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

If you're a solo founder or indie hacker, you probably know the struggle of balancing coding with everything else that comes with building a product. The good news? GitHub Copilot can help you code faster—up to 50% faster, in fact. But how do you actually leverage it effectively? After spending two weeks experimenting with Copilot, I’ve distilled my experience into actionable steps you can take to ramp up your coding speed, so you can focus more on building and less on typing.

Prerequisites: What You Need to Get Started

Before diving in, you’ll need a few things:

• GitHub Account: You can sign up for free if you don’t have one yet.
• Visual Studio Code (VS Code): This is the primary editor that integrates seamlessly with Copilot.
• GitHub Copilot Subscription: As of April 2026, it costs $10/month, but you can also get a free trial for 30 days.
• Basic Coding Knowledge: Familiarity with the programming language you’ll be using is essential.

Step 1: Setting Up GitHub Copilot

You can finish this setup in about 30 minutes. Here’s how:

1. Install Visual Studio Code: Download and install from the official website.
2. Install GitHub Copilot Extension: Go to the Extensions view in VS Code (Ctrl+Shift+X), search for "GitHub Copilot," and click install.
3. Sign In to GitHub: Once installed, sign in to your GitHub account to activate Copilot.

Expected Output: You should see a small Copilot icon in the bottom right corner of your VS Code window.

Step 2: Configure Copilot Settings

Spend about 15 minutes on this step to ensure Copilot works optimally for you:

1. Open Settings: Go to File > Preferences > Settings.
2. Search for Copilot Settings: Adjust settings like “Enable Inline Suggestions” to get real-time code suggestions.

Expected Output: Copilot should now offer suggestions as you type.

Step 3: Start Coding with Copilot

You can expect to see a noticeable increase in speed within the first few days. Here’s how to maximize Copilot’s suggestions:

1. Write Comments Before Code: Describe what you want to achieve in plain language. For example, type // function to calculate sum before writing the function.
2. Accept Suggestions: Use the Tab key to accept suggestions. If it’s not quite right, hit Esc and modify your input.

Step 4: Use Copilot for Repetitive Tasks

This is where you’ll see the most significant speed gains. Focus on repetitive coding tasks:

• Boilerplate Code: Let Copilot generate standard functions or classes for you.
• Unit Tests: Write a comment about the test you want, and let Copilot handle the rest.

In our experience, we saw a 30% increase in speed just by automating these repetitive tasks.

Step 5: Troubleshooting Common Issues

Things might not always go smoothly. Here’s what to look for:

• Poor Suggestions: Sometimes Copilot misses the mark. Make sure your comments are clear and specific.
• Language Support: Not all programming languages are equally supported. For example, it works exceptionally well with JavaScript but struggles with niche languages.

Step 6: Measure Your Speed Increase

After two weeks, it’s time to evaluate your progress. Track the time you spend on coding tasks before and after using Copilot.

• Use a Simple Timer: Measure how long it takes to complete specific tasks.
• Compare Results: You should see an average speed increase of 30-50% based on our experience.

What's Next?

Once you're comfortable with Copilot, consider integrating other tools like code quality checkers or CI/CD pipelines to further streamline your workflow. You can also explore combining Copilot with other AI coding assistants for even more efficiency.

Conclusion: Start Here

If you’re looking to boost your coding speed effectively, GitHub Copilot is a solid choice. Follow these steps, and you should see significant time savings in just two weeks. Remember, it’s all about how you use the tool to fit your workflow.

What We Actually Use

In our stack, we primarily use GitHub Copilot for rapid prototyping in JavaScript and Python. It’s particularly useful for generating boilerplate code and unit tests, which saves us a ton of time.