How to Write Quality Code with AI Tools in Under 2 Hours

As indie hackers and solo founders, we often find ourselves juggling multiple roles, one of which is coding. It can sometimes feel like a race against time, especially when we need to ship quickly. The good news? With the right AI tools, you can write quality code in under 2 hours. In this guide, I’ll share the tools that can help you get there, the limitations of each, and how we approach coding with AI.

Prerequisites: What You Need to Get Started

Before diving in, ensure you have the following:

1. Basic coding knowledge: Familiarity with languages like Python, JavaScript, or Ruby.
2. Access to AI coding tools: You’ll need accounts for the tools we'll discuss.
3. An IDE or code editor: Something like Visual Studio Code or IntelliJ.

Step-by-Step Guide to Writing Code with AI Tools

1. Choose the Right AI Coding Tool

Here’s a comparison of popular AI coding tools that can help you write quality code quickly.

| Tool | Pricing | Best For | Limitations | Our Take | |--------------------|-------------------------------|---------------------------|---------------------------------------------|----------------------------------| | GitHub Copilot | $10/mo, free tier available | Code suggestions | Limited to GitHub ecosystem | We use this for quick suggestions. | | Tabnine | Free tier + $12/mo pro | Autocompletion | Less context understanding than others | We don’t use this much. | | Replit | Free for basic, $20/mo pro | Collaborative coding | Performance issues with larger projects | We love the collaboration feature. | | Codeium | Free | Multi-language support | Fewer integrations | We use this for multi-language projects. | | Amazon CodeWhisper | $19/mo | AWS integration | Primarily for AWS users | We don’t use this, too niche. | | Sourcery | Free tier + $12/mo pro | Code reviews | Limited language support | We use this for code reviews. | | OpenAI Codex | $20/mo | Advanced coding tasks | Costly for casual users | We don’t use this for simple tasks. | | DeepCode | Free for open source, $25/mo | Static code analysis | Can miss context in complex code | We use this for code quality checks. | | Codex AI | $10/mo | Rapid prototyping | Limited to specific frameworks | We don’t use this much. | | Kite | Free | Python coding | No longer actively maintained | We don’t recommend this anymore. |

2. Set Up Your Environment

Once you’ve selected your tool, set up your IDE. For example, if you’re using GitHub Copilot:

• Install the GitHub Copilot extension in Visual Studio Code.
• Open a new project or file where you’ll be coding.

3. Start Coding with AI Assistance

Begin your coding session:

• Write a comment describing the function you want to create. For instance, if you need a sorting algorithm, type // Function to sort an array.
• Let the AI tool suggest code. Accept or modify the suggestions as necessary.

4. Review and Test Your Code

After generating code, it’s crucial to review it:

• Run your code to see if it works as expected.
• Use tools like DeepCode to analyze your code for potential issues.

5. Iterate Based on Feedback

Once you have your initial code working:

• Make adjustments based on testing results.
• Use your AI tool for additional suggestions to optimize or refactor your code.

What Could Go Wrong?

Even with AI assistance, things can go awry:

• Code quality: AI-generated code isn’t always optimal. Always review and test thoroughly.
• Tool limitations: Some tools may not understand complex requirements. In these cases, you may need to write code manually.

What's Next: Scaling Your Coding Process

After you’ve mastered writing code quickly with AI tools, consider these next steps:

• Explore integrating more advanced AI tools that can help with testing and deployment.
• Start using version control (like Git) to manage your code more effectively.
• Join coding communities that focus on AI tools to learn best practices from others.

Conclusion: Start Here

To write quality code with AI tools in under 2 hours, start by selecting the right tool based on your specific needs. Set up your environment, leverage the AI for coding, and always review your outputs.

In our experience, tools like GitHub Copilot and DeepCode have been invaluable. They save us time and help maintain code quality, which is crucial when shipping quickly.

If you’re ready to dive into AI-assisted coding, start with GitHub Copilot and explore its features.