Walk into any college hackathon today, and you’ll sense the difference immediately. The energy is the same — groups huddled over laptops, arguing over design ideas, rushing to meet submission deadlines — but the tools have changed. Instead of just text editors and C compilers, you’ll see AI assistants, drag-and-drop builders, and auto-complete tools doing half the heavy lifting.
Welcome to the era of vibe coding — a prompt-first, AI-assisted approach to building apps and prototypes fast. It’s transforming how students learn to code, how hackathons run, and even how companies think about technical talent.
So what exactly is “vibe coding”? Why does it matter? And what do employers actually expect from students in this new world of AI-driven software development? Let’s explore.
A Short History: Why This Moment Matters
In the early 2000s, India’s IT boom changed the landscape of technical education. Computer Science became the most sought-after branch, as software services and startups flourished. Students mastered C and C++, learned Java for enterprise systems, and eventually embraced Python for its simplicity and versatility.
Hackathons became the testing ground for new talent. Students stayed up all night building apps, debugging endlessly, and competing for prizes and internships. It wasn’t just about winning — it was about learning fast and solving problems creatively.
Then, over the next decade, coding education began to evolve. Schools introduced block-based learning tools like Scratch and micro:bit, allowing even young children to learn core logic concepts — loops, conditions, and sequences — without worrying about syntax errors.
That foundation set the stage for what we see today: AI tools that can understand plain English and generate real, working code.
What Exactly Is Vibe Coding?
Vibe coding is a term popularized in 2025 by AI researcher Andrej Karpathy. He described it as “a new kind of coding where you fully give in to the vibes — you focus on ideas and let the AI handle the code.”
In essence, vibe coding replaces most of the “typing” with talking to an AI. You describe what you want — for example, “Create a simple to-do app with notifications and login.” The AI assistant then writes the code, tests it, and even helps debug it if something breaks.
Instead of memorizing syntax, the coder’s role shifts to prompting, reviewing, and refining. It’s about turning ideas into prototypes — fast.
Researchers are now studying how this workflow changes learning, productivity, and creativity. Early evidence shows that vibe coders can produce working demos 3x faster than traditional teams — but may struggle with deeper understanding of code structure and long-term maintenance.
Which Colleges and Events Are Already Doing This?
Across India and the world, vibe coding has already entered mainstream hackathons and university curricula.
- The Smart India Hackathon, one of the country’s largest innovation events, now allows and even encourages students to use AI tools for brainstorming and coding.
- Kongu Engineering College (Erode, Tamil Nadu) is hosting an “Exploring VIBE Coding Techniques using AI 2025” workshop, open to students from both technical and non-technical backgrounds.
- Google Developer Groups (GDG) on Campus chapters and local hack clubs have started organizing vibe coding sessions, where students practice prompt engineering and AI-assisted app building.
- Internationally, universities in the US, Europe, and Singapore are running “AI-first hackathons,” where tools like GitHub Copilot, Replit Agents, and Cursor Composer are part of the competition workflow.
The takeaway? Vibe coding is no longer a niche experiment — it’s becoming the default mode of innovation on campus.
The Tools Behind the Movement
To understand vibe coding, you need to know the tools that power it. Here are the main categories students are using:
- AI Coding Assistants:
Tools like GitHub Copilot, Codeium, Amazon Q, and Cursor help write code, explain bugs, and even generate test cases — all within your IDE. - Rapid Prototype / No-Code Platforms:
Platforms such as Replit Agents, Glide, and Notion AI let students build full web apps using natural language prompts and minimal manual coding. - Hackathon Platforms:
Devpost, HackerEarth, Unstop, and Major League Hacking (MLH) host challenges, connect mentors, and showcase AI-powered projects to recruiters. - Educational Kits:
Devices like micro:bit, Arduino, and visual coding tools like Scratch still play a role in teaching problem-solving and computational thinking.
The Upside: Speed, Access, and Real-World Problem Solving
Vibe coding brings undeniable benefits to the table — especially for students and teams without formal programming training.
- Speed: You can build prototypes in hours, not days.
- Accessibility: Designers, management students, or mechanical engineers can all contribute to building a working demo.
- Focus on Problems: Teams spend less time on syntax errors and more time understanding real-world needs — improving UX, business logic, and scalability.
- Interdisciplinary Learning: Hackathons now attract mixed teams — coders, marketers, designers — making projects more complete and market-ready.
For instance, a business student can describe a workflow in natural language, while an AI model converts it into a functioning web dashboard. The coder’s job shifts to reviewing and optimizing, not starting from scratch.
But What About “Real Coding”?
Critics argue that vibe coding can create shallow understanding. If students rely on AI to generate code, will they ever learn what’s happening under the hood?
That’s a fair question. Real coding — the traditional kind — still teaches valuable skills: debugging logic, understanding data structures, managing performance, and thinking algorithmically.
In workplaces, engineers who understand these fundamentals remain irreplaceable. AI can generate code, but only humans can design scalable systems, ensure security, and handle edge cases.
Employers today expect a blend of both — the efficiency of AI-assisted development combined with the depth of real coding knowledge.
What Companies Really Want
The hiring trend is clear: companies don’t just want coders; they want problem-solvers who can work with AI.
Here’s what modern recruiters are looking for:
- Prompt engineering skills — the ability to communicate intent clearly to AI tools.
- Code literacy — being able to read, edit, and verify AI-generated code.
- Collaboration — working in mixed teams that include designers, product leads, and AI models.
- Ethical and secure development practices — ensuring AI-generated code is safe and compliant.
Simply put, the best candidates are AI-native engineers — people who use automation intelligently but still understand the underlying logic.
What Students Can Do to Prepare
If you’re a student stepping into this new era, here’s how to future-proof your skills:
- Master the Basics.
Understand core programming fundamentals — logic, data structures, algorithms — so that you can spot AI mistakes quickly. - Learn to Prompt.
Practice giving precise, context-rich instructions to AI tools. It’s a skill that improves with iteration. - Build Projects Publicly.
Participate in AI-assisted hackathons and post your work on GitHub or LinkedIn. Employers love seeing initiative. - Collaborate Across Disciplines.
Join teams with designers, marketing students, and hardware engineers. Learn how to turn prototypes into real-world solutions. - Stay Curious.
Experiment with multiple AI tools — from Copilot to Cursor — and learn how each one helps you think differently about software creation.
Conclusion: The New Coding Mindset
Vibe coding isn’t replacing real coding — it’s reshaping it. The new generation of developers will still need logic, structure, and debugging skills, but they’ll also need communication, creativity, and the confidence to collaborate with AI.
As Andrej Karpathy put it, the goal isn’t to forget code — it’s to focus on the vibes that make coding exciting again: curiosity, experimentation, and fast creation.