"Vibe coding" has been buzzing around tech sphere lately, and if you're like me, you might be wondering if this is just another buzzword or something actually transformative.
Spoiler alert: it's a bit of both! But what exactly is this trendy approach that has developers either cheering or rolling their eyes? Is it the coding revolution we've been waiting for, or just another tech meme gone viral?
When Andrej Karpathy casually dropped his now-famous tweet about barely touching his keyboard anymore while coding, he unknowingly sparked a movement.
There's a new kind of coding I call "vibe coding", where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It's possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Also I just talk to Composer with SuperWhisper
The concept? Using AI to handle your syntax while you focus on the ideas. Type a natural language prompt, watch the code materialize before your eyes, and channel those productive vibes. It sounds almost too good to be true—which is precisely why we need to talk about it.
As someone who's spent countless hours debugging regex patterns (see my previous post for that adventure), the idea of simply describing what I want and having AI write the implementation feels like a superpower. But is it making us better developers or just more dependent on tools? Let's dive in and explore this brave new world of coding by vibes.
What is vibe coding, really?
At its core, vibe coding is a prompt-driven approach to software development. Instead of manually typing every line of code, you describe what you want in natural language, and let an AI tool generate the implementation. Think of it as pair programming with an AI that handles the tedious parts while you focus on the vision and architecture.
As Emmanuel Mumba explains on Apidog's blog, vibe coding is:
when a developer enters a trance-like state, where every keystroke feels like a drumbeat, and every function written flows as smoothly as a well-composed jazz solo.
It's that magical flow state where you're "bobbing your head to a song that doesn't exist" while the code just happens.
But beyond the meme-worthy definition, vibe coding represents a genuine shift in how we approach development. According to Niall McNulty in its blog post, this approach allows developers to focus more on the creative aspects of software development rather than the nitty-gritty details of syntax and implementation. It's about leveraging AI to handle the mundane so you can focus on the meaningful.
Let's watch a quick demo by Matthew Berman of what vibe coding looks like in practice:
The tools that make vibe coding possible
The vibe coding movement wouldn't exist without some powerful AI tools that have emerged in the past year or two:
Cursor
Cursor is a code editor built specifically for AI-assisted coding. It integrates AI capabilities directly into your development environment, allowing you to generate code, debug issues, and get explanations without leaving your editor.
I've been using Cursor for about two months now, and the experience is genuinely mind-blowing. Just last week, I was working on my personal portfolio website and needed to implement a filterable tech stack page. Instead of starting from scratch, I simply described what I wanted to the AI: "Create a system that displays my tech stack categorized by type, with chips to filter by category."
Within minutes, Cursor generated a well-structured solution with toggleable category filters. The code was not only functional but also incorporated best UI practices with responsive design and smooth animations.
function TechStackPage() {
const [searchQuery, setSearchQuery] = useState("");
const [activeCategories, setactiveCategories] = useState<string[]>([]);
const [filteredTechs, setFilteredTechs] = useState(TECH_STACK);
useEffect(() => {
setFilteredTechs(
TECH_STACK.filter(
(tech) =>
(tech.name.toLowerCase().includes(searchQuery.toLowerCase()) ||
tech.category.toLowerCase().includes(searchQuery.toLowerCase())) &&
(activeCategories.length === 0 ||
activeCategories.includes(tech.category))
).sort((a, b) => a.name.localeCompare(b.name))
);
}, [searchQuery, activeCategories]);
const toggleCategory = (category: string) => {
if (category === "all") {
setactiveCategories([]);
return;
}
setactiveCategories((prev) =>
prev.includes(category)
? prev.filter((c) => c !== category)
: [...prev, category]
);
};
return (
<div>
<div className="flex flex-wrap gap-2">
<button
role="checkbox"
aria-checked={activeCategories.length === 0}
className={`cursor-pointer rounded-full px-2 py-1 text-xs ${
activeCategories.length === 0
? "bg-primary text-primary-foreground"
: "bg-transparent border border-input"
}`}
onClick={() => toggleCategory("all")}
>
{activeCategories.length === 0 && <CheckIcon size={16} />}
All
</button>
{/* Category chips rendering */}
{/* Tech stack items rendering */}
</div>
</div>
);
}The best part? I didn't have to remember React's useEffect syntax or figure out the filtering logic from scratch. I could focus on customizing the appearance and behavior to match my portfolio's design system.
When I needed to add the smooth fade-in animations for each tech category, I just asked: "Add staggered animations when the categories appear" and the AI suggested using the BlurFade component I had already created elsewhere in my project:
{
visibleCategories.map((category, index) => (
<BlurFade key={category} delay={BLUR_FADE_DELAY * index}>
<h2 className="text-xl font-semibold flex items-center gap-2">
<CategoryIcon category={category} size={20} />
{category}
</h2>
<div className="flex flex-wrap gap-3 mt-4">
{techsInQuadrant.map((tech, techIndex) => (
<BlurFade key={tech.name} delay={0.05 * techIndex + 0.1}>
<TechBadge tech={tech} />
</BlurFade>
))}
</div>
</BlurFade>
));
}This is where vibe coding truly shines - I could describe my intention in natural language, and the AI understood not only what components I needed but how they should integrate with my existing codebase.
GitHub CoPilot
GitHub CoPilot is one of the most reliable and official AI coding assistants on the market. Backed by GitHub and built on OpenAI's technology, it offers seamless integration into existing development workflows.
While it might appear less powerful than other tools on certain advanced features (nothing comparable to Cursor's predictive "cursor tab" for instance), its strength lies in its reliability and legitimacy. For security-conscious companies and enterprises concerned with compliance, Copilot often represents the ideal entry point into the world of AI-assisted coding.
The tool transforms coding into a conversation rather than a typing exercise, with developers describing their needs through comments and letting the AI handle the implementation.
Lovable, Bolt, v0.dev & more...
Beyond text-based coding assistants, a new generation of visual generation tools is transforming how we create interfaces. These platforms represent a natural extension of the vibe coding concept applied to UI design and implementation.
v0.dev by Vercel stands as one of the pioneers in this space, enabling developers to generate React/Tailwind CSS components directly from text descriptions. A simple prompt like "a modern landing page for a productivity app" produces a fully functional component ready to be integrated into your Next.js project.
Lovable, another promising entrant in this category, focuses on generating entire user interfaces with seamless integration into existing development workflows. What particularly sets Lovable apart is its ability to generate not just frontend code but also suggest the data structures and APIs needed to fully implement a feature.
Bolt is another tool worth noting. Focused on the e-commerce aspect, it allows developers to quickly create smooth shopping interfaces without worrying about the implementation details of payment processing or inventory management.
The emergence of these tools represents a fascinating evolution of vibe coding, where we're no longer just describing what the code should do but also what it should look like visually. This fusion between design and development blurs the traditional boundaries between these disciplines, enabling even developers without design expertise to create visually impressive interfaces.
Claude, ChatGPT, Grok & more...
Let's not forget the large language models powering much of this revolution. Tools like Claude and ChatGPT aren't specifically code editors, but many developers use them as on-demand pair programmers.
What's particularly exciting is Anthropic's recently released Model Context Protocol (MCP), which is poised to dramatically change the landscape of AI-assisted coding. This open standard allows AI assistants like Claude to connect directly to data sources where your code lives, including development environments and code repositories. Instead of being isolated from your actual codebase, MCP enables AI to access relevant context & API calls in real-time, producing more accurate and helpful responses for coding tasks.
These advancements are fundamentally changing how we interact with code, allowing us to work with entire projects at a conceptual level rather than just isolated snippets. I'm planning to write a detailed article about MCP and its implications for the future of development—let me know if you'd be interested in reading more about this transformative technology.
Real-world vibe coding: A day in the life
To give you a concrete sense of vibe coding in practice, here's how my typical development session looks these days:
- 9:00 AM: Open Cursor, load up the project I'm working on
- 9:05 AM: Write a comment describing a new feature: "Add a dark mode toggle that persists user preference in local storage"
- 9:06 AM: Review and lightly edit the generated code, adding project-specific context
- 9:15 AM: Test the implementation, make tweaks as needed
- 9:25 AM: Move on to the next feature
Compare that to my pre-vibe workflow:
- 9:00 AM: Open VSCode, load up project
- 9:05 AM: Google "React dark mode toggle best practices"
- 9:20 AM: Read various Stack Overflow posts and blog articles
- 9:35 AM: Start implementing based on what I've learned
- 10:15 AM: Debug issues with localStorage persistence
- 10:30 AM: Finally have a working feature
The time savings are significant, especially for features I've implemented dozens of times before. Why reinvent the wheel when AI can just hand you a perfectly good one?
Vibe coding vs. traditional coding
Let's compare these two approaches:
| Traditional coding | Vibe coding |
|---|---|
| Writing every line manually 🤓 | Describing what you want in natural language 💬 |
| Googling solutions to common problems 🔍 | Asking AI to solve problems for you 🤖 |
| Meticulously designing architecture 📐 | Focusing on high-level concepts and letting AI handle details |
| Debugging line by line 🐛 | Pasting error messages for AI to fix 🔧 |
| Learning syntax rules 📚 | Learning how to prompt effectively 🎯 |
| Spending time on Stack Overflow 📊 | Spending time refining your vision 🧠 |
| "How do I center a div?" moments 😅 | "How do I architect this system?" moments 🏗️ |
| Satisfaction from solving a problem yourself 💪 | Satisfaction from building something quickly 🚀 |
| Hours spent debugging off-by-one errors ⏰ | Minutes spent reviewing generated solutions ⚡ |
| Detailed knowledge of language internals 🔬 | Broader knowledge across multiple domains 🌐 |
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The pros of going with the vibe
1. Accelerated development
The most obvious benefit is speed. Vibe coding lets you "focus on the what rather than the how", and dramatically accelerating development cycles. Tasks that once took hours can now be completed in minutes.
2. Lower barrier to entry
Vibe coding is democratizing software development. You no longer need to memorize complex syntax or spend years mastering a programming language to build functional applications. This opens coding to people with great ideas but limited technical background.
I recently mentored a designer who wanted to build a simple website with some interactive elements. Instead of sending his down the traditional learning path (HTML → CSS → JavaScript → React), we set up Cursor and focused on teaching her how to describe what she wanted to build. Within a week, he had a fully functional site that would have taken months to build with traditional methods.
3. Reduced cognitive load
Let's be honest: coding can be mentally exhausting. By offloading the syntax details to AI, developers can preserve their mental energy for solving the truly challenging problems that require human creativity and insight.
I've noticed that I end my workdays with more creative energy left since adopting vibe coding techniques. Instead of being drained from wrestling with implementation details, I can focus on higher-level architecture and user experience considerations.
4. Multilingual superpowers
One of the most underrated benefits of vibe coding is how easily it lets you work across different languages and frameworks. Need to port a React component to Vue? Ask AI to translate it. Need to write a Python script but you're primarily a JavaScript developer? No problem.
The Concerns: Not all vibes are good
1. The black box problem
When AI generates your code, do you really understand what's happening under the hood? As Pete Sena warns in UX Collective, "Not all vibes are good." Relying too heavily on AI-generated code without understanding its implementation can lead to security vulnerabilities, performance issues, and maintenance nightmares.
I experienced this firsthand when an AI-generated authentication system looked perfect at first glance but contained a subtle security flaw that would have been obvious to someone with deeper knowledge of authentication best practices. The lesson? Trust but verify, especially for critical systems.
2. Weakening skills
Will our coding muscles atrophy if we outsource too much to AI? There's a legitimate concern that developers who rely heavily on vibe coding might lose their ability to solve problems independently or understand complex systems thoroughly.
This is especially concerning for junior developers who might miss out on the formative struggles that help build deep understanding. There's value in wrestling with difficult problems and building that mental model of how code works.
3. Prompt engineering complexity
Effective vibe coding requires skilled prompt engineering. I've experienced this prompt engineering paradox many times. You think you're being clear, but the AI misinterprets your intent. You refine your prompt, but now it's ignoring an important constraint. Before you know it, you've spent 15 minutes trying to get the perfect prompt for what would have taken 5 minutes to code manually.
4. Technical debt and maintainability
AI-generated code can sometimes prioritize "working" over "working well." This can lead to bloated, inefficient implementations that become maintenance headaches down the road.
Finding the sweet spot
Like most technological advances, vibe coding isn't about completely replacing the old way—it's about augmenting our capabilities. Here's how I think about the balance:
-
Use AI for the repetitive stuff: Boilerplate code, standard functions, and common patterns are perfect candidates for vibe coding. Why manually write your 50th authentication system when AI can generate a solid starting point?
-
Maintain deep understanding: Even when using AI, take time to understand what it's generating. Read the code, ask the AI to explain sections you don't understand, and ensure you could maintain it if needed. I often ask the AI to add detailed comments explaining its implementation choices.
-
Verify critical components: For security-sensitive or performance-critical code, always validate AI-generated solutions against best practices and consider having them reviewed by human experts. No matter how good AI gets, some things are too important to trust blindly.
-
Learn prompt engineering: Just as we've invested time learning programming languages, invest in learning how to effectively communicate with AI. Clear, specific prompts yield better results. I keep a journal of effective prompts that I can reuse and adapt for common tasks.
-
Blend human and AI strengths: Use AI for rapid prototyping and initial implementations, then apply your human expertise to refine, optimize, and ensure the code aligns with your project's architecture and standards. The best results come from this collaborative approach. This guide from OpenAI about prompt engineering is a great resource to get started.
-
Code reviews matter more than ever: When reviewing AI-generated code, be extra vigilant about security, performance, and maintainability. AI might not be aware of your project's specific constraints or future plans.
Ethical considerations in the age of vibe coding
As we embrace this new paradigm, we should also consider its broader implications:
Job displacement concerns: Will vibe coding lead to fewer developer jobs? My take is that it will change the nature of development work rather than eliminate it. We'll need fewer people writing basic CRUD applications but more people solving complex problems and directing AI tools effectively.
Educational shifts: How should we teach programming in a world where AI can generate code? Should we focus more on fundamentals and concepts rather than syntax and implementation details? Computer science education may need to evolve to emphasize the skills that complement AI rather than compete with it.
Accessibility and inclusion: On the positive side, vibe coding could make software development more accessible to people with different learning styles, backgrounds, and abilities. Natural language interaction removes many of the formal barriers to entry that have historically made coding exclusive.
Environmental impact: Training and running large AI models consumes significant computational resources and energy. As we rely more heavily on these models for coding, we should be mindful of the environmental footprint of our development practices.
My personal take
After spending several months experimenting with vibe coding in my own projects, I've found it to be incredibly powerful for certain tasks. Generating boilerplate React components? AI nails it. Setting up database schemas? The vibes deliver. But for complex algorithms or security-critical code, I still prefer to write things manually or at least heavily review and modify what the AI produces.
The most valuable lesson I've learned is that vibe coding isn't about being lazy or cutting corners—it's about being strategic with your time and mental energy.
As a developer who remembers the days of memorizing syntax from printed reference manuals, I'm embracing this shift while remaining mindful of its limitations. After all, even the best vibes need a reality check sometimes.
I find myself in a middle ground—using AI as a productivity multiplier while still maintaining my core coding skills. I'll often ask the AI to generate a first draft, then refine it manually based on my knowledge of the specific project context and requirements. This collaborative workflow gives me the best of both worlds: the speed of AI and the precision of human expertise.
Try it yourself
If you're curious about vibe coding (and you should be!), let's try out yourself!
Remember, the goal isn't to never write code again—it's to write better code with less effort. The vibiest code is the one that solves real problems effectively!
Embracing the vibe while keeping your skills sharp
As we wrap up this exploration of vibe coding, I want to leave you with a balanced perspective. This technology is transformative but not transcendent. It's a powerful tool in our developer toolkit, not a replacement for the toolkit itself.
The developers who will thrive in this new era are those who can seamlessly blend AI assistance with human expertise—knowing when to lean on each and how to create synergy between them. They'll understand both the code and the context, using AI to implement their vision rather than surrendering to it.
So go ahead and ride the vibe coding wave—it's making development more accessible, efficient, and creative than ever before. But don't forget to keep your coding muscles toned and your fundamentals strong. The best developers in the age of AI will be those who can both direct the orchestra and play the instruments when needed.
What are your thoughts on vibe coding? Have you tried it? Let me know in the comments or reach out on LinkedIn. I'd love to hear about your experiences in this brave new world of AI-assisted development!