How to Use GitHub Copilot to Boost Your Coding Speed by 50% in One Week

As indie hackers and solo founders, we often find ourselves strapped for time and resources. You might be juggling multiple side projects while trying to keep your coding skills sharp. Enter GitHub Copilot: an AI-powered coding assistant that promises to supercharge your coding speed. But can it really deliver on that promise? In this guide, I’ll show you how to leverage GitHub Copilot effectively, sharing my experience and tips to help you boost your coding speed by 50% within just a week.

What is GitHub Copilot?

GitHub Copilot is an AI pair programmer that suggests code snippets and entire functions as you type. It’s built on OpenAI’s Codex model, trained on a vast amount of code from public repositories.

Pricing Breakdown

• Free: For individuals with limited usage.
• $10/month: Individual Pro plan for unlimited usage.
• $19/month: Team plan with additional features for collaboration.

Best For

• Developers who want to speed up repetitive coding tasks.
• Beginners looking for guidance on syntax and best practices.

Limitations

• It can sometimes suggest incorrect or insecure code.
• Not a replacement for understanding programming concepts—it's an assistant, not a teacher.

Getting Started: Prerequisites

Before diving in, here’s what you need:

1. GitHub Account: Sign up if you don’t have one.
2. Visual Studio Code: Install this IDE, as Copilot integrates seamlessly here.
3. GitHub Copilot Extension: Install the extension from the Visual Studio Code marketplace.

Step-by-Step Guide to Boosting Your Coding Speed

Step 1: Set Up GitHub Copilot in VS Code

1. Open Visual Studio Code.
2. Go to the Extensions view (Ctrl+Shift+X).
3. Search for "GitHub Copilot" and click "Install".
4. Sign in with your GitHub account.

Expected Output: You should see Copilot suggestions as you start typing code.

Step 2: Get Familiar with Copilot's Suggestions

Spend the first couple of days experimenting with Copilot. Write simple functions and observe how it suggests code. The more you interact with it, the better it learns your style.

Expected Output: Suggestions should appear as you type, like autocomplete.

Step 3: Use Contextual Prompts

To get the best suggestions, provide context. For example, instead of just typing function, write function calculateTotalPrice(items). This will give Copilot more information to work with.

Step 4: Review and Refine Suggestions

Copilot’s suggestions might not always be perfect. Make it a habit to thoroughly review the code it suggests. This will not only help you catch errors but also improve your understanding.

Step 5: Incorporate Copilot into Your Workflow

Dedicate at least 30 minutes each day to using Copilot for your coding tasks. Whether you’re building a new feature or fixing a bug, involve Copilot in the process.

Step 6: Measure Your Progress

At the end of the week, compare your coding speed before and after using Copilot. Track metrics like lines of code written per hour or the number of completed tasks.

Troubleshooting Common Issues

• Irrelevant Suggestions: If Copilot is suggesting irrelevant code, try providing more context in your prompts.
• Performance Lag: If you experience slowdowns, check your internet connection, as Copilot requires real-time interaction with GitHub’s servers.

What's Next?

Once you’ve seen improvements with Copilot, consider exploring other AI coding tools for additional support. Tools like Tabnine and Codeium can complement your workflow by offering different suggestions and features.

Conclusion: Start Here

To boost your coding speed by 50% in one week, start by integrating GitHub Copilot into your daily coding routine. Follow the steps outlined above, and remember to review suggestions critically. With consistent practice, you'll become more efficient and confident in your coding abilities.

What We Actually Use

In our workflow, we primarily rely on GitHub Copilot for its contextual suggestions. We've found it particularly useful for writing boilerplate code and repetitive functions, but we always double-check its outputs for accuracy.