I Went Back to Writing My Own Code After a Month With GitHub Copilot

For the past month, I let GitHub Copilot guide my hands. Line after line, suggestion after suggestion — the AI pair programmer promised speed, shortcuts, and fewer keystrokes. And at first, it felt magical. My screen filled with auto-completed functions, boilerplate vanished in seconds, and I was shipping features at lightning speed.

But then reality set in.

The more I leaned on Copilot, the more I found myself stuck in a cycle: fixing subtle bugs, re-checking logic, and untangling code that “looked right” but didn’t quite work. Instead of building a direct Minimum Viable Product (MVP), I was firefighting issues I hadn’t planned for.

So last week, I made a decision: I’m going back to writing my own code.

The Honeymoon Phase With Copilot

The first week felt like an upgrade.

• Boilerplate code? Gone.
• API calls? Autocompleted.
• Unit tests? Suggested before I could think of edge cases.

I felt like I had an invisible senior engineer sitting beside me, whispering code snippets I could copy-paste. Productivity metrics looked great on the surface — more commits, more features, more speed.

But then came the tradeoffs.

When Fast Becomes Fragile

The core problem with Copilot wasn’t speed — it was precision.

Copilot often generates plausible-sounding code. And plausible is dangerous. Code that looks “right enough” sneaks past your tired eyes during late-night coding sessions. It runs once, maybe twice, but then it crumbles under real-world inputs.

I noticed three patterns emerging:

1. Hidden Bugs Multiply Copilot didn’t always understand the business logic or domain-specific quirks. It would guess. And those guesses led to subtle errors that weren’t obvious until later.

2. Debugging Took Longer Fixing AI-generated bugs often took more time than writing clean code from scratch. I wasn’t just debugging my logic — I was debugging Copilot’s assumptions.

3. Lost Product Focus Instead of shipping a lean MVP, I got caught chasing fixes. Each bug pulled me further from my original goal: build something small, functional, and testable.

Speed without direction is a mirage.

Why I Chose to Go Back

After a month, I realized something: Copilot is a tool, not a strategy.

If your goal is to learn, experiment, or quickly scaffold an idea, Copilot shines. But if your goal is to build a direct MVP, with as little friction as possible, the tool can distract more than it helps.

For me, going back to manual coding meant:

• Clarity: I fully understood each function I wrote.
• Control: No hidden assumptions creeping in.
• Confidence: Bugs were my bugs, not “AI’s bugs.”

There’s also a creative joy in writing code yourself. Copilot may autocomplete, but it doesn’t truly design. That design — the architecture, the thought process — that’s where the craft lives.

The Balanced Approach Going Forward

I’m not uninstalling Copilot. It still has value. But I’m reframing how I use it.

• ✅ For boilerplate: Scaffolding React components, configuring routes, writing simple API wrappers.
• ✅ For exploration: Seeing alternative patterns or testing quick snippets.
• ❌ For business logic: Anything that defines the core of my product, I’ll write myself.
• ❌ For MVP builds: When speed to clarity matters more than speed to commit, I’ll go manual.

The balance is simple: let Copilot handle the repetitive tasks, but keep the steering wheel firmly in my hands.

Final Thoughts

GitHub Copilot is fast. But speed isn’t the same as progress.

After a month, I’ve learned that an MVP isn’t just about getting code on the screen — it’s about building something small, reliable, and testable. For that, clarity matters more than shortcuts.

So yes, I’m back to writing my own code. Copilot will stay in the toolbox, but not in the driver’s seat.

Because sometimes the fastest way to ship is to slow down and code with intent.