Vibe coding is the practice of describing what you want in plain language and letting an AI write, run and adjust the code—while you stay in the role of product director rather than typist. The term, popularized by Andrej Karpathy in early 2025, captured a real shift: building software now feels closer to having a conversation than to typing semicolons. This guide explains what vibe coding actually is, when it works, where it breaks, and how to use it without losing the plot.
What is vibe coding?
At its core, vibe coding is a workflow where the human focuses on intent and the AI handles the syntax. You write something like "make the dashboard show last week's revenue grouped by client, with a small line chart on top" and the AI produces the changes, runs them, and reports back. You read the result, give feedback, and keep going. The friction of typing every character disappears. What remains is taste, judgment and clarity of intent.
Vibe coding is not the same as no-code. No-code tools usually give you visual builders with predefined blocks. Vibe coding—sometimes called AI coding—gives you actual source code, written by an AI, that you (or the AI) can keep editing in any direction. The result is closer to "AI pair programming" than to "Lego for software".
Why vibe coding works in 2026?
Three things converged. First, frontier models became genuinely good at writing and editing whole programs, not just snippets. Second, tooling caught up: agents that can run code, install dependencies, read your files, and report errors back to themselves. Third, ecosystems standardized: TypeScript, React, Tailwind, Supabase, Postgres, Stripe—the same stack everywhere means the AI sees patterns it has seen ten thousand times.
The combination is what makes vibe coding feel almost magical: you describe a feature, the AI writes the file, runs it, sees an error, fixes it, deploys, and shows you the result. The bottleneck shifts from typing speed to thinking speed.
Vibe coding vs traditional coding
- Speed: vibe coding is dramatically faster for the first 80% of a feature; the last 20% is where it can stall.
- Skill required: less syntax knowledge, much more product judgment and architectural taste.
- Iteration cost: changes feel cheap—rename a field, restructure a screen, redo the auth flow without flinching.
- Debugging: harder when you don't read the code at all. Light reading and tests still pay off.
- Code quality: highly dependent on the model and the prompts; structure and naming need active care.
Tools that enable vibe coding
The vibe coding stack is moving fast, but the categories are stable. The right tool depends on whether you're shipping a quick prototype, a full app, or working inside an existing codebase.
- AI app builders for non-developers: Cadrant, Lovable, Bolt.new, v0—they generate whole apps from prompts and host them.
- AI-powered IDEs for developers: Cursor, Windsurf, Zed—they sit on top of real codebases and let you edit by chat.
- Coding agents in the terminal: Claude Code, Codex CLI, OpenAI Agents—they run autonomously over your repo.
- Inline assistants: GitHub Copilot, Cursor Tab—they accelerate typing without taking the wheel.
When vibe coding shines?
There are situations where vibe coding crushes traditional development. Most of them share one trait: the spec is fuzzy and you need to feel the product before you can finish defining it.
- Prototyping a new product idea—you want to see it before deciding if it's worth building.
- Internal tools where the user is one team and the requirements evolve weekly.
- Side projects where joy and momentum matter more than polish.
- Custom dashboards built around an existing API or data warehouse.
- MVPs aiming to validate before investing in a real engineering team.
When vibe coding breaks?
Vibe coding has real limits, and pretending it doesn't will burn you. The pattern of failure is consistent: as a project grows in complexity, the feedback loop between prompt and result gets noisier and noisier until you're stuck.
- Heavy regulatory or security requirements: you cannot vibe your way through SOC 2, PCI-DSS, or healthcare compliance.
- Performance-critical systems: low-latency trading, video processing, custom database engines—still demand engineers.
- Large legacy codebases: the AI loses context fast and starts making changes that don't fit the existing architecture.
- Edge cases that hide in the data: vibe coding generates the obvious branches; the weird ones often need real debugging.
- Long-lived production systems: someone still needs to own the architecture, the migrations, the on-call.
Best practices for vibe coding
- Keep prompts intent-first. Describe outcomes, not implementations. Trust the AI to pick the right pattern.
- Commit often. Treat each working step as a checkpoint you can return to.
- Read enough to keep mental models. You don't need every line, but skim the structure regularly.
- Refuse to ship anything you can't read at all if it touches money, auth or personal data.
- Add small tests around the parts that scared you. They future-proof your iterations.
- Re-prompt instead of patching. If the AI made a mess, it is often faster to ask again with a sharper prompt than to manually fix.
- Keep one source of truth—a single canonical README or doc the AI reads at the start of each session.
A typical vibe coding session
Imagine you want a tiny CRM for your freelance business. A vibe coding session might look like this. You open Cadrant. You write: "I want a list of clients with name, email, project status, and last contact date. Add a button to log a new contact. Make it pretty." Two minutes later, you have a working app. You ask: "Add a filter by status." Done. "Make the list responsive on mobile." Done. "Authenticate with my Google account." Done. By the end of an hour, you have an app that would normally take days to scaffold from scratch.
The future of vibe coding and AI coding
Vibe coding will quietly become the default for a huge slice of software work—not because it replaces engineers, but because it absorbs the boilerplate, the scaffolding, the CRUD apps and the internal tools nobody wanted to build by hand. AI coding is reshaping who can build software and how fast. Engineers move up the stack: architecture, performance, security, distributed systems, anything that requires deep understanding. Non-developers move into territory that used to be locked behind dev teams: their own CRMs, their own dashboards, their own apps. The line between "user" and "builder" is blurring on purpose.
Vibe coding with Cadrant
Cadrant is built around the vibe coding workflow, with a strong opinion: real apps need a real database, real auth and real payments to be useful past the demo. You describe the app you want; Cadrant scaffolds a React codebase backed by Supabase, with auth, file storage and payment hooks ready to use. Then you iterate by chat. The code is yours: you can export it, host it, hand it to a developer, or keep going inside Cadrant. The vibe coding feel, with the safety of owning what you build.