How to Use GitHub Copilot to Boost Your Coding Speed by 50% in Just 2 Weeks

If you're anything like us, you know the frustration of staring at a blank screen, waiting for inspiration to strike. As indie hackers and side project builders, time is our most precious resource, and anything that can help us code faster is worth exploring. Enter GitHub Copilot, an AI-powered code completion tool that promises to boost your coding speed significantly. But does it really work? In this guide, I'll share how we've used Copilot to improve our productivity and how you can too.

What is GitHub Copilot?

GitHub Copilot is an AI tool that suggests entire lines or blocks of code based on the context of what you're writing. It's like having a pair of extra hands (or a super-smart intern) that can help you code faster and more efficiently.

• Pricing: GitHub Copilot is $10/month or $100/year.
• Best for: Beginners to intermediate developers looking to speed up coding tasks.
• Limitations: It can struggle with complex logic and may generate incorrect code that you'll need to debug.
• Our take: We found it particularly useful for boilerplate code and repetitive tasks, but we still double-check its suggestions.

Prerequisites to Get Started

Before diving into Copilot, you’ll need:

1. GitHub Account: Create one if you don’t have it already.
2. Visual Studio Code: This is where Copilot works best. Download it from here.
3. Copilot Extension: Install the GitHub Copilot extension from the marketplace.

Setting Up GitHub Copilot (Estimated Time: 30 minutes)

1. Install Visual Studio Code: Follow the installation instructions for your OS.
2. Install GitHub Copilot: Open VS Code, go to the Extensions view (Ctrl+Shift+X), search for "GitHub Copilot," and click "Install."
3. Authenticate: Sign in with your GitHub account and authorize the extension.
4. Start Coding: Open a new file and start typing. Copilot will automatically suggest code.

Expected Outputs

As you type, you'll see suggestions appear in a faded text format. You can accept a suggestion by hitting Tab or dismiss it with Esc.

Tips to Maximize Your Copilot Experience

1. Provide Context

The more context you give Copilot, the better it performs. Use comments to describe what you want to achieve.

• Example:

  // Function to calculate the factorial of a number
  function factorial(n) {
  

2. Experiment with Different Languages

While Copilot works best with popular languages like JavaScript, Python, and TypeScript, don't hesitate to try it with others. You might be surprised by its versatility.

3. Review and Refine Suggestions

Always review the code Copilot suggests. It may not always be correct, especially for complex logic or edge cases.

4. Use It for Learning

Copilot can also serve as a learning tool. If you're unsure how to implement a function, start typing and see what Copilot suggests. It can help you understand syntax and patterns.

5. Pair Programming

Treat Copilot like a coding partner. Use its suggestions to spark ideas or alternative approaches to a problem you’re tackling.

Troubleshooting Common Issues

• Wrong Suggestions: If Copilot suggests incorrect code, don’t hesitate to modify it. It’s a learning tool, not a perfect solution.
• Slow Suggestions: Sometimes, suggestions can lag. Make sure your internet connection is stable and try restarting VS Code if it gets too slow.

What’s Next?

Once you feel comfortable using Copilot, consider integrating it with other tools to further streamline your coding process. For instance, using GitHub Actions for CI/CD can automate your deployment pipeline, allowing you to focus on coding.

Conclusion: Start Here

If you're looking to boost your coding speed by 50% in just two weeks, GitHub Copilot is a solid investment. Start with the setup steps above, and focus on writing clear comments and providing context. With consistent use, you'll find your coding speed and efficiency improve dramatically.

What We Actually Use

In our workflow, we rely heavily on GitHub Copilot for writing boilerplate code and for experimenting with new libraries. For more complex logic, we still trust our judgment.