Right now, vibe coding is everywhere.
You prompt an AI, stay in flow, generate code quickly, and ship something that looks correct. Demos work. Tests pass. It feels productive.
And then production reminds you why engineering exists.
This post isn’t anti-AI. I use AI daily. But after working on real systems, handling incidents, and reviewing production code, one thing has become clear:
Vibe coding accelerates output. Engineering judgment protects systems.
You need both—but they solve very different problems.
What People Actually Mean by Vibe Coding
Vibe coding usually looks like this:
- rapid AI-assisted code generation,
- minimal planning,
- trusting the output because “it works”,
- prioritizing momentum over structure.
It’s popular because it removes friction. No blank page. No syntax struggles. No context switching.
For the right use cases, that’s a real advantage.
The problem starts when speed becomes the goal instead of correctness.
Why Vibe Coding Feels So Effective
Vibe coding works because it eliminates pain points developers hate:
- staring at an empty file,
- boilerplate work,
- repetitive setup.
AI fills gaps instantly and keeps you moving. For small scopes, this is a genuine productivity boost.
But momentum without judgment doesn’t equal progress—it just means you’re moving fast in some direction.
Where Vibe Coding Works Well ✅
Let’s be honest about where vibe coding shines.
Disposable Prototypes
If the code will be thrown away—POCs, demos, experiments—vibe coding is ideal. Longevity doesn’t matter. Speed does.
Hackathons & Side Projects
When the goal is learning or validation, not production stability, vibe coding is a net win.
Boilerplate & Scaffolding
CRUD APIs, basic UI layouts, config setup—AI handles these well and saves real time.
In all these cases, the cost of mistakes is low.
Where Vibe Coding Fails ❌
This is where experience matters.
Large, Long-Lived Systems
Code that must survive:
- multiple teams,
- refactors,
- scaling,
- years of maintenance.
AI-generated structure often looks clean early and becomes painful later.
Security-Critical Code
Auth, permissions, payments, secrets.
Vibe coding here is risky because insecure defaults don’t fail loudly—they fail later.
Complex Business Logic
Business rules are messy and full of exceptions. AI tends to simplify them incorrectly and encode assumptions that break in edge cases.
A Real Mistake I Made Using Vibe Coding
I once used AI to quickly implement a background job flow. It worked perfectly in staging. Clean retries. Nice abstractions.
What I missed:
- retries weren’t idempotent,
- partial failures caused duplicate writes,
- logs hid the real issue.
It took a production incident to uncover it.
The AI didn’t make the mistake.
I did—by trusting speed over judgment.
How Much Vibe Coding Is Too Much?
A simple rule that works:
If you can’t explain why the code is safe, you shouldn’t ship it.
AI can help write code.
Only humans can own the consequences.
Use AI as:
- an assistant,
- a drafting tool,
- a productivity multiplier.
Not as the architect or decision-maker.
Security Risks Are the Quiet Problem
The biggest danger of vibe coding isn’t broken code—it’s unquestioned code.
Common issues:
- hardcoded secrets “temporarily” left in place,
- overly permissive access checks,
- unsafe dependency choices,
- missing validation paths.
Everything looks fine until it isn’t.
Security requires skepticism. AI doesn’t have that instinct.
Best Practices for Safe Vibe Coding
What’s worked for me:
- Slow down at boundaries (auth, data writes, integrations)
- Review AI code as if someone else wrote it
- Ask how it fails, not just how it works
- Never skip human code review
- Treat AI output as untrusted by default
Speed without discipline just moves problems downstream.
Final Verdict
Vibe coding isn’t bad.
Blind vibe coding is.
Use vibe coding to:
- explore ideas,
- reduce friction,
- move fast where risk is low.
Rely on engineering judgment to:
- protect users,
- secure systems,
- design for failure,
- own production outcomes.
Speed ships features. Judgment keeps systems alive.
That’s the difference experience teaches you.
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