🚀 Dev Automation Platform

A full-stack SaaS platform for developer automation tools Built with a modern React frontend and a distributed Node.js backend using async job processing

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🌐 Live Website

👉 https://dev-automation-platform.vercel.app/

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✨ Features

🛰️ API Monitoring

• Monitor APIs with custom intervals
• Detect incidents (errors, slow responses, failures)
• Generate AI-powered summaries
• Send email alerts on status changes (UP ↔ DOWN)
• View monitoring history in dashboard

📸 Screenshot Capture

• Capture full-page screenshots of any website
• Process jobs asynchronously via worker queue
• Store images in AWS S3 for distributed access
• Track job status in real time

🤖 AI Commit Generator

• Generate structured commit messages from git diff
• Enforce conventional commit format
• Includes fallback logic for reliability

🔐 Authentication

• JWT-based authentication
• Per-user data isolation
• Guest login for quick access

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🏗️ Architecture

Frontend (React + TypeScript)
        ↓
Backend API (Node.js + Express)
        ↓
Redis Queue (BullMQ)
        ↓
Worker Services
        ↓
PostgreSQL

🔑 Key Concepts

• Distributed backend with separate worker services
• Asynchronous job processing using Redis + BullMQ
• Production-safe file storage using AWS S3
• Real-time UI updates via polling
• Multi-user system with secure data isolation

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⚙️ Tech Stack

🎨 Frontend

• React
• TypeScript
• Tailwind CSS
• shadcn/ui
• React Query

🧠 Backend

• Node.js
• Express
• PostgreSQL
• Redis
• BullMQ
• Puppeteer

☁️ Infrastructure

• AWS S3
• Railway (API, workers, Redis, PostgreSQL)
• Vercel (frontend)

🤖 AI

• Groq API (OpenAI-compatible)

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🔄 System Flow

📸 Screenshot Flow

User submits URL
→ Job added to Redis queue
→ Worker processes with Puppeteer
→ Upload to S3
→ UI polls and displays result

🛰️ Monitoring Flow

User creates monitor
→ Scheduled job runs
→ Worker checks API
→ Result stored in PostgreSQL
→ Incident detected + summarized
→ Email alert sent

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🛡️ Production Considerations

• Rate limiting to prevent abuse
• Per-user quotas for monitors and jobs
• Ownership checks for secure access
• Async processing to avoid blocking API
• Fallback handling for AI failures
• Distributed storage (S3) for multi-service setup

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🌍 Deployment

• Frontend → Vercel
• Backend API → Railway
• Workers → Railway
• Database → Railway PostgreSQL
• Queue → Railway Redis

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📌 What I Learned

• Designing async systems with queues and workers
• Building distributed backend architecture
• Handling real production issues (CORS, worker isolation, storage)
• Integrating AI features into real applications
• Deploying and debugging fullstack systems

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📎 Repository

🔗 https://github.com/hereisben/dev-automation-platform

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