Vibe Coding: What It Is and Why Developers Love It

Vibe coding sounds like a meme. It is not. It is a real workflow shift that has quietly changed how thousands of developers write software. The term, coined by Andrej Karpathy in early 2025, describes a development style where you work with AI by describing what you want in natural language, reviewing the output, and iterating through conversation rather than typing every line yourself.

It is not about replacing coding skills. It is about spending your expertise differently.

What vibe coding actually looks like

Imagine you are building a settings screen for a mobile app. In traditional development, you open your editor, import components, lay out the UI, wire up state management, handle user interactions, add error states, and write tests. That is 200-400 lines of code and 45 minutes of focused work.

In vibe coding, you describe the screen: "Settings screen with toggles for notifications, dark mode, and biometric lock. Include a section for account info (name, email) with edit capability. Logout button at the bottom with a confirmation dialog." The AI generates the full implementation. You review it, ask for adjustments ("make the logout button red and move it inside a danger zone section"), and iterate until it matches what you need.

The key difference is not speed, although it is faster. The key difference is where you spend your cognitive energy. Instead of thinking about syntax and boilerplate, you think about product decisions.

Why experienced developers love it

You might expect experienced developers to resist vibe coding. The opposite happened. Senior engineers were among the first to adopt it, and they are its biggest advocates. Why?

Because senior developers have already solved most problems before. They know what a settings screen should look like. They know the patterns for form handling, state management, and API calls. Writing those patterns by hand for the hundredth time is not rewarding work. It is repetitive. Vibe coding lets them skip the repetition and focus on the parts that require genuine thought: architecture decisions, edge cases, and user experience details.

Junior developers get a different benefit. They can build things beyond their current skill level, then study the generated code to learn patterns they would not have discovered on their own. Vibe coding becomes a teaching tool. You describe what you want, see how the AI implements it, and absorb the patterns.

The workflow in practice

A productive vibe coding session follows a pattern:

Start with architecture. Before generating any code, describe the overall structure. "I am building a React Native app with tab navigation, Supabase auth, and four main screens." The AI sets up the foundation.

Build screen by screen. Describe each screen's purpose and layout. Review the generated code. Make adjustments. Move on. Do not try to generate the entire app in one prompt. Screen-by-screen gives you checkpoints.

Handle edge cases explicitly. The AI will produce the happy path by default. You need to ask about error states, loading states, empty states, and offline behavior. "What happens when the API call fails? Add error handling with a retry button."

Review everything. This is not optional. AI-generated code works 80-90% of the time. The remaining 10-20% contains subtle bugs, security issues, or performance problems. You need to read and understand every file. Vibe coding means you direct the work. It does not mean you stop thinking.

The 10-20% rule

AI-generated code works 80-90% of the time. The remaining 10-20% contains subtle bugs, security issues, or performance problems. Always review everything.

Where vibe coding fits in the builder ecosystem

Vibe coding tools like Cursor and Copilot occupy one segment of the AI building spectrum. They are powerful for developers who want to stay hands-on but move faster.

At the other end of the spectrum, full-lifecycle builders take the concept further. Instead of vibing with an AI about one screen at a time, you describe the entire app and let an automated pipeline handle architecture, code generation, testing, and deployment. Think of it as industrial-scale vibe coding, where the system handles the prompting and iteration for you.

Both approaches have their place. Vibe coding gives you control. Full-lifecycle building gives you speed. Some builders use both: they generate an app with a full-lifecycle tool, then vibe-code their customizations on top.

Common mistakes

Generating too much at once. Asking the AI to build your entire app in one prompt produces mediocre results. Break it into components. One screen, one module, one function at a time.

Not reading the output. If you accept AI-generated code without reading it, you are building on a foundation you do not understand. When something breaks (and it will), you will not know where to look.

Ignoring types and tests. Vibe coding works best with typed languages (TypeScript, not JavaScript) and a test suite that catches regressions. Ask the AI to generate tests alongside the implementation. It takes 30 seconds and saves hours of debugging later.

Over-describing implementation details. "Use useState with a Record<string, boolean> for the toggle state" is fighting the AI. "Add toggles for each setting that persist when the user leaves the screen" lets the AI pick the right pattern. Describe behavior, not implementation.

Describe behavior, not implementation

Tell the AI what the feature should do, not how to code it. "Add toggles that persist" beats "Use useState with a Record." Let the AI pick the right pattern.

Vibe coding vs traditional development

The difference between vibe coding and traditional development is not just speed. It is a fundamentally different workflow.

Traditional development follows a linear path: gather requirements, design the system, write code, write tests, fix bugs, deploy. Each phase depends on the previous one. A single feature might take a team of developers days or weeks to move through this pipeline. You need full-stack expertise across frontend, backend, databases, and infrastructure to build anything meaningful.

Vibe coding compresses this into a loop: describe what you want, review what the AI generates, adjust your description, repeat. A screen that takes 45 minutes to hand-code ships in 5. A feature that takes a sprint ships in a day. The skill requirement shifts from "can you write a Redux reducer from memory" to "can you clearly describe the behavior you need and evaluate whether the output is correct."

Both approaches produce production-quality software when done well. The difference is leverage. Traditional development scales with headcount. Vibe coding scales with clarity of thought.

The limits of vibe coding

Vibe coding handles code generation well. Surprisingly well. But code is only one piece of launching a successful app.

What about finding the right idea in the first place? Validating that real demand exists before you build? Optimizing your app store listing so people actually find your app? Running marketing campaigns, managing social content, handling outreach to press and communities?

Most vibe coding tools start after you have already decided what to build. They assume you have a clear idea, a validated market, and a plan for getting users. That is a big assumption. The majority of apps fail not because the code was bad, but because nobody needed the product or nobody could find it.

Here is the uncomfortable math: code generation is maybe 20% of what it takes to launch a successful app. The other 80% is discovery, validation, design decisions, app store optimization, marketing, and iteration based on user feedback. Vibe coding gives you a 10x improvement on that 20%. The other 80% still takes the same amount of work.

Building the wrong thing faster is still building the wrong thing. The hardest part of launching an app was never writing the code. It was knowing what to build and getting it in front of the right people.

Beyond vibe coding: the full app lifecycle

If vibe coding is step one, what does the full picture look like? At Goodspeed, we think about app building as four stages, and code generation is just one of them.

Step 1: Discover. Before writing a single line of code, find ideas backed by real market data. Goodspeed scans 16 signal sources (Reddit, Hacker News, Product Hunt, app store reviews, GitHub issues, Google Trends, and more) to identify problems people are actively complaining about. Every idea gets a demand score, a monetization score, and a competitive analysis. You start with evidence, not guesses.

Step 2: Define. Once you have a validated idea, the system generates a complete architecture: screen layouts, navigation structure, data models, feature set, and cross-cutting concerns like offline support, push notifications, and authentication. This is the blueprint that makes code generation reliable instead of random.

Step 3: Build. This is where vibe coding lives, but done at scale. Instead of prompting one screen at a time, Goodspeed's build pipeline generates complete apps from a 246-feature production template. Auth flows, theming, analytics, subscriptions, error handling -- all baked in. The AI only generates the parts that are unique to your app.

Step 4: Grow. After the app is built, automated ASO generates optimized metadata and keywords. Outreach campaigns target Product Hunt, Reddit, Hacker News, and Indie Hackers. Social content gets scheduled across platforms. The same AI pipeline that built the app now markets it.

The question is no longer "can AI write code?" That is settled. The real question is "can AI build a business?" That requires handling the full lifecycle, not just the coding step.

Getting started with vibe coding

You do not need to change your entire workflow at once. Start with one task you find tedious. Maybe it is writing tests for existing code, or building form validation, or setting up a new screen layout. Let the AI handle that one thing while you continue coding everything else manually.

A few principles that separate productive vibe coding from frustrating vibe coding:

Start with a problem, not a feature list. "I need a way for users to track their daily water intake and see weekly trends" is a better starting prompt than "build a screen with a counter component, a bar chart, and a SQLite database." Describe the outcome you want. Let the AI figure out the implementation.

Test with real users early. Vibe coding makes it trivially fast to build a prototype. Use that speed advantage to get something in front of real people within days, not months. Their feedback is worth more than any amount of prompt engineering.

Do not skip validation. Building fast does not mean building right. If you can go from idea to working prototype in a weekend, you can also validate three ideas in the time it used to take to build one. Use the speed to test more hypotheses, not to ship untested assumptions faster.

Expand your scope gradually. As you build confidence, let the AI generate more. Keep reviewing everything. Over time, you will develop an intuition for which tasks are best handed to the AI and which need your direct attention.

If you want to skip straight to validated ideas, Goodspeed's free idea scoring tells you whether your app concept has real demand before you write a single prompt.

Key takeaway

Vibe coding is not a replacement for knowing how to code. It is a force multiplier for people who already know what they want to build. But building the right thing matters more than building it fast. Start with validation, describe behavior instead of implementation, review everything, and think beyond code to the full lifecycle of discovery, building, and growth.

Explore Goodspeed's approach to see how we apply this concept across the entire app building pipeline, from idea scoring to app store submission.