Beyond Vibe Coding

What is Vibe Coding?

"Vibe coding" is a playful approach to development that relies on high-level prompting. You give broad instructions, accept AI suggestions, and focus on the overall "vibe" of the project rather than implementation details.

Here's a reality check: The 70% Problem

While vibe coding enables rapid progress (getting 70% of the way to a functional app quickly), the final 30% becomes extremely challenging without deep engineering knowledge:

• Two steps back pattern is where fixing one bug introduces others
• Hidden costs is where requires expertise to ensure maintainability
• Diminishing returns is where AI tools help experienced developers more than beginners
• Security vulnerabilities is where "Vibe coding is fun until you start leaking database credentials"

What is AI-Assisted Engineering?

This goes beyond vibe coding. AI-assisted engineering is a more structured approach that combines the creativity of vibe coding with the rigor of traditional engineering practices. It involves specs, rigor and emphasizes collaboration between human developers and AI tools, ensuring that the final product is not only functional but also maintainable and secure.

Mastering the art of prompting

Prompt engineering is the discipline of crafting precise, effective instructions for AI models. It's the difference between getting generic responses and receiving tailored, production-ready solutions.

Why is my code not working?

This JavaScript function using React hooks is expected to update the user's profile when the form is submitted, but instead it's throwing "Cannot read property 'name' of undefined". Here's the code:

```javascript
const updateProfile = (userData) => {
  setUser(userData.name);
};
```

Error occurs on line 2. Using React 18.2.0.

Create a function that formats currency values.

Examples:
- formatCurrency(2.5) should return "$2.50"
- formatCurrency(1000) should return "$1,000.00"
- formatCurrency(0.99) should return "$0.99"

Optimize this database query step by step:

1. First, analyze the current query and identify performance bottlenecks
2. Then, suggest specific index optimizations
3. Next, rewrite the query using more efficient joins
4. Finally, explain the expected performance improvement

Current query: [paste query here]

Create a user registration function that:
1. Validates email format
2. Checks password strength
3. Handles duplicate users gracefully

After writing the function:
1. Test it with valid inputs
2. Test it with invalid inputs
3. Verify error messages are user-friendly
4. Check that it follows our existing code patterns

Mastering Command-line AI and multi-agent systems

CLI coding agents and orchestrators represent the cutting edge of AI-assisted development, transforming terminals into powerful development environments and enabling teams of AI agents to work collaboratively on complex projects.

# User Authentication Feature Spec

## Goal
Implement secure user authentication with JWT tokens

## Requirements
- Email/password login
- Password hashing with bcrypt
- JWT token generation
- Protected route middleware
- Input validation and sanitization

## Acceptance Criteria
- [ ] User can register with email/password
- [ ] User can login and receive JWT token
- [ ] Protected routes verify JWT tokens
- [ ] Passwords are properly hashed
- [ ] Input validation prevents injection attacks

# Pipe logs to AI for anomaly detection
tail -f /var/log/app.log | gemini "Monitor for errors and anomalies"

# Analyze Git history with AI
git log --oneline | claude "Summarize recent changes and identify patterns"

# Process test results
npm test 2>&1 | aider "Fix failing tests"

#!/bin/bash
# Automated code review with AI

# Get changed files
CHANGED_FILES=$(git diff --name-only HEAD~1)

# Run AI code review
echo "$CHANGED_FILES" | xargs cat | claude "Review this code for:
- Security vulnerabilities
- Performance issues  
- Code quality problems
- Best practice violations"

# Run automated tests
npm test

# Generate commit message
git diff --cached | gemini "Generate a conventional commit message"

pipeline:
  - stage: analysis
    agent: code-analyzer
    task: "Analyze codebase for technical debt and security issues"
    
  - stage: implementation  
    agent: developer
    task: "Fix issues identified in analysis stage"
    depends_on: analysis
    
  - stage: testing
    agent: tester
    task: "Write and run comprehensive tests"
    depends_on: implementation
    
  - stage: documentation
    agent: documenter  
    task: "Update documentation based on changes"
    depends_on: [implementation, testing]