The Day My Boss Asked Me to Explain My "Clever" Code
A story about writing code for humans, not just computers
Hi, I'm Maya. I was three months into my first real Python job when Marcus, my tech lead, pulled up a chair next to my desk.
"Hey, can you walk me through this?" He pointed at my screen.
My stomach dropped. It was the data processing module I'd submitted for code review the day before. I'd been particularly proud of it. The entire validation pipeline fit into a single line.
validate = (lambda check: check(check))(lambda check: lambda data:
True if not data else (data[0].isvalid() and check(check)(data[1:])))
"It's a recursive validator," I explained, feeling my confidence waver as I watched his expression. "It goes through the dataset and checks each item. All in one lambda. No function definitions needed."
Marcus was quiet for a moment. "Can you add comments explaining what each part does?"
I opened my mouth, then closed it. Where would I even start?
"I learned this pattern from a functional programming article," I said. "It's supposed to be more Pythonic."
He nodded slowly. "I can see you put thought into this. But here's the thing—in six months, when you're debugging a validation failure at 2am, will you remember how this works?"
I thought about it. Honestly? Probably not.
"And what about when Sarah needs to modify the validation rules? Or when we onboard a new developer?"
I felt my face getting warm. "I guess they'd have to ask me."
"Exactly. You've created a dependency where there doesn't need to be one."
What I Learned That Day
Marcus didn't tell me the code was wrong. He asked me to refactor it with one principle in mind: write code that explains itself.
Here's what I came up with:
def validate_dataset(data):
"""
Validates all items in the dataset.
Returns True only if all items pass validation.
"""
if not data:
return True
first_item = data[0]
remaining_items = data[1:]
if not first_item.isvalid():
return False
return validate_dataset(remaining_items)
It's longer. It takes up more lines. But something interesting happened when I rewrote it—I found a bug. In my original version, I wasn't handling empty validation results correctly. The nested lambdas obscured the edge case where an empty list was passed initially, something that became immediately obvious when I wrote out the logic explicitly.
The Real Cost of "Clever" Code
Over the next year, I learned something that changed how I think about programming: the computer doesn't care how you write your code. Every programmer knows this intellectually, but it took me time to internalize it.
Your code could be elegant lambdas or verbose functions—the machine compiles it the same way. But your teammates? They care a lot.
Code is read far more often than it's written. For every hour I spent writing that validator, the team would spend collective hours reading it, modifying it, debugging it, testing it.
When I wrote "clever" code, I was optimizing for the wrong thing. I was optimizing for the ten minutes of writing instead of the hours of maintenance.
What "Pythonic" Really Means
I thought being Pythonic meant using advanced features, writing compact code, showing off what Python could do.
I was wrong.
Being Pythonic means writing code that other Python developers can understand quickly. It means using the language's features in expected ways. It means valuing clarity over cleverness.
The Zen of Python says it better than I can:
- Explicit is better than implicit
- Simple is better than complex
- Readability counts
My nested lambda was implicit, complex, and hard to read. The refactored version was explicit, simple, and readable.
The Questions I Ask Myself Now
Before I submit code for review, I ask:
- Can I understand this after not looking at it for a month?
- Could a junior developer figure out what this does?
- If this breaks in production, can someone debug it without calling me?
- Am I being clever, or am I being clear?
That last question is the most important. Cleverness feels good in the moment, but clarity serves the team.
When Complex Code Is Necessary
Sometimes you genuinely need complex code. Performance-critical sections, sophisticated algorithms, framework internals—these all justify complexity.
But even then, you can make it readable:
- Write extensive comments explaining the "why"
- Break it into well-named functions
- Add clear docstrings
- Include examples in your tests
Complex code is fine. Unnecessarily obscure code is not.
The Compliment That Changed Everything
Six months after the validator incident, Marcus stopped by my desk again.
"I was reviewing your latest PR. The data transform pipeline."
Here we go again, I thought.
"Really clean work. I could understand exactly what it does just from reading it. That's the goal."
I've gotten lots of compliments in my career since then. But that one might still be my favorite.
Not because my code was clever. Because it was clear.
The Bottom Line
Write code for the next person who reads it. That person might be your teammate, your future self, or someone who's never met you trying to fix a critical bug at midnight.
Show them the respect of clarity. They'll thank you for it.
And your 2am debugging self will definitely thank you for it.
Aaron Rose is a software engineer and technology writer at tech-reader.blog and the author of Think Like a Genius.
Comments
Post a Comment