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Content Summary
The post discusses how the author built 9 mobile and macOS apps in 6 months using AI, with 99% of the code generated by AI. They share 11 rules for effective AI-assisted development, including using high-quality models for architecture, breaking tasks into small units, resetting when stuck, committing code frequently, and refactoring regularly. The author emphasizes efficiency and speed, but acknowledges that none of their apps have gained significant traction or generated revenue. Many comments discuss the trade-offs between quantity and quality, the role of AI in development, and the importance of marketing and product-market fit.
Opinion Analysis
Mainstream opinions suggest that the author's approach is efficient for rapid prototyping and idea testing, but there is debate about whether AI-generated apps can achieve long-term success. Some commenters support the focus on speed and experimentation, while others criticize the lack of polish and potential for low-quality products. There is also discussion about the ethical implications of using AI for development and the importance of marketing. A few users express skepticism about the effectiveness of AI in creating truly valuable applications, while others see it as a powerful tool for indie developers. The conversation highlights the tension between building quickly and building something that people actually want to use.
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| Cursor | https://cursor.com | AI Coding Assistant | Used for AI-assisted coding, supports multiple models like Claude and ChatGPT |
| Claude | https://www.anthropic.com | AI Model | Used for architecture and complex tasks, with versions like Claude 4.1 Opus MAX and Claude Sonnet 4 |
| ChatGPT | https://chat.openai.com | AI Model | Used for general coding and prompt generation |
| GitHub | https://github.com | Version Control | Mentioned as a tool for committing code regularly |
| Figma | https://figma.com | Design Tool | Mentioned as an inspiration source for UI design |
| React Native | https://reactnative.dev | Mobile Development Framework | Used by the author for app development |
| SwiftUI | https://developer.apple.com/swiftui/ | Apple Development Framework | Mentioned as an alternative for iOS apps |
| Jetpack Compose | https://developer.android.com/jetpack/compose | Android Development Framework | Mentioned as an alternative for Android apps |
USER NEEDS
Pain Points:
- Difficulty in managing AI-generated code and ensuring quality
- Overwhelmed by the volume of changes from AI, leading to mental fatigue
- Fear of losing progress due to lack of version control
- Challenges in designing visually appealing interfaces without designer expertise
- Uncertainty about whether AI-generated apps will gain traction or be successful
Problems to Solve:
- How to efficiently use AI for coding without sacrificing quality or maintainability
- How to manage the mental load of working with AI
- How to ensure that AI-generated code is well-structured and maintainable
- How to create visually appealing designs without traditional design tools
- How to determine which ideas are worth building and have potential for success
Potential Solutions:
- Using version control (e.g., Git) to save progress regularly
- Breaking down tasks into small, manageable units for AI
- Using high-quality models for complex architecture and cheaper models for smaller tasks
- Creating detailed feature specifications before coding
- Testing ideas before investing time in development
GROWTH FACTORS
Effective Strategies:
- Building multiple apps quickly using AI to test different ideas and niches
- Focusing on marketing rather than over-polishing individual apps
- Using efficient workflows and tools like Cursor and AI models to speed up development
- Refactoring code regularly to maintain quality and reduce complexity
- Committing code frequently to avoid losing progress
Marketing & Acquisition:
- The author emphasizes that marketing is more important than polishing apps
- No specific marketing strategies were mentioned, but the focus was on rapid app development
Monetization & Product:
- The author's apps do not generate revenue, suggesting that the focus is on experimentation rather than monetization
- The approach highlights the importance of building quickly to test ideas, even if they may not be profitable
User Engagement:
- The author encourages users to experiment and iterate rapidly
- Community discussions highlight the importance of feedback loops and testing ideas before full development