Subtitle: From prototypes to production—real-world experiments that prove what’s possible (and what’s not) with AI-powered development.
Introduction: Something Is Changing—Are You Ready?
You’ve probably seen the hype: “AI writes your code now.” Maybe you’ve watched someone on YouTube build an app by just talking to an AI. Maybe you’ve even tried a tool like GitHub Copilot or Cursor. But somewhere in the back of your mind, you’re wondering:
“Is this actually usable in real development? Or is it just another cool demo?”
You’re not alone in asking that.
That’s where Vibe Coding comes in—a concept that’s been picking up steam among developers, product teams, and even agile coaches. In this post, you'll get a clear, practical breakdown of what Vibe Coding is, how it actually works, and what happens when it moves from weekend hackathons to real production environments.
This isn’t theory. It’s based on experiments run over several weeks, across actual teams, with measurable results. Whether you’re sceptical or curious, this post will help you see where things are really heading—and how to stay ahead of the curve.
What Is Vibe Coding?
In February 2025, Andrej Karpathy tweeted a line that caught the tech world’s attention:
“I call it Vibe Coding. You just go with the vibe. Let the code disappear. It’s possible because tools like Cursor Composer and Sonnet are just that good now.”
At its core, Vibe Coding is about describing what you want to build in natural language, then letting AI agents do the heavy lifting—generating code, designs, architecture, and even documentation.
It’s not just autocomplete on steroids. It's the beginning of a new development paradigm, where your intent becomes the new input, and AI agents—like Cursor, SuperWhisper, Claude, and ChatGPT—become your team.
Use Cases: From Side Projects to Serious Work
You might think, “This sounds cool, but probably only for toy projects, right?”
That’s what I thought—until I tried it.
Here’s what happened when I went beyond just playing around and actually ran a full Vibe Coding sprint:
- Project Goal: Build a functioning mobile app on a tech stack I’d never used (Flutter, Dart, Firebase).
- Team Setup:
- Cursor as developer & designer
- ChatGPT as product manager
- Me as PM, Scrum Master, Dev Lead, and Design Lead
The result? A working prototype of a fishing app—with 99% of the code written by Cursor. I handled the strategic decisions and reviewed the outputs, but the agents handled nearly everything else.
And I’m not alone. Inside our company, we’ve seen multiple successful applications:
- Sue (non-developer): Built internal tools solo.
- Hook: Built an MCP registry service in under 6 hours.
- Herschel: Created an automated reporting system for ML model evaluations.
- Beans: Built a platform-based MCP server with zero prior experience.
The key takeaway? Vibe Coding is no longer just a playground for experimentation—it’s becoming a practical tool for real, high-value work.
From Experiment to Production: What We Learned
Let’s be clear: pure Vibe Coding, where you never look at the code, won’t cut it in production. That’s not how professional software gets made.
But when paired with thoughtful human oversight and strategic intervention, it absolutely works.
💡 Two essential practices:
- Human guidance during AI execution
- Thorough review and validation of AI-generated outputs
We tested this in a real production environment using OLIVE, Kakao’s open-source verification platform. The results?
🔍 Summary of Results
Experiment setup: 3 developers using AI agents (Cursor, Claude, etc.) across the full dev lifecycle—feature dev, refactoring, testing, documentation, code reviews, and migration.
Productivity: Tasks were completed in half the expected time, with a roughly 2x improvement in delivery speed.
Quality: AI-assisted work passed peer reviews with no major rework.
Case Studies:
- Dependency Analyzer feature expansion → Estimated: 2–3 days | Actual: 1 day
- Manual Component Registration → Estimated: 3–5 days | Actual: 2.5 days
- Angular to React Migration (junior dev) → Estimated: 5 days | Actual: 2.5 days
“AI agents significantly improved productivity across refactoring, testing, and frontend work. And the team actually enjoyed using them—it’s driving organic adoption.”
— Violet Blue, Open Source Tech Lead
How Developers Should Respond to This Shift
There’s a fear: “Will AI take our jobs?” Or even, “Will juniors be wiped out?”
Let’s be honest. If AI starts writing incomprehensible, machine-optimised code that no human can understand… maybe we should all open fried chicken shops. But we’re not there yet.
In fact, as productivity increases, the demand for skilled people often increases too. That’s Jevons Paradox in action.
The reality is, your relevance now depends not on seniority, but on how quickly you can adapt and absorb new tools and workflows.
The developers and teams who lean into this shift will ship better software, faster, more often. The ones who don’t? They’ll be left behind.
What Should You Do First?
Don’t overthink it. Don’t wait for the perfect course or strategy.
Just start.
Start working with coding agents. Start conversations with tools like Cursor or GPT. Treat them like teammates, not search engines. You’ll be amazed how quickly your mindset—and your output—will shift.
This is the groundwork for what’s next: Agent Fleets—multiple AI agents collaborating across your SDLC. We’ve already seen the early signs, like Google’s A2A (Agent-to-Agent) demo, where an AI Kanban board coordinates across an entire development lifecycle.
Final Thoughts: Ride the Wave or Get Washed Away
Vibe Coding isn’t a gimmick. It’s not magic either. But it’s real. It’s happening. And it works—when used right.
It’s not about replacing developers. It’s about augmenting them, freeing them up from repetitive tasks so they can focus on what really matters: creativity, problem-solving, and delivering real value.
I’ll leave you with a quote from a Korean drama that sums this up perfectly:
“Will you be swept away by the wave—or will you ride it? That choice is yours.”
📌 Explore More on Vibe Coding:
- Prototype to Production: Real-World Use Cases
- AI Agent Collaboration Frameworks
- Getting Started with Cursor and ChatGPT
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