Most Common Mistakes Vibe Coders Make

You've discovered AI coding tools like Cursor or GitHub Copilot. The promise is intoxicating: describe what you want, watch the code materialize, ship features in hours instead of days. Within a weekend, you've built what would have taken weeks using traditional methods.

But hold on just yet.

Three weeks into your shiny new SaaS product, strange things start happening. Users report intermittent login failures. Your database queries slow to a crawl under real traffic. Security researchers discover vulnerabilities you didn't even know existed. And when you try to fix these issues, the AI generates solutions that break three other features.

Welcome to the vibe coding hangover.

I've watched hundreds of founders ride the vibe coding wave with enthusiasm, only to crash into the rocks of production reality. The 2025 Stack Overflow Developer Survey paints a sobering picture: while 84% of developers now use AI tools (up from 76% in 2024), trust in these tools has plummeted. Only 60% view AI favorably in 2025, down from over 70% in 2023-2024. The top frustration, cited by 66% of developers? "AI solutions that are almost right, but not quite."

Let me elaborate on what's actually happening here. Vibe coding – building software by accepting AI-generated code without deep review – creates a dangerous illusion. The code compiles. Tests pass. The demo works beautifully. But beneath the surface lurks a tangled mess of inconsistent patterns, security vulnerabilities, and architectural decisions that will haunt you for months.

This being said, I'm not anti-AI. I use AI coding tools daily. They're genuinely transformative when used correctly. But there's a massive difference between AI-assisted development and vibe coding. The former amplifies your engineering capabilities. The latter outsources your judgment to a system that doesn't understand the consequences of its suggestions.

So let's walk through the seven most common mistakes I see vibe coders make – and more importantly, how to avoid them without sacrificing the speed advantages that drew you to AI tools in the first place.

Mistake #1: Treating AI Output as Gospel Instead of Suggestions

Here's the brutal truth: AI coding tools are trained on millions of code samples from the internet, including countless examples of terrible code.

A recent Veracode study found that leading AI models now produce code that compiles successfully 90% of the time. Sounds impressive, right? But compilation success tells you nothing about code quality, security, or long-term maintainability. A security firm analyzing Fortune 50 companies discovered that AI-assisted developers produced three to four times more code but generated 10 times more security issues.

This isn't a theoretical problem. In May 2025, researchers discovered that 170 out of 1,645 applications created with the vibe coding platform Lovable had critical security vulnerabilities allowing unauthorized access to personal information. The startup Enrichlead, whose founder proudly announced their platform was "100% written by Cursor AI with zero hand-written code," shut down just days after launch due to fundamental security flaws.

The Fix: Implement Mandatory Code Review

Every single line of AI-generated code needs human review. Not a quick scan – an actual line-by-line review where you ask:

• Do I understand what this code does?
• Could this introduce security vulnerabilities?
• Will this scale under production load?
• Does this follow our established patterns?
• Are there edge cases this doesn't handle?

Set up a simple rule: if you can't explain the code to a junior developer, you don't understand it well enough to merge it. According to the 2025 Stack Overflow survey, 77% of professional developers say vibe coding is not part of their workflow. The successful developers using AI tools treat them as copilots that require constant supervision, not autopilots they can walk away from.

Create a review checklist specifically for AI-generated code:

1. Security validation: Run static analysis tools (SonarQube, Snyk, or similar)
2. Pattern consistency: Does this match your existing codebase architecture?
3. Error handling: Does it gracefully handle failures?
4. Performance implications: Will this create bottlenecks at scale?
5. Test coverage: Are edge cases actually tested?

Think of it like using a chainsaw. In skilled hands, it increases productivity 5x. But you need proper training, protective equipment, and constant attention – because the consequences of mistakes are severe.

Mistake #2: Building Without Architecture (The "Generate and Hope" Approach)

One of the most damaging patterns I see: founders start prompting their AI tool without any architectural planning. They ask for a login system, then a dashboard, then a payment flow – each feature generated in isolation without considering how everything fits together.

The result? What one developer called "vibe-coded messes" – codebases that look like they were written by multiple junior developers, all using completely different approaches.

A March 2025 industry analysis found that AI-generated code often lacks structure, documentation, and clarity necessary for long-term maintenance. One experienced architect reviewed a vibe-coded application and was "shocked to see how bad the code was in a lot of places." The inconsistent patterns made bug tracking nearly impossible.

The Fix: Design Before You Generate

Spend time planning your architecture before writing a single line of code:

1. Sketch your data models: What entities exist? How do they relate?
2. Define your module boundaries: What are the core features? How do they interact?
3. Establish conventions: Naming patterns, file organization, error handling approaches
4. Document key decisions: Why did you choose this database? This authentication approach?

Then – and only then – start using AI to generate code that fits within this architecture.

For example, instead of prompting: "Create a user authentication system"

Break it down:

1. Design your auth flow: JWT tokens? Session-based? OAuth?
2. Define your database schema: What user fields do you need?
3. Plan your security requirements: 2FA? Password complexity rules?
4. Map out error scenarios: What happens when login fails?

Now you can use AI to implement specific, well-defined pieces while maintaining architectural coherence. You're the architect; AI is your construction crew.

At Two Cents Software, we structure our boilerplate with clear architectural boundaries precisely because we know teams will use AI tools. When you have established patterns for authentication, multi-tenancy, and billing, AI can generate feature code that naturally fits within these proven structures.

Mistake #3: Ignoring the "Almost Right" Problem

This might be the most insidious mistake. AI generates code that looks correct, passes initial testing, and even handles some edge cases. It's 95% there. So you ship it.

The 2025 Stack Overflow survey identified this as the number one developer frustration: 66% report struggling with "AI solutions that are almost right, but not quite." The second biggest frustration? Debugging AI-generated code takes longer than writing it yourself (45% of developers).

Here's a real example shared by a software architect: A junior developer used AI to build a user permissions system. It passed QA testing and looked fine initially. Two weeks after launch, they discovered a critical flaw – users with deactivated accounts still had access to admin tools because the AI had inverted a boolean check. The subtle bug slipped through because nobody deeply understood the generated code.

Stack Overflow's own data shows trust in AI accuracy has cratered: 46% of developers actively distrust AI tool accuracy, compared to only 33% who trust it. Among experienced developers, the skepticism is even higher.

The Fix: Test Obsessively, Especially Edge Cases

The "almost right" problem manifests in edge cases and error conditions. AI often generates happy-path code that works perfectly under ideal conditions but fails catastrophically when things go wrong.

Build a comprehensive testing strategy:

1. Unit tests for core logic

• Test normal inputs
• Test boundary conditions (empty strings, null values, maximum values)
• Test invalid inputs
• Test error scenarios

2. Integration tests for component interactions

• What happens when the database is unavailable?
• How does the system behave under concurrent requests?
• What if external APIs timeout?

3. Manual testing of realistic scenarios

• Have someone who didn't write the code try to break it
• Test with production-like data volumes
• Simulate network failures and slow connections

4. Security-specific testing

• Input validation: Can users inject SQL or scripts?
• Authentication bypass attempts
• Authorization edge cases: Can users access others' data?
• Rate limiting: Can the system be overwhelmed?

Create a testing checklist for every AI-generated feature. Don't merge until every item passes. The time invested in thorough testing is minuscule compared to the cost of production incidents.

Mistake #4: Creating Technical Debt Faster Than You Can Pay It Down

Vibe coding's speed advantage is also its greatest danger. You can generate features so quickly that technical debt accumulates like interest on a predatory loan.

Ben Lorica noted in a March 2025 analysis: "AI-generated code often lacks the structure, documentation, and clarity necessary for long-term maintenance. This can lead to increased technical debt, making future modifications and debugging significantly more difficult."

The pattern is consistent across organizations. A CodingIT blog post highlighted: "A team that leans too heavily on AI might seem efficient at first, but if they're constantly revisiting past work and fixing AI-generated messes, they're not moving forward, they're just running in circles."

The Fix: Enforce Documentation and Refactoring Discipline

Make documentation non-negotiable:

1. Code-level documentation

1/**
2 * Validates user subscription status and entitlements
3 * 
4 * IMPORTANT: This function checks both:
5 * - Active subscription (via Stripe webhook events)
6 * - Feature flags (for beta access overrides)
7 * 
8 * @param userId - The user to check
9 * @returns Object containing subscription status and available features
10 * 
11 * @example
12 * const status = await validateSubscription(user.id);
13 * if (status.hasFeature('advanced-analytics')) {
14 *   // Show premium feature
15 * }
16 */
17

2. Architecture documentation

• Maintain a README for each major module explaining its purpose
• Document integration points between systems
• Keep a decision log: Why did you choose this approach?

3. Regular refactoring sessions Schedule weekly refactoring time:

• Identify code that's duplicated across files
• Look for inconsistent patterns AI generated
• Consolidate similar solutions into reusable utilities
• Add missing error handling or validation

4. Establish coding standards before generating code Create a style guide for your AI:

• Naming conventions (camelCase? snake_case?)
• Error handling patterns (throw exceptions? return error objects?)
• Logging approaches
• File organization structure

Then include these standards in your AI prompts: "Generate a user service following our error handling pattern (return Result<T, Error>) and using our logging utility."

Mistake #5: Skipping Security Fundamentals

Security vulnerabilities in AI-generated code are not edge cases – they're systematic problems.

Kaspersky's analysis of vibe coding security risks identified several critical issues. The EscapeRoute vulnerability (CVE-2025-53109) in Anthropic's MCP server allowed reading and writing arbitrary files. A malicious MCP server was discovered forwarding all email correspondence to hidden addresses. A Gemini CLI vulnerability allowed arbitrary command execution when developers simply asked AI to analyze project code.

These aren't theoretical attacks. In July 2025, a vulnerability in the Base44 platform allowed unauthenticated attackers to access any private application. Two high-profile incidents made headlines: Google's AI assistant erased user files during folder reorganization, and Replit's AI deleted code despite explicit instructions not to modify it.

The Fix: Security-First Development Practices

1. Input validation everywhere Never trust user input. AI often generates code that assumes valid data:

1// AI might generate this:
2function updateUser(userId, data) {
3  return db.users.update(userId, data);
4}
5
6// You need this:
7function updateUser(userId, data) {
8  // Validate userId is a valid UUID
9  if (!isValidUUID(userId)) {
10    throw new ValidationError('Invalid user ID');
11  }
12  
13  // Sanitize and validate data fields
14  const sanitized = sanitizeUserData(data);
15  validateUserData(sanitized);
16  
17  // Ensure user can only update their own data
18  if (!canUserModify(currentUser, userId)) {
19    throw new AuthorizationError('Unauthorized');
20  }
21  
22  return db.users.update(userId, sanitized);
23}

2. Authentication and authorization checks Every endpoint needs proper auth:

• Is the user authenticated?
• Does the user have permission for this action?
• Can the user access this specific resource?

3. Secure dependency management AI loves pulling in npm packages or NuGet libraries without vetting them:

• Run security scans (npm audit, Snyk)
• Review dependencies before adding them
• Keep dependencies updated
• Understand what each package actually does

4. Environment variable security Never, ever commit secrets:

• Use environment variables for all sensitive data
• Add .env to .gitignore immediately
• Use tools like git-secrets to prevent accidental commits
• Rotate secrets if they're accidentally exposed

5. Regular security audits Schedule monthly security reviews:

• Run automated security scanners
• Review authentication flows
• Check authorization logic
• Validate input handling
• Test rate limiting and DDoS protection

Mistake #6: Not Understanding What You're Deploying

Here's a scenario that plays out constantly: You've built a feature with AI assistance. It works on your laptop. You deploy to production. Within hours, everything breaks under real user load.

The problem? AI generates code for ideal conditions. It doesn't consider production realities like concurrent users, network latency, database connection limits, or memory constraints.

According to industry analysis, experienced developers show the most caution with AI tools, with the lowest "highly trust" rate (2.6%) and highest "highly distrust" rate (20%). They've learned the hard way that deployment is where theoretical code meets brutal reality.

The Fix: Production-Ready Development from Day One

1. Database query optimization AI often generates N+1 query problems:

1// AI generates this:
2const users = await db.users.findAll();
3for (const user of users) {
4  user.posts = await db.posts.findByUserId(user.id);
5}
6
7// You need this:
8const users = await db.users.findAll({
9  include: [{
10    model: db.posts,
11    where: { userId: db.users.id }
12  }]
13});

2. Proper error handling Don't let errors crash your application:

1// Instead of:
2const result = await externalAPI.call();
3
4// Do this:
5try {
6  const result = await externalAPI.call();
7  return result;
8} catch (error) {
9  logger.error('External API failed', { error, context });
10  // Graceful degradation
11  return fallbackBehavior();
12}

3. Rate limiting and throttling Protect your APIs from abuse:

• Implement rate limits per user/IP
• Add request queuing for heavy operations
• Use caching for expensive queries
• Set timeouts for external calls

4. Monitoring and observability You can't fix what you can't see:

• Log errors with context (Sentry, LogRocket)
• Track performance metrics (response times, database query duration)
• Set up alerts for critical failures
• Monitor resource usage (memory, CPU, database connections)

5. Staged deployments Never deploy straight to production:

• Development environment for initial testing
• Staging environment that mirrors production
• Gradual production rollout (feature flags, canary releases)
• Quick rollback capability

Mistake #7: Building in Isolation Instead of Learning Fundamentals

The most damaging long-term mistake: using AI as a replacement for understanding rather than a tool for acceleration.

Andrew Ng criticized the term "vibe coding" precisely because it "misleads people into assuming that software engineers just 'go with the vibes' when using AI tools." The reality is that successful AI-assisted development requires deep understanding of software engineering principles.

Y Combinator reported in March 2025 that 25% of startups in its Winter 2025 batch had codebases that were 95% AI-generated. But in September 2025, Fast Company reported on the "vibe coding hangover" with senior engineers citing "development hell" when working with AI-generated code.

The problem is clear: when you don't understand the code you're deploying, you can't debug it, extend it, or maintain it. As one analysis put it: "Since the developer did not write the code, they may struggle to understand syntax/concepts that they themselves have not used."

The Fix: Use AI to Learn, Not to Replace Learning

1. Understand before you accept When AI generates code:

• Read through every line
• Look up unfamiliar patterns or libraries
• Ask the AI to explain its approach
• Try to write it yourself first, then compare

2. Study the fundamentals AI assumes you know

• Authentication and authorization patterns
• Database design principles
• API design and REST conventions
• Error handling strategies
• Security best practices
• Testing methodologies

3. Learn from AI's suggestions Treat AI as a senior developer pair programming with you:

• Why did it choose this approach over alternatives?
• What edge cases is this code handling?
• What would break if requirements changed?

4. Build a mental model of your codebase You should be able to:

• Explain the data flow for any feature
• Identify where bugs are likely occurring
• Predict the impact of changes
• Understand dependencies between components

5. Gradually reduce AI dependency Challenge yourself:

• Write functions yourself before asking AI
• Refactor AI code into your own style
• Solve debugging problems before prompting AI for fixes
• Contribute to code reviews with substantive feedback

The developers thriving with AI tools aren't the ones who've stopped learning – they're the ones using AI to learn faster than ever before. Three engineers interviewed by IEEE Spectrum agreed that vibe coding is "a way for programmers to learn languages and technologies they are not yet familiar with."

The Right Way to Use AI Coding Tools

After walking through all these mistakes, you might wonder: is vibe coding salvageable? Should we even be using AI tools?

Absolutely yes – but with clear guardrails.

The 2025 Stack Overflow survey shows that 52% of developers agree AI tools have positively affected their productivity. When used correctly, the efficiency gains are too significant to ignore. As one experienced developer put it: "In skilled hands, the sheer amount of work you can get done with AI assistance is huge."

Here's the framework I use and recommend:

1. AI for acceleration, not abdication

• Use AI to generate boilerplate code
• Let AI handle repetitive patterns
• Ask AI for implementation suggestions
• But always maintain architectural control

2. Clear responsibilities

• You design the architecture
• You define the requirements
• You make security decisions
• You review every line
• AI implements within your constraints

3. Incremental adoption

• Start with low-risk features
• Build your review process
• Develop team expertise
• Gradually increase scope

4. Documentation and knowledge sharing

• Document AI-generated code thoroughly
• Share learnings across your team
• Build internal guidelines
• Create reusable patterns

Real-World Success: How to Actually Build with AI

Let me show you what this looks like in practice.

At Two Cents Software, we built our SaaS boilerplate specifically for teams using AI coding tools. The foundation includes production-grade authentication, multi-tenancy, billing integration, and security patterns that we know work at scale.

When our customers use AI tools to build features, they're generating code that integrates with proven infrastructure. Instead of prompting AI to "build user authentication," they're asking it to "create a new group-scoped feature following our existing authorization patterns."

The architecture provides guardrails that prevent common vibe coding mistakes:

• Clear module boundaries: Features know how to interact with auth, billing, and data layers
• Established patterns: AI generates code matching existing conventions
• Security by default: Authorization checks are built into the infrastructure
• Production-ready foundation: Database optimization, error handling, and monitoring already implemented

This is how you get the speed advantages of AI without the technical debt nightmare. You're not starting from scratch each time. You're building features on infrastructure that's already solved the hard problems.

Think of it like building a house. You wouldn't ask an AI to generate plans for plumbing, electrical, framing, and roofing all at once. You'd start with a solid foundation and proven systems, then use AI to help customize the floor plan and interior design.

The Future of AI-Assisted Development

The vibe coding movement of 2025 taught us valuable lessons. The industry went through a hype cycle, crashed into reality, and emerged with clearer understanding of what works.

Even Andrej Karpathy, who popularized vibe coding, has stepped back. His latest project, Nanochat, was entirely hand-coded. He explained: "I tried to use Claude/Codex agents a few times but they just didn't work well enough at all."

But this doesn't mean AI tools failed. It means we're learning to use them properly. The developers succeeding with AI in late 2025 aren't the ones blindly accepting every suggestion. They're the ones who've developed systematic approaches to AI-assisted development.

The key insight: AI tools are incredibly powerful assistants, but they're not replacements for engineering judgment, architectural thinking, or security expertise. They accelerate developers who know what they're doing. They don't transform non-developers into competent engineers.

Your Action Plan: Moving Forward with AI Tools

If you're currently vibe coding your way through a project, here's how to course-correct:

This Week:

1. Pause new feature development
2. Review your existing codebase for security issues
3. Set up automated security scanning
4. Create a code review checklist
5. Document your current architecture

This Month:

1. Establish coding standards and patterns
2. Implement comprehensive testing
3. Add monitoring and error tracking
4. Create development/staging/production environments
5. Build your team's AI usage guidelines

Ongoing:

1. Review every line of AI-generated code
2. Refactor for consistency weekly
3. Learn the fundamentals behind AI suggestions
4. Build security checks into your workflow
5. Share knowledge across your team

The Bottom Line

Vibe coding – building software by blindly accepting AI output – is a recipe for disaster. The data is clear: projects built this way accumulate crushing technical debt, security vulnerabilities, and maintenance nightmares.

But AI-assisted development – using AI tools within a disciplined engineering framework – is genuinely transformative. The developers and teams succeeding in 2025 are those who've learned to harness AI's strengths while guarding against its weaknesses.

The seven mistakes we've covered aren't just theoretical problems. They're patterns I see repeatedly in real projects, often with expensive consequences. But they're also completely avoidable with the right approach.

Use AI to move faster, learn quicker, and build more ambitiously. But never outsource your judgment to a tool that doesn't understand the consequences of its suggestions. Review rigorously. Test obsessively. Understand deeply. Document thoroughly.

That's how you get the speed of vibe coding without the technical debt hangover.