“Vibe coding” — describing what you want and letting AI write the code while you supervise the vibes. But what is vibe coding, and is it a real workflow? Or just a meme for people who don’t code? Honest assessment from someone who’s done both.
What is Vibe Coding and is it a Real Workflow
The term (coined by Andrej Karpathy) describes fully AI-driven development: you describe features in natural language, the AI writes the code, you test and iterate conversationally — often without reading the generated code closely. “Give the AI the vibes, accept the code.” It ranges from casual (weekend prototypes) to serious (founders shipping products without engineering teams).
Where it genuinely works
- Prototypes and MVPs: idea → working demo in hours. The highest-leverage use — validating ideas before investing.
- Personal tools: scripts and apps for yourself, where bugs are annoyances not liabilities.
- Boilerplate-heavy work: CRUD apps, landing pages, standard integrations — patterns AI has seen a million times.
- Learning: building while asking the AI to explain — accelerated, hands-on education.
- Non-critical features: internal dashboards, marketing sites, experiments.
Where it falls apart
- Security-critical code: AI generates plausible-but-vulnerable code (SQL injection, auth flaws) at scale. Vibe-coded auth is a breach waiting to happen.
- Complex systems: distributed systems, concurrency, performance-critical paths — where understanding matters more than output.
- Debugging hell: when the vibe-coded app breaks mysteriously, you can’t fix what you don’t understand. Technical debt compounds invisibly.
- Scale: prototypes that “work” collapse under real load, real users, real edge cases.
- Maintenance: code nobody understands is code nobody can safely change.
The real workflow: supervised vibe coding
The meme version (never read the code) is reckless. The real workflow that professionals use:
- You architect: decide the structure, stack, and key design choices. AI implements within your architecture.
- Small iterations: one feature at a time, tested before moving on. Never “build the whole app” in one prompt.
- Read the critical parts: auth, payments, data handling — review these like your business depends on it (it does).
- Test deliberately: write or demand tests for important logic. “It runs” ≠ “it works.”
- Refactor regularly: AI code accrues cruft fast; periodic cleanup keeps it maintainable.
- Know when to hand-write: the tricky 10% (algorithms, security, performance) deserves human authorship.
Is it “real”? (verdict)
Yes — as a prototyping and acceleration workflow, it’s transformative and here to stay. No — as a replacement for software engineering, it’s a fantasy that produces fragile systems. The professionals winning with AI coding aren’t vibe-coding blindly; they’re directing AI with engineering judgment. The skill that matters isn’t prompting — it’s knowing what’s good.
Vibe coding in teams: making it work
Solo vibe coding is one thing — teams need more structure. What works: AI-assisted, human-reviewed as the team standard (AI drafts, humans review — like junior developers with infinite patience). Architecture decisions stay human — the team agrees on structure; AI fills in implementation. Shared prompts and patterns — team prompt libraries for common tasks, ensuring consistency. Code review still mandatory — AI-generated code gets the same review rigor as human code (more, arguably, since the author can’t explain their reasoning).
What doesn’t work: everyone vibe-coding independently without coordination (integration nightmare), skipping tests because “the AI said it works” (the AI is not QA), and no documentation (AI-generated code without docs is write-only code). Teams that thrive with AI coding treat it as leverage for engineers, not replacement of engineering discipline.
Tools for professional AI-assisted coding
The serious toolkit: Cursor or Windsurf (AI-native editors — the current state of the art), GitHub Copilot (ubiquitous, improving), Claude Code / Codex (agentic coding — describe tasks, review diffs), and v0/Lovable (rapid UI prototyping). The Cursor documentation covers the workflows professionals actually use. But remember the verdict from the main article: these are power tools for people with judgment, not replacements for it.
Try vibe coding this weekend: guided first project
Saturday: pick a tiny project (a personal dashboard, a simple tool you’ve always wanted); choose your AI coding tool; describe the project in plain language and let the AI scaffold it. Build iteratively — one feature at a time, testing each. Sunday: review the generated code for the critical parts (data handling, any auth); add tests for important logic; refactor anything confusing; deploy it for personal use.
What you’ll learn: where AI coding genuinely accelerates (boilerplate, standard patterns) and where it needs your judgment (architecture, edge cases, security). That firsthand calibration is worth more than a hundred opinions about vibe coding. You’ll finish the weekend with a working tool and an informed perspective — the two most valuable outputs of any learning project.
Practical takeaway
- Vibe coding = natural-language-driven development; transformative for prototypes, dangerous for critical systems.
- Works: MVPs, personal tools, boilerplate, learning. Fails: security, complex systems, scale.
- Real workflow: you architect, AI implements, you review critical parts, test everything.
- The durable skill isn’t prompting — it’s engineering judgment.
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