How It Actually Works

You describe what you want in plain English. An AI agent writes the code. You review it, adjust, iterate.

Before vibe coding, I wrote every line myself. Now the workflow looks like this:

1. Describe the feature — "add Google OAuth with Supabase"
2. The agent generates code across multiple files
3. I check if it matches my intent
4. Fix what is wrong, move on

Andrej Karpathy coined the term in February 2025. He described it as "fully giving in to the vibes, embrace exponentials, and forget that the code even exists." Collins Dictionary named it Word of the Year for 2025. Merriam-Webster added it as slang that same month.

By early 2026, 92% of US-based developers report using some form of AI-assisted coding in their daily workflow.

The Prototype Trap

Anthropic's 2026 Agentic Coding Report shows 79% of Claude Code users automate code generation tasks. The speed is real. An AI agent can scaffold a login screen in 30 seconds.

But there is a gap between generated code and shipped product.

The demo looks great. Then you try to deploy. The database has no schema. Auth tokens vanish between sessions. Push notifications are not wired. Apple rejects your build because the privacy policy URL is missing.

I call this the prototype trap. Vibe coding produces code. Code is not a product.

About 70% of the work that makes an app real — deployment, infrastructure, store submission, error handling — never shows up in a demo. This is where most vibe-coded projects quietly die.

What Separates Prototypes from Shipped Apps

Between March and April 2026, I shipped 4 native iOS and Android apps to the App Store as a solo developer. No team, no funding, no CS degree. 10 years of full-stack experience, self-taught from the start.

I used vibe coding for all of them. But vibe coding is not what made me fast.

A system made me fast. Vibe coding made the system faster to operate.

The difference is structural:

• Vibe coding in an empty project produces a prototype that breaks in production
• Vibe coding inside a structured system produces an app that actually ships

When AI agents work within clear boundaries — typed schemas, pre-configured auth, a deployment pipeline, defined folder structure — they produce code that fits. Remove those boundaries and the output looks right but falls apart under real usage.

The best analogy I have found: AI is an Iron Man suit. It amplifies capability, not judgment. A developer who knows exactly what to build becomes dramatically more productive. A developer who does not know what to build just moves in the wrong direction faster.

Anthropic's own research supports this. In what they call "Feedback Loop" patterns, human involvement remains essential even when AI handles most of the implementation. The thinking part is still on you.

Who Should (and Should Not) Vibe Code

Works well for:

• Developers with a clear product idea and a solid codebase to build on
• Teams accelerating implementation after architecture decisions are made
• Builders who understand what they are delegating — not just the code, but the intent behind it

Does not work well for:

• Anyone expecting AI to decide what to build. It cannot.
• Projects with no foundation — no boilerplate, no CI/CD, no database schema
• Security-critical systems where unreviewed code creates liability

The rule I follow: only delegate what you already understand. Not what you can already code — what you understand architecturally. Learn the big picture first. Then let AI handle the implementation.