How to Write and Debug Code in 2 Hours Using AI

As indie hackers and solo founders, we often find ourselves stuck in the weeds of coding and debugging. You might have a feature to implement or a bug that just won’t quit, and you’re staring at the clock, knowing you need to ship soon. The good news? With the right AI tools, you can dramatically reduce the time it takes to write and debug code. In this guide, I’ll walk you through how to leverage AI coding tools effectively in just two hours.

Prerequisites: What You Need to Get Started

Before you dive in, here’s what you'll need:

• A computer with internet access
• A basic understanding of programming concepts (Python, JavaScript, etc.)
• Accounts set up on AI coding platforms (more on this below)

Step 1: Choose the Right AI Coding Tool

In 2026, the market is saturated with AI coding tools, each offering unique features. Here are some of the top contenders:

| Tool Name | Pricing | Best For | Limitations | Our Take | |-------------------|-----------------------------|------------------------------|---------------------------------------|-----------------------------------| | GitHub Copilot | $10/mo per user | Quick code suggestions | Limited to certain languages | We use this for rapid prototyping. | | Tabnine | Free tier + $12/mo pro | Autocompletion | Can struggle with context in larger files | Good for smaller projects. | | Codeium | Free | Multi-language support | Lacks advanced debugging features | A solid choice for new coders. | | Replit | Free tier + $20/mo pro | Collaborative coding | Performance issues on heavy apps | Great for team projects. | | OpenAI Codex | $0-20/mo based on usage | Natural language queries | Sometimes misinterprets commands | Powerful, but requires fine-tuning. | | Sourcery | Free + $29/mo for pro | Code quality improvement | Limited to Python | Helps keep our code clean. | | Ponic | $29/mo, no free tier | Full-stack development | Pricing can add up quickly | We don’t use this due to cost. | | Codex AI | $49/mo, no free tier | Advanced code generation | High price for solo founders | Only for serious projects. | | Kodezi | $15/mo | Debugging | Limited language support | Useful for quick fixes. | | Codeium Debugger | Free | Debugging | Basic analytics only | Use alongside other tools. | | Firefly | $19/mo | Frontend code generation | Limited backend support | Good for frontend-focused projects. | | AI Dungeon | Free + $15/mo for premium | Creative coding | Game-focused, not practical for apps | Skip unless you need something fun. |

Step 2: Writing Code Efficiently

Once you’ve selected your tool, spend about 30 minutes writing the code. Here’s a streamlined workflow:

1. Define Your Task: Clearly outline what you want to build. For example, “Create a simple CRUD API in Python.”
2. Use AI Suggestions: Start typing your code. Use the AI tool to autocomplete functions and suggest snippets. Most tools provide context-aware suggestions.
3. Iterate Quickly: Don’t dwell too long on any one part of the code. If you hit a roadblock, ask the AI for help or switch contexts.

Expected Output: A functional prototype of your desired feature.

Step 3: Debugging Your Code

After writing your code, allocate another 30 minutes for debugging. Here’s how to do it effectively:

1. Run Initial Tests: Execute your code to see if it runs as expected.
2. Use Debugging Features: Leverage the debugging tools of your AI platform. For instance, GitHub Copilot can suggest fixes based on error messages.
3. Refactor with AI Help: If your code runs but isn’t efficient, ask the AI to suggest improvements.

Expected Output: A debugged version of your code that works without errors.

Troubleshooting Common Issues

What Could Go Wrong?

• Misinterpretation of Commands: Sometimes the AI may not grasp your intent. If this happens, clarify your request or provide more context.
• Performance Issues: If the tool slows down, try restarting your environment or switching to a lighter tool.

Solutions

• Rephrase your query if you’re not getting the right suggestions.
• If you encounter persistent issues, consider using a different tool from your list.

What’s Next?

Once you’ve successfully written and debugged your code, consider integrating your AI tool into your regular workflow. This could mean:

• Using it for all new features.
• Regularly checking code quality with tools like Sourcery.
• Exploring advanced features like multi-language support with Codeium.

Conclusion: Start Here

If you’re looking to save time and increase productivity in coding and debugging, start by testing out GitHub Copilot for its balance of features and pricing. In our experience, it’s the best tool for quickly generating and refining code. You can get started for just $10 a month, making it a cost-effective choice for indie hackers.