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Vibecoding is Real: How AI Changed How I Build Apps

Paweł Karniej·February 2026

Vibecoding is Real: How AI Changed How I Build Apps

February 2026

"Vibecoding" sounds like a meme. It's not.

It's how I built 3 profitable React Native apps in the last 18 months while the rest of the dev world was still debating whether AI is "just autocomplete."

Here's what vibecoding actually looks like in practice.

Table of Contents

  1. What is Vibecoding?
  2. Before AI: The Old Way
  3. After AI: The Vibe Way
  4. Real Examples: Apps I Built Vibecoding
  5. The Vibecoding Toolkit
  6. Workflows That Actually Work
  7. What AI Can't Do (Yet)
  8. The Future is Already Here

What is Vibecoding?

Vibecoding: Building apps by describing what you want and having AI handle the implementation details.

Not: Replacing developers with AI

Actually: Amplifying developer productivity 10x

The shift:

  • 2017-2022: Write every line of code by hand
  • 2023-2026: Describe the outcome, AI writes the code

What it feels like:

  • You have a senior developer pair programming with you
  • They know every API, every pattern, every edge case
  • They never get tired, never forget documentation
  • They implement your ideas at the speed of thought

Real example from last week:

Me: "Create a React Native hook that records audio, transcribes it with Whisper, and stores the result in Convex with proper error handling."

AI (Claude): Generates 200+ lines of production-ready code with TypeScript types, error boundaries, loading states, and proper cleanup.

Time saved: 4-6 hours → 15 minutes

That's vibecoding.

Before AI: The Old Way

How I Built Apps in 2020

Building a voice memo feature:

Day 1: Research React Native audio libraries

  • Compare react-native-audio-recorder-player vs expo-av
  • Read 47 Stack Overflow posts
  • Try 3 different approaches that don't work

Day 2: Get basic recording working

  • Fight with iOS permissions
  • Debug Android-specific crashes
  • Realize audio format isn't compatible with transcription API

Day 3: Add transcription

  • Research speech-to-text APIs
  • Build backend endpoint for Whisper
  • Handle audio file upload/conversion

Day 4: Error handling and edge cases

  • What happens when recording fails?
  • How to handle network errors during upload?
  • Audio permission denied scenarios

Day 5: Polish and testing

  • Loading states
  • Progress indicators
  • Device-specific testing

Total: 5 days, 800+ lines of code, endless debugging

Result: Basic voice memo feature that works... sometimes.

The Research Hell

Remember this flow?

  1. Google "react native audio recording"
  2. Find 12 different approaches
  3. Spend 2 hours reading documentation
  4. Try approach #1 → Doesn't work on Android
  5. Try approach #2 → iOS permissions broken
  6. Try approach #3 → Library abandoned 2 years ago
  7. Stack Overflow research → Conflicting answers
  8. GitHub issues hunting → Bug reports, no solutions
  9. Finally get it working → Breaks in production

The productivity killer: 80% research, 20% building.

After AI: The Vibe Way

How I Build Apps in 2026

Building the same voice memo feature:

Me: "I need a React Native hook for audio recording with Whisper transcription and Convex storage. Include proper TypeScript types and error handling."

AI: Here's a complete implementation:

// Generated in 30 seconds
import { useState, useCallback } from 'react'
import { Audio } from 'expo-av'
import * as FileSystem from 'expo-file-system'
import { convex } from '../services/convex'

interface Recording {
  id: string
  uri: string
  transcription?: string
  duration: number
  createdAt: Date
}

export const useAudioRecording = () => {
  const [recording, setRecording] = useState<Audio.Recording | null>(null)
  const [isRecording, setIsRecording] = useState(false)
  const [isTranscribing, setIsTranscribing] = useState(false)
  const [recordings, setRecordings] = useState<Recording[]>([])

  const startRecording = useCallback(async () => {
    try {
      const permission = await Audio.requestPermissionsAsync()
      if (!permission.granted) {
        throw new Error('Audio permission required')
      }

      await Audio.setAudioModeAsync({
        allowsRecordingIOS: true,
        playsInSilentModeIOS: true,
      })

      const { recording } = await Audio.Recording.createAsync(
        Audio.RecordingOptionsPresets.HIGH_QUALITY
      )

      setRecording(recording)
      setIsRecording(true)
    } catch (error) {
      console.error('Failed to start recording:', error)
      throw error
    }
  }, [])

  const stopRecording = useCallback(async () => {
    if (!recording) return null

    try {
      setIsRecording(false)
      await recording.stopAndUnloadAsync()
      
      const uri = recording.getURI()
      const status = await recording.getStatusAsync()

      if (!uri) throw new Error('Recording failed')

      const newRecording: Recording = {
        id: Date.now().toString(),
        uri,
        duration: status.durationMillis || 0,
        createdAt: new Date(),
      }

      setRecordings(prev => [newRecording, ...prev])
      setRecording(null)

      // Auto-transcribe
      transcribeRecording(newRecording)

      return newRecording
    } catch (error) {
      console.error('Failed to stop recording:', error)
      throw error
    }
  }, [recording])

  const transcribeRecording = useCallback(async (recording: Recording) => {
    setIsTranscribing(true)
    try {
      const formData = new FormData()
      formData.append('audio', {
        uri: recording.uri,
        type: 'audio/m4a',
        name: 'recording.m4a',
      } as any)

      const { data, error } = await convex.action('transcribe-audio', {
        body: formData,
      })

      if (error) throw error

      const transcription = data.transcription

      // Update recording with transcription
      setRecordings(prev =>
        prev.map(r =>
          r.id === recording.id
            ? { ...r, transcription }
            : r
        )
      )

      // Store in database
      await useMutation(api.recordings.create)({
        id: recording.id,
        transcription,
        duration: recording.duration,
        created_at: recording.createdAt.toISOString(),
      })

      return transcription
    } catch (error) {
      console.error('Transcription failed:', error)
      throw error
    } finally {
      setIsTranscribing(false)
    }
  }, [])

  return {
    recordings,
    isRecording,
    isTranscribing,
    startRecording,
    stopRecording,
    transcribeRecording,
  }
}

Total time: 30 seconds for AI generation + 15 minutes for customization

Result: Production-ready feature with error handling, TypeScript types, and proper cleanup.

The New Flow

  1. Describe what you want (natural language)
  2. AI generates implementation (30 seconds)
  3. Customize for your needs (10-20 minutes)
  4. Test and iterate (30 minutes)

The productivity multiplier: 20% specification, 80% building.

Real Examples: Apps I Built Vibecoding

YapperX: Voice Memos with AI (2024)

Traditional estimate: 4-6 months

Vibecoding actual: 6 weeks

AI-generated components:

  • Audio recording/playback engine
  • Whisper transcription pipeline
  • GPT-4 summarization
  • Search functionality
  • User authentication flows
  • Payment integration

My contributions:

  • Product vision and user experience
  • Custom UI design
  • Business logic and edge cases
  • App Store optimization

AI productivity examples:

Feature: Smart audio compression

Me: "Create a function that compresses React Native audio files to optimal size for Whisper transcription while maintaining quality."

AI: Generated complete implementation with:

  • Multiple compression algorithms
  • Quality vs. size optimization
  • Platform-specific handling
  • Progress callbacks
  • Error recovery

Saved time: 8 hours → 20 minutes

Feature: Intelligent text search

Me: "Build a search system that finds recordings by transcription content, with highlighting and relevance ranking."

AI: Generated:

  • SQLite full-text search setup
  • Relevance scoring algorithm
  • Text highlighting component
  • Search result optimization
  • Fuzzy matching support

Saved time: 12 hours → 45 minutes

VidNotes: AI Video Summarization (2024)

Traditional estimate: 3-4 months

Vibecoding actual: 4 weeks

The breakthrough moment:

Me: "Create a React Native app that extracts audio from video files, transcribes with Whisper, summarizes with GPT-4, and exports notes to other apps."

AI: Generated the entire core pipeline in one response:

  • Video-to-audio extraction
  • Chunk processing for long videos
  • Streaming transcription
  • Summary generation with timestamps
  • Export functionality to 8+ formats

What would have taken weeks: Done in hours.

BeatAI: AI Music Practice Coach (2024)

Traditional estimate: 6-8 months

Vibecoding actual: 8 weeks

The complex part: Building an AI practice companion

Me: "Build a React Native app that generates music practice exercises with AI, tracks practice sessions, and gives feedback."

AI: Generated:

  • Practice session tracking
  • AI exercise generation
  • Progress visualization
  • Smart preset system
  • UI for practice flows

The magic: AI handled the boilerplate while I focused on what musicians actually need during practice.

The Vibecoding Toolkit

Essential AI Tools

Claude (Anthropic): Best for complex React Native code

ChatGPT: Good for general programming tasks

GitHub Copilot: Real-time code suggestions

Cursor: AI-powered code editor

My Daily Workflow

1. Morning Planning (with AI)

Me: "I want to add push notifications to my React Native app. 
    What's the complete implementation plan?"

AI: Generates step-by-step plan with:
- Expo Notifications setup
- Permission handling
- Backend integration
- Testing strategy
- Edge cases to consider

2. Implementation (with AI)

Me: "Implement step 1: Expo Notifications setup with TypeScript"

AI: Generates complete implementation with:
- Proper TypeScript types
- Error handling
- Platform differences
- Best practices

3. Debugging (with AI)

Me: "This push notification code isn't working on Android. 
    Here's the error: [error message]"

AI: Analyzes error and provides:
- Root cause explanation
- Step-by-step fix
- Prevention strategies
- Related edge cases

4. Optimization (with AI)

Me: "How can I optimize this notification system for better performance?"

AI: Suggests:
- Caching strategies
- Background processing
- Memory optimization
- Battery impact reduction

Prompting Strategies That Work

Bad prompt:

"Make a button"

Good prompt:

"Create a React Native Button component with TypeScript that supports primary/secondary variants, loading states, icons, and follows Material Design 3 principles."

Great prompt:

"Create a React Native Button component with:

  • TypeScript with strict types
  • Variants: primary, secondary, ghost, danger
  • Sizes: small, medium, large
  • Loading state with spinner
  • Optional left/right icons
  • Haptic feedback on press
  • Accessibility labels
  • Dark mode support
  • Animation on press
  • Follow iOS/Android design guidelines"

The pattern: Be specific about what you want, include technical requirements, mention edge cases.

Code Review with AI

My process:

  1. AI generates implementation
  2. I review for business logic
  3. AI reviews for code quality
  4. I test edge cases
  5. AI suggests optimizations

Example review prompt:

"Review this React Native component for performance issues, accessibility problems, and edge cases I might have missed: [component code]"

AI finds:

  • Memory leaks in useEffect
  • Missing accessibility labels
  • Potential race conditions
  • Platform-specific issues

Workflows That Actually Work

New Feature Development

Phase 1: Planning with AI (15 minutes)

Prompt: "I want to add [feature] to my React Native app. 
        Create a detailed implementation plan including:
        - Technical architecture
        - Required dependencies
        - Potential challenges
        - Testing strategy
        - Step-by-step roadmap"

Phase 2: Core Implementation (1-2 hours)

For each step:
Prompt: "Implement [step] with TypeScript, error handling, 
        and following React Native best practices"

Phase 3: Integration (30 minutes)

Prompt: "Help me integrate this [feature] with my existing 
        [auth/state/navigation] system. Here's my current setup: [code]"

Phase 4: Polish (30 minutes)

Prompt: "Review and optimize this implementation for:
        - Performance
        - Accessibility  
        - Error handling
        - User experience"

Total time: 2-3 hours vs 2-3 days traditionally

Bug Fixing with AI

Traditional debugging:

  1. Read error message (cryptic)
  2. Google the error (20 irrelevant results)
  3. Check Stack Overflow (answers from 2018)
  4. Read library documentation (outdated)
  5. Try random fixes (make it worse)
  6. Finally figure it out (6 hours later)

Vibecoding debugging:

  1. Copy error to AI
  2. Get immediate analysis
  3. Get specific fix
  4. Understand root cause
  5. Implement prevention

Real example:

Error: TypeError: Cannot read property 'navigate' of undefined

Traditional approach: 45 minutes of Stack Overflow searching

AI approach:

Me: "Getting this React Navigation error: [error message]
    Here's my navigation setup: [code]"

AI: "This error occurs when the navigation object isn't available 
     in your component context. Here are 3 solutions:
     1. Use useNavigation hook if you're in a navigator screen
     2. Pass navigation as prop if calling from outside navigator
     3. Use router.push if using Expo Router
     
     Based on your code, you need solution #1: [specific fix]"

Resolution time: 2 minutes

Performance Optimization

AI-driven performance reviews:

Prompt: "Analyze this React Native component for performance issues:
        [component code]
        
        Focus on:
        - Unnecessary re-renders
        - Memory leaks
        - Expensive operations
        - List optimization opportunities"

AI finds issues I miss:

  • Missing dependency arrays in useEffect
  • Expensive calculations not memoized
  • FlatList without proper keyExtractor
  • Image components without optimization

Result: Apps that feel native because performance is built-in, not bolted-on.

What AI Can't Do (Yet)

Product Vision and User Experience

AI can't:

  • Decide what users actually want
  • Understand market positioning
  • Design intuitive user flows
  • Make business decisions

I still do:

  • User research and interviews
  • Product strategy and roadmap
  • UX design and user flows
  • Business model decisions

Complex Business Logic

AI struggles with:

  • Domain-specific edge cases
  • Complex state interactions
  • Business rule validation
  • Integration with existing systems

Example:

Payment processing flow with subscription upgrades, prorations, and edge cases requires human oversight.

Creative Problem Solving

AI is great at: Implementing known patterns

AI struggles with: Novel solutions to unique problems

Example:

Building a custom audio waveform visualizer required creative problem-solving that AI couldn't handle alone.

Testing and Quality Assurance

AI can: Generate test code

AI can't: Understand if tests actually validate business requirements

My approach:

  • AI generates test boilerplate
  • I design test scenarios based on user behavior
  • AI helps implement complex test setups

The Future is Already Here

What's Coming Next

Code generation is just the beginning.

Already happening:

  • AI writes entire features from descriptions
  • AI debugs complex issues instantly
  • AI optimizes performance automatically
  • AI generates tests from specifications

Near future (6-12 months):

  • AI handles complete app architecture decisions
  • AI manages dependencies and updates
  • AI provides real-time performance monitoring
  • AI suggests user experience improvements

Long term (2-5 years):

  • AI understands user behavior patterns
  • AI optimizes apps based on usage data
  • AI handles deployment and DevOps
  • AI manages entire product lifecycles

The Developer Role Evolution

2017: Write every line of code

2026: Orchestrate AI to implement your vision

2030: Manage AI teams that build entire products

Skills that matter more:

  • Product thinking
  • User empathy
  • Business understanding
  • AI prompting and direction

Skills that matter less:

  • Memorizing API documentation
  • Syntax and boilerplate
  • Copy-pasting Stack Overflow solutions

The Vibecoding Advantage

Developers who adopt vibecoding now:

  • Ship 5-10x faster than traditional developers
  • Build more ambitious products
  • Focus on user value instead of implementation
  • Stay ahead of the automation curve

Developers who resist:

  • Get left behind by faster-shipping competitors
  • Spend time on solved problems
  • Miss the productivity revolution

The choice is clear.

The Bottom Line

Vibecoding isn't about replacing developers. It's about amplifying human creativity with AI capability.

Before AI: I spent 80% of my time fighting with implementation details and 20% building user value.

After AI: I spend 20% of my time on implementation and 80% on user value.

Result: 3 profitable apps in 18 months, each built faster and better than anything I created in my first 6 years of development.

The future: Developers who embrace vibecoding will build the next generation of apps while others are still debating whether AI is "real programming."

Want to start vibecoding? Ship React Native includes all the AI-integrated patterns, prompts, and workflows that make vibecoding possible in React Native.

Get Ship React Native and start shipping at AI speed.

Written by Paweł Karniej, who has built 3 AI-powered React Native apps using vibecoding techniques. Follow @thepawelk for more real-world insights on building apps in the AI era.

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