How I Integrated GitHub Copilot into My Daily Workflow in 30 Days

As a solo founder, I often find myself juggling multiple projects, and time is always of the essence. Enter GitHub Copilot, the AI-powered coding assistant that promises to boost productivity. But does it really work, or is it just another shiny tool that distracts us from actual coding? In this post, I’ll share my 30-day journey integrating GitHub Copilot into my daily workflow, highlighting what worked, what didn’t, and how you can do the same.

What is GitHub Copilot?

GitHub Copilot is an AI pair programmer that suggests code snippets and entire functions as you type. It leverages OpenAI’s Codex model to understand context and offer relevant suggestions.

• Pricing: $10/month per user, with a free trial available for 30 days.
• Best for: Developers looking to speed up their coding process or learn new coding patterns.
• Limitations: It might suggest incorrect code, and it requires an internet connection to function.

My 30-Day Plan

Week 1: Setting Up and Getting Familiar

Time Estimate: 2 hours to set up and familiarize yourself with the tool.

Prerequisites:

• An active GitHub account.
• Visual Studio Code (VS Code) installed.
• GitHub Copilot subscription.

Step-by-Step:

1. Install the GitHub Copilot extension in VS Code.
2. Create a new coding project or open an existing one.
3. Start coding and pay attention to the suggestions that Copilot offers.

Expected Output: By the end of the week, I was able to see Copilot’s suggestions in real-time, which helped me understand its capabilities and limitations.

Week 2: Integrating into Daily Tasks

During the second week, I started integrating Copilot into my daily coding tasks. I focused on simple functions and repetitive tasks:

• Use Case: Writing boilerplate code for API integrations.
• Output: Copilot generated code snippets based on comments I wrote, significantly reducing the time I spent on boilerplate.

Week 3: Experimenting with Complex Projects

In the third week, I decided to test Copilot on more complex projects, like building a small web application.

• What Worked: Copilot excelled at suggesting entire functions based on a brief description.
• What Didn’t: It struggled with more intricate logic and edge cases.

Week 4: Refining My Workflow

By the final week, I was refining my workflow. Here’s how I utilized Copilot effectively:

• Daily Standups: I shared Copilot-generated code snippets with my team for feedback.
• Code Reviews: I used Copilot to suggest improvements during code reviews, which helped streamline the process.

Troubleshooting and Limitations

While Copilot has enhanced my workflow, there were some hiccups along the way:

• What Could Go Wrong: Copilot sometimes suggested outdated practices or incorrect code. Always review suggestions critically.
• Our Take: We use Copilot for speed, but we double-check its outputs, especially for critical code.

Pricing Breakdown

| Feature | GitHub Copilot Pricing | Best For | Limitations | Our Verdict | |------------------------|----------------------------|------------------------------|-------------------------------------|-------------------------------| | GitHub Copilot | $10/month | Speeding up coding | May suggest incorrect code | Essential for quick prototyping | | Alternatives (e.g., Codeium, Tabnine) | $0-20/month (varies) | Specific use cases, depending on project | Varies by tool | Good to explore, but Copilot stands out |

Conclusion: Start Here

If you’re looking to integrate GitHub Copilot into your workflow, start with the setup and familiarize yourself with its capabilities. Focus on repetitive tasks in the first few weeks, and gradually introduce it into more complex projects. Remember to critically evaluate its suggestions.

What we actually use: GitHub Copilot for coding speed, combined with manual reviews for accuracy.