Greg Isenberg - How he built iOS apps that PRINT (only using Cursor)
Chris Baroque, a developer without a traditional tech background, has successfully built a portfolio of native iOS apps using AI tools like Cursor and Open Router. He emphasizes the importance of using AI to enhance productivity and create complex apps, which would otherwise require a larger team. Chris shares his workflow, which involves using Cursor for coding and ChatGPT for asset generation, highlighting the efficiency and cost-effectiveness of these tools. He demonstrates how to integrate AI features into apps, such as adding a chat function to a budgeting app using Open Router for model switching and tool calling for dynamic data handling. Chris also discusses the importance of prompt engineering in AI development and shares techniques for generating high-quality prompts using tools like Claude. Additionally, he showcases how to use AI for generating app assets, enhancing the visual appeal of apps. Chris encourages both developers and non-developers to embrace AI tools to improve their skills and productivity, while also cautioning about potential security risks when using these tools.
Key Points:
- Use AI tools like Cursor and Open Router to build native iOS apps efficiently.
- Integrate AI features into apps using tool calling and model switching for dynamic data handling.
- Generate high-quality prompts using Claude to improve AI app responses.
- Utilize AI for creating app assets to enhance visual appeal.
- Developers should embrace AI tools to boost productivity, but be cautious of security risks.
Details:
1. 🎙️ Introduction to Creating Mobile Apps with AI
1.1. AI Tools and Techniques for App Development
1.2. Personal Journey and Practical Applications
2. 🤖 Advanced Techniques in AI-driven iOS Development
2.1. AI Tools and Models for iOS Development
2.2. AI-driven Development Workflow and Challenges
3. 📱 Native iOS App Development Using AI Tools
3.1. AI-Driven UI Development
3.2. Tagging and Contextual Integration
3.3. Integrating Chat Features
4. 🛠️ Building and Integrating AI Features in Apps
- Open Router allows integration with over 300 models, switching between them with a single line of code, facilitating rapid testing and cost comparison during development.
- Integrating Open Router into a chat application enables toggling between different AI models like GPT and Claude with ease, enhancing flexibility in AI functionality.
- Using Open Router, developers can feed three months of transaction history as context to improve AI responses, optimizing accuracy and relevance.
- Documentation can be fed into cursor to reduce AI hallucinations during Apple-specific app development, significantly improving accuracy by providing real-time API updates.
- A structured codebase with separate UI, models, and services folders was used, promoting organized development and easier management of AI integrations.
- API keys should be kept in the back end for security; however, they were temporarily placed in the front end for demonstration purposes, highlighting the need for secure coding practices.
- The integration achieved functionality with just two major prompts, showcasing an efficient development process leveraging AI capabilities.
- Local data usage was preferred over database calls for transaction data to reduce latency and costs, optimizing application performance.
5. 🧩 Implementing Tool Calling and Functionality Enhancements
- Improving the prompt's quality is crucial for better AI responses; the initial one-liner prompt led to poor answers, highlighting the importance of detailed instructions.
- Using Claude to generate prompts in XML format enhances AI performance by providing structured instructions and concise responses, leading to more accurate outputs.
- Testing with realistic mock data, such as adding specific local details, improves the practical usability and user engagement of AI features, making demos more appealing and realistic.
- AI-generated realistic mock data significantly reduces the time required to create test scenarios, increasing efficiency and allowing for more thorough testing processes.
- A refined prompt resulted in concise answers, improving the user experience compared to verbose, less effective responses, demonstrating the impact of prompt refinement on AI interaction quality.
6. 💡 Using AI for Asset Generation and App Polish
- Implemented AI integration with multiple model switching capability, allowing for flexible adaptation to various tasks.
- Encountered significant cost issues when processing large transaction histories, highlighting the need for efficient data management strategies.
- Introduced function calling to manage transaction data efficiently, significantly optimizing processing time and reducing costs.
- Developed specific tools for retrieving transactions within a specific date range and for budget management, facilitating more precise financial tracking.
- Managed tool generation errors through iterative fixes, improving system reliability and performance.
- Showcased the application of tool calling in AI apps, demonstrating enhanced local tool development capabilities.
- Implemented in-app cost tracking by displaying token usage and cost, providing transparency and aiding in budgeting.
- Identified models that support function calling and noted their context window sizes, aiding in the selection of appropriate models for various tasks.
- Facilitated model cost analysis, enabling more informed budgeting decisions during development.
7. 🚀 Final Thoughts and Advice for Developers
- AI like GPT-40 efficiently generates high-quality assets, surprising developers with its capabilities.
- Secondary assets can be generated from a primary design, allowing for customization in app visuals.
- Specificity in prompts is crucial as AI accuracy is around 60-70%, highlighting the need for refined prompts.
- AI-generated assets can enhance app depth and visual appeal, leading to more engaging applications.
- Developers should adopt AI to remain competitive, as adaptability is key to future success.
- Non-developers should start with platforms like Replet or Lovable, which offer more guidance to avoid mistakes.
- Security concerns are significant, as AI tools can inadvertently expose sensitive information, demonstrated by an incident involving a hardcoded key being exploited.