How to Debug Code with AI in 60 Minutes

Debugging can be a real pain, especially when you’re racing against the clock. As indie hackers and solo founders, we often juggle multiple roles, and finding the root cause of a bug can suck up precious time. Enter AI coding tools—these can help speed up the process, but they aren’t all created equal. In this guide, I’ll walk you through how to set up an AI-assisted debugging workflow in just 60 minutes using some of the best tools available in 2026.

Prerequisites: What You’ll Need

Before diving in, make sure you have:

• A codebase with bugs to debug (preferably in a language supported by the tools).
• An IDE or code editor (like Visual Studio Code).
• Accounts set up for the AI tools you’ll be using.
• A willingness to try out different suggestions from the AI.

Step-by-Step Debugging Workflow

Step 1: Choose Your AI Debugging Tool

Here’s a list of AI coding tools that can help you debug your code efficiently.

| Tool Name | What It Does | Pricing | Best For | Limitations | Our Take | |--------------------|------------------------------------------------|---------------------------|---------------------------|-------------------------------------------|-----------------------------------| | GitHub Copilot | AI pair programmer that suggests code snippets | $10/mo, free for students | General coding tasks | Limited language support | We use it for quick suggestions. | | Tabnine | AI code completion tool for faster coding | Free tier, $12/mo pro | JavaScript, Python, etc. | May not understand complex logic | Good for repetitive tasks. | | Codeium | AI code assistant that helps find bugs | Free, $19/mo for pro | Bug fixing | Can struggle with large codebases | We don’t use it often. | | Replit AI | Code suggestions and debugging in Replit IDE | Free, $20/mo for pro | Quick prototyping | Limited to Replit environment | Great for quick tests. | | DeepCode | Static analysis tool that finds bugs | Free tier, $29/mo for pro | Static code analysis | Not real-time, requires uploads | We prefer real-time solutions. | | Snyk | Security-focused bug detection | Free tier, $49/mo for pro | Security vulnerabilities | More focused on security than logic bugs | Useful for security checks. | | Sourcery | Suggests code improvements and bug fixes | Free, $12/mo for pro | Python projects | Limited to Python only | We don’t use it because we work in JS. | | Codex | Natural language to code conversion | $100/mo | Complex projects | Expensive for side projects | We use it sparingly. | | AI Dungeon | Fun way to debug through storytelling | Free, $7/mo for pro | Educational purposes | Not practical for serious debugging | Skip unless you want a laugh. | | Ponic | Collaborative debugging tool | Free, $19/mo for pro | Team projects | Requires team collaboration | Not relevant for solo founders. |

Step 2: Set Up Your Tool

1. Install the Tool: Depending on the tool you choose, install it in your IDE or set it up on a cloud platform.
2. Connect Your Codebase: Allow the tool access to your codebase. This often involves linking your GitHub or GitLab account.
3. Configure Settings: Most tools allow you to tweak settings like language preferences or level of assistance.

Step 3: Run the Debugging Process

1. Identify the Bug: Start with a clear understanding of what’s not working in your code.
2. Ask the AI: Use the tool to generate suggestions or ask it specific questions about the bug.
3. Review Suggestions: Carefully read the AI’s suggestions. Not everything it says will be correct, and you’ll need to apply your own judgment.
4. Test Changes: Implement the suggested changes and run your tests. Monitor for any new errors or warnings.

Step 4: Troubleshooting Common Issues

• AI Suggests Incorrect Code: If the AI’s suggestion doesn’t work, try asking it in a different way or providing more context.
• Tool Compatibility: Ensure your codebase’s language is supported by the tool. If not, switch to a different tool.
• Performance Issues: If the AI is slow, it may be due to server load; try again later or check your internet connection.

Step 5: Document Your Process

Once you’ve resolved the bug, take a moment to document what you did. This helps you (and your team) learn from the experience and improves your debugging workflow for the future.

What We Actually Use

In our experience, we rely heavily on GitHub Copilot for its robust suggestions and integration with our workflow. We also use Snyk for security checks, particularly when we’re concerned about vulnerabilities in our code.

Conclusion: Start Here

To debug your code with AI in 60 minutes, choose a tool that fits your needs (I recommend GitHub Copilot), set it up, and follow the steps outlined above. Remember that while AI can significantly speed up the debugging process, it’s not infallible. Always apply your own judgment and experience.