4 min read3 hours ago

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While my classmates were submitting standard lab assignments, I decided to build a real production system. Here’s what I learned.

TL;DR: I’m an 18-year-old student who built SkillSwapAI — a skill-exchange platform with microservices architecture (NestJS + Python AI service). I spent my entire student stipend on servers, dealt with race conditions in WebSockets, and learned how real systems work. Here’s the story.

The Challenge That Started It All

Let me be honest: I was tired of building todo lists and e-commerce clones. Every programming course seems to end with the same projects. But I wanted to understand how real, scalable systems work — the kind that tech companies actually build.

So I set myself a challenge: create SkillSwapAI, a platform for skill exchange and mentorship matching, powered by a proper microservices architecture with AI integration.

Spoiler: It was painful, expensive, and absolutely worth it.

Why This Tech Stack?

I could have built everything in Node.js. It would’ve been simpler. But here’s the thing — I wanted to use the right tool for each job, not just the easiest one.

Here’s what I chose and why:

Frontend: Next.js 14 (App Router)

Server-side rendering for SEO and better performance. The new App Router made routing cleaner and more intuitive.

Core Backend: NestJS

TypeScript-first framework with dependency injection, guards, and a modular structure. It’s like having guardrails that prevent you from writing spaghetti code.

AI Engine: Python (FastAPI)

A separate microservice for generating personalized learning paths and skill recommendations. Python is the standard for AI work, and FastAPI makes it blazingly fast.

Infrastructure

• Docker & Docker Compose for containerization
• AWS S3 for file storage (avatars)
• PostgreSQL (Neon.tech) for the database

Architecture: How It All Connects

The key principle: separation of concerns.

Here’s the flow:

1. Client (Next.js) sends requests to the API Gateway (NestJS)
2. NestJS handles business logic (auth, database operations, validation)
3. When a user needs a learning plan or recommendations, NestJS makes an internal request to the Python service
4. Python service processes data, calls OpenAI API, and returns structured results
5. All files (avatars) upload directly to AWS S3 — keeping containers clean

The beauty? Each service can scale independently. If the AI service gets hammered with requests, I can spin up more Python instances without touching the main backend.

The Real Pain Point: Real-Time Communication

The biggest challenge wasn’t setting up Docker or connecting services. It was implementing real-time chat with WebSockets.

At first, it seemed simple: just plug in Socket.io and you’re done. Wrong.

The Race Condition From Hell

Messages were being sent before the socket connection was fully established on first load. Users would send a message, refresh the page, and only then see the chat history.

Classic race condition.

I had to dive deep into:

• Socket.io lifecycle hooks
• React useEffect dependencies and cleanup
• Connection state management
• Event ordering and buffering

The solution involved properly sequencing the connection establishment, waiting for acknowledgment before allowing message sends, and implementing a reconnection strategy with exponential backoff.

Lesson learned: Real-time systems are unforgiving. You need to think about every edge case — network failures, concurrent connections, message ordering, and state synchronization.

What I Built (And What I Learned)

Right now, the project is deployed and running (still MVP stage, but functional).

Features implemented:

• User authentication and profiles
• Skill posting and matching
• AI-powered learning plan generation
• Real-time messaging between users
• File uploads to cloud storage

What this taught me:

1. Think in systems, not functions. It’s not about “how to write a loop” — it’s about “how does Service A talk to Service B when Service C goes down?”
2. Infrastructure matters. Docker Compose locally is very different from production deployment. Networking between containers, environment variables, health checks — all critical.
3. AI integration is complex. It’s not just calling an API. You need prompt engineering, error handling for when OpenAI is down, rate limiting, and cost management.
4. Real-time is hard. WebSockets, connection states, reconnection logic — there’s a reason companies hire specialists for this.

What’s Next?

I’m actively improving the codebase. Next up:

• Message queues with Redis for better scalability
• Kubernetes deployment for production-grade orchestration
• Monitoring with Prometheus and Grafana
• Rate limiting and better API security

I’m also looking for my first team as a Junior Full-Stack Engineer where I can continue growing and learning from experienced developers.

Final Thoughts

Building this project cost me my entire student stipend (those AWS bills hit different). But I learned more in 3 months than in an entire semester of traditional coursework.

If you’re a student or junior developer, my advice: build something that scares you. Not another tutorial project — something where you’ll inevitably get stuck and have to figure it out.

That’s where the real learning happens.

I’m building in public and sharing my journey. Follow me on X/Twitter [@GLOBEST7] for more updates on this project and my path from student to engineer.

What was your first “real” project? What challenges did you face? Let me know in the comments!

Tags: #Microservices #NestJS #Python #AI #WebDevelopment #StudentDeveloper #BuildInPublic #FullStack