As a software developer, I’m always curious about tools that promise to make my life easier. AI-powered coding assistants have been one of those exciting yet slightly skeptical areas for me—cool in theory, but will they actually deliver? Over the past few months, I’ve had the chance to explore Amazon Q, JetBrains AI, and GitHub Copilot, and I’d love to share my experience with these tools.
Each one brings something different to the table, and the right choice really depends on your workflow, the tools you’re already using, and how deep you want the integration to go.
Amazon Q: The AWS Ecosystem’s Brainy Assistant
When I first tried Amazon Q, I’ll admit, I wasn’t sure what to expect. Amazon has a knack for building things that are incredibly powerful but sometimes overwhelming for everyday developers. But Amazon Q surprised me in a good way. It’s a coding assistant that integrates seamlessly with AWS services and various IDEs, including JetBrains IDEs, Visual Studio Code, and Eclipse.
Here’s what stood out to me:
- AWS-Centric Integration: If your projects live and breathe in the AWS ecosystem (mine often do), Amazon Q feels like an extension of your AWS workflow. It’s great at understanding AWS-specific setups and suggesting improvements.
- Natural Language Queries: The chat interface is incredibly intuitive. Instead of writing convoluted SQL queries, I could just type, “Show me the EC2 instances with the highest CPU usage last month,” and it pulled up exactly what I needed. This felt less like using a tool and more like having a really smart colleague on call.
- Feature-Rich Plugins: Amazon Q’s plugins for JetBrains IDEs and Visual Studio Code bring a lot of power directly into your coding environment. It’s not just about autocompletion; it can help you refactor code, generate unit tests, and even document your work.
But here’s the catch: If you’re not fully embedded in the AWS ecosystem, you might not feel the full value of Amazon Q. It really shines when it’s working with AWS data and services.
JetBrains AI: Purpose-Built for JetBrains Users
I’ve been a long-time JetBrains user, so naturally, I was curious to see how their AI assistant stacks up. What I loved about JetBrains AI is how deeply it integrates with their IDEs like IntelliJ IDEA, PyCharm, and WebStorm.
Here’s my take:
- Seamless Experience: Because JetBrains AI is built by JetBrains for JetBrains, the integration feels effortless. It’s like the assistant already knows what you’re working on and how you like to work.
- Context-Aware Suggestions: I noticed that JetBrains AI was particularly good at understanding the context of my code and making precise recommendations. Whether I was refactoring a messy function or debugging a tricky issue, it felt like having a second brain.
- Focused Language Support: For now, JetBrains AI works best with Java, Python, and a few other core languages. If you’re in that wheelhouse, it’s fantastic. If you’re not, it might feel limiting.
The downside? It’s very JetBrains-centric. If you’re hopping between multiple IDEs or working with a variety of tools, it doesn’t have the broad compatibility that Amazon Q or GitHub Copilot offers.
GitHub Copilot: The Generalist
GitHub Copilot is probably the most well-known AI coding assistant out there, and for good reason. It works with pretty much every IDE I’ve ever used: Visual Studio Code, JetBrains IDEs, Neovim, and more.
Here’s why Copilot is so appealing:
- Wide Language Support: No matter what language I’m using—Python, JavaScript, Go, Rust—Copilot just works. This makes it incredibly versatile for someone like me, who’s constantly switching between projects.
- Fast and Fluid: Copilot excels at inline code suggestions and completions. If you’re writing boilerplate or working on repetitive tasks, it’s a huge time-saver.
- Simple Integration: Setting up Copilot is ridiculously easy. A couple of clicks, and you’re good to go.
However, Copilot sometimes feels like it’s trying to be helpful when it’s not. Its suggestions can be a bit off when working on complex or highly customised codebases. And while it’s great for general use, it doesn’t have the same level of AWS-specific or JetBrains-focused integration as the other two.
So, Which One Do I Prefer?
Honestly, it depends on the project. If I’m doing something heavily tied to AWS, Amazon Q wins hands down. Its ability to understand AWS services and work seamlessly within that ecosystem is unbeatable. When I’m in my JetBrains IDEs working on something like a Python backend or a JS/Web app, JetBrains AI feels like the natural choice—it’s like the IDE and assistant are one.
But if I’m working on a side project or jumping between different tools, GitHub Copilot is my go-to. Its flexibility and broad language support make it invaluable for those “jack-of-all-trades” moments.
Final Thoughts
These tools aren’t just gimmicks—they’re genuinely useful assistants that can make a real difference in how we code. Each one has its strengths and weaknesses, and the right choice ultimately depends on your workflow. For me, having access to all three has been a game-changer, and I’m excited to see how they evolve in the coming years.
Have you tried any of these tools? I’d love to hear your experiences—what’s worked for you, and what hasn’t? Let’s compare notes!