How to Use GitHub Copilot to Boost Your Coding Productivity in 60 Minutes

If you're a solo founder or indie hacker, you know that every minute counts when you're building your project. The coding part can be a major bottleneck, especially when you're juggling multiple tasks. Enter GitHub Copilot: an AI-powered code completion tool that promises to supercharge your coding productivity. But does it really deliver? In this guide, I'll walk you through how to get started with GitHub Copilot in just 60 minutes, including its limitations and what you can expect.

Prerequisites: What You Need to Get Started

Before diving into GitHub Copilot, make sure you have:

• A GitHub account (free)
• Visual Studio Code installed (free)
• The GitHub Copilot extension (requires a subscription)

Step-by-Step Guide to Getting Started with GitHub Copilot

Step 1: Install GitHub Copilot Extension

1. Open Visual Studio Code.
2. Go to the Extensions view by clicking on the Extensions icon in the Activity Bar.
3. Search for "GitHub Copilot" and click "Install."
4. Sign in with your GitHub account when prompted.

Expected Output: You should see a Copilot icon in the sidebar once the installation is complete.

Step 2: Set Up Your Project

1. Create a new project folder and open it in Visual Studio Code.
2. Create a new file (e.g., app.js for JavaScript projects).
3. Start typing a function or a comment describing what you want to do.

Expected Output: GitHub Copilot will suggest code snippets based on your input.

Step 3: Review and Accept Suggestions

1. As you type, suggestions will appear. Use the arrow keys to navigate through suggestions.
2. Press Tab to accept a suggestion or Esc to dismiss it.

Expected Output: Your code will auto-complete, significantly reducing the time spent writing boilerplate code.

Step 4: Experiment with Different Languages

1. Try using Copilot with different programming languages (Python, TypeScript, etc.).
2. Create files with different extensions and see how Copilot adapts to your coding style.

Expected Output: Copilot should provide relevant suggestions regardless of the language, but results may vary.

Step 5: Explore Advanced Features

1. Use comments to describe complex functions or algorithms you want to implement.
2. Check out the Copilot Labs feature for experimental features and improvements.

Expected Output: You may gain access to new functionalities that can enhance your coding experience.

Troubleshooting: What Could Go Wrong

• No Suggestions Appearing: Ensure you are connected to the internet and that your subscription is active.
• Irrelevant Suggestions: Sometimes Copilot misses the mark. Refine your comments or context for better results.
• Performance Issues: If Visual Studio Code is lagging, try disabling other extensions to see if that improves performance.

Pricing Breakdown

Here's how GitHub Copilot stacks up in terms of pricing:

| Plan | Pricing | Best For | Limitations | |--------------------------|-------------------------|----------------------------------|-----------------------------------| | GitHub Copilot | $10/month or $100/year | Individual developers and teams | Limited to Visual Studio Code only |

Our Take

We've been using GitHub Copilot since its launch, and it has genuinely improved our coding speed. However, it’s not perfect. If you’re working on highly specialized code, you might find yourself writing more comments to guide Copilot. It shines for boilerplate and common patterns but can falter with niche cases.

Conclusion: Start Here

To get the most out of GitHub Copilot, dedicate just 60 minutes to set it up and experiment with its features. It can be a game-changer for your productivity, especially when tackling repetitive tasks. Just remember that while it’s a powerful tool, it’s not a substitute for your own coding skills.

If you find Copilot isn’t meeting your needs, consider alternatives like Tabnine or Codeium, which offer similar functionality but might work better for your specific use case.

What We Actually Use

In our stack, we rely heavily on GitHub Copilot for rapid prototyping and boilerplate generation. We also use it alongside traditional documentation and coding practices to ensure we maintain code quality.