Vibe Coding Your Way Out of Vibe Coding

March 20, 2025

There's a lot of talk lately about "vibe coders." The term itself was popularized by Andrej Karpathy—one of the original developers behind Tesla's autonomous driving and OpenAI's GPT models. Nowadays, Karpathy spends his time democratizing AI, making videos that teach complex concepts in simple ways. In one of his famous tweets, he described vibe coding as the practice of just speaking your thoughts into an AI tool and having it generate code for you—building websites, creating features, fixing bugs, all without needing deep programming knowledge.

But as more people dive into these AI-powered tools, a new debate emerges: when does a vibe coder actually become a developer?

Vibe coders are typically people without formal programming backgrounds. They jump onto platforms like Cursor or other AI-driven websites and say something like, "Build me this feature," or "Make me a website that does X." And the AI dutifully generates the code. At first glance, this feels revolutionary—and in many ways, it is. But beneath the surface, some serious issues start to emerge.

One big problem is that vibe coders often don't fully understand the underlying architecture or technologies their apps rely on. AI tools today are great at generating initial versions of websites or apps—especially front-end interfaces—but they struggle with deeper complexities and edge cases. Over time, as vibe coders keep adding new features and tweaking existing ones through AI prompts, their codebases become cluttered. Old, unused code piles up because the AI doesn't always know to delete it when replacing features. After dozens of iterations, your repository is full of dead code.

This isn't just messy—it actively hurts your development process. Why? Because AI tools have limited "context length." Think of context length like your own memory: if you read an entire textbook in one sitting, you'll barely remember details from chapter two by the time you reach chapter fifty. Now imagine someone sprinkled random unrelated sentences throughout each chapter—your memory would get even fuzzier. Similarly, when your codebase is filled with unused or irrelevant pieces of code, the AI struggles to clearly understand what's relevant for your next feature request.

And that's just scratching the surface.

When you're building software products, there's a lot more than just front-end design:

  • You need a robust back-end to handle logic and data processing.
  • You need databases to store information reliably.
  • You need DevOps skills to manage hosting infrastructure.
  • And crucially—you need security measures to protect your users and data.

Initially, AI tools handle front-end tasks pretty well. Ask them for a website with charts or tables—they'll deliver quickly. But things get tricky once you start dealing with databases and back-end logic. Even small mismatches between your database columns and front-end expectations can cause errors that aren't immediately obvious—I've personally run into this issue multiple times.

Then there's security—often overlooked by beginners because they assume nobody will bother attacking their tiny project. But once your app gains even minimal traction, vulnerabilities become targets. Experienced developers know how to set proper security roles, rate limits (to prevent excessive database requests), and infrastructure safeguards. Vibe coders usually don't have this knowledge yet—and unfortunately, it's easy for attackers to exploit these gaps.

It's tempting for seasoned developers to mock vibe coders for these mistakes—but that's unfair. Every developer was once inexperienced; every programmer has made countless mistakes along their journey. The difference today is that vibe coders' mistakes are more visible because they're launching products faster and publicly.

In traditional companies, junior developers had safety nets: senior engineers reviewing their code, automated testing pipelines catching errors before deployment, security teams monitoring threats proactively. Vibe coders typically don't have these support structures—they're often solo founders wearing every hat at once: front-end developer, back-end engineer, database admin, DevOps specialist, security analyst—all rolled into one.

This complexity isn't something previous generations faced as directly; roles were specialized precisely because mastering all these areas simultaneously is incredibly challenging.

So how does a vibe coder transition into becoming a genuine developer?

The answer lies in intentional practice:

  1. Start big with AI: Let AI tools help you quickly prototype large projects—this gives you momentum and motivation.
  2. Then go small manually: After building something impressive via AI assistance, create tiny test projects from scratch without relying on AI prompts.
    • Build a simple database yourself.
    • Write basic front-end code manually.
    • Experiment with inserting data into databases directly through your own scripts.
  3. Break things intentionally: Change small pieces of your manual projects deliberately; see what breaks and why.
  4. Understand fundamentals deeply: Spend time reading documentation for each technology you're using—even if your main project already works fine thanks to AI-generated code.

By alternating between large-scale AI-powered projects (to maintain excitement) and small-scale manual experiments (to build deep understanding), you'll gradually internalize how everything fits together: front-end interfaces connecting seamlessly with back-end logic; databases reliably storing data; infrastructure securely hosting apps online.

Over time—through intentional trial-and-error—you'll find yourself naturally transitioning from being purely a "vibe coder" into becoming an experienced full-stack developer who truly understands their entire tech stack inside-out.

That's how you vibe-code your way out of vibe coding—and into genuine mastery as a developer.