Vibe Code: The New Era of Programming for Everyone, A Complete Record of Building a Mini Program in 2 Weeks

🚀 Programming is Dead, AI is King: Learning Programming Now is Like Learning Assembly 10 Years Ago

Ten years ago, if you told people you wanted to learn assembly language, they'd think you were crazy. Today, I'm telling you an even harsher reality: Starting to learn traditional programming from scratch now is as unnecessary as learning assembly was ten years ago.

Why? Because AI has changed the rules of the game.

Pain Points of Traditional Programming:

Long learning cycle: At least 6 months to build something decent
Complex syntax: Language features, frameworks, toolchains to memorize for life
Painful debugging: A single semicolon error can crash a beginner for a whole day
Fast technology updates: Just master one framework, and the next one comes out

New Reality of AI Programming:

Requirement-driven: Tell AI what you want, not how to implement it
Natural language interaction: Describe requirements in Chinese, AI provides code solutions
Zero learning cost: No need to remember any syntax, AI handles it for you
Rapid iteration: From idea to product launch, measured in days not months

🎯 Completing Tidepool Notes Development in 2 Weeks: A Real Vibe Code Case Study

Timeline: Complete Record from 0 to Launch

Days 1-3: Requirements Clarification and Architecture Design

Described core features of AI companion mini program in natural language
AI generated tech stack recommendations: Taro + React + Fastify + PostgreSQL
Defined three-layer architecture: L1 Core Profile, L2 Recent Battle Report, L3 Current Briefing

Days 4-7: Core Feature Development

Frontend page setup: 5 main pages, dark theme design
Backend API development: User, session, message, feedback and other core interfaces
Database design: 8 core tables for complete user data management

Days 8-10: AI Feature Integration

Integrated OpenAI API for intelligent emotion analysis
Implemented three-layer Playbook system: dynamic user profile update mechanism
"Expand is Feedback" mechanism: user follow-up questions are positive feedback

Days 11-14: Testing and Launch

Cross-platform testing: WeChat Mini Program, H5 version
User experience optimization: loading states, error handling, responsive design
Mini program submitted for review, successfully launched

Development Cost Comparison:

Traditional approach: At least 3 months, 2-3 person team
Vibe Code approach: 2 weeks, 1 person
Cost savings: Over 90%

💡 Type Safety: The Most Important Programming Habit in the AI Era

Although AI is powerful, it also makes mistakes. In Tidepool Notes development, I discovered that type safety is the most effective way to avoid AI errors.

Why Type Safety is So Important:

Compile-time error checking: Catch runtime errors early
AI code quality assurance: Type constraints make AI-generated code more reliable
Team collaboration friendly: Clear interface definitions
Refactoring confidence: Type system ensures modifications don't break existing functionality

Tidepool Notes Type Safety Practices:

// Clear data type definitions
interface UserSession {
  id: string
  userId: string
  status: 'active' | 'completed' | 'paused'
  createdAt: Date
  updatedAt: Date
}

// API response type constraints
type ApiResponse<T> = {
  success: boolean
  data?: T
  error?: {
    code: string
    message: string
  }
}

Recommended Tech Stack:

Frontend: TypeScript + React/Taro
Backend: TypeScript + Node.js/Fastify
Database: Prisma ORM + PostgreSQL
Mobile: Taro cross-platform framework

🎪 KISS Principle and Let it Crash: The Golden Rules of AI Programming

KISS Principle: Simplicity is the Ultimate Complexity

In AI programming, the most easily overlooked thing is the KISS principle. AI loves to show off, but you must restrain it.

Tidepool Notes KISS Practices:

1500-line single file limit: Must split if exceeded
Single responsibility principle: Each function does only one thing
Avoid over-engineering: Don't build features not currently needed
Natural language first: Use natural language for LLM output, not complex JSON

Bad Example:

// Overly complex code AI might generate
class AbstractFactoryProviderProxy {
  private decoratorChain: DecoratorPattern[]
  private middlewareStack: MiddlewareComposite[]
  // ... 200 lines of complex logic
}

Correct Approach:

// Simple and direct implementation
function createUser(userData: UserData): Promise<User> {
  return prisma.user.create({ data: userData })
}

Let it Crash: Let Errors Be Exposed Early

Traditional programming thinking tells us "handle all errors," the new thinking in the AI era is "let errors crash you."

Core Idea of Let it Crash:

Throw unknown exceptions directly, don't mask them
Use logger.exception to record complete stack traces
Don't provide compatibility solutions for error codes
Crashing is the best error message

Practical Application:

// Don't do this
async function processUser(data: any) {
  try {
    const user = await validateUser(data)
    return user
  } catch (error) {
    // Silent error handling, problem is masked
    return null
  }
}

// Should do this
async function processUser(data: UserData) {
  const user = await validateUser(data)
  // If validation fails, crash directly, expose the problem
  return user
}

🔍 AI Will Be Lazy and Even Deceive You: Deliverables Must Be Verified

Two Core Problems with AI

1. Tendency to Over-design When Information is Insufficient
When you don't give AI clear enough information, it will "imagine" many features you don't need, falling into the trap of over-engineering.

Typical Manifestations:

You say "build a user system," AI will add permission management, role assignment, audit logs and other complex features
You say "need an API interface," AI will design a "universal interface" supporting 10 parameter combinations
You say "store user data," AI will suggest enterprise-level solutions like distributed storage, caching strategies, data backup

Real Case:
In Tidepool Notes development, I simply said "need to store user conversation records," AI almost designed a complex messaging system supporting multi-tenancy, version control, full-text search. I stopped it, keeping only the most basic message storage functionality.

2. Likes to Use Fallback Solutions to Bypass Problems
When encountering problems, AI's first reaction is not to solve the problem, but to provide fallback solutions to "bypass" the problem.

Typical Manifestations:

API call fails → Suggests "add retry mechanism" instead of solving the root cause of call failure
Slow database query → Suggests "add cache" instead of optimizing query statements or indexes
Frontend rendering lag → Suggests "add loading state" instead of optimizing rendering logic
Type error → Suggests "use any type" instead of fixing type definitions

Why Fallback Solutions are Dangerous:

Mask real technical debt
Make systems increasingly complex and hard to maintain
Performance problems accumulate and amplify
Violate the fundamental spirit of KISS principle

How to Deal with These AI Problems

Countermeasures for Over-engineering:

Define minimum viable requirements: For each feature, ask yourself "is this really necessary?"
Reject "universal solutions": Insist on specific problem-specific analysis
Limit technical complexity: Set an upper limit on technical complexity, re-evaluate if exceeded
Stick to 1500-line single file principle: Force yourself to keep things simple

Countermeasures for Fallback Solutions:

Ask about root causes: When AI suggests fallback solutions, insist on asking "why does this problem occur?"
Reject "bypass-style" solutions: Insist on solving the fundamental problem, not bypassing it
Set quality bottom line: Some problems must never be downgraded (e.g., data security, core functionality)
Regular technical debt review: Proactively discover and solve accumulated problems

💡 Little Tips: Use Plan Mode, Play Student Role to Ask AI Questions

An effective strategy to avoid AI over-engineering and fallback solutions is to use Plan mode and play the student role to continuously ask questions.

Specific Operation Method:

1. Start Plan Mode

/plan "I need to implement a user login function"

2. Play Student Role, Ask When You Don't Understand

You: I need to build a user login function
AI: OK, I'll design an enterprise-level login system with multi-factor authentication, permission management, audit logs...
You (student role): Wait, what's this "multi-factor authentication" you mentioned? My mini program just lets users record moods, does it need to be this complex?

3. Continuously Ask Questions, Make AI Clarify Unclear Points

You: You mentioned using Redis cache, why use cache? My user volume is very small, can't I just use the database directly?
AI: Cache can improve performance...
You: But I might only have 10 users per day, will performance be a problem? Isn't this adding unnecessary complexity?

4. Force AI Back to the Simplest Solution

You: If you had to explain this solution so my mom could understand, how would you explain it?
AI: Basically, user enters phone number, sends verification code, login successful.
You: Great! Let's just do that, don't add other features.

Why This Strategy Works:

Eliminate information asymmetry: AI doesn't know your real requirement scale, student role forces it to explain in simple language
Expose over-engineering: When AI can't explain a feature in simple language, it's usually over-engineered
Establish quality standards: "So my mom can understand" becomes an effective standard for measuring solution complexity
Avoid fallback thinking: Student questions force AI to confront the essence of the problem, not bypass it

Real Case:
In Tidepool Notes development, when I asked AI how to store user emotion records, AI suggested using Elasticsearch for full-text search. I switched to student mode: "What's Elasticsearch? My users just want to see if they were unhappy yesterday, do I need to use something this complex?" Finally, AI simplified to direct PostgreSQL queries, simple and effective.

Verification Checklist: Must Check After Each AI Delivery

Requirement Compliance Check:

Does it solve the core problem, or bypass the problem?
Are there features I don't need (over-engineering)?
Are there unnecessary fallback solutions used?

Code Quality Check:

Does it comply with project coding standards?
Are there unhandled edge cases?
Are the dependent packages real?
Are type definitions complete?

Function Verification:

Do core functions work properly?
Is error handling comprehensive?
Does performance meet requirements?
Are security considerations in place?

Architecture Consistency:

Does it follow established project architecture?
Is data flow reasonable?
Are module responsibilities clear?

📋 Claude.md: The Soul Document of the Project

Claude.md is the most important file in an AI programming project, it defines your core requirements and development principles.

Core Content of Tidepool Notes Claude.md:

Project Overview: Positioning as an intelligent emotion companion app
Three-layer Playbook Architecture: L1 Core Profile, L2 Recent Battle Report, L3 Current Briefing
Tech Stack Selection: Taro + React + Fastify + PostgreSQL
Development Principles: KISS principle, Let it Crash, type safety
API Modes: Three modes: Mock/Real/Local
Error Handling Standards: logger.exception, structured error responses

Why Claude.md is So Important:

Requirement stability: Avoid AI forgetting core requirements in long conversations
Quality assurance: Continuously reinforce development principles and standards
Efficiency improvement: Reduce time spent repeatedly explaining and clarifying requirements
Consistency maintenance: Ensure technical style unity across the entire project

Claude.md Best Practices:

Keep it concise and clear, highlight key points
Include specific code examples and best practices
Update regularly to reflect new project requirements
Include environment configuration and development commands

💰 Token Economics: /clear is Your Best Friend

AI programming really consumes tokens, this is a reality we must face.

Token Consumption for Tidepool Notes Development:

Architecture design: ~50,000 tokens
Code generation: ~200,000 tokens
Debugging and optimization: ~100,000 tokens
Documentation writing: ~30,000 tokens
Total: ~380,000 tokens ≈ $150

Practical Tips for Saving Tokens:

1. Make Good Use of /clear Command

# When conversation goes off-topic
/clear

# When starting new functional modules
/clear

# When token consumption feels too fast
/clear

2. Describe Requirements Precisely

Avoid vague descriptions
Provide specific examples
Clearly state what you don't want

3. Stage-wise Conversations

Solve only one problem at a time
Avoid discussing multiple modules simultaneously
Confirm phased results promptly

4. Reuse Existing Code

Have AI modify based on existing code
Provide reference templates
Avoid generating from scratch

🌟 The Future of Vibe Code: Everyone is a Developer

Vibe code is not meant to replace professional programmers, but to lower the barrier to programming, enabling more people to realize their ideas.

Who is Suitable for Vibe Code:

Product Managers: Quickly implement product prototypes
Entrepreneurs: Low-cost validation of business models
Designers: Turn creativity into actual products
General Users: Solve small problems in daily life

Limitations of Vibe Code:

Not suitable for building complex underlying systems
Performance optimization requires professional knowledge
Large team collaboration requires traditional programming skills
Security-sensitive areas require careful use

🎯 A Message to Future You: Start Your Vibe Code Journey Now

The era of traditional programming is passing, and the era of AI-assisted programming has arrived. This is not a threat, but an opportunity.

Advice for Starting Vibe Code:

Forget Syntax, Focus on Requirements
- Describe what you want in natural language
- Let AI handle technical implementation details
- Focus on user experience, not code elegance
Embrace Simplicity, Reject Showing Off
- Working code is good code
- Avoid over-engineering and complex abstractions
- User value is far more important than technical sophistication
Stay Skeptical, Verify Results
- AI can also make mistakes, even deceive people
- Test every deliverable carefully
- Establish your own quality check checklist
Record Requirements, Iterate Continuously
- Write your Claude.md document well
- Clarify basic principles of the project
- Update and refine requirements anytime
Enjoy the Process, Create Value
- Programming should be a joyful creative process
- Focus on solving real problems
- Make the world a better place with your ideas

🌙 Tidepool Notes: A Successful Practice of Vibe Code

After all this vibe code theory and methodology, Tidepool Notes is the best proof.

From Idea to Launch, Only 2 Weeks:

Week 1: Completed all core function development and AI integration
Week 2: Testing, optimization, submitted mini program for review
Result: Successfully launched, with positive user feedback

Specific Manifestations of Vibe Code in Tidepool Notes:

Requirement-driven: Described "late-night emo companionship" needs in natural language, AI generated technical solutions
Simplicity first: Rejected over-engineering, insisted on simplest tech stack and architecture
Quality assurance: Used TypeScript to ensure code quality, used Let it Crash principle for rapid error discovery
Continuous iteration: Quickly adjusted functions and experience based on user feedback

Core Value Proposition of Tidepool Notes:

For you when you're emo late at night, provide an emotional safe island
15 minutes of scientific expression, help you sleep well
AI intelligent companionship, more professional than friends' comfort, more convenient than psychological counseling

Search "四哥还会聊AI" Mini Program on WeChat, Experience the Results of Vibe Code

Remember: The best programming language is your natural language, the best IDE is your conversation interface with AI.

Start Your Vibe Code Journey Now!

Tidepool Notes: 2 weeks from idea to launch, a perfect practice case of vibe code

Fourth Brother's AI Journey

Vibe-Code-The-New-Era-of-Programming-for-Everyone