How to Use GitHub Copilot to Write Code 50% Faster in 3 Simple Steps

As indie hackers and solo founders, we often find ourselves pressed for time, juggling multiple tasks while trying to build our next big project. If you're like me, you've probably wished for a way to speed up your coding process without sacrificing quality. Enter GitHub Copilot—a tool that promises to help you write code 50% faster. But does it actually deliver? In this guide, I'll walk you through how to effectively use GitHub Copilot with actionable steps, honest trade-offs, and personal insights.

Prerequisites for Getting Started

Before diving in, here’s what you’ll need:

• A GitHub account (Free or Pro)
• Visual Studio Code installed on your machine
• GitHub Copilot extension (available for free during the trial period, then $10/month for individual users)
• Basic understanding of programming concepts

Step 1: Install GitHub Copilot

The first step is straightforward. Here's how to set it up:

1. Open Visual Studio Code: If you haven’t installed it yet, you can download it for free from Visual Studio Code.

2. Install the GitHub Copilot Extension:

   • Go to the Extensions view in VS Code (Ctrl+Shift+X).
   • Search for "GitHub Copilot."
   • Click "Install."

3. Authenticate with GitHub: You’ll need to log in with your GitHub credentials to activate Copilot.

Expected Output: You should see a Copilot icon in the sidebar, indicating that it's ready to assist you.

Troubleshooting:

If you encounter issues during installation:

• Ensure your Visual Studio Code is updated to the latest version.
• Check your internet connection since Copilot requires online access.

Step 2: Start Coding with Copilot

Once you have Copilot installed, it’s time to put it to work.

1. Create a New File: Open a new file in VS Code and start typing your code or comments describing what you want to achieve.

2. Leverage Suggestions: As you type, Copilot will suggest code completions. You can cycle through suggestions using the arrow keys and hit "Tab" to accept a suggestion.

3. Use Comments for Context: If you're building a function, you can type a comment first. For example, // function to calculate the sum of two numbers. This helps Copilot understand what you want to do.

Expected Output: You should see context-aware suggestions that can help you complete your code much faster than typing it out manually.

Limitations:

While Copilot is impressive, it sometimes struggles with complex logic or domain-specific tasks. It’s not infallible and may suggest inefficient or incorrect code, so always review its outputs carefully.

Step 3: Optimize Your Workflow

To truly take advantage of GitHub Copilot, consider the following strategies:

1. Use Copilot for Boilerplate Code: Let Copilot handle repetitive tasks like setting up API requests or creating data models. This saves time on boilerplate and lets you focus on the logic.

2. Iterate Quickly: As you refine your code, use Copilot to suggest variations or optimizations. For example, if you’re refining a function, modify a few parameters and see how Copilot adapts.

3. Pair with Other Tools: Combine Copilot with linting tools and version control to maintain code quality. For instance, use ESLint for JavaScript projects to catch issues Copilot might miss.

Expected Output: You should notice a significant reduction in time spent on repetitive coding tasks, allowing you to focus on building features that matter.

What Could Go Wrong:

• Over-reliance: Don’t become too dependent on Copilot. It’s essential to understand the code being generated.
• Quality Control: Always test and review code thoroughly, as Copilot may not always adhere to best practices.

Conclusion: Start Here

If you’re looking to boost your coding speed by 50%, GitHub Copilot is worth exploring. With a simple setup and practical usage tips, it can significantly streamline your workflow. Just remember to maintain a balance between leveraging its power and ensuring code quality.

What We Actually Use

In our projects, we find GitHub Copilot particularly useful for generating boilerplate code and quickly prototyping new features. However, we always pair it with code review practices to ensure we maintain high quality.