What Is Vibe Coding? The Trend Honestly Assessed

The Idea Behind Vibe Coding

Vibe coding is the practice of building software by describing what you want in plain language, letting an AI generate the code, and iterating on the result through more prompts. You don’t write the code yourself. You describe it, review it, test it, and redirect the AI when something’s off.

The term was coined by Andrej Karpathy in early 2025. His description was deliberately casual: you give in to the vibe, let the AI handle the implementation details, and stop worrying too much about the underlying code. The framing caught on immediately, partly because it named something people were already doing, and partly because it captured the slightly reckless spirit of the approach.

It’s an honest description of a real phenomenon. And it’s worth understanding clearly — both what it does well and where it runs into walls.

Where the Term Came From

Karpathy’s original framing was specific. He wasn’t describing a polished workflow or a production methodology. He was describing a mode of exploration — using AI to build things quickly without needing to care deeply about every line of code.

That framing has since been stretched in every direction. Marketing teams use it. Developers use it pejoratively. Non-technical founders use it as a badge of pride. The meaning has blurred.

For this article, we’re using the practical definition: vibe coding means building apps primarily through natural language prompts to an AI, with minimal direct code authorship.

That’s distinct from using AI to assist while you code. A developer using GitHub Copilot or Cursor is still writing code — the AI is helping. Vibe coding inverts that. The AI writes the code. You guide it.

How Vibe Coding Actually Works

The basic loop looks like this:

You describe what you want — a feature, a screen, an interaction, an entire app concept.
The AI generates code.
You run it or preview it.
If it’s wrong or incomplete, you say what’s wrong and the AI tries again.
Repeat until you have something that works.

This is less like programming and more like directing. You’re making decisions about what should happen. The AI is figuring out how to make it happen in code.

The process feels surprisingly productive at first. A form that would have taken an hour to build from scratch appears in seconds. A dashboard that would have required wiring up a chart library, writing queries, and dealing with CSS comes together in a few back-and-forth messages.

The experience is genuinely different from anything that existed five years ago. Non-coders are shipping real software using tools and workflows that simply didn’t exist before.

The Tools That Make It Possible

Vibe coding as a practice runs on a specific category of tools: AI-native app builders that handle more than just code generation. They typically wrap a development environment, a deployment pipeline, and an AI agent into a single browser-based interface.

The main players in this space include:

Bolt — Generates full-stack apps from prompts, with a focus on speed and clean output. Read more about Bolt.
Lovable — Stronger on frontend polish, particularly for React-based UIs. Read more about Lovable.
Replit Agent — Browser-based, with good support for backend features and live deployment. Read more about Replit Agent.
Vercel v0 — Focused specifically on UI generation, not full apps.

If you want a detailed side-by-side on these tools, the full-stack AI app builders comparison covers them in depth.

These platforms lower the barrier enough that someone with no prior coding experience can ship a working app. That’s real. The question is what “working” actually means in practice.

What Vibe Coding Is Actually Good At

Let’s be honest about the wins before we get to the limitations.

Prototyping and validation

Vibe coding is excellent for building fast, throwaway prototypes. If you have an idea and you want to know whether it makes sense — whether the UX flow works, whether someone would actually use it, whether the concept holds up — you can build a version in hours instead of weeks. That’s genuinely valuable.

Founders and product people use this to test ideas before committing engineering resources. The prototype doesn’t need to be maintainable. It needs to be good enough to show someone.

Personal tools and internal apps

Automation tools, dashboards, data processors, admin panels — the kind of software that would take a developer a day or two but only needs to work reliably for a handful of people. These are excellent candidates for vibe coding.

The bar is lower, the user base is forgiving, and if something breaks, you fix it. Stakes are manageable.

Learning by doing

For people who want to understand software but don’t have a technical background, vibe coding offers a way to build real things while gradually learning what the pieces mean. You’re not learning programming syntax — you’re learning how systems fit together.

There’s a version of this that’s genuinely educational. Watching the AI generate code and asking it to explain what it wrote is a viable learning path.

Specific app categories

Some app types are well-served by current AI app builders. CRUD apps (create, read, update, delete) with straightforward data models. Simple SaaS interfaces with a small number of features. Landing pages with embedded forms. Marketing tools. If the problem is well-defined and the scope is narrow, vibe coding often works well.

If you’re curious about how a real consumer app came together using this approach, the Cal AI framework breakdown is worth reading.

Where Vibe Coding Breaks Down

This is the part that often gets glossed over in the enthusiasm.

The complexity cliff

Vibe coding works smoothly up to a certain level of complexity. After that, it gets harder fast.

Simple features get generated cleanly. Multi-step features with interdependencies start generating inconsistent code. Complex features with edge cases, async behavior, error states, and data integrity requirements produce code that looks right but fails in subtle ways.

The AI doesn’t have a mental model of your app. It has the conversation history. As that history grows and the app accumulates features, the AI’s ability to make consistent, coherent changes degrades. You end up in a loop of fixing one thing while breaking another.

The legibility problem

If you don’t understand the code the AI generates, you can’t debug it. You can describe the symptoms to the AI and ask it to fix things, but you’re flying blind. You don’t know whether the fix the AI applied is correct or just suppressing the symptom.

This is manageable for simple apps. For anything with real data, real users, or real money moving through it, flying blind is a significant risk.

The backend gap

Many vibe coding tools generate impressive-looking frontends that mask a shallow backend. Forms that don’t actually persist data. Authentication flows that look complete but skip edge cases. API integrations that work in the happy path and fall apart on errors.

This isn’t a flaw in the concept — it’s a limitation of the current tools. But it’s worth being clear-eyed about. A vibe-coded app isn’t automatically a complete, production-ready app. It might be, but you need to check.

Maintenance compounds the problem

Software needs to change. Users request features. Requirements evolve. Bugs surface. With traditionally written code, you have a clear source of truth — the codebase — and a team that understands it.

With a vibe-coded app, your source of truth is a chat log. There’s no shared mental model. Every time you add a feature or fix a bug, you’re relying on the AI to understand enough of the existing structure to make the right change. As the app grows, that gets harder.

This is the central structural weakness of the approach as it’s currently practiced.

Vibe Coding vs. More Structured Approaches

Vibe coding sits on a spectrum. On one end, you have fully manual coding — every line written by hand. On the other, you have tools that work from a higher-level description and compile down to code.

AI coding agents sit somewhere in the middle — they assist with code generation but you’re still working at the code level. Tools like Windsurf and Cursor are in this category. They make coding faster. They don’t replace it.

Vibe coding as practiced in tools like Bolt or Lovable goes further. You’re not editing code. You’re directing an AI that produces it. The upside is speed and accessibility. The downside is the loss of a reliable source of truth.

The next step beyond vibe coding — and the thing the field is actively working toward — is structured specification. Instead of a loose conversation, you describe your app in a format that captures actual precision: data types, validation rules, behavior at edge cases, authentication logic. That description becomes the source of truth. The code is compiled from it.

This is what spec-driven development looks like in practice. It’s more structured than vibe coding, but it’s still accessible to non-coders. The difference is that iteration stays coherent because you have a document that reflects what the app is supposed to do — not just a chat log of what you asked for.

The Bigger Question: What Changes

Vibe coding is a symptom of a real shift. The question isn’t whether non-programmers can now build software (they can). The question is what building software actually means when the code is no longer where you work.

There’s a reasonable argument that most of what software engineers do is already moving. Not the architecture thinking, not the system design, not the judgment about what to build and why — but the mechanical translation of intent into code. That part is getting automated.

The “is software engineering dead” debate is worth reading with some nuance. The short version: the role is changing faster than the title is. Engineers who adapt are building at higher levels of abstraction. Those who don’t are competing with AI on tasks the AI is increasingly better at.

Vibe coding sits right at this inflection point. It gives non-technical people real building capability. It also surfaces what a skilled developer actually brings — judgment, architecture, reliability, maintainability — as things that matter a lot more than typing speed.

Where Remy Fits

If you’ve been following what’s described above, you can see the structural problem with vibe coding at scale: the conversation is the only source of truth, and conversations are lossy.

Remy approaches this differently. Instead of prompting an AI in a chat interface, you write a spec — a structured markdown document that describes what your app does. The readable prose captures the intent. Annotations carry the precision: data types, validation rules, edge cases, business logic.

Remy compiles that spec into a full-stack app: backend, database, auth, tests, deployment. All of it.

The spec is the program. The code is the compiled output.

This means when something needs to change, you update the spec — not the chat log, not the code directly. The AI has something coherent to work from because the source of truth is structured and explicit, not implicit in a conversation history.

It’s more structured than vibe coding. That’s the point. The vibe coding experience is fast and accessible, but it trades precision for speed. Remy tries to give you both: accessible enough that non-technical builders can use it, structured enough that the app stays coherent as it grows.

You can see what this looks like in practice at mindstudio.ai/remy.

Frequently Asked Questions

What exactly is vibe coding?

Vibe coding is building software by prompting an AI in natural language and iterating on the generated output. The developer (or non-developer) describes what they want. The AI writes the code. The person tests it, identifies problems, and prompts again. The term was coined by Andrej Karpathy in early 2025 and spread quickly because it accurately described what a lot of people were already doing with tools like Bolt, Lovable, and Replit Agent.

Is vibe coding just for non-programmers?

No. Experienced developers use vibe coding workflows too, typically for prototyping, for internal tools, or for parts of a project where speed matters more than precision. The main difference is that a developer can read and debug the generated code, which changes the risk profile significantly. For non-programmers, the inability to audit the output is a bigger constraint.

Can you ship a real product with vibe coding?

Yes, with caveats. Simple apps with limited scope, low-stakes use cases, and manageable user bases have shipped as real products built through vibe coding workflows. The risk increases with complexity, with the number of users, and with the consequences of failure. A personal productivity tool built with vibe coding is a very different risk profile than a fintech app with real money moving through it.

What are the main limitations of vibe coding?

The biggest limitations are: coherence over time (the conversation history degrades as a source of truth), the legibility problem (you can’t debug what you can’t read), and the backend gap (many vibe coding tools generate better-looking frontends than real backends). There’s also the question of what happens when something breaks in production and you don’t have a clear understanding of how the system works.

How does vibe coding compare to using Cursor or GitHub Copilot?

Tools like Cursor and GitHub Copilot assist developers who are still writing code. They’re code-level tools — they make existing workflows faster. Vibe coding replaces the coding step itself with AI generation. The comparison is less “fast coding vs. slow coding” and more “coding vs. directing.” Different skills apply, different outputs result.

Is vibe coding the same as no-code?

Not exactly. Traditional no-code tools give you visual builders — forms, drag-and-drop interfaces, logic blocks. Vibe coding generates actual code from natural language. The output is real TypeScript (or Python, or whatever the tool targets), not a configuration layer on top of a no-code platform. The distinction matters when you need to extend or debug the output. For more context on where these approaches differ, the no-code vs. low-code vs. code-first breakdown is useful.

Key Takeaways

Vibe coding means building apps through natural language prompts to an AI, with minimal direct code authorship.
It’s genuinely useful for prototyping, personal tools, and low-complexity apps — especially for non-technical builders.
The main limitations are: degrading coherence as apps grow, inability to audit generated code, and backend gaps in current tooling.
The underlying shift is real: the code-writing step is getting automated, and the skills that matter most are moving toward judgment, architecture, and precise specification.
Structured alternatives like spec-driven development address the coherence problem by giving the AI a reliable source of truth to work from, rather than a conversation history.

If you want to see what a more structured version of this approach looks like — one that compiles annotated specs into full-stack apps with real backends, real auth, and real deployment — take a look at Remy.