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

As a solo founder or indie hacker, you're likely juggling multiple tasks while trying to ship your product. The reality is that coding can be time-consuming and draining, especially when you're working alone. What if I told you that you could boost your coding output by 50% in just two weeks? Enter GitHub Copilot, an AI-powered coding assistant that can significantly enhance your productivity. In this guide, we’ll walk through how to effectively use GitHub Copilot, the potential limitations, and actionable steps to get you started.

What is GitHub Copilot?

GitHub Copilot is an AI coding assistant developed by GitHub and OpenAI. It provides code suggestions in real-time as you write, helping you complete functions, write tests, and even generate entire code blocks based on comments.

Pricing

• Free tier: Available for individuals with limited features.
• Pro tier: $10/month for individuals, $19/month for businesses.

Best for

• Solo developers looking to speed up their coding process.
• Teams that want to streamline their workflows and reduce repetitive coding tasks.

Limitations

• Copilot can struggle with understanding complex business logic.
• It may generate code that is not optimal or secure, requiring manual review.

Our Take

We've been using GitHub Copilot for several months now, and while it certainly speeds things up, it's important to maintain a critical eye on the code it generates.

Prerequisites

Before diving in, make sure you have:

• A GitHub account (free or paid).
• Visual Studio Code installed on your machine.
• The GitHub Copilot extension installed in Visual Studio Code.

Step-by-Step Guide to Boosting Your Coding Output

Step 1: Set Up GitHub Copilot

1. Install Visual Studio Code: If you haven't already, download and install VS Code.
2. Install the GitHub Copilot extension: Go to the Extensions view in VS Code (Ctrl+Shift+X), search for "GitHub Copilot," and install it.
3. Sign in to GitHub: Open VS Code, and you'll be prompted to sign in to your GitHub account. Follow the instructions.

Step 2: Familiarize Yourself with Copilot's Features

Spend a few hours exploring Copilot's capabilities. Here are some key features to test:

• Code Completion: Start typing a function, and Copilot will suggest completions.
• Comment-based Generation: Write comments describing what you want to achieve, and let Copilot generate the code for you.
• Testing: Use it to write tests for your functions, which can save time in the debugging process.

Step 3: Create a Daily Routine

Time Estimate: Spend about 1 hour each day for the next two weeks using Copilot in your coding sessions.

1. Daily Goals: Set specific coding tasks for each day. For example, focus on writing API endpoints or implementing UI components.
2. Review Suggestions: As you code, review Copilot's suggestions but don't accept them blindly. Assess the quality of the generated code.

Step 4: Measure Your Productivity

At the end of each week, track your output. Use simple metrics:

• Number of functions written.
• Lines of code generated.
• Time spent coding versus time saved.

Step 5: Troubleshooting Common Issues

What Could Go Wrong:

• Copilot may suggest outdated or insecure code.
• It might generate code that doesn't fit your specific use case.

Solutions:

• Always review and test the generated code.
• Use tools like ESLint for JavaScript or other linters for your language to catch potential issues.

What's Next?

After two weeks of using GitHub Copilot, evaluate your productivity. If you’ve seen a significant boost, consider:

• Continuing to integrate Copilot into your workflow.
• Exploring other AI coding tools like TabNine or Codeium for different perspectives.

Conclusion

To truly leverage GitHub Copilot, commit to a daily practice of using it in your coding workflow. By setting clear goals and regularly reviewing your output, you can realistically aim to boost your coding productivity by 50% in just two weeks. Start today, and see how much more you can accomplish with a little AI assistance.

What We Actually Use

In our experience, we combine GitHub Copilot with traditional coding practices. We often use it for boilerplate code but prefer manual coding for complex logic to ensure quality and security.