AI is redefining how software is built. Today, you no longer have to write lines of code to develop an app, system, network, or anything for that matter. With AI coding tools like Lovable, Replit, and Cursor, anyone can build functional applications by simply describing what they want to achieve. This is what “Vibe Coding” or “programming by vibes” is all about.
For old-school programmers who have been burning the midnight oil over the years, mastering how to design, code, debug, and test software, the idea that you can simply blink software into reality with a few prompts is far-fetched. Nonetheless, Vibe Coding is real.
In this article, we’ll explore how this trend is bridging the gap between technical and non-technical individuals, developers, and users, a feat that was previously impossible. Let’s break it down.
What Is Vibe Coding?
Unlike other trends, which often leave you wondering how and when they started, this can be traced back to March 2025, when Andrej Karpathy tweeted, “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”.
Simply put, vibe coding is a technique where you lean on AI agents to write most of the code for the apps you want to build by using descriptive prompts or “vibes.” In other words, it has less to do with knowing how to write every line of code and more to do with knowing what you want to build and how to describe it clearly.
Your role as the engineer shifts to interacting with the tool, examining its output, and steering it in the right direction. Even when errors occur, the expectation is that the agent will iterate, debug, and fix its own mistakes.
The upside of vibe coding
- Rapid prototyping: With less code to write by hand, vibe coding accelerates the time it takes to turn an idea into a working project, making it easier to spin up early versions of applications.
- Streamlines routine tasks: All the “boring” parts of engineering, such as setting up environments and scaffolding, are handled in minutes instead of hours, freeing developers to focus on high‑level design instead of boilerplate.
- Boosts creativity: By lowering the cost of experimentation, vibe coding encourages a more fluid, intuitive approach to building and iterating on ideas.
Practical applications of vibe coding
Regardless of whether “vibe coding” becomes the official name, the trend itself is here to stay, and it is already gaining traction in several areas:
Startups
Vibe coding has proven to be a game‑changer for startups looking to move fast with limited resources. It enables a handful of people, or even solo founders, to rapidly create prototypes and MVPs, bringing ideas to market quickly and efficiently.
Agile teams
Initially, the adoption of AI by agile teams was gradual. Over time, though, we have seen a complete shift, with AI not only assisting with agile development but becoming the backbone of how many high‑performing teams operate. By automating repetitive tasks, vibe coding frees developers to focus on problem‑solving and innovation.
Enterprises
Maintaining and updating legacy systems in a large organization is no small feat, especially when done traditionally. The process can take weeks or even months. With vibe coding, enterprises can modernize their systems more easily as AI assists with code refactoring, identifies outdated patterns, and suggests more efficient solutions.
Open source
In the open‑source world, vibe coding streamlines the contribution process for developers of all skill levels. It helps contributors get up to speed quickly by suggesting relevant code, surfacing documentation, and checking for common errors.
From these examples, you can get a glimpse of how different organizations are already using vibe coding. But to understand its power, you have to see it in practice. It is one thing to talk about automating tasks and increasing efficiency; it is another to watch it unfold in front of you. So let’s move from the conceptual to the tangible.
Vibe coding in action
The first time vibe coding really “clicked” for me was when I decided to build a small AI‑powered task manager from scratch, without touching a code editor. Instead of opening an IDE, I opened an AI coding assistant, pasted a single sentence — “Create a simple AI task manager app with JavaScript and HTML” — and waited to see how far the machine could take me.
For that experiment, I used Lovable, but the same approach works with tools like Cursor, Replit, or Windsurf, and with general‑purpose LLMs such as ChatGPT or Gemini.
Within minutes, Lovable scaffolded both the frontend and backend, wired up a basic UI, and even suggested improvements: better validation, nicer styling, and small UX tweaks I had not explicitly asked for. My role shifted from “person who writes every line” to “person who describes the outcome, reviews diffs, and presses the accept button when the vibes feel right.
Linking GitHub turned it into a real workflow instead of a one‑off demo. Each push to the main branch triggered Lovable to re‑ingest the codebase, propose changes, and keep the app deployable, so I could alternate between manual edits and AI‑guided refactors without losing the thread. It felt like pair‑programming with a very fast, slightly overconfident junior developer that never gets tired of trying again.
How it compares with traditional coding methods
Putting vibe coding next to a traditional IDE makes the trade‑offs obvious. On the AI side, the learning curve is shallow: you describe the app in natural language, iterate on prompts, and rely on the model to generate boilerplate, glue code, and even tests far faster than a human could. In a traditional setup, you retain full control over every line, but you also take on all the cognitive load of designing, wiring, and maintaining that code yourself.
Traditional IDEs, on the other hand, give developers full control over the project from the very beginning, which is ideal for complex systems that demand a high degree of precision and customization. This control comes with a steep learning curve, though, because it requires deep knowledge of languages like Python, Java, or C, plus a broad set of engineering skills that can take years to master.
Here’s a quick summary:
Challenges of vibe coding with AI
The first AI‑generated prototype feels like magic; the third or fourth reminds you why software engineering is still a job. The problems only really show up after the “wow” moment, when you try to turn a demo into something robust enough to survive real users.
Three pain points kept repeating in my experiments:
- AI agents are great for simple CRUD prototypes, but struggle as soon as you add non‑trivial business logic, tricky state, or performance constraints. At that point you need to guide them with very specific prompts and be ready to step in with manual fixes.
- The model will confidently tell you “Done!” even when the feature silently fails. You only discover the lie when you click the button, nothing happens, and you end up debugging AI‑written code you did not fully read.
- Progress is front‑loaded: you sprint to 80 percent completion, then crawl through the last 20 percent as edge cases, integration bugs, and weird regressions pile up. Without a basic understanding of software engineering, it is very easy to accumulate a graveyard of almost‑finished apps that never quite ship.
Vibe coding can absolutely multiply the output of someone who already knows how to design, test, and debug systems; it is much less forgiving for people who hope to outsource all of that thinking to the model.
What the community and developers are saying
Ever since Karpathy coined the term, vibe coding has ignited a passionate, often divisive conversation within the developer community. While some are hyping it up, others are approaching it with caution and skepticism, and others are outright rejecting it, calling it a “gimmick.” But behind all the noise, people out here are benefiting and profiting from it.
For example, after realizing that things could move way faster with AI, Rowan Trollope, the CEO of Redis, approved the use of AI‑assisted coding tools. Other companies following the same trend include Reddit, Google, Visa, and Bayer, among others. In fact, according to Y Combinator, 25% of its startups are using AI for 95% of their code base.
And it is not just big companies. Harry Roper, who founded Imaginary Space Agency, created a $100,000‑per‑month business using Lovable to develop production‑ready applications. We also have examples of non‑programmers, such as Kevin Roose, who shared his experience in an article in the New York Times, as shown below.
What lies ahead
What we are currently seeing is just the tip of the iceberg. It is not just about meeting today’s needs, but about building toward a future development experience that feels more like a conversation than a chore. Here is what is on the horizon:
Multi‑language support
Vibe coding will go beyond popular programming languages like Python, JavaScript, and Java. It will increasingly support niche and emerging languages like Go, Julia, and Swift, and make it possible to shift projects between languages with minimal manual rewriting.
Voice‑to‑code
Soon enough, you will be able to dictate features in plain speech and watch your AI agent translate them directly into functional code. Speaking your intent instead of typing it will make prototyping even more accessible.
Collaborative, real‑time AI dev rooms
Vibe coding will enable you and your team to join virtual development rooms where ideas can be shared, built, edited, and deployed in real time. Each teammate will be able to work in parallel with AI assistance, without constantly overwriting each other’s changes.
Final thoughts
To sum it up, vibe coding is a new, more powerful approach to building software, not a replacement for human expertise. It is a force multiplier for developers who have put in the work and built a strong command of their craft.
For up‑and‑coming engineers, it is not a shortcut to avoid learning, but a glimpse of what they can achieve with a solid foundation. The most successful developers of the future will not be the ones who choose between traditional coding and vibe coding, but the ones who blend both to build what was previously impossible.
Do not just take this on faith. Experiment with an AI coding tool today and see how this new approach can accelerate your next project.
Top comments (0)