wling

Writing code should be a process of understanding, reasoning, and making trade-offs. Copy-pasting tells you 'it runs', but doesn't tell you 'why'.

The emergence of similar code generation tools like GPT in recent years has had a huge impact on us, affecting every aspect of our lives and even those around us.

Have you noticed—

In the past, when using GPT, we could still learn something, just during that initial period.
We were all using it with a problem-solving and learning attitude,
checking its thought process, asking questions when we didn't understand.

Now? Everyone relies on it, copy-pasting.

And then there's the so-called Coding Vibe, Coding Agent.
Not looking, not modifying, not testing.
If it runs, it goes live, and three months later, you don't even know what you wrote, or the overall structure and package references of the project.

Debugging feels like a cloud of little question marks:

“Who wrote this **code?”
“Oh my god, it’s GPT.”

I strongly oppose handing all coding work over to GPT.#

Writing code has turned into a process of copying answers; you no longer think.
Understanding requirements? Code design? Project maintainability? You’ve skipped all of these.

It seems highly efficient, but in reality, you are continuously losing your basic judgment and growth space as a programmer.
Writing code is not just about writing; it’s also about learning.

Bugs don’t decrease; they actually become harder to debug.#

The project code you pasted might not have even run through tests.
Why would GPT help you consider edge cases? Why would it care about your project context?
They just need to get the project done and the code right.

The apparent “Wow, the functionality is implemented!”
But the result is a bunch of hidden issues; even if triggered, debugging becomes even harder.
Because this isn’t your code; you don’t understand it at all.
After an error, you have no idea where the problem lies and can only go back to ask GPT
to fix the code it generated itself.

The more you write, the more unclear the code quality and style become.#

Today you paste an async function, tomorrow an old callback, the day after a strange, more abstract one.
The project looks like a jigsaw puzzle, with maintainers passionately debugging freestyle.
Every piece of code runs, but no one wants to maintain it.

Outsourcing projects to LLMs, deceiving oneself.#

I still don’t understand why a bunch of people are responding,
“Oh, our coding AI tool has new features!”
In the past, when people said “directly pasting GPT code is harmful,” some would rebut.

“We are reformists; we have Cursor, Trae, Tongyi, Copilot Chat… full automation for writing projects, automated debugging.”

They claim one-click bug localization, but the truth is the tool takes your error log and keeps guessing your exception sources,
giving you a seemingly good, possibly useful patch.
What’s the result? Fix A and break B.

Automatic refactoring is not magic.#

So-called refactoring = Find-&-Replace.
It doesn’t understand what the complete testing process of this project looks like, doesn’t grasp implicit agreements, and often leaves a bunch of hidden dangers when merging into old code.

It seems friendly to beginners, completely shielding the learning cost.
Low-threshold usage, a novice clicks a couple of times to “fix” a bug but doesn’t understand the cause.
The next time they encounter the same problem, they can only rely on tools to put out fires.

Time savings are superficial; the actual cost is high.#

Saving time may be valid in some situations, but what about in the future?
You spend time reading its diffs, running that private regression, and still have to fix new issues.
In the end, the total time often exceeds that of a novice debugging.

Relying on tools ≠ solving problems.#

Now a bunch of people throw out “Then just use Cursor, and it’s done.”
This is a conceptual shift; it’s not solving problems but extending reliance from writing code to debugging, with the dosage supercharged.

These tools purely create illusions, leading some novices to believe, “As long as I have Cursor, I don’t need to understand debugging or code; I can also become an engineer.”
When it goes live and issues arise, what will you do as a novice? Keep relying on Cursor?

Short-term progress is rapid, but long-term maintenance feels like hell.
Those chaotic project structures, interface references (even allowing the creation of completely identical interfaces)
Whether it’s your company’s team collaboration or the open-source community,
the person taking over feels like they are in a mess.
You look at this pile of code and first need to understand which parts were written by humans and which are AI “seasoning,”
then roll back or refactor each one.

In short, on top of the already “CPDD (Copy-Paste Driven Development)” wound, another layer of so-called “automatic debugging ointment” is applied, causing the code’s genes to mutate faster and making it even less daring to touch.

Novices: It used to be just “can’t write,” now it’s bundled with “can’t debug.”
One-click generation + one-click patching = “automatically cruising towards the cliff.”
Novices generate - paste - auto-fix, thinking everything is fine after one round, but they haven’t formed a debugging mindset: setting breakpoints, checking the stack, validating hypotheses...

The more you write, the larger the project becomes, and the less salvageable it is towards the end.

Final choice: Trust yourself and stay proactive.#

I don’t even need to think; after reading this long rant, looking at those repetitive viewpoints, I say “makes sense,” but when it comes time to write features, I still open GPT and LLM without hesitation, continuing to paste and fix.
Pasting a whole new “magic code.”

I have to say I use it too; blindly rejecting new technology isn’t pure **.
But using it wisely and using it mindlessly yield very different results.
You can ask, you can reference, you can even let it help you write tests and fill in documentation.

But the code in the project ultimately needs to reflect your own understanding, not GPT’s output ability.

In the end: GPT is not you, and it won’t serve only you.
The person who should write good code is you.
Don’t let tools think for you; otherwise, you will slowly lose your ability to solve problems.
Please trust yourself and stay proactive.

This article is synchronized and updated to xLog by Mix Space.
The original link is https://ling.crashvibe.cn/posts/think/thinking-about-coding-and-the-impact-of-gpt