Full-Stack AI Development in 2025

After spending 6 months building the Ethical AI Stack - 84,000 lines of code across four interconnected AI projects - I've learned a lot about what works and what doesn't in modern AI development.

The Architecture

The stack consists of four layers, each built with different technologies:

• Philosophy Layer: TypeScript + Next.js (UI)
• Memory Layer: Python + FastAPI (MCP Server)
• Knowledge Layer: Python + SQLAlchemy (Vector Search)
• Application Layer: TypeScript + React (Dashboard)

This polyglot approach works better than trying to use a single language everywhere.

Tech Stack Decisions

Next.js 15 → 16 Migration

Upgrading Next.js mid-project was... interesting. The Turbopack + MDX incompatibility forced us to get creative:

• Initially tried Turbopack with `@next/mdx` → Runtime errors
• Fell back to webpack → Slow builds
• Finally upgraded to Next.js 16 + mdxRs → Success!

Lesson: Don't bet on experimental combinations in production.

MDX vs Markdown

We chose MDX for articles, but in hindsight, plain Markdown with code highlighting would have been simpler:

• MDX promises "components in Markdown" → rarely needed
• Build complexity increased significantly
• Tooling support is still maturing

For future projects, I'll evaluate whether MDX is actually necessary or just "nice to have".

Performance Learnings

Turbopack Real-World Performance

After disabling Turbopack temporarily, we measured actual build times:

• With Turbopack: 38s (when it worked)
• With Webpack: 62s (fallback)
• Next.js 16 + Turbopack + mdxRs: <30s (final solution)

The speedup is real but requires stable combinations of dependencies.

Database Choices

For the Knowledge Layer, we chose SQLite over PostgreSQL for simplicity:

• Zero configuration required
• Great for development and testing
• Can migrate to PostgreSQL later if needed

For the Memory Layer, we used file-based storage with JSON.

Career Perspective

Building this stack has been the most challenging and rewarding project of my career.

Skills Developed

• Polyglot programming (TypeScript + Python)
• System architecture across multiple languages
• Integration testing across stack boundaries
• Performance optimization across multiple runtimes
• Technical documentation for complex systems

Market Positioning

Being a "full-stack AI developer" in 2025 means:

• Understanding both frontend and backend AI integration
• Being able to ship complete features independently
• Communicating technical decisions clearly
• Balancing speed with code quality

What's Next?

The Ethical AI Stack is now functional, but there's always more to build:

Short Term (Q1 2025)

• Add more sample articles to the Knowledge layer
• Implement user authentication
• Add commenting system for articles
• Deploy to production environment

Long Term (2025+)

• Multi-user support with role-based access
• Advanced analytics dashboards
• Integrate with external AI providers (OpenAI, Anthropic, etc.)
• Open source the framework for others to use

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Lessons Learned

#1: Stability Over Speed

It's tempting to use the latest features, but stable combinations are more important than cutting-edge speed.

Example: We waited for Next.js 16 stability before upgrading.

#2: Document Decisions

Writing down the architecture rationale saved us multiple times when debugging tricky integration issues.

Result: This entire article documents our technical decisions.

#3: Test Early and Often

Integration testing across the stack caught issues that unit tests missed.

We added 21 integration tests for MDX components.

Conclusion

Full-stack AI development in 2025 is complex but rewarding. The key is to choose stable technologies, document decisions, and test thoroughly.

The Ethical AI Stack demonstrates that with careful architecture and attention to ethical considerations, we can build AI systems that users can trust and developers can maintain.

Ready to see the code? Check out the{' '} projects page {' '} or explore the{' '} GitHub repository .