I Built a Production App with Cursor and Claude 3.7 — Here's What Broke

I Built a Production App with Cursor and Claude 3.7 — Here's What Broke

By Ebuka Onah

AI coding tools are incredible—until they aren't.

A few weeks ago, I decided to build a real production application using Cursor and Claude 3.7. Not a toy project. Not a weekend demo. A real app with authentication, payments, APIs, and users who would absolutely complain if something broke.

The experience was impressive, frustrating, fast, and occasionally terrifying.

AI can write code at lightning speed. It can also create bugs at exactly the same speed.

What AI Did Extremely Well

For the first 70% of development, it felt like cheating.

  • Generated boilerplate instantly
  • Built CRUD operations in minutes
  • Handled repetitive components beautifully
  • Wrote database schemas quickly
  • Saved hours on documentation

Tasks that normally take an afternoon were done before my coffee got cold.

Then Reality Arrived

Production software is where the easy part ends.

The moment I moved beyond simple features, cracks started showing.

  • Authentication edge cases were missed
  • Error handling was often incomplete
  • Generated code sometimes ignored existing architecture
  • Dependency versions occasionally conflicted
  • Refactoring introduced subtle regressions
AI is brilliant at local optimization and terrible at remembering your entire system.

The Biggest Problem: False Confidence

The code looked polished. Clean variable names. Proper formatting. Sensible comments.

And yet, some of it was wrong.

That's the dangerous part. Bad human code often looks suspicious. Bad AI code can look professional while quietly sabotaging your weekend.

Debugging Changed Completely

I spent less time writing code and more time reviewing it.

That sounds efficient, but it shifts the bottleneck. You stop being a builder and become an editor.

  • Reading generated code carefully
  • Testing every assumption
  • Validating API integrations
  • Catching hallucinated libraries
  • Fixing architectural shortcuts

Where Cursor and Claude Shined

They were exceptional for:

  • Rapid prototyping
  • Writing tests
  • Explaining unfamiliar code
  • Generating migration scripts
  • Refactoring repetitive logic

They felt less like replacement developers and more like tireless pair programmers.

What Still Required Human Judgment

No surprise here: the hard parts remained hard.

  • System architecture
  • Security decisions
  • Performance trade-offs
  • Database design
  • Product decisions
AI can suggest. It cannot own consequences.

The Real Lesson

AI coding assistants do not eliminate engineering. They amplify it.

A strong developer becomes dramatically faster. A weak developer simply makes mistakes more efficiently.

That's not criticism—it's leverage.

Would I Use It Again?

Absolutely.

But I would never ship AI-generated code without rigorous review, tests, and healthy skepticism.

Trust, but verify. Then verify again.

AI won't replace software engineers. But engineers who use AI well will absolutely replace those who don't.