I really dislike the term vibe coding.

Not because it sounds silly, although it does. And not because people should not have fun with technology. They should. The problem is that the term quietly suggests something bigger: that building with AI is mostly casual, experimental, and a bit unserious. Like you are just throwing prompts at a machine, getting lucky sometimes, and calling it product development.

That is not how I see it.

For small businesses, freelancers, and solo founders, AI-assisted development is not a toy. It is not a side show. It is a serious way to build real products faster, with less overhead, and with much more independence than most people realize.

But there is a catch.

Using AI to build products only works well when you stop treating it like magic.

I am not speaking theoretically here. I am speaking from how I work now. I stopped hiring developers in early 2025, and today I rely on Codex as the only solo developer across the projects I am handling. That does not mean I sit down, type a vague idea, and watch perfect software appear like a cooking show reveal. It means I changed the way I think about product development.

And that is exactly where most of the confusion around “vibe coding” starts.

Using AI is not the problem. Vagueness is.

The first rule many of us learned when we started learning computer science in the late 1990s was simple: garbage in, garbage out.

That rule did not disappear because AI arrived. If anything, it became more important.

If you give an AI coding tool a half-idea, a blurry goal, and a pile of unmade decisions, it will still produce something. That is part of the danger. It is very easy now to get output that looks impressive before you realize it is built on fuzzy thinking.

That is what I would call vibe coding.

Not using AI.

Not building quickly.

Not shipping with AI assistance.

Vibe coding, to me, is when someone gives AI a semi-idea and expects it to somehow fill in the product thinking, business logic, edge cases, user needs, and technical tradeoffs by itself. That may produce demos. It may even produce code that runs. But it usually does not produce a reliable product.

The issue is not the intelligence of the tool. The issue is the laziness of the input.

My workflow does not start with Codex

This is the part that matters most.

I do not start with Codex.

I start with ChatGPT.

That surprises some people because if the goal is to build software, the instinct is to go directly to the coding tool. But in my experience, that is too early. Before implementation, I need clarity. I need pressure. I need questions. I need to discover whether the idea in my head is actually a product or just a direction wearing a confident face.

So I use ChatGPT first for thinking, not coding.

I use it to discuss what already exists in the market. I use it to brainstorm approaches. I use it to challenge weak assumptions. I use it to force me into specifics when I am still speaking too generally. Sometimes the most useful thing it does is not answering me. It is asking me the questions I should have asked myself earlier.

That stage is extremely important because most product ideas are incomplete when they first show up. They sound clear because they are familiar to the person thinking about them. But once you start discussing them properly, all the hidden ambiguity comes out.

What exactly is the first version?

What is essential and what is not?

What should happen when something fails?

What does the user actually need, not what sounds nice in a feature list?

What should be postponed even if it looks attractive now?

That is where the serious work starts.

The goal is not a better prompt. The goal is a clear product.

A lot of people talk about prompting as if the secret is finding the perfect sentence to unlock perfect software.

I do not think that is the real game.

The goal is not to write a clever prompt. The goal is to create a clear picture of the product before asking an AI coding tool to build it.

Sometimes that ends up becoming a proper PRD. Sometimes it is a technical specification. Sometimes it is just a very well-structured breakdown of scope, flows, decisions, and constraints. The format matters less than the clarity.

What matters is that by the time I move into implementation, I am no longer asking the AI tool to invent the product for me. I am asking it to execute against something I have already thought through.

That changes everything.

When the product is clear, the AI becomes dramatically more useful. It stops guessing as much. It makes fewer wrong assumptions. It can move faster without dragging the whole project into chaos. Review becomes easier. Iteration becomes sharper. The output becomes more reliable because the target is more reliable.

This is why I do not see serious AI development as “prompting.” I see it as structured product thinking followed by accelerated execution.

A small real example

One public example I can talk about is a WordPress backup plugin I am designing.

If I had gone straight to a coding tool with the raw idea, the prompt would have sounded something like this: build me a WordPress plugin that backs up a site to Google Drive.

That sounds fine until you realize how many decisions are hidden inside that one sentence.

What exactly gets backed up?

Should it create large local zip files or upload directly?

Does it support manual backups, scheduled backups, or both?

What belongs in version one and what should wait?

How should it behave on limited shared hosting?

What errors deserve admin alerts?

Should it support restore? Migration? Multiple cloud providers? Incremental backup?

If you skip those questions and just start coding, the AI will still build something. But now the product is being shaped by whatever the model guesses, not by your actual priorities.

That is not a development strategy. That is delegation by wishful thinking.

Instead, I used ChatGPT first to work through the product properly. The discussion narrowed the scope, removed unnecessary features from the first version, and focused heavily on reliability over feature count. The result was a much clearer first release: what it should do, what it should not do, how it should behave, and what kind of architecture made sense for the real hosting environments it would run on.

Only after that kind of clarification does a coding tool become truly powerful.

That is the difference I keep trying to explain when people reduce AI development to “vibes.”

My tool preference is personal. The method is broader.

For my own work, I use Codex.

I have also tried Google AI assistance and Google Firebase Studio, and in my own experience they did not come close to Codex for the way I work. I have not had the chance to try Claude Code yet, so I am not pretending to publish a grand ranking of all AI coding tools from a mountaintop.

But honestly, that is not the most important part.

The methodology matters more than the vendor.

If you start with vague thinking, weak scope, and missing decisions, most AI development tools will give you unreliable results sooner or later. If you start with a clear product picture, a defined first version, and a realistic understanding of the problem, you give any strong AI development tool a much better chance of producing useful work.

Tool choice matters, yes.

But thought process matters more.

AI did not remove the need for product thinking. It increased it.

This is the part many people still miss.

AI does not remove the need to think clearly. It increases the cost of not thinking clearly.

When development becomes faster, confusion also becomes faster. Wrong assumptions spread quicker. Bad scope decisions show up earlier. Weak product thinking gets amplified instead of hidden behind longer timelines.

That is why I reject the framing behind vibe coding.

Using AI to build products is not unserious. In many cases, it is the opposite. It demands sharper thinking because the execution layer has become much faster. If you are careless, the tool will happily help you build the wrong thing efficiently. That is not innovation. That is just a quicker route to regret.

Serious AI development, at least in the way I use it, starts before the first line of code. It starts with clarity. It starts with the discipline to define what you want, what you do not want, and why.

That is not a vibe.

That is product work.

And if you do it properly, it can absolutely produce real products.