Brothers, today let’s talk about a hot tech topic—Vibe-Coding.

Have you noticed that everywhere you go on GitHub or in tech groups, people are discussing “AI writing code”? Some even claim that a novice can create a runnable application just by chatting with AI?

It sounds appealing to run code without typing it yourself—doesn’t it?

But hold on, let me share some hard truths: what you think is an “efficient shortcut” might be planting a time bomb for you and your entire team.

The Shocking Data Behind the Glamour

Let’s start with some alarming data:

Research shows that engineers using AI-assisted programming introduce security vulnerabilities at a rate 10 times higher than manual coding.

Even more critically, due to a lack of basic identity verification, nearly 380,000 assets are exposed to the public due to “Vibe-Coding,” akin to running naked! Among these, nearly 2,000 applications have directly leaked medical records, financial data, and trade secrets—these are fatal vulnerabilities that can plunge a company into crisis.

A 10 times vulnerability rate and 380,000 assets exposed—that’s the painful cost of coding by intuition.

You might think you’re improving efficiency and saving time, but in reality, you’re digging a bottomless pit for yourself and your company.

Now, even Apple has stepped in—beginning to block and even remove various AI coding applications; core contributors to Node.js have jointly petitioned to ban the use of LLM-generated code for rewriting core modules.

This isn’t an overreaction; they are genuinely scared.

What Exactly is Vibe-Coding?

Before we criticize, let’s clarify what Vibe-Coding really is.

This concept was first proposed by Andrej Karpathy, co-founder of OpenAI, and it’s quite simple: you no longer need to type functions and write logic line by line; just converse with AI in natural language, and if it feels right, let it generate the code, then run it to see the effect.

Doesn’t it sound like magic?

Its original intention is good: to lower the barriers to programming, quickly realize ideas, and free programmers from tedious CRUD tasks to focus on more valuable work.

However, the key issue is that code has never run on “feelings”; it relies on rigorous logic, standardized writing, and uncompromising security.

When you throw complex business requirements at AI, and it spits out hundreds or thousands of lines of code in no time, do you really dare to deploy it directly?

Have you scrutinized every line of code? Can you guarantee there are no hidden logical flaws? Do you know if it has secretly introduced an outdated dependency with a CVE vulnerability?

The harsh truth is: you can’t.

This is the most dangerous illusion of Vibe-Coding—you think you are mastering AI, but in reality, AI is leading you by the nose, and you have already lost control over the code.

Why is Your Team Being Bitten Back by AI Programming?

I’ve talked with many peers in tech groups and offline salons about this issue, and I’ve summarized that the most severe pitfalls currently stem from these three situations. Do you see them in your team?

1. Newcomers Misusing AI: Creating “Spaghetti Code” and Feeling Proud

New team members often think AI is omnipotent—when a requirement comes in, they don’t think or check documentation; they just throw it at Copilot or Cursor. As long as the generated code runs, they consider it done.

What’s the result?

The code style is ridiculously chaotic, mixing four different writing styles in one file; there’s repeated logic everywhere, requiring changes in ten places for a single modification; comments are almost non-existent, and variable names look like passwords; hidden logical errors remain undisclosed until deployment.

When you take over such code, don’t you feel like cursing?

2. Veteran Employees Cutting Corners: Sacrificing Security for Speed

Even scarier than newcomers are some seasoned employees who let their guard down. To speed up project progress, they let AI generate sensitive core code like authentication modules, payment logic, and database queries.

And then?

SQL injection? AI doesn’t care; permission bypass? AI doesn’t care; hard-coded sensitive information? AI still doesn’t care.

The previously mentioned “10 times vulnerability rate” largely comes from this—seeking short-term convenience while burying lifelong risks.

3. Managers Blindly Praising AI: Uninformed Directives Leading to Chaos

The worst scenario is when non-technical leaders, dazzled by online hype about AI programming, come back and demand “everyone must develop with AI, and efficiency must double.”

The result is obvious: while the volume of code produced indeed increases, the online failure rate, security incidents, and subsequent maintenance costs explode.

Half of the meaningless overtime you’ve put in might have been cleaning up after AI.

Some may ask: does this mean AI programming cannot be used?

Of course not. AI itself is not the problem; the issue lies in our “misuse of AI”. Based on countless experiences from peers who have stumbled, here are three actionable suggestions to help you avoid all pitfalls:

Suggestion One: Treat AI as a “Junior Assistant”, Not an “Architect”

AI has clear advantages: writing glue code, generating test cases, explaining unfamiliar code snippets, and quickly validating prototypes—these repetitive, simple tasks are faster for it than for you.

However, it is absolutely unsuitable for tasks like designing core business logic, writing sensitive security modules, or making system architecture decisions.

Remember this: you are the architect, and AI is just your intern. The code written by an intern must be reviewed, tested, and modified—you cannot deploy it directly.

Suggestion Two: Enforce Code Review + Automated Security Scanning for Double Assurance

From now on, establish a rule: any AI-generated code must not be merged into the main branch without human review.

Additionally, ensure you integrate SAST (Static Application Security Testing) tools to automatically scan AI-generated code—many IDE plugins can achieve this without extra costs, so don’t skimp on this effort, or you’ll face significant losses later.

Suggestion Three: Establish Team “AI Code Standards” to Avoid Pitfalls from the Start

For example, you can set clear rules that are simple to execute:

1. Limit the number of lines of code generated by AI at one time; if it exceeds 200 lines, it must be split for review;
2. Require that AI-generated code must include complete comments and unit tests; otherwise, it will not be approved;
3. Prohibit direct AI generation of sensitive modules like payment, authentication, and encryption; such code must be manually written and undergo multiple reviews;
4. Hold a weekly “AI Code Review Meeting” to share pitfalls experienced by everyone and avoid repeating mistakes.

This isn’t about adding burden; it’s about reducing future “technical debt”. The more standardized you are, the easier it will be later.

AI is a Sword, Not a Crutch

Brothers, let me say this sincerely: I have never opposed using AI to write code; I use it every day, and the efficiency boost is indeed visible.

But I firmly believe that AI programming is not about “earning money while lying down”; it’s about “working standing up to do it better.”

The biggest problem with Vibe-Coding is not that the technology is immature, but that our mindset has become complacent—we think that with AI, we no longer need to practice the basics, understand underlying principles, or think about architectural design.

That’s a huge mistake.

AI will make excellent programmers even better: it helps you eliminate tedious tasks, allowing you to focus more on thinking, designing, and optimizing, maximizing your technical capabilities.

But AI will also make lazy programmers more dangerous: the speed at which you create bugs will exceed the speed at which you fix them by tenfold, ultimately not just harming yourself but also your entire team and company.

Looking back at those shocking data: a 10 times vulnerability rate, 380,000 assets exposed, Apple’s ban, and the open-source community’s resistance…

This has never been the fault of AI; it’s the fault of our misuse of AI.

So, my advice is simple:

Embrace AI, but don’t blindly trust it; enjoy the efficiency, but maintain your bottom line; coding can be fast, but your mind must not be lazy.

What do you think?

Has your team ever stumbled due to AI programming? Or do you have any useful “pitfall prevention” experiences?

Let’s discuss in the comments; I’m waiting for you.