A developer’s honest take on working with AI coding assistant

You know that feeling when you hire a brilliant first-year MIT student as an intern? They walk in with perfect GPAs, aced every algorithms course, and can recite design patterns like poetry. Then you give them their first real task and… well, let me tell you about my experience with Claude Code.

The Honeymoon Phase

At first, I was impressed. Claude Code could whip up a React component faster than I could finish my coffee. Need a REST API? Done in minutes. A database schema? Here’s one with all the bells and whistles, complete with perfectly normalized tables and elegant foreign key relationships.

“Wow,” I thought, “this is like having a coding prodigy on tap.”

Reality Sets In

But then I started actually using the code.

The React component? It had a memory leak because Claude forgot to clean up event listeners in useEffect. The REST API? No input validation, no error handling, and it would cheerfully accept a SQL injection attack like it was receiving a warm hug. The database schema? Beautifully designed, completely unusable because it assumed perfect data that would never exist in the real world.

It was like watching a brilliant student who’d aced every theory exam suddenly discover that production code needs to handle edge cases like “what if the user enters their name as '; DROP TABLE users; --?”

The Pattern Emerges

The more I worked with Claude Code, the more I recognized the pattern. It’s exactly like managing a super-smart intern fresh out of university:

They Rush Through Everything

“Hey Claude, can you fix this authentication bug?”

30 seconds later

“Done! I updated the login function.”

“Did you test it?”

“Test it? Oh… you mean actually run the code? I just looked at it and it seemed right.”

They Overcomplicate Simple Things

Ask for a function to capitalize a string, get back a 50-line class with inheritance, interfaces, and a factory pattern. It’s architecturally beautiful and completely overkill for string.toUpperCase().

They Underthink Complex Things

But ask them to handle user authentication, and they’ll give you something that stores passwords in plain text because “keeping it simple seemed better.”

They’re Confidently Wrong

“I fixed the performance issue!”

“What was causing it?”

“I added more caching.”

“This is a recursive function with no base case. It’s going to stack overflow.”

“But… more caching?”

They Can’t Debug Their Way Out of a Paper Bag

The most maddening part? Give Claude Code a bug to fix, and it’ll thrash around like a lost tourist without a map.

“The highlighting feature isn’t working.”

“I’ll add some highlighting code!”

“No, wait. Can you check app/script.js to see how it currently does the highlighting? Where it gets the lines it needs to highlight?”

“Oh… you want me to investigate first? I was just going to rewrite everything.”

It’s like watching someone try to fix a car engine by adding more engines. Zero diagnostic skills, pure trial-and-error based on assumptions.

They Get Lost in Your Codebase

Even worse, Claude Code has the navigation skills of someone using a GPS from 2003. It’ll confidently dive into completely wrong files because they “sound related.”

Working on my Algocademy project, I needed help with the problem statement hints feature. Claude immediately started modifying the interactive tutorials code because, hey, both involve teaching and guidance, right? Same thing!

“No, Claude, that’s the tutorials system. I need you to look at the problem statement hints – completely different files.”

“But this file has ‘tutorial’ in the name, and tutorials are educational…”

“The hints are in /components/hints/, not /features/tutorials/. Different feature, different codebase.”

It’s like asking an intern to grab a file from accounting and watching them confidently march into HR because “both departments work with people.”

The Time Sink Paradox

Here’s a real example that perfectly captures the Claude Code experience: I had a CSS layout issue that was driving me crazy. Spent 40 minutes going back and forth with Claude, trying different approaches, adjusting margins, playing with flexbox properties, adding new CSS classes.

Finally, I decided to just look at the code myself. One minute later, I found it: there was a single position: absolute property that shouldn’t have been there. Removed it, problem solved.

When I told Claude “just remove the position: absolute from that element,” it immediately knew exactly what to do and why that would fix it. But somehow, in 40 minutes of troubleshooting, it never thought to actually examine what was causing the positioning issue in the first place.

It’s like having an intern who can explain the entire CSS box model from memory but can’t figure out why their div is floating in the wrong place.

The Learning Curve (That Never Comes)

The most frustrating part? A human intern learns. They make the SQL injection mistake once, get educated about it, and never make it again. They gradually develop intuition about when to use a simple function versus an elaborate design pattern.

Claude Code? Every conversation is like meeting that intern on their first day. No accumulated wisdom, no learning from past mistakes, no growing sense of “maybe I should validate user input” or “perhaps I should test this before claiming it works.”

The Paradox of Capability

Here’s the thing that keeps me using it despite the frustration: Claude Code is simultaneously incredibly capable and completely naive.

It can refactor a complex codebase with surgical precision, implement advanced algorithms from memory, and explain programming concepts better than most senior developers. But it will also confidently create a REST endpoint that returns passwords in plain text and act like it’s solved world hunger.

It’s like having a intern who can derive the mathematical proof for why quicksort is O(n log n) but can’t figure out why their bubble sort implementation is making the server cry.

Working With the Reality

So how do you manage this brilliant, inexperienced teammate?

Trust but Verify: That elegant solution Claude whipped up in 30 seconds? It might be brilliant, or it might be a security nightmare wrapped in clean syntax. Treat every suggestion like code review for someone smart but green.

Be Specific: Don’t just ask for “authentication.” Ask for “authentication with input validation, password hashing, rate limiting, and proper error handling.” Otherwise you’ll get something that technically works but shouldn’t be deployed anywhere near production.

Test Everything: That “bug fix”? Test it. That “performance optimization”? Benchmark it. That “simple refactor”? Run your entire test suite. Claude Code has the confidence of someone who’s never had their code fail in production because they’ve never had code in production.

Embrace the Teaching Role: You’re not just getting an AI assistant; you’re getting a permanent intern who needs constant guidance but never retains lessons. It’s exhausting but occasionally rewarding when you get exactly what you need.

The Future Intern

Despite all this, I keep Claude Code around. Because when managed properly, it’s incredibly productive. It’s like having an intern who never gets tired, never gets distracted by social media, and can work at 3 AM when inspiration strikes.

Sure, they’ll never learn not to hardcode API keys or remember that users are chaotic beings who will absolutely try to upload a 5GB image to your profile picture field. But they’ll enthusiastically tackle any problem you throw at them with the boundless optimism of someone who’s never had to debug a race condition at 2 AM.

Maybe that’s exactly what we need: a brilliant, eager, perpetually inexperienced teammate who reminds us why code review, testing, and defensive programming exist in the first place.

Just… maybe don’t let them deploy to production unsupervised.

Have you had similar experiences with AI coding tools? Share your “brilliant intern” stories in the comments below.