Vibecoding means creating software in flow mode: start from an idea, describe it with text or voice, and use AI tools to build fast, prototype, and test without locking yourself into rigid rules from day one. It sounds liberating, but it also introduces real dilemmas when you want to ship to production.
What does “vibecoding” mean and why is it so trendy?
Vibecoding is often understood as an informal and creative way to code. The priority is not strictly following formal architecture rules or best practices, but:
- getting into flow
- experimenting fast
- building guided by intuition and speed
- shipping something to see what happens in the real world
The trend comes from something very concrete: today you can ask an AI “build me an app,” “build me a game,” or “prototype this,” and get iterable results in minutes.
How is vibecoding related to low-code and no-code?
It connects with a discussion we already know: low-code/no-code. The logic is similar:
- they are tools to build faster
- they let more people create things
- they bother people who believe “if you are a programmer, you should not use them”
The key idea is simple: they are tools. Conflict appears when “making it work” is confused with “building it well.”
Can you vibecode without knowing how to program?
Yes. With ChatGPT or similar tools, someone can build with:
- text
- audio
- trial-and-error iteration
Even with everyday examples: ask for an app or a game, get a result, iterate, and compose a product. This drastically lowers the barrier to entry.
Where does the pain show up when vibecoding for real users?
The problem is not creating. The problem starts when real people will use it.
Once you launch an app, factors appear that a prototype does not reveal:
- users find edge cases
- friction and expectations show up
- the real problem is sometimes not solved, even if it looks good
That is the dilemma: many vibecoded apps can remain as demos, or reach production with weak foundations for growth.
What about scalability and architecture in vibecoding?
Vibecoding in an unstructured way can work for testing ideas, but it can fail when you need to:
- scale
- sustain a solid technical foundation
- make intentional architectural decisions
The app can be built just for the sake of building it, only because the prompt asked for it, without answering questions like:
- how will data be stored?
- can the user have multiple palettes?
- can a palette be iterated?
- what structure supports that tomorrow?
How can someone with technical experience vibecode without losing best practices?
You can vibecode with a more disciplined approach. Before asking for code, it helps to do prior work:
- break down and understand requirements
- validate them and test ideas with other tools
- create a phased developer plan
- guide the AI so it respects architecture and best practices
This way you do not only flow, you flow in a technically correct way, aiming for a scalable result.
Why does working in phases improve vibecoding results?
A practical rule that works: split work into phases and keep each phase to a maximum of 4 tasks or subtasks.
The benefit is direct:
- you reduce chaos
- you can validate each delivery
- you analyze outcomes before moving on
- you avoid letting AI do everything at once
This turns vibecoding into a more controlled process without killing speed.