What Is Vibe Coding? How AI Is Changing Software Development
Vibe coding lets non-developers build working apps by describing what they want in plain language. Here's what it is, how it works, and its real limits.
Typing in Plain English and Watching Code Appear
Not long ago, building software meant knowing how to code — or paying someone who did. Then a shift happened quietly but quickly: people started describing what they wanted in plain language and watching working applications appear in response.
That’s vibe coding. And it’s changing who gets to build software, how fast it gets built, and what “knowing how to code” even means anymore.
The term was coined by Andrej Karpathy in early 2025. In a post on X, he described a mode of programming where you “fully give in to the vibes” — where you tell an AI what you want, accept the output, and keep iterating without reading or fully understanding the code underneath. The phrase stuck because it described something real that a lot of people were already doing.
This article breaks down what vibe coding actually is, how it works in practice, where it genuinely helps, and where it falls apart.
What Vibe Coding Actually Means
Vibe coding is the practice of building software primarily through natural language prompts to an AI, rather than writing code manually. You describe what you want — “build me a dashboard that pulls from my Google Sheet and shows weekly trends” — and an AI generates the code. You test it, describe what’s broken or what to change, and the AI updates the code again.
How Remy works. You talk. Remy ships.
The “vibe” part refers to the feel-first, iterate-fast approach. You’re not studying the code line by line. You’re describing intent, reviewing results, and steering the direction.
It’s worth distinguishing this from AI-assisted coding, which is when experienced developers use AI tools to write code faster. Vibe coding is more radical: it’s the idea that someone with little or no coding background can ship working software by prompting well and iterating quickly.
Where the Term Comes From
Andrej Karpathy — former director of AI at Tesla and a founding member of OpenAI — described vibe coding in a February 2025 post. He wrote about building programs by just telling an AI what he wanted, letting it handle the code, and not really reading what it produced. “It’s not really coding,” he said, “I just see stuff, say stuff, run stuff, and copy paste stuff.”
That description resonated because it matched what thousands of people were already doing with tools like Cursor, Claude, and ChatGPT. The phrase gave a name to a behavior that was spreading fast.
Vibe Coding vs. Traditional Development
Traditional software development involves writing code, understanding logic, debugging manually, and maintaining a codebase over time. It requires deep knowledge of languages, frameworks, and systems.
Vibe coding inverts much of that. The AI writes the code. You direct it. The understanding is optional — at least in the beginning.
This is genuinely useful for prototyping, internal tools, and simple applications. It’s also genuinely risky for anything complex, security-sensitive, or long-lived. More on that shortly.
How Vibe Coding Works in Practice
The basic loop looks like this:
- Describe what you want — In plain language, explain the application, feature, or problem you’re trying to solve.
- Review the AI’s output — The AI generates code. You run it, test it, see what works.
- Describe what to fix or change — Tell the AI what’s broken, what you want added, or what feels wrong.
- Repeat — Keep iterating until the output does what you need.
In practice, this can produce a working prototype in under an hour for simple tools. A basic CRUD app, a data dashboard, a form with logic, a simple API wrapper — these are well within reach for someone with no coding background.
The skills that matter in vibe coding aren’t traditional programming skills. They’re:
- Clarity — Describing exactly what you want, not vaguely
- Iteration — Testing quickly and giving precise feedback
- Judgment — Knowing whether the output is actually doing what you intended
- Scope management — Keeping the complexity low enough for AI to handle reliably
The Tools People Use
Several tools have become closely associated with vibe coding:
Cursor
Cursor is a code editor built on top of VS Code with deep AI integration. You can describe changes in natural language, have the AI write or edit code across files, and ask questions about your codebase. It’s the most widely used tool among people doing serious vibe coding.
Claude (by Anthropic)
Plans first. Then code.
Remy writes the spec, manages the build, and ships the app.
Claude, particularly Claude 3.5 Sonnet and Claude 3.7 Sonnet, has become a go-to for vibe coders because of its long context window and strong code generation. Many people paste their entire codebase into a Claude conversation and work from there.
ChatGPT
ChatGPT’s code interpreter and GPT-4o capabilities make it practical for generating and explaining code. Less integrated into an IDE than Cursor, but widely used for one-off scripts and prototyping.
GitHub Copilot
Microsoft’s Copilot integrates directly into VS Code and other editors, completing code in real time as you type. It’s more assistive than generative — better suited for developers who know what they’re building but want to move faster.
Replit
Replit has leaned heavily into AI-assisted development, offering a browser-based environment where you can prompt an AI to build applications without installing anything. It’s particularly popular with beginners.
Bolt and v0
Bolt (from StackBlitz) and v0 (from Vercel) generate full-stack web applications from a prompt, running in the browser. These are among the most beginner-friendly vibe coding tools.
Who Is Actually Using It
Vibe coding isn’t just for curious hobbyists. It’s spread across several distinct groups:
Non-technical founders — People with business ideas who couldn’t previously build a prototype without hiring a developer. Vibe coding lets them test concepts quickly and cheaply.
Operators and analysts — People who understand their workflows deeply but don’t code. They’re building internal tools: custom dashboards, automation scripts, data cleaners.
Developers moving faster — Even experienced engineers use AI coding tools to accelerate work, generate boilerplate, and explore solutions. This overlaps with AI-assisted coding but often involves the same tools.
Students and learners — People using vibe coding as a way to learn, watching what the AI produces and trying to understand it.
Entrepreneurs building MVPs — The ability to go from idea to working demo in a day or two has real business value. Vibe coding has shortened the path from concept to something you can show investors or customers.
What You Can Build With Vibe Coding
The sweet spot for vibe coding is relatively contained, well-defined applications. Things that work well:
- Internal tools — Simple admin panels, data viewers, form processors
- Prototypes and MVPs — Early-stage versions of apps, enough to test an idea
- Scripts and automation — One-off scripts to process files, scrape data, or automate repetitive tasks
- Simple web apps — Landing pages with logic, basic CRUD applications
- Integrations — Small connectors between services
What doesn’t work as well is covered in the next section. But the range of what’s possible is genuinely wider than most people expected two years ago.
The Real Limits of Vibe Coding
This is where honest reporting matters. Vibe coding has real, documented limitations that don’t go away just because the tools are improving.
Complexity Doesn’t Scale Linearly
Simple apps work well. But as the codebase grows — more features, more edge cases, more integration points — AI-generated code starts to accumulate errors, inconsistencies, and technical debt. The AI doesn’t have a coherent model of your system the way a human developer does. It generates plausible code, not necessarily correct code.
Not a coding agent. A product manager.
Remy doesn't type the next file. Remy runs the project — manages the agents, coordinates the layers, ships the app.
Many vibe coders hit a wall at some point where the AI starts breaking previous features while fixing new ones, or where the code becomes so tangled that even the AI can’t navigate it.
You Can’t Debug What You Don’t Understand
When something breaks in a vibe-coded application — and things will break — you often can’t diagnose the problem if you don’t understand the code. You’re dependent on the AI to identify and fix issues, which it sometimes can and sometimes can’t.
This creates a fragile dependency. If the AI misdiagnoses the bug, you’re stuck.
Security Is a Genuine Risk
AI-generated code often contains security vulnerabilities. SQL injection risks, improper authentication handling, exposed API keys, insecure data storage — these are all real problems documented by security researchers studying AI-generated code.
If you’re building anything that handles user data, financial information, or sensitive operations, vibe coding without security review is not safe.
Maintenance Is Hard
Code that no one on your team fully understands is hard to maintain. If the original prompt context is lost, or the AI model changes, or requirements shift significantly, updating the application can require essentially rebuilding it from scratch.
Legal and Licensing Questions
AI code generation models are trained on publicly available code, some of which is under open-source licenses. The legal status of AI-generated code — particularly whether it might reproduce licensed snippets — is not fully settled.
The Skill Floor Is Real
Despite what the most optimistic takes suggest, effective vibe coding still requires some technical literacy. You need to understand enough to evaluate outputs, identify when something is wrong, and give useful feedback. Pure technical naivety produces broken apps faster than working ones.
How MindStudio Fits Into This Shift
Vibe coding represents a broader trend: the line between “technical” and “non-technical” is blurring, and the tools that work best for building AI-powered applications are increasingly visual, prompt-driven, and no-code.
MindStudio sits squarely in that shift — but for AI agents and workflows rather than traditional software.
Where vibe coding often still produces raw code that needs to be understood, deployed, and maintained, MindStudio’s visual builder lets you assemble AI agents without that dependency. You’re describing what the agent should do, connecting integrations, and deploying — without managing a codebase at all.
The average build on MindStudio takes 15 minutes to an hour. You can connect to 1,000+ tools — Google Workspace, Slack, HubSpot, Salesforce, Airtable, and more — without writing integration code. And you have access to 200+ AI models built in, from Claude and GPT to Gemini and FLUX, without needing API keys or separate accounts.
For someone who wants to build something like an AI-powered customer intake form, an automated report generator, or an agent that monitors data and sends alerts — MindStudio is a faster and more stable path than vibe coding a custom app.
Other agents start typing. Remy starts asking.
Scoping, trade-offs, edge cases — the real work. Before a line of code.
If you do want to go deeper technically, MindStudio supports custom JavaScript and Python functions. And for developers who are building with AI agents like Claude Code or LangChain, the Agent Skills Plugin (@mindstudio-ai/agent) gives those agents access to 120+ typed capabilities — things like agent.sendEmail(), agent.searchGoogle(), or agent.runWorkflow() — without building the infrastructure from scratch.
The distinction is worth understanding: vibe coding produces software artifacts (code) that require ongoing maintenance. MindStudio produces deployed agents that run without a codebase to manage.
You can try MindStudio free at mindstudio.ai.
Vibe Coding vs. No-Code: What’s the Difference?
These two terms get conflated, but they refer to different things.
Vibe coding involves generating actual code through AI prompts. The output is code — usually in a real language like JavaScript, Python, or TypeScript — that runs as software. The code exists and needs to be hosted, maintained, and updated.
No-code tools (like MindStudio, Webflow, or Airtable) use visual interfaces and configuration instead of code. There’s no codebase to maintain. The platform handles infrastructure. You’re assembling behavior from building blocks.
Both approaches let non-developers build things. The tradeoffs differ:
| Vibe Coding | No-Code | |
|---|---|---|
| Output | Actual code | Configuration / visual flows |
| Flexibility | Higher (you can build anything) | Bounded by platform capabilities |
| Maintenance | Requires managing a codebase | Platform handles it |
| Debugging | Harder without code knowledge | Easier, more visual |
| Hosting | You manage it | Platform manages it |
| Best for | Custom software, unique logic | Workflows, agents, standard business tools |
For building AI workflows and automation, no-code is often the faster and more durable choice. For building software with unique or complex requirements, vibe coding may be the better path — especially with some technical oversight.
Is Vibe Coding Making Developers Obsolete?
Short answer: no. Slightly longer answer: it’s changing what developers spend their time on.
Experienced developers using AI coding tools report significant productivity gains — estimates range from 30% to 2x faster depending on the task and tool. But the gains are concentrated in code generation and boilerplate. Architecture decisions, systems design, debugging complex issues, and security review still require human judgment.
What’s shifting is the floor: you need less baseline knowledge to start building something functional. The ceiling — truly complex, reliable, secure, scalable software — still requires expertise.
For AI-powered application development, the most practical framing isn’t “developers vs. non-developers.” It’s “what’s the right tool for what I’m building?” A simple internal tool? Vibe code it or use a no-code platform. A production application with real security requirements? You want a developer involved.
The broader shift is real though. The economics of early-stage software development are changing. Prototyping is faster and cheaper. The path from idea to something functional is shorter. That creates real value for founders, operators, and teams who couldn’t previously access that.
Frequently Asked Questions
What is vibe coding in simple terms?
Vibe coding is building software by describing what you want in plain language to an AI, rather than writing code yourself. You prompt the AI, review what it builds, describe changes or fixes, and keep iterating. The term was coined by Andrej Karpathy in 2025 to describe a style of development where you focus on intent and output rather than on reading or writing code directly.
Do you need any coding knowledge to vibe code?
Seven tools to build an app. Or just Remy.
Editor, preview, AI agents, deploy — all in one tab. Nothing to install.
Some baseline helps significantly, but it’s not required to start. You need to understand enough to evaluate whether the output does what you intended, identify when something is broken, and give useful feedback. Completely technical naivety tends to produce dead ends faster than working applications. But you don’t need to know a specific programming language or have formal training.
What are the biggest risks of vibe coding?
The main risks are security vulnerabilities (AI-generated code often has them), maintainability problems (code no one understands is hard to fix or update), and accumulated technical debt as complexity grows. For anything handling sensitive user data or running in production at scale, vibe coding without expert review introduces real risk.
What tools do people use for vibe coding?
The most commonly used tools are Cursor (an AI-native code editor), Claude (especially for longer context), ChatGPT, GitHub Copilot, Replit, Bolt, and v0. Cursor is the most integrated option for serious development work. Bolt and v0 are the most beginner-friendly for generating web apps from scratch.
Is vibe coding the same as no-code?
No. Vibe coding generates actual code through AI prompts — the output is real code in a programming language that you need to host and maintain. No-code tools use visual interfaces and configurations that don’t produce a codebase. Both let non-developers build things, but no-code is generally more stable and lower-maintenance for standard business applications and workflows.
Can vibe coding be used for professional or production software?
With appropriate oversight, yes — but with caveats. Many teams are using AI-generated code in production, but with code review, security audits, and developer involvement to catch problems. Using vibe coding to ship production software without any technical review is high-risk, particularly for security-sensitive applications.
Key Takeaways
- Vibe coding means building software through natural language prompts to an AI, iterating quickly without necessarily reading or writing code directly.
- The term was coined by Andrej Karpathy in 2025 and describes something that was already widely happening across tools like Cursor, Claude, and Replit.
- It works well for prototypes, internal tools, and simple applications — and it genuinely lowers the barrier to building functional software.
- Real limits exist: security vulnerabilities, maintenance difficulty, and reliability problems at higher complexity are documented and worth taking seriously.
- Vibe coding and no-code are different. No-code platforms like MindStudio offer a more stable path for building AI agents and workflows — with no codebase to maintain and built-in access to models and integrations.
- Developers aren’t becoming obsolete. The economics and speed of early-stage development are shifting — and so is how technical and non-technical people collaborate.
If you want to build AI-powered tools without managing a codebase, MindStudio’s no-code builder is worth exploring. You can start free and build your first agent in under an hour.
