Intermediate Full-Stack Projects

You’ve mastered the basics of CRUD. Now, it's time to dive into the world of distributed systems. These projects require you to think about concurrency, real-time communication, and background processing.

Project 1: Miniature Video Streaming Platform

Think of this as a "CodeHarborHub Clips" platform.

• The System:
  • Frontend: A video player with "Netflix-style" hover previews.
  • Backend: Node.js API that handles metadata (titles, tags).
  • Storage: AWS S3 for the video files.
  • The Master's Twist: Implement Transcoding. Use a background worker (or AWS Lambda) to automatically create a "Low Resolution" (360p) version of any video a user uploads.

Project 2: Real-Time Collaborative Workspace

A simplified version of Slack or Discord for study groups.

• The System:
  • Communication: Use WebSockets (Socket.io) for instant messaging.
  • Database: PostgreSQL for message history and Redis for "Who's Online" status.
  • Cloud: Deploy using Docker Containers.
  • The Master's Twist: Implement Horizontal Scaling. If you have two servers, how do you make sure a user on Server A can chat with a user on Server B? (Hint: Use the Redis Pub/Sub pattern).

Project 3: Real-Time Analytics Dashboard

An app that tracks "Live Visitors" on a website and shows them on a map.

• The System:
  • Data Ingestion: A tiny script (Pixel) that sends a "ping" to your server whenever a page is viewed.
  • Processing: Use a Message Queue (like RabbitMQ or BullMQ) to handle thousands of pings per second without crashing the main API.
  • Visualization: Use D3.js or Chart.js on the frontend.
  • The Master's Twist: Use Database Sharding. Store visitor data in different table partitions based on the month to keep queries fast.

Project 4: AI-Powered Resume Screener

An automated tool that reads PDFs and ranks them based on keywords.

• The System:
  • Frontend: Drag-and-drop file upload.
  • Backend: Python or Node.js service that parses PDF text.
  • AI Integration: Send the text to an LLM (like Gemini or OpenAI) to summarize the skills.
  • The Master's Twist: Implement an Asynchronous Workflow. Don't make the user wait for the AI to finish. Give them a "Processing..." status and send an email notification when the result is ready.

5. The "Intermediate" Architecture Standard

To pass the "Master" level for these projects, your architecture must include:

1. Containerization: Everything must run in Docker.
2. API Gateway: A single entry point (like Nginx) that routes traffic to different microservices.
3. Centralized Logging: Use a tool (like Winston or ELK stack) to see logs from all your services in one place.
4. Health Checks: A /health route that monitors if your database and cache are actually connected.

Practice: The "Architecture First" Approach

Before you write a single line of code for these projects:

1. Draw a System Diagram showing how the Frontend, API, Database, and Cache interact.
2. Identify the Bottleneck. (Where will the system break first if 100,000 people join?)
3. Choose your Cloud Strategy. (Will you use EC2, Lambda, or a Managed Kubernetes service?)

The Cost

At the intermediate level, you are using more cloud resources. Always check the pricing of the services you use. A Master never leaves a "Heavy" database or an "Autoscaling Group" running over the weekend if they aren't using it!