Recently, a shiny new add-on hit the marketplace: GitHub Copilot. Naturally, I had to dive in and see what all the buzz was about. As a tech enthusiast who dabbles in React for frontend and Python for backend and scripting, I couldn’t resist the allure of this AI-powered coding assistant.
But why use GitHub Copilot ?
GitHub Copilot is like having a supercharged assistant right inside your code editor. It suggests entire lines or blocks of code as we type code.., making coding faster and more efficient. It supports a wide range of languages and frameworks ( 🤔 almost every one ), understands the coding context, and even helps with documenting and writing tests. Imagine having a tool that not only speeds up coding but also helps learn new coding patterns and best practices along the way. Now, that’s amazing !!
For developers, time is precious. GitHub Copilot helps save time by automating repetitive tasks, reducing need to search for code snippets online, and minimizing coding errors.
Whether you’re a seasoned developer or just starting out, Copilot can enhance your productivity and make coding more enjoyable. It’s like having a knowledgeable coding buddy who’s always ready to help, without the need for coffee breaks!
My GitHub Copilot adventure
To start my journey, I signed up for a 30-day trial of GitHub Copilot. The setup process was a breeze—seriously 🙂, it was smoother than a freshly buttered scone. I found the extension for VS Code, clicked "Install," and voilà ! Most of the configurations were handled automatically.
After logging into my GitHub account, the happy little robot icon popped up in the status bar, eager to help. It was like having a coding buddy at my fingertips, ready for action.
First Impressions
To spice things up, I decided to build a trivia API project. This API would challenge users with questions, offer multiple-choice answers, and deliver the results.
Eager to test Copilot's capabilities, I prompted it to create a template for a Python-based API app using Flask.
Within seconds, it generated boilerplate files with helpful content. With just one click, my project setup was complete. It felt like witnessing coding magic đź’« unfold before my eyes.
The template included all the necessary project files, complete with relevant code and configurations to jumpstart the project.
I appreciated how it allowed me to review each file's template code before accepting and creating the workspace—a thoughtful touch that gave me control over the setup process.
Coding time
With the boilerplate files in place, I jumped on filling the gaps and putting the logic to build the API services. During all the time, the coding assistant by my side helping me follow best practices, and handing errors and code refactoring.
Copilot helped me
- set up the endpoints,
- handle user inputs, and
- manage the scoring logic.
It was like having a trivia game helper engine at my fingertips
The Debugging Companion
For most of the actions, we can just right click on the editor to get the copilot options to start an inline chat for cursor position or with code selection. For users to be very specific, can use the built-in copilot commands explain , fix , generate docs or generate tests for the file or selected code.
Fixing errors and debugging with GitHub Copilot is a breeze. Simply select the code or error, then choose the explain command from the IDE's right-click context menu in the editor or terminal. Copilot then scans your workspace's source code to provide the most likely explanation for the error and offers code snippets to clarify the issue or fix the problem.
I made a few minor adjustments using the inline chat feature. It was fast, smooth, and incredibly convenient—a real game-changer for quick edits.
To enhance the app's user-friendliness, I decided to add an HTML template with a home page to display the questions and allow users to answer them.
A great feature of GitHub Copilot is that it allows users to review code changes before applying them. It also scans the entire code file to identify the snippet that needs modification, reassuring developers that it won't disrupt the existing code logic.
Finishing the project
Testing is usually my least favorite part of development, so I handed the task over to my AI pair programmer. Copilot suggested performing a test setup for the app.
I chose to go with the pytest framework for my app and then followed Copilot's prompts and suggestions. In no time, I was able to configure and write tests for all my API routes, adhering to testing best practices.
Containerizing the Application
To finish the app build, I decided to containerize my application. I asked GitHub Copilot to create a Dockerfile for my app, preparing it for a Docker build and deployment in cloud-native environments.
Copilot generated a Dockerfile following best practices. I then tested the containerized app locally to ensure everything was working correctly.
Final Thoughts
With both the backend and frontend complete, my full-fledged app was ready for sharing with my team. A big shout-out to Copilot, my AI pair programmer, for making the process swift and enjoyable.
If you're a developer or DevOps engineer looking to supercharge your coding experience, I wholeheartedly recommend giving GitHub Copilot a spin. It's like having a tireless, supercharged assistant at your fingertips.
Now, go forth and code with gusto!
Top comments (0)