
Liveblocks
Real-time collaboration features
The frontend is built with Next.js, offering server-side rendering for optimal performance and SEO. Tailwind CSS and ShadCN provide a consistent design system, while Liveblocks enables real-time collaboration features.
Next.js
React framework for server-rendered applications
Tailwind CSS
Utility-first CSS framework
ShadCN
Accessible component library
Lucide
High-quality icon set
Liveblocks
Real-time collaboration features
The frontend is built with Next.js, offering server-side rendering for optimal performance and SEO. Tailwind CSS and ShadCN provide a consistent design system, while Liveblocks enables real-time collaboration features.
Python 3.10+
Core programming language
FastAPI
High-performance API framework
Uvicorn
ASGI web server
Poetry
Python dependency management
Docker
Containerization
The backend services are developed with Python and FastAPI, providing asynchronous request handling with high performance. Uvicorn serves as the ASGI server, while Poetry manages dependencies and Docker containerizes the application for consistent deployment.
MongoDB
NoSQL document database
Mongoose
MongoDB object modeling
MongoDB serves as the primary database, offering flexibility for varying data structures. Mongoose provides a schema-based solution for modeling application data.
Azure OpenAI
Language model services
NVIDIA NIM
Accelerated inference microservices
Cloudinary
Media asset management
Azure OpenAI services power the AI code generation capabilities, while NVIDIA NIM accelerates AI inference for faster performance. Cloudinary handles media asset management and optimization.
Vercel
Frontend deployment platform
Azure Container Apps
Backend service hosting
GitHub Actions
CI/CD pipeline automation
Vercel hosts the frontend applications with global edge deployment, while Azure Container Apps runs the containerized backend services. GitHub Actions automates testing and deployment processes.
System Flows
User Interface Generation Flow
User Input
User provides a description of the desired UI component or page
AI Processing
The description is processed by the AI generative core
Code Generation
AI generates React/Next.js component code with Tailwind styling
Preview Rendering
Generated code is rendered in real-time for user preview
Customization
User can modify generated components through the visual editor
Integration
Component is integrated into the user’s project codebase
API Creation Flow
API Definition
User defines API requirements through the visual interface
Schema Generation
The system generates OpenAPI schema based on requirements
Logic Implementation
Backend logic is created through visual flow builder
Database Connection
API endpoints are connected to database collections
Testing
Endpoints are tested through the built-in testing interface
Deployment
API is deployed as serverless functions or container services
Scalability Design
The platform is designed for horizontal scalability:
- Stateless services: Backend components are stateless for easy scaling
- Containerization: All services are containerized for consistent deployment
- Microservices: Independent scaling of individual services based on load
- Database sharding: Data distribution for high-volume applications
- Edge computing: Global CDN for frontend assets and API caching
- Auto-scaling: Automatic resource adjustment based on demand