How I Used AI Coding Tools to Build My First App in 2 Weeks

Building an app can feel like a daunting task, especially when you're a solo founder or an indie hacker trying to juggle multiple responsibilities. I knew I wanted to create something useful, but the thought of coding from scratch was intimidating. Enter AI coding tools. In just two weeks, I managed to build my first app using these tools, and I’m here to share exactly how I did it.

What Are AI Coding Tools?

AI coding tools leverage machine learning to assist in writing, debugging, and optimizing code. They can suggest code snippets, help with syntax errors, and even generate entire functions or components. If you’re short on time and coding experience, these tools can be game-changers.

Pricing Breakdown of AI Coding Tools

Here’s a breakdown of the AI coding tools I explored during my two-week journey:

| Tool Name | Pricing | Best For | Limitations | Our Take | |--------------------|----------------------------|-----------------------------------|--------------------------------------|-------------------------------| | GitHub Copilot | $10/mo | Code suggestions and completions | Limited to supported languages | We use this for quick code hints. | | OpenAI Codex | $20/mo | Natural language to code | Requires API knowledge | Great for generating code from descriptions. | | Tabnine | Free tier + $12/mo pro | Autocomplete suggestions | Less effective for complex code | We switched to this for better context. | | Replit | Free tier + $7/mo pro | Collaborative coding | Limited debugging features | We love it for quick prototypes. | | Codeium | Free | Code suggestions | Still in beta, may lack stability | Worth trying for free. | | Ponic | $15/mo | Building full-stack apps | Limited community support | We don’t use it due to cost. | | Sourcery | Free + $29/mo pro | Code reviews | Doesn’t support all languages | Useful for team projects. | | DeepCode | Free | Code analysis | Focused on error detection | We use this for quality checks. | | CodeGPT | $19/mo | Code generation | Limited by API call limits | Great for small tasks. | | Snippet AI | Free + $5/mo for extra features | Code snippets management | Basic functionality | We don’t use this. | | AI Buddy | $10/mo | General coding assistance | Not as robust as others | We prefer other tools. | | Cogram | Free | Writing and debugging | Limited language support | We tried it, but it’s basic. |

What We Actually Use

• GitHub Copilot: For quick reference and code suggestions.
• OpenAI Codex: When I needed to convert natural language to code.
• Tabnine: For autocomplete and context-aware suggestions.
• DeepCode: To maintain code quality with automated checks.

Setting Up for Success

Before diving into coding, I spent a few hours setting up my environment:

Prerequisites:

1. GitHub Account: For version control and collaboration.
2. Code Editor: I used Visual Studio Code with the necessary extensions.
3. OpenAI API Key: For Codex access.
4. Basic Knowledge of JavaScript: I had some experience but was not an expert.

Step-by-Step Process

Week 1: Planning and Initial Coding

1. Define Your App Idea: I spent the first two days brainstorming what I wanted to build. I settled on a simple task manager app.

2. Create Wireframes: Using Figma, I sketched out the user interface.

3. Set Up Your Environment: Installed all the necessary tools and created a GitHub repository.

4. Start Coding: I began writing the basic functionality using GitHub Copilot. The suggestions sped up the process significantly.

   • Expected Output: A basic version of the app’s core functions (add, delete, view tasks).

Week 2: Refining and Testing

1. Integrate AI Tools: I switched to Tabnine and OpenAI Codex for more advanced features and to fill in the gaps.

2. Testing: I used DeepCode to analyze my code for potential issues.

3. Gather Feedback: I shared the app with a few friends to gather insights.

4. Launch: By the end of the second week, I had a functional app ready for a small beta testing group.

What Could Go Wrong

• AI Limitations: Sometimes the code generated by AI tools was not optimal or contained bugs. I had to review everything carefully.
• Dependency Management: I faced issues with package dependencies that required manual fixes.

Troubleshooting Tips

• Always test generated code immediately after integration to catch issues early.
• Use version control effectively to roll back changes if necessary.

What’s Next

Now that my app is live, it’s time to focus on user acquisition and feedback. I'll be iterating on the app based on user feedback and exploring additional features. I’m also planning to document this process further, as sharing my learnings could help others in the community.

Conclusion

If you're a solo founder looking to build your first app, I highly recommend leveraging AI coding tools. They can save you a significant amount of time and help you overcome the hurdles of coding. Start by trying out GitHub Copilot and OpenAI Codex, as they were essential in my journey.