What is Vibe Coding? The 2026 Guide to AI-Powered Development

The term "vibe coding" was coined by Andrej Karpathy in early 2025, and within months it had split the engineering community in half. One camp saw it as the future of software development. The other saw it as a recipe for unmaintainable, insecure spaghetti pushed to production by people who don't understand what they're shipping.

Both camps are partially right. That's what makes the topic worth understanding carefully.

What is Vibe Coding?

Vibe coding is AI-assisted software development — a workflow where you describe what you want in natural language and an AI tool generates the code, which you then review, test, and iterate on.

The "vibe" part is a nod to the intuitive, intent-driven nature of the process. Instead of writing every function from scratch, you're directing an AI that's been trained on billions of lines of code. You describe the intent; the AI handles much of the implementation.

In practice this looks like: opening Cursor, typing "build a user authentication flow with email/password login, magic links, and Google OAuth — Next.js App Router, Supabase for the database," and getting 90% of the boilerplate written in seconds. Then you review it, fix what's wrong, and ship.

That's vibe coding. It's not magic. It's not a cheat code. It's a new category of developer tooling that changes the ratio of thinking to typing — heavily in favor of thinking.

The Tools

Understanding vibe coding requires understanding the tools, because they vary significantly in what they're good at.

Cursor is currently the dominant vibe coding IDE. It's a fork of VS Code with deep AI integration — you can select any block of code and ask Cursor to modify it, generate tests for it, explain it, or rewrite it entirely. Cursor's "Composer" mode lets you describe a multi-file feature and watch it build across your entire codebase. For most professional vibe coders, Cursor is the primary environment.

Claude (Anthropic) is the AI model of choice for code generation in 2026. Claude 3.7 Sonnet (and the more powerful Opus variants) produces code that is generally cleaner, better-structured, and more likely to handle edge cases than competing models. It reasons through problems before generating code, which catches a surprising number of issues. Claude is available directly, through the Claude.ai web app, and through the API that powers Cursor's backend when you select Claude as the model.

GitHub Copilot was the first major vibe coding tool and remains widely used, particularly inside legacy enterprise codebases where switching IDEs is painful. Copilot is strong at autocomplete — suggesting the next line or block as you type. It's weaker than Cursor + Claude at larger multi-file generation tasks.

v0 by Vercel occupies a specific niche: generating React UI components from a description or a design reference. If you describe a pricing page or a dashboard layout, v0 will generate the Tailwind + React component. It's not a full-stack tool — it's a UI scaffolding accelerator. Many teams use v0 for front-end and Cursor for back-end.

Bolt.new is the most accessible entry point — a browser-based environment where non-technical founders can describe an app and get a running prototype. It's powerful for exploration and zero-to-one ideation, but the output often needs significant engineering review before it's production-safe.

How the Workflow Actually Works

The vibe coding workflow isn't "describe everything and ship." That's how you end up with exposed API keys and no error handling. The actual workflow that produces quality output has five stages:

1. Architect first. Before you type a single prompt, sketch the data model and system design. What are the main entities? How do they relate? What are the API surface areas? This doesn't need to be a formal ERD — a quick whiteboard sketch or even a bullet-point list is enough. AI is excellent at generating code for a well-understood design. It's mediocre at inventing architecture.

2. Describe intent, not implementation. The most effective prompts are written at the level of behavior, not code. "Create a Stripe webhook handler that updates the user's subscription status in Supabase when a customer.subscription.updated event fires, with idempotency checks" is better than "write some Stripe code." Specificity about behavior — including edge cases — produces dramatically better output.

3. Generate in bounded chunks. Don't ask for an entire application in one shot. Break features into discrete, testable units. Authentication first. Database schema second. API routes third. The AI performs better when the context is focused, and you can review and test each piece before moving on.

4. Read every line. This is non-negotiable. You are responsible for the code in your repository. AI generates plausible-looking code that is sometimes subtly wrong — a missing await, an N+1 query in a loop, a permission check that only runs on the happy path. If you don't read it, you won't catch it.

5. Test with real data and real failure modes. Hit your API with malformed inputs. Try to break your auth flow. Run your database queries against a realistic dataset to catch slow queries before they hit production. AI-generated code often skips edge case handling. Manual testing surfaces the gaps.

When to Use It

Vibe coding is not the right tool for every project. It is the right tool for specific scenarios.

MVPs and prototypes. This is where vibe coding shines. You need to validate whether an idea is worth pursuing before you invest months building it the "right" way. A vibe-coded MVP built in a week gives you a real, deployed product that real users can interact with. The feedback you get from that is worth more than any amount of planning.

Internal tools. The stakes are lower, the user base is known, and the requirements are usually clear. Vibe coding can produce a surprisingly good internal dashboard or admin tool in a few days. These typically don't need to scale to millions of users and don't get the kind of adversarial inputs a public-facing API does.

Boilerplate-heavy features. Authentication, email notifications, payment flows, CRUD APIs — these are patterns AI has seen thousands of times. The generated code is usually solid. Use vibe coding here and spend your careful attention on the business logic that's specific to your product.

Well-understood domains. The AI performs best when generating code for patterns it has seen many times. A standard REST API on Express? High quality output. A novel distributed consensus algorithm for a custom database engine? You're on your own.

The Pitfalls — Real Examples

This is the section that gets glossed over in most vibe coding content, so I'm going to be specific.

Exposed API keys and secrets. This is the most common serious error in AI-generated code. The AI will often demonstrate a pattern using hardcoded example values like sk-1234567890 or your_api_key_here — and then when you tell it "make this work with my real key," it puts the real key directly in the code. I've personally seen Next.js apps deployed to public GitHub repos with Anthropic API keys, Stripe secret keys, and Supabase service role keys all committed in plain text. The fix is environmental variable hygiene from day one, but the AI doesn't always enforce this.

Missing auth checks on API routes. A classic failure mode: the AI generates an API route that reads or modifies user data but only checks authentication on the happy path. Something like an endpoint that takes a userId parameter and trusts it without verifying that the authenticated user is actually the owner of that userId. This is an IDOR (Insecure Direct Object Reference) vulnerability and it's extremely common in AI-generated code.

N+1 database queries. If you have a list endpoint that loads 50 posts and then, for each post, makes a separate database query to load the author — that's 51 queries per request. The AI doesn't usually generate optimized JOIN queries or use techniques like DataLoader. This code looks fine in development with 5 test records and completely falls apart under any real load.

Hallucinated APIs. Older models had this problem more than current ones, but it still happens. The AI confidently generates code that calls a library method that doesn't exist, or uses a configuration option that was removed two major versions ago. It compiles. It fails at runtime. Always check the docs for the specific library version you're using.

No error handling. AI-generated code often has the happy path working beautifully and no handling for failures. What happens if the third-party API times out? What if the database returns null instead of a row? What if the uploaded file is too large? These paths often just... crash, silently, or return unhelpful 500 errors.

Why Human Review is Non-Negotiable

Given the failure modes above, the "vibe coding factory" model — where you describe something, accept the output without review, and ship — is not a viable approach for anything that touches real users or real data.

Human review is what separates vibe coding as a productivity tool from vibe coding as a liability. Specifically, you need engineers who can:

• —Read generated code and identify structural problems, not just syntax errors
• —Recognize common vulnerability patterns (OWASP Top 10 is a good baseline)
• —Know when the AI has taken a shortcut that will cause problems at scale
• —Test beyond the happy path

This is why "AI-assisted" is the accurate description, not "AI-built." The AI is doing the typing. The engineer is still doing the thinking.

How We Do It at Greta Agency

At Greta Agency, vibe coding is our primary development method for client projects. Here's what that actually looks like in practice:

What we trust the AI with:

• —Boilerplate: project setup, routing configuration, common UI components
• —Standard patterns: auth flows (reviewed post-generation), payment integrations, form handling
• —First drafts of business logic (which we then review and often rewrite)
• —Test scaffolding (the structure; we write the assertions)

What we always write or heavily modify ourselves:

• —Permission and authorization logic
• —Anything that touches money or billing
• —Database schema and migrations (we review for index coverage and normalization)
• —Error handling strategies
• —Security-sensitive operations (token validation, session management)

Our review process: Every feature goes through a pull request with at least one senior engineer reviewing specifically for the failure modes above. We run a security checklist against every new API route. We do a load test against any endpoint that touches the database before we consider it production-ready.

This is not a slow process. A feature that would take an experienced engineer two days to write from scratch takes us a few hours to generate and review. The velocity gain is real. The review process is what keeps it from being reckless.

A Practical Framework for Doing It Well

If you're going to adopt vibe coding for your own projects, here's the framework that produces good outcomes:

1. —

   Start with architecture, not code. Spend 30 minutes on the data model and system design before you open Cursor.

2. —

   Use the right tool for the right layer. v0 for UI scaffolding, Cursor + Claude for business logic and API routes, direct Claude (via the API or web) for complex reasoning tasks.

3. —

   Enforce environment variable discipline from line one. Never hardcode secrets. Set up .env.local and .gitignore before you write your first prompt.

4. —

   Read the output. Every line. Not skimming — actually reading. This is the one rule you can't skip.

5. —

   Audit before you ship. Run the OWASP Top 10 checklist against your API routes. Check for exposed secrets in your repo history. Profile your database queries against realistic data.

6. —

   Test the unhappy paths. Specifically: malformed inputs, authentication bypass attempts, concurrent requests, database failures. These are the paths AI doesn't test and often doesn't handle.

FAQ

Is vibe coding just for non-technical people?

No — and this is a common misconception. The most effective vibe coders are experienced engineers who use it as a multiplier on their existing skills. They generate code faster and review it with expert judgment. Non-technical founders can use tools like Bolt.new to explore ideas, but they should get engineering review before shipping anything to real users.

How do I know if AI-generated code is secure?

You don't, unless you read it and test it against known attack patterns. The OWASP Top 10 is the standard baseline. For a production application, we recommend getting a security audit from someone who wasn't involved in building it — fresh eyes catch things the original author misses.

Does vibe coding work for large, complex applications?

It works well for bounded features within a larger application. It gets harder as the codebase grows, because the AI's context window has limits and understanding a 100,000-line codebase fully is not something current AI handles well. The workflow at scale is: vibe code individual features, review carefully, maintain clear architectural separation so AI can work on bounded contexts.

What happens to the code quality over time?

This depends entirely on the review process. Vibe-coded projects that are reviewed carefully and refactored regularly can have excellent code quality — often better than code written under time pressure without AI, because the AI tends to follow idiomatic patterns. Vibe-coded projects that are never reviewed accumulate technical debt faster than traditionally-built projects, because the failure modes compound. The review process is the variable that determines long-term code health.