What Is Vibe Coding? AI-Assisted Development Explained

Mar 18, 2026

Problem

I keep seeing developers ship code they don’t understand. They use AI assistants to generate entire projects, then publish them on GitHub for “stars” without ever reviewing what the AI produced.

This trend has a name: vibe coding.

From a Reddit thread about this phenomenon:

"vibecoded AI-powered, blockchain compatible organizer... I spent hundreds of
thousands of tokens on this without knowing anything about coding"

"The problem isn't using AI itself—it's the fact that people don't look over
their code before they showcase it"

"it's a weird blend between expertise and vibecoding, some of these projects
are genuinely good, others are totally trash"

Vibe coding is shipping AI-generated code without understanding it.

What is Vibe Coding?

Vibe coding describes a style of development where:

Developers rely heavily on AI to generate code
Minimal or no review of what the AI produces
Speed and experimentation over quality and comprehension
Projects built for attention rather than utility

The term captures the “vibe” of using AI carelessly—just letting it flow without checking the output.

What vibe coding is NOT:

Using AI assistants thoughtfully isn’t vibe coding. Reviewing, understanding, and taking responsibility for AI-generated code is responsible AI-assisted development.

Why Vibe Coding is Problematic

1. You Can’t Fix What You Don’t Understand

When I generate code with AI and skip the review, I create technical debt I can’t resolve later.

def process_user_data(data):
    # No idea what this does, but it works
    result = ai_generated_function(data)
    return result  # Hope there's no security issues!

This function might work. But when it breaks in production at 3 AM, I’m stuck because I never understood what ai_generated_function actually does.

2. Security Vulnerabilities Ship at Scale

AI can generate code with security flaws—SQL injection, XSS, exposed secrets, improper authentication. Without review, these vulnerabilities go straight to production.

3. Open-Source Quality Drops

GitHub fills with projects that look impressive but have no substance. As one Redditor satirized:

"SubMurderer 9000 - exclusively AI coding, I personally contributed the
project name and approximately one 'hello claude'"

This floods ecosystems with low-quality projects, making it harder to find genuinely useful code.

4. Your Skills Atrophy

If I stop understanding my code, I stop growing as a developer. In interviews, I can’t explain decisions I didn’t make. My resume shows projects I can’t discuss in depth.

Responsible AI-Assisted Development

The solution isn’t avoiding AI—it’s using it responsibly.

Always Review Before Shipping

I read every line of AI-generated code. I ask:

What does this function do?
Why did the AI choose this approach?
Are there edge cases or security issues?
Can I explain this to another developer?

Treat AI as a Collaborator, Not a Replacement

def process_user_data(data: dict) -> ProcessedResult:
    """
    Process user data with validation and error handling.

    AI generated initial structure, I reviewed and added:
    - Input validation
    - Type hints
    - Error handling
    """
    # Validate input - I added this for safety
    if not isinstance(data, dict):
        raise ValueError("Expected dict input")

    # Core processing (AI-suggested, I reviewed and understood)
    validated_data = validate_user_data(data)
    result = transform_data(validated_data)

    return ProcessedResult(
        data=result,
        processed_at=datetime.utcnow(),
        version="1.0"
    )

The AI did the heavy lifting. But I understood every line before shipping.

Use AI to Accelerate Learning, Not Skip It

When AI suggests a solution, I ask “why does this work?” I use AI explanations to deepen understanding, not bypass it entirely.

Maintain Quality Standards

Run linters and formatters
Write and run tests
Follow established patterns
Document decisions

AI generates code faster than I can write it manually. But quality checks still take time—and they’re worth it.

Common Mistakes

Mistake	Why It’s a Problem	Better Approach
Shipping AI code without review	Bugs, security issues, misunderstood behavior	Review every function, understand dependencies
Using AI to bypass learning	Skill gaps, interview failures, technical debt	Use AI to accelerate learning, not skip it
Building for GitHub stars	Wasted effort, ecosystem pollution	Build for real users and real problems
Ignoring generated code quality	Maintenance debt, security vulnerabilities	Treat AI code like junior developer PRs—review carefully
No testing of AI output	Hidden bugs, runtime failures	Always test, especially edge cases

The Path Forward

AI coding assistants are powerful tools. Used well, they multiply productivity. Used carelessly, they create unmaintainable software and degrade our skills.

The key distinction: Am I shipping code I understand?

If yes, I’m using AI responsibly. If no, I’m vibe coding—and I need to slow down and review.

Summary

In this post, I explained vibe coding—shipping AI-generated code without understanding it. The key point is that vibe coding prioritizes speed over comprehension, creating technical debt and security vulnerabilities. Responsible AI-assisted development means reviewing, understanding, and taking ownership of every line you ship.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Reddit Discussion: Vibe Coding
👨‍💻 GitHub Copilot Documentation

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!