Tutorials  

Introduction

Vibe coding is a breakthrough in software development where developers (even amateurs) rely heavily on AI-driven large language models (LLMs) to generate code. Instead of manually writing every line, you feed natural language prompts or simple instructions to an AI, then guide, test, and refine the results. Originally coined by Andrej Karpathy in February 2025 [5][2][4][1], vibe coding has stirred excitement—and some concerns—about how it transforms the coding process.

In a typical vibe coding scenario, you might say, “Hey, write a JavaScript function to shuffle a deck of cards.” The AI then drafts the solution, letting you bypass the labor-intensive details. You remain in control, checking functionality and ensuring the output does what you want. This tutorial explores the origins of vibe coding, its practical uses, potential pitfalls, and an example code snippet that an AI might generate.

Definition of Vibe Coding

Vibe coding emerged from Karpathy’s claim that “the hottest new programming language is English,” meaning humans can increasingly rely on conversational AI to handle code implementation [7]. The method focuses on:

  • LLM-Dependent Prompts: You describe what you want in natural language; the AI delivers a workable draft.
  • Hands-On Testing: You then test, tweak, or ask the AI to fix issues until the program meets your needs.
  • Emphasis on Speed & Convenience: By deferring most coding tasks to AI, you accelerate prototyping and hobby projects.
  • Limited Deep Understanding: A core characteristic is that many vibe coders accept AI-generated code without fully grasping how it works [1].

The term itself was introduced by Karpathy, co-founder of OpenAI, in February 2025 [5][2][4][1] and, remarkably, appeared in dictionaries like Merriam-Webster the following month [6].

How Vibe Coding Works in Practice

The typical vibe coding process can look like this:

  1. Describe the Task: Speak or type a brief prompt into an AI code editor like Cursor: “Please build a function to shuffle a deck of cards.”
  2. AI Drafts Code: The AI outputs JavaScript, Python, or another language snippet, depending on your request.
  3. Validation & Testing: You run the code, see if it works, and either accept it or ask the model for revisions.
  4. Iterate: Repeat until you’re satisfied with the solution.

According to Karpathy, “It’s not really coding—I just see things, say things, run things, and copy-paste things, and it mostly works.” [4]

Sample Code: Shuffling a Deck of Cards

Below is a quick JavaScript function that demonstrates vibe coding in action. Imagine asking ChatGPT to create a “shuffle” function for an array-based deck of cards: 

function shuffleDeck(deck) {
  for (let i = deck.length - 1; i > 0; i--) {
    const j = Math.floor(Math.random() * (i + 1));
    [deck[i], deck[j]] = [deck[j], deck[i]];
  }
  return deck;
}

You might say, “Hey, write a JavaScript function to shuffle a deck of cards.” The LLM then outputs this snippet. You test it in your environment, confirm it works, and you’re done. That’s vibe coding in a nutshell!

Why Vibe Coding Matters

Advocates claim vibe coding removes the steep learning curve traditionally required for programming [1][2][3]. Non-experts can quickly generate functional prototypes without extensive background knowledge. Tech journalists like Kevin Roose have described vibe coding as “software for one,” enabling personal, hyper-specific apps built via AI [3]. Y Combinator also reports startups are increasingly building codebases that are over 95% AI-generated [8].

However, critics caution that final accountability still rests on the “coder.” If you don’t review or understand what the AI wrote, you risk buggy or even malicious outcomes. As Simon Willison puts it, “If you use AI-generated code without comprehension, that’s vibe coding in its pure form” [1]. It can be risky for production-level projects that demand robustness and long-term maintainability.

Drawbacks & Controversies

  • Lack of True Understanding: If everything is generated by AI, you may miss subtle logic errors.
  • Difficulty Maintaining Code: Future debugging or feature extensions might become more difficult if no one fully understands the codebase.
  • Potential Ethical Concerns: Code can “hallucinate” reviews or other artificial data. AI might reuse copyrighted snippets inadvertently [3].
  • Limited Originality: Critics like Gary Marcus argue that AI often reproduces patterns it has seen before, rather than innovating [7].

Conclusion

Vibe coding marks a paradigm shift: programming with an AI assistant that handles the heavy lifting. Enthusiasts love its speed and accessibility, while skeptics worry about quality, security, and overreliance on black-box models. Regardless, the trend is here to stay, with more individuals and companies relying on natural language prompts to generate code.

Before you go all-in on vibe coding for mission-critical projects, remember to test and verify thoroughly. AI is a valuable tool, but human judgment—especially in software engineering—remains irreplaceable.

Vibe Coding Crash Course Training

Vibe Coding 101 with Cursor AI – Full Crash Course Video Tutorial

References

  1. Ars Technica. (2025). Vibe Coding: Accepting AI-Generated Code Without Full Understanding.
  2. Karpathy, A. (2025). Original Podcast on Vibe Coding.
  3. Roose, K. (2025). New York Times Articles on AI-Generated Software.
  4. Business Insider. (2025). Silicon Valley’s New Buzzword.
  5. OpenAI Founder’s Blog. (2025). Introducing the Term ‘Vibe Coding’.
  6. Merriam-Webster Dictionary. (2025). Slang & Trending: “Vibe Coding”.
  7. Marcus, G. (2025). Critiques of AI Reproduction vs. Originality.
  8. Y Combinator. (2025). Winter 2025 Batch Reports.
  9. Ars Technica. (2025). On AI Coding Assistants Refusing Requests.