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OnlyAutomator Architecture

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System Overview

OnlyAutomator is a comprehensive system designed to automate interactions with the OnlyFans platform. The architecture consists of the following core components:
  1. Web Application: A Next.js-based web interface for users to manage their automation workflows
  2. Chrome Extension: A browser extension that interacts with OnlyFans
  3. Microservice Backend: A Node.js-based service for handling background processes and scheduled tasks
  4. Database: Supabase PostgreSQL database for storing user data and automation configurations
  5. Email Marketing System: For user communications and notifications

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Detailed Components

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Web Application

The web application provides the main user interface for OnlyAutomator:
  • Technology: Next.js, React, TailwindCSS
  • Key Features:
    • User authentication and account management
    • Dashboard with analytics and insights
    • Automation workflow configuration
    • Fan management tools
    • Subscription tracking
    • Mass messaging features

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Chrome Extension

The Chrome extension is responsible for executing automation tasks directly on OnlyFans:
  • Technology: JavaScript, Chrome Extension API
  • Key Features:
    • Secure authentication with OnlyFans
    • Fan data collection
    • Automated messaging
    • Content scheduling
    • Engagement tracking

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Microservice Component

The microservice handles background processing and scheduled tasks:
  • Technology: Node.js, Express, TypeScript
  • Key Features:
    • Scheduled automation tasks
    • Data aggregation and processing
    • Webhook handling for external integrations
    • API endpoints for extension and webapp communication
    • Email notifications generation

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API Endpoints

The microservice exposes several key endpoints:
  • /api/automation/schedule: Manages scheduled automation tasks
  • /api/fans/sync: Synchronizes fan data from OnlyFans
  • /api/messages/queue: Handles queued messages for fans
  • /api/analytics/generate: Generates analytics reports

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Data Flow

  1. User configures automation in the web app
  2. Configuration is stored in the database
  3. Microservice schedules and manages execution tasks
  4. Chrome extension receives instructions and executes them on OnlyFans
  5. Results are sent back to the microservice and stored in the database
  6. Web app displays results to the user

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Database

The database layer stores all application data:
  • Technology: PostgreSQL via Supabase
  • Key Schemas:
    • Users: User account information
    • Fans: Fan profiles and metadata
    • Messages: Automated message templates and history
    • Schedules: Automation scheduling configurations
    • Analytics: Performance data and metrics

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Deployment Architecture

The OnlyAutomator system uses a multi-environment deployment strategy:
  • Web Application: Hosted on Vercel for seamless CI/CD integration
    • Development, staging, and production environments
    • Automatic previews for pull requests
    • Edge functions for API routes
  • Microservice Backend:
    • Primary deployment: Cloudflare Workers for global distribution
    • Secondary deployment: Contabo VPS for specialized workloads
      • Deployed using GitHub Actions workflow
      • Managed with PM2 for process management
      • Configured for auto-restart and logging
      • Runs on Node.js 18.x
  • Database: Hosted on Supabase
    • PostgreSQL with row-level security
    • Real-time subscriptions for live updates
    • Automated backups and point-in-time recovery
  • Chrome Extension: Distributed through the Chrome Web Store
    • Manual release process with version control
    • Beta testing channel for early adopters

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Security Architecture

The system implements several security measures:
  • Authentication: OAuth 2.0 with JWT tokens
  • Data Encryption: All sensitive data encrypted at rest and in transit
  • API Security: Rate limiting, CORS policies, and request validation
  • Secrets Management: Environment variables for all credentials, no hardcoded secrets
  • Access Control: Role-based permissions system

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Scaling Strategy

The architecture is designed to scale horizontally:
  • Stateless Components: All services are designed to be stateless
  • Database Scaling: Connection pooling and read replicas for high load
  • Caching Layer: Redis cache for frequently accessed data
  • Load Balancing: Distributed traffic across multiple instances
  • Microservices: Modular design allows independent scaling of components

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Monitoring and Observability

The system includes comprehensive monitoring:
  • Application Metrics: Response times, error rates, and throughput
  • System Metrics: CPU, memory, and network utilization
  • User Metrics: Active users, conversion rates, and engagement
  • Alerting: Automated alerts for system anomalies
  • Logging: Centralized log collection and analysis