How to Utilize GitHub Copilot to Cut Your Coding Time in Half

As indie hackers and solo founders, we often find ourselves juggling multiple roles. When it comes to coding, the pressure to ship quickly can lead to burnout. Enter GitHub Copilot—a tool that promises to cut your coding time significantly. But does it live up to the hype? In this article, I’ll share how we’ve leveraged Copilot in our projects, along with practical steps to maximize its effectiveness.

What is GitHub Copilot?

GitHub Copilot is an AI-powered code completion tool that integrates seamlessly with your coding environment. It suggests entire lines or blocks of code based on the context of what you’re working on.

• Pricing: $10/month for individuals, $19/month for businesses
• Best for: Developers looking to speed up repetitive coding tasks
• Limitations: It can generate incorrect or suboptimal code, and it may not understand complex business logic
• Our take: We use Copilot for boilerplate code and simple functions, but we always review its suggestions carefully.

Getting Started with GitHub Copilot

Time Estimate: 30 minutes

You can set up GitHub Copilot in about 30 minutes and start seeing productivity gains almost immediately.

Prerequisites

• A GitHub account (free tier is sufficient)
• Visual Studio Code (VS Code) installed
• GitHub Copilot subscription

Step-by-Step Setup

1. Install the GitHub Copilot Extension in VS Code:

   • Open VS Code and navigate to the Extensions view (Ctrl+Shift+X).
   • Search for "GitHub Copilot" and click "Install".

2. Sign in to GitHub:

   • After installation, you’ll be prompted to sign in to your GitHub account.
   • Authorize the Copilot to access your GitHub account.

3. Start Coding:

   • Open a new file or an existing project.
   • Begin typing a comment or a function name, and watch as Copilot suggests completions.

4. Review and Accept Suggestions:

   • Use the Tab key to accept suggestions or continue typing for more options.
   • Always review the code it generates for accuracy and relevance.

Expected Outputs

You should see a noticeable reduction in the time it takes to write functions and boilerplate code. For example, a function that traditionally takes 10 minutes can often be completed in 2-3 minutes with Copilot's assistance.

Troubleshooting Common Issues

• Inaccurate Code Suggestions: If Copilot suggests code that doesn’t work, try rephrasing your comment or providing more context.
• No Suggestions Appearing: Ensure your internet connection is stable and that you’re logged into your GitHub account.

What's Next?

Once you're comfortable with Copilot, consider integrating it with other tools in your stack to further streamline your development process. For instance, combine it with tools like Postman for API testing or Figma for design handoffs.

Tool Comparison: Alternatives to GitHub Copilot

While GitHub Copilot is a powerful tool, it's not the only option. Here’s a comparison of several AI coding tools:

| Tool | Pricing | Best for | Limitations | Our Verdict | |------------------------|-------------------------|------------------------------|-----------------------------------------------------|----------------------------------| | GitHub Copilot | $10/mo | Speeding up coding tasks | May generate incorrect code | Great for boilerplate code | | Tabnine | Free + $12/mo Pro | Autocompleting code snippets | Limited language support | Good for specific languages | | Codeium | Free | General code completion | Lacks advanced suggestions | Good starter tool | | Replit | Free + $20/mo Pro | Collaborative coding | Performance issues with larger projects | Best for team projects | | Sourcery | Free + $12/mo Pro | Code quality improvement | Focuses more on refactoring than code generation | Use alongside Copilot | | Polycoder | Free | Experimental AI coding | Still in development, not production-ready | Skip for now |

Conclusion

GitHub Copilot can significantly reduce your coding time, especially for repetitive tasks. However, it’s essential to combine its power with your own coding knowledge to ensure quality. Start by integrating it into your workflow for boilerplate code, and see how it fits into your existing stack.

If you’re just starting with Copilot, give it a try in a small project first. As you become more comfortable, you can start relying on it for larger tasks.

What We Actually Use

In our experience, we primarily use GitHub Copilot for generating boilerplate code and simple functions, alongside tools like Tabnine for specific language support.