How to Write and Debug Code Using AI in Just 60 Minutes

In 2026, coding has transformed significantly with AI tools taking center stage. As indie hackers and solo founders, we often find ourselves juggling multiple roles, and the last thing we need is to struggle with writing or debugging code. The good news? With the right AI coding tools, you can streamline this process and get your projects moving faster. In this guide, I’ll share how you can leverage AI to write and debug code in just 60 minutes, complete with tool recommendations, pricing, and our honest take based on real experiences.

Prerequisites: What You Need to Get Started

Before diving in, make sure you have the following:

• A clear coding task: Know what you want to build or fix.
• An AI coding tool: Choose one from the list below that fits your needs.
• A coding environment: Set up your IDE (like VSCode) with the necessary plugins for AI integration.

Step 1: Choose Your AI Coding Tool

Here’s a breakdown of some of the best AI coding tools available in 2026, along with their pricing and limitations.

| Tool Name | What It Does | Pricing | Best For | Limitations | Our Take | |--------------------|-------------------------------------------|---------------------------|-------------------------------|------------------------------|---------------------------------| | GitHub Copilot | Autocompletes code and suggests solutions | $10/mo, free tier available | Quick code suggestions | Limited in complex tasks | We use it for writing boilerplate code quickly. | | Tabnine | AI-powered code completions and suggestions | $12/mo, free tier available | JavaScript-heavy projects | Not as robust for Python | Great for pair programming with AI. | | Codeium | AI code assistant for various languages | Free, $19/mo for Pro | Multi-language support | May struggle with niche languages | We love its versatility. | | Replit's Ghostwriter| Code suggestions in your browser | $20/mo | Web development | Browser-dependent | Perfect for quick prototypes. | | Sourcery | Code quality improvements and suggestions | Free, $15/mo for Pro | Python projects | Limited to Python | Helps catch bugs before they happen. | | ChatGPT Code Plugin| Conversational coding help and examples | $20/mo | Learning and debugging | Can be verbose | Excellent for understanding concepts. | | Codex | Generates code from natural language prompts | $30/mo | Rapid prototyping | Needs specific prompts | Great for turning ideas into code. | | Ponic | Debugging assistance and code analysis | Free, $25/mo for Pro | Debugging complex issues | Limited language support | We use it for catching tricky bugs. | | AI Code Reviewer | Automated code review and feedback | $15/mo | Quality assurance | Not a replacement for human review | Useful for maintaining code standards. | | Kodezi | Real-time coding assistance | $10/mo | Collaborative coding | Needs internet connection | We found it helpful in team settings. |

Step 2: Write Code Using AI

1. Set Up Your IDE: Install the chosen AI tool’s plugin. For instance, if you’re using GitHub Copilot, ensure it's enabled in your IDE.

2. Start Coding: Begin typing your function or feature. The AI tool will suggest code snippets. For example, if you’re writing a function to fetch data from an API, start with fetchData and see what Copilot suggests.

3. Refine Suggestions: Don’t accept the first suggestion blindly. Modify it as needed. AI tools can give you a solid foundation, but your input will make it fit your specific needs.

Step 3: Debug Your Code

1. Run Your Code: Execute your code in your IDE. If it doesn't work as expected, take note of the error messages.

2. Use AI for Debugging: Input the error message into your AI tool. For instance, with ChatGPT’s Code Plugin, you can ask, “What does this error mean?” or “How can I fix this function?”

3. Iterate: Based on the AI’s suggestions, make changes to your code. Rerun it to see if the issue is resolved.

Troubleshooting Common Issues

• The AI isn’t understanding my requests: Be specific in your prompts. Instead of asking “How do I fix this?”, ask “How do I fix the TypeError in my fetch function?”

• Suggestions are irrelevant: Sometimes, the AI might not grasp the context. Refine your input or try a different tool.

What’s Next?

Once you’ve successfully written and debugged your code, consider these next steps:

• Integrate your code into your main project: Test it thoroughly with real data.
• Gather feedback: Use automated code review tools to ensure quality.
• Iterate based on user feedback: Continue refining your code with the help of AI tools.

Conclusion: Start Here

To write and debug code effectively using AI in just 60 minutes, start by selecting the right tool from the list above. Personally, I recommend GitHub Copilot for writing and Ponic for debugging, as they complement each other well. With practice, you’ll find that these tools not only save time but also enhance your coding skills.

For a deeper dive into how we leverage these tools weekly, check out our podcast, Built This Week, where we share our experiences and tools that actually work.