At first glance, vibe coding feels like magic.

You feed an LLM a thought, a document, or a concept. It responds with HTML scaffolds, snippets of JavaScript, and maybe even a fully working app. The gap between idea and execution seems to collapse. For many, especially those new to coding, this approach feels revolutionary—and in many ways, it is.

But as any experienced engineer will tell you: getting something to work is not the same as getting it to work reliably, at scale, across time and teams.

That’s where things get tricky.

At its core, vibe coding is about aligning your intent with the generative capabilities of an AI model to spin up working code quickly. It’s not about planning out each component. Instead, you can use it to express a concept in natural language and refine what the model gives back until it behaves the way you want. 

I’m not saying that detailed component-level programming is not possible. That is far from the truth. But at its core, that is coding supported by AI. Vibe coding is a different aim entirely. The goal is to build with as little code-level interaction as possible.

When done well, this workflow can:

It feels agile. It’s fun. Fast. Fluid. 

But it’s not free from the rules of complexity. For many, getting something that works at the end of the vibe coding experience is more art than skill. Some call this getting “Vibe Tipsy”. 

When you first start vibing on a project, you are excited and blown away by what is possible, only to be 500 resends and error responses later with a broken project, disillusioned at how you ended up here, i.e., “Vibin Hangover”.

Precision isn't just tricky in traditional software development. It’s fragile.

The more systems you build, the more interdependencies emerge. As the number of components grows, the number of relationships between them grows exponentially.

This is what we mean by entropy in code:

This is more than just bugs. It includes invisible complexity. Each feature adds more potential points of failure—not linearly, but geometrically.

When you’re coding “by vibe,” this entropy doesn’t go away. It can sneak up faster, because you’re skipping some of the guardrails and checks that traditional methods rely on. Plus, the larger the code base you never coded directly, the harder it will be for you or anyone down the road to manage it. 

Agile development wasn’t born from a desire to go faster. Developers needed to manage complexity.

In the late 90s, waterfall development meant planning everything up front, then building for months before knowing whether anything worked. Projects bloated. Misaligned specs led to millions lost. And software systems got so complex that no one person could hold the whole thing in their head.

Agile changed that by:

Ironically, vibe coding inherits the spirit of agile but skips many of its structures. It accelerates iteration but also increases the risk of generating fragile, brittle, or non-scalable code if not balanced by engineering discipline.

To use vibe coding effectively beyond a demo or prototype, you must bridge the gap between creative fluidity and technical precision. Here’s how:

AI is stunning at creating code fragments. But it doesn’t fully understand your system as a whole—not yet. That’s still your job.

That means vibe coding is not the end of engineering. It’s a new first mile. A way to think through building, not just build what you’ve already thought.

But don’t mistake that starting point for the finish line.

Because real software lives in the edges, the connections, the scale, and the stories behind the system. And that still takes intention, structure, and discipline.

So yes—vibe high. But also… architect wisely.