My Experience with AI Coding Assistants: ChatGPT, GitHub Copilot, and Gemini
I am constantly looking for ways to optimize my workflow on my ThinkPad; I wanted to share my experience using AI coding assistants over the past year. Many of you have probably heard of or experimented with tools like ChatGPT, GitHub Copilot, and Google's Gemini. I've been using all three extensively, and I'd like to break down how they've transformed my coding process.
The Impact on My Development Workflow
When AI coding assistants first emerged, I was skeptical. Would they help or create more work through endless corrections? After a year of daily use, they've become indispensable tools in my development arsenal.
GitHub Copilot: The Coding Companion
Copilot has been the most seamlessly integrated tool in my IDE experience. It anticipates what I'm trying to build as I type and offers contextually relevant suggestions. What impresses me most is how it understands the broader architecture of my projects:
It completes entire functions based on docstrings or comments
Suggests test cases that I might have overlooked
Helps implement complex algorithms with proper error handling
The productivity boost is substantial - I'm completing routine coding tasks about 40% faster. Copilot particularly shines when working with boilerplate code or implementing standard patterns.
ChatGPT: The Problem-Solving Consultant
While Copilot works within my code, ChatGPT has become my go-to for higher-level problem-solving conversations. When I'm stuck on a complex issue, I can explain the problem in natural language and receive:
Multiple approaches to solving the problem
Explanations of tricky concepts with examples
Debugging assistance for particularly stubborn bugs
Optimization suggestions for performance bottlenecks
What's particularly valuable is the ability to refine solutions through conversation iteratively. I've had multi-turn discussions that helped me understand complex systems like WebRTC implementation or GraphQL optimization, which would have taken hours of documentation diving.
Gemini: The Research Assistant
Google's Gemini has carved out its niche in my workflow. Its stronger connection to web information makes it particularly useful for:
Understanding new frameworks or libraries
Finding relevant documentation and examples
Suggesting modern best practices
Working with Google Cloud services and APIs
Gemini is beneficial when I need to integrate with Google services or research the latest approaches to technical challenges.
Real Productivity Gains
The proof is in the results. These tools have helped me:
Complete client projects ahead of schedule by automating repetitive coding tasks
Debug a particularly nasty memory leak that had plagued our application for months
Learn and implement a new framework in days rather than weeks
Refactor legacy code with greater confidence thanks to AI-assisted test generation
The Learning Curve and Limitations
These tools aren't perfect. Learning to use them effectively involves:
Understanding how to write clear prompts that get you applicable code
Developing the judgment to evaluate the quality of AI suggestions
Knowing when to rely on AI and when to code manually
Maintaining security awareness (never sharing sensitive code or credentials)
How I Use Them on My Lenovo Setup
My Lenovo Yoga handles these tools remarkably well. I run VS Code with the Copilot extension, keep a browser tab open with ChatGPT, and occasionally use the Gemini app for quick reference. The integration is smooth, and performance remains excellent despite running multiple development environments.
Has anyone else in the community been using these AI coding assistants? What has your experience been like? Are there any particular workflows or setups you've found especially effective on your Lenovo machines?
Looking forward to hearing your thoughts!