3 Fatal Mistakes "Vibe Coders" Make

Introduction

AI has fundamentally changed software engineering.

Five years ago, building a web app required months of work: architecture decisions, database design, backend APIs, frontend components. Today, you can describe an idea in natural language and have a working prototype in hours.

This is incredible. The velocity you can achieve is unlike anything we've seen before.

But it comes with a catch.

When you let AI do the heavy lifting without an engineering mindset, you're trading immediate velocity for hidden technical debt. You're shipping features without thinking about security. You're deploying infrastructure without understanding what breaks when.

We call this "vibe coding"—writing code based on vibes, on what seems good, on what the AI generated without a deeper engineering perspective.

Vibe coding works great for prototypes and MVPs. But the moment real users interact with your app, vibe coding becomes dangerous.

This guide covers the three fatal mistakes vibe coders make and how to avoid them.

Mistake #1: Ignoring Basic Security Vulnerabilities (Priority 1) 🔴

The Problem:

AI assistants are optimized for making things work, not making things secure. They'll happily generate code that ships faster but opens security holes wide open.

Hard-Coded Secrets

// ❌ Generated by AI - NEVER do this
const DATABASE_PASSWORD = "prod_password_12345";
const STRIPE_API_KEY = "sk_live_1234567890";

const dbConnection = mysql.createConnection({
  host: "prod.db.example.com",
  user: "admin",
  password: DATABASE_PASSWORD,
});

The fix:

// ✅ CORRECT: Use environment variables
const DATABASE_PASSWORD = process.env.DATABASE_PASSWORD;
const STRIPE_API_KEY = process.env.STRIPE_API_KEY;

---

Missing Row-Level Security (RLS)

-- ❌ NO RLS: Anyone can query anyone's data
SELECT * FROM user_profiles WHERE user_id = ANY_ID;

-- ✅ WITH RLS: Database enforces ownership
CREATE POLICY "Users can only view their own profile"
ON user_profiles
FOR SELECT
USING (auth.uid() = user_id);

---

S3/Cloud Storage Buckets Left Open to the Public

// ❌ Generated code that makes your bucket public
const s3Client = new S3Client({});
s3Client.send(new PutBucketAclCommand({
  Bucket: "my-app-uploads",
  ACL: "public-read", // DANGEROUS
}));

The fix:

// ✅ Keep buckets private by default
const s3Client = new S3Client({});

// For files that should be public, generate signed URLs
const signedUrl = await getSignedUrl(s3Client, new GetObjectCommand({
  Bucket: "my-app-uploads",
  Key: "public/image.jpg",
}), { expiresIn: 3600 }); // Expires in 1 hour

---

Mistake #2: Flying Blind Without Observability (Priority 2) 📊

The Problem:

AI will generate working code, but it won't generate the monitoring infrastructure to understand how that code behaves in production.

You ship your app, real users interact with it, and then... you have no idea what's happening.

The Two-Tier Observability Framework

Split your visibility into two distinct buckets:

Tier 1: System Metrics (Infrastructure health)

• API endpoint error rates
• API endpoint latency (response times)
• Database query performance
• Server CPU/memory usage

Tier 2: Product Analytics (User behavior)

• How far are users scrolling?
• What buttons are users clicking?
• Where are users dropping off?
• What pages are most visited?

Implementing System Metrics

import * as Sentry from "@sentry/nextjs";

export async function handler(req: NextApiRequest, res: NextApiResponse) {
  const start = Date.now();
  const endpointName = req.url;

  try {
    const result = await processRequest(req);
    
    const duration = Date.now() - start;
    Sentry.captureMessage(`${endpointName}: ${duration}ms`, {
      level: "info",
      tags: {
        endpoint: endpointName,
        method: req.method,
      },
    });

    return res.status(200).json(result);
  } catch (error) {
    Sentry.captureException(error);
    return res.status(500).json({ error: "Internal server error" });
  }
}

Implementing Product Analytics

import { usePostHog } from 'posthog-js/react'

export function MyComponent() {
  const posthog = usePostHog()

  const handleButtonClick = () => {
    posthog.capture('button_clicked', {
      button_name: 'signup_cta',
      location: 'hero_section',
    })
  }

  return <button onClick={handleButtonClick}>Sign Up</button>
}

---

Mistake #3: Not Understanding Your Scale Ceiling & Costs (Priority 3) 💰

The Problem:

You build an app locally, it works great. You deploy it to production, and it's still great. But then your app goes viral, or a feature gets popular, and suddenly you're:

• Getting paged because the database is locked up
• Getting a $40,000 cloud bill
• Having cascading failures because you've hit your infrastructure limits

All because you never asked: "How many concurrent users can this handle? How much will it cost if we 10x?"

Calculating Your Scale Ceiling

Every infrastructure component has a breaking point:

YOUR APP ARCHITECTURE:
├── Frontend: Vercel (serverless)  → Can handle unlimited traffic
├── API: Node.js (4 servers)       → Can handle ~500 req/s per server = 2000 req/s total
├── Database: PostgreSQL           → Can handle ~1000 connections max
└── Cache: Redis                   → Can handle ~10000 ops/sec

BREAKING POINTS:
─────────────────
1. Database connections (1000 max)
2. Database query latency (if P95 > 2 seconds, users experience slowness)
3. API server CPU (if CPU > 80%, requests queue and latency increases)
4. Disk space (when you're full, database stops accepting writes)

---

Mapping Infrastructure Costs

COST ANALYSIS:
==============

CURRENT SETUP:
─────────────
Vercel Frontend:     $0 - $30/month
Node.js API (4x):    $192/month
PostgreSQL DB:       $100/month
Redis Cache:         $15/month
Monitoring:          $50/month

Total: ~$370/month (for ~500 concurrent users)

10x TRAFFIC:
────────────
- API servers: 40 × $50 = $2,000/month
- Database: $1,000/month
- Cache: $100/month
- Load balancing: $50/month
- Monitoring: $200/month

Total: ~$3,500/month (for ~5000 concurrent users)

---

The Vibe Coder's Action Plan

If you've been vibe coding, don't panic. Here's how to get your app production-ready:

Week 1: Security Audit

Monday: Audit secrets and environment variables
Tuesday: Enable RLS on all database tables  
Wednesday: Make all cloud buckets private
Thursday: Review authentication and authorization
Friday: Run security checklist

Week 2: Observability

Monday: Set up error tracking (Sentry)
Tuesday: Set up system metrics monitoring
Wednesday: Add product analytics (PostHog)
Thursday: Create monitoring dashboard
Friday: Set up alerting

Week 3: Scale Planning

Monday: Load test your app
Tuesday: Calculate scale ceiling
Wednesday: Map cost projections
Thursday: Review with team/stakeholders
Friday: Document runbooks for scaling

---

Checklist: Before You Ship (Priority 1-2-3)

🔴 PRIORITY 1: SECURITY
=======================
☐ No hardcoded secrets in code
☐ All secrets in environment variables
☐ RLS enabled on database tables
☐ Cloud buckets are private
☐ Authorization enforced on all APIs
☐ Input validation on all endpoints

🟡 PRIORITY 2: OBSERVABILITY
=============================
☐ Error tracking configured
☐ API latency monitoring active
☐ Product analytics implemented
☐ Alerts set up for critical metrics
☐ Can view last 7 days of logs
☐ Team can see user behavior

🟢 PRIORITY 3: SCALE
====================
☐ Load tested under 10x traffic
☐ Scale ceiling calculated
☐ Cost projections reviewed
☐ Scaling runbook documented
☐ Budget reserved for growth

---

Key Takeaways

1. Vibe coding is great for speed, dangerous without engineering discipline. Use AI to generate code faster, but apply engineering rigor to security, observability, and scale.

2. Security isn't optional. A fast, broken, hacked app is worthless. Fix secrets, enable RLS, and keep buckets private.

3. You can't fix what you can't see. Invest in observability. System metrics + product analytics let you understand what's actually happening.

4. Scale matters. You don't need to over-engineer on day one, but you need to understand your breaking points and cost projections.

5. Different code deserves different rigor. Experimental features can be scrappy. Payment processing and user data handling cannot.

---

The difference between a prototype and a production app isn't the code—it's the thinking. Use AI to code faster. Use your engineering brain to ship responsibly.