Programming has always been as much about intuition as it is about logic. While software development emphasizes rigid methodologies, extensive planning, and structured approaches, a new philosophy has emerged that celebrates the developer's instinctive understanding of code flow, user needs, and system architecture.
This approach, known as "vibe coding," represents a fundamental shift in how developers think about building software, prioritizing intuitive decision-making and rapid iteration over exhaustive documentation and formal processes.
Andrej Karpathy, a co-founder of OpenAI and former AI leader at Tesla, introduced the term "Vibe Coding" in February 2025.
Vibe coding leverages the power of large language models (LLMs) to transform natural language ideas into functional code. Far more than a passing trend, it signals a potential shift in how we create software, promising accessibility while raising questions about quality and control.
Understanding Vibe Coding
At its essence, vibe coding redefines the software development process. Instead of writing the whole code from scratch, a programmer generates the code by providing simple sentences, say, “Build a app or a tool to track my daily fitness,”—and an AI model generates that are trained with coding and programming generates the corresponding code.
This shift moves the programmer’s role from manual coding to supervising and refining AI output. It’s a method that prioritizes intent over syntax, making software creation feel less like a technical chore and more like a creative conversation.
The term itself, “vibe coding,” carries a lighthearted yet meaningful origin. Andrej Karpathy described it as “giving into the vibes,” a nod to the intuitive, almost carefree way developers can lean on AI to handle the heavy lifting. The phrase stuck, resonating with a community eager to embrace exponential advancements in AI technology.
Why Vibe Coding Is Gaining Traction
The rise of vibe coding isn’t hard to understand. Traditional software development demands years of learning, mastering languages, debugging errors, and grasping complex algorithms. Vibe coding, by contrast, flattens that curve dramatically.
A beginner with no coding background can describe a simple app idea, and within moments, an AI tool delivers a working prototype. For seasoned developers, it’s a time-saver, offloading repetitive tasks like writing boilerplate code or troubleshooting minor bugs.
This accessibility is a game-changer. It empowers non-programmers—designers, entrepreneurs, or students—to bring their ideas to life without needing a computer science degree.
Meanwhile, professionals can redirect their focus to big-picture challenges, like system architecture or user experience, rather than getting bogged down in syntax. The result? Faster development cycles and a broader pool of creators contributing to the digital landscape.
Some of the security researchers have also shown the discovery of a security lapse via an AI chatbot, like from the Google Project Team and CVE-2025-37899, a remote zero-day vulnerability in the Linux kernel’s SMB implementation by Sean Heelan.
Is Vibe Coding Bad? Weighing the Risks
Yet, for all its promise, vibe coding stirs unease among some in the tech community. A pressing concern is comprehension—or the lack of it. When AI generates code, developers might not fully grasp how it functions beneath the surface.
This opacity can hide bugs, inefficiencies, or even security flaws, risks that grow more serious in professional environments where reliability is non-negotiable.
In an interview with Ars Technica, Simon Willison, a respected voice in software development, has cautioned that “vibe coding your way to a production codebase is clearly risky.” His point underscores a critical tension: while vibe coding excels at rapid prototyping, it may falter under the scrutiny of enterprise-grade standards.
A hastily generated script might work for a personal project, but in a banking app or healthcare system, an unnoticed vulnerability could spell disaster.
Limitations and Drawbacks of Vibe Coding
Beyond comprehension, vibe coding has other hurdles. The code AI produces isn’t always pristine. Large language models, while impressive, can churn out solutions that function but lack elegance—think bloated algorithms or ignored best practices.
For simple tasks, this might not matter, but in complex applications—like a distributed cloud system or real-time data processor—efficiency and scalability are paramount, and AI output often falls short.
Debugging poses another challenge. When code is hand-written, developers know its structure intimately, making errors easier to spot and fix. AI-generated code, however, can feel like a black box, with logic that’s dynamic or unconventional. Tracking down a glitch in such code can be a frustrating scavenger hunt, especially for those less experienced in programming fundamentals.
How Does Vibe Coding Compare to Traditional Coding Methods?
To appreciate vibe coding’s place, it helps to contrast it with traditional coding. The conventional approach is meticulous, requiring fluency in languages like C++ or Ruby, plus a firm grip on data structures and design patterns. It’s a slow burn—rewarding precision and depth with robust, maintainable software. Developers wield total control, tailoring every line to meet exacting standards.
Vibe coding flips this script. It’s fast and forgiving, letting you bypass syntax for high-level ideas. Need a quick website mockup? Vibe coding delivers in minutes what might take hours otherwise. But that speed trades off with quality.
Traditional methods shine in scenarios demanding airtight security or intricate logic—think aerospace software or financial platforms—where vibe coding’s approximations might crumble.
In practice, the two aren’t mutually exclusive. Many developers blend them, using vibe coding to draft initial versions and traditional skills to polish the final product. It’s a hybrid model that balances efficiency with excellence, adapting to the task at hand.
How Does Vibe Coding Work in Practice?
So, what does vibe coding look like day-to-day? The process hinges on AI-powered tools—think Cursor Composer, Replit Agent, or even ChatGPT. A developer starts with a prompt: “Write a script to email me a weather update every morning.” The AI interprets this, pulling from its vast training data to craft a solution, perhaps a Python script using an API like OpenWeatherMap. Within seconds, the code appears.
But it’s rarely a one-and-done affair. The developer reviews the output, tests it, and tweaks the prompt if needed—“Add a check for rain alerts.”
This back-and-forth refines the result, with the human steering the AI toward precision. It’s less about coding from scratch and more about sculpting something usable from the AI’s raw material.
We have also tried to create some of the basic apps using AI, like Image to Webp converter, Nepali Date Converter (code), Morse Code Decoder and Encoder Tool (code), JSON Web Tokens Decoder (code), etc.
How to Learn Vibe Coding: Practical Steps
Eager to try vibe coding yourself? It’s more approachable than you might think. Start by experimenting with AI tools. ChatGPT, free and widely available, is a solid entry point—type in a coding request and see what it spits out.
GitHub Copilot, integrated into many code editors, offers real-time suggestions as you type, easing you into the vibe coding mindset.
Next, hone your prompting skills. The clearer your instructions, the better the AI performs. Instead of “Make a game,” try “Create a Python script for a number-guessing game with three difficulty levels.” Specificity reduces guesswork, yielding more accurate code. Online communities, like Reddit’s r/learnprogramming, often share prompt-writing tips—tap into those for inspiration.
A basic grasp of programming helps, too. You don’t need to be a guru, but knowing what a loop or variable does lets you spot flaws in AI output. Free resources like Codecademy’s intro courses can build that foundation. Finally, start small—code a to-do list app or a basic calculator. As you gain confidence, scale up to meatier projects.
The Future of Vibe Coding
Where is vibe coding headed? It’s unlikely to dethrone traditional coding soon—mission-critical systems will always need human rigor. But its role is growing.
As AI models improve, generating cleaner, smarter code, vibe coding could become a staple for rapid innovation—think startups churning out MVPs or educators teaching coding through exploration.
It’s also a democratizing force. By lowering barriers, it invites more voices into tech, fostering diversity in what gets built. Yet, human oversight remains vital. The best outcomes will come from pairing Vibe Coding’s speed with traditional coding’s depth—a synergy that could redefine software development for decades.
FAQ: Common Questions About Vibe Coding
Q. Is vibe coding reliable for production-level applications?
A. Not entirely. It’s great for prototypes or personal tools, but production code needs rigorous testing and optimization—areas where human expertise still outshines AI. Use it as a starting point, not a finish line.
Q. What’s the learning curve for vibe coding?
A. It’s gentle compared to traditional coding. You’ll need to master prompting and basic code review, but you can start seeing results in hours, not months. A little programming knowledge accelerates your progress.
Q. Will vibe coding replace traditional coding methods?
A. Not likely. It complements rather than competes, excelling in speed but lacking the precision complex projects demand. Think of it as a co-pilot, not a replacement.
Q. How can I get started with vibe coding?
A. Grab a tool like ChatGPT or Replit Agent, write a simple prompt—“Build a tip calculator”—and experiment. Refine as you go, and brush up on coding basics to polish the results.
Vibe coding represents a significant shift in how we think about software development, emphasizing the human elements of programming that traditional methodologies sometimes overlook. While it's not appropriate for every situation, understanding and applying vibe coding principles can lead to more satisfying development experiences and often superior results when used in appropriate contexts.
The key to success with vibe coding lies in developing the experience and intuition necessary to make good decisions quickly while maintaining the flexibility to adapt and improve as new insights emerge.