How to Debug Code Faster Using AI in 2 Hours

As a solo founder or indie hacker, you know that debugging can become a black hole of time and frustration. In 2026, AI tools have evolved to help us tackle this issue head-on, but not all tools are created equal. In this guide, I’ll walk you through how to leverage AI for debugging, showcasing specific tools that can genuinely save you time and sanity. You can expect to spend about two hours getting set up with these tools and processes.

Prerequisites: What You Need Before Starting

Before diving into the tools, make sure you have the following ready:

1. A programming environment: Set up your code editor (like VSCode or JetBrains).
2. Basic familiarity with your codebase: Understand the structure and logic of your application.
3. An AI debugging tool: You’ll need to sign up for one or more of the tools listed below.

Top AI Tools for Debugging Code

Here’s a rundown of 12 AI tools that can help you debug faster, along with their pricing, best use cases, limitations, and our thoughts on each.

| Tool Name | Pricing | Best For | Limitations | Our Take | |--------------------|-------------------------------|------------------------------|------------------------------------------|--------------------------------------------| | GitHub Copilot | $10/mo, free trial available | Code suggestions, quick fixes| Can suggest incorrect code | We use it for quick code snippets. | | Tabnine | Free tier + $12/mo pro | Autocompletion, context-aware suggestions | Limited to supported languages | Great for JavaScript, but less effective for Python. | | DeepCode | Free tier + $12/mo pro | Static code analysis | Doesn’t always catch runtime errors | Useful for catching common mistakes. | | Sourcery | Free tier + $19/mo pro | Python code improvement | Limited to Python | We love it for refactoring Python code. | | Kite | Free version, $19.90/mo pro | Autocompletion | Less support for non-Python languages | We don’t use it because of language limits. | | Codeium | Free, $10/mo for pro features | General code completion | Not as mature as others | A solid free alternative worth trying. | | Replit Ghostwriter | $20/mo | Collaborative code writing | Mainly for Replit users | Good for real-time collaboration. | | Codex | $0.01 per token | Natural language code generation| Cost can add up with large requests | We’ve used it for quick prototypes. | | AI Bug Fixer | Free with premium options | Automated bug fixing | Still in beta, may miss edge cases | Experimental but promising. | | Ponicode | Free tier + $15/mo pro | Unit testing automation | Limited to JavaScript and Python | We use it for testing, but it has a steep learning curve. | | Jupyter AI | $10/mo | Data science debugging | Works best with Jupyter notebooks only | Great for data projects. | | CodeGuru | $19/mo | Java and Python code review | Limited to specific languages | We haven’t used it due to language constraints. |

What We Actually Use

In our experience, we primarily use GitHub Copilot for quick fixes and DeepCode for static analysis. Sourcery is a go-to for Python projects.

Step-by-Step: Debugging with AI Tools

Step 1: Set Up Your Tools

1. Choose your AI tool from the list above.
2. Sign up and install any necessary plugins for your code editor.
3. Familiarize yourself with the interface and features.

Step 2: Integrate the AI Tool

1. Open your codebase in your preferred editor.
2. Activate the AI tool. For example, in GitHub Copilot, start typing code and watch for suggestions.
3. Adjust settings based on your preferences (like language support).

Step 3: Start Debugging

1. Identify the bug: Understand the problem you're facing.
2. Use the AI tool: Input your code or describe the issue. For instance, if you’re using Copilot, type a comment explaining what you want to achieve.
3. Review suggestions: Carefully evaluate the AI's suggestions. Not all will be perfect—make adjustments as necessary.

Step 4: Test Your Fixes

1. Run your code to see if the issue is resolved.
2. Use tools like DeepCode to check for any other potential bugs or improvements.
3. Iterate as needed, using the AI to refine your approach.

Troubleshooting Common Issues

• AI suggestions don't work: Check if your tool is properly configured and compatible with your programming language.
• Performance issues: If the tool slows down your editor, consider disabling other extensions or upgrading your hardware.
• Incorrect suggestions: AI isn't perfect; always verify code changes manually.

What's Next?

Once you've streamlined your debugging process with AI tools, consider exploring more advanced features or branching into automated testing. You could also look into integrating these tools into a CI/CD pipeline for even faster development cycles.

Conclusion: Start Here

To get started with debugging faster using AI, I recommend trying GitHub Copilot for its versatility and ease of use. Pair it with DeepCode for static analysis to catch additional issues. This combination can significantly cut down your debugging time, allowing you to focus more on building and less on fixing.