How AI Tools Are Reshaping Software Development: Our Experience, Thoughts, and Comparisons
- Ozan Akdemir & Orhan Şentürk
- Jun 12
- 6 min read
Updated: Jul 8
Ever since ChatGPT was released, artificial intelligence has quickly started transforming the way we work. As a software developer, I’ve seen this change from the inside. At first, like many others, I had doubts: Would these tools really help or just get in the way? Would they replace human logic or support it?
But over time, I found myself naturally integrating these tools into my daily development process. In this post, I’d like to share my honest thoughts on how different AI tools perform in real software development tasks — not just in theory, but from actual use. I'll walk you through their strengths, weaknesses, and unique features.
Why AI Makes Sense in Software Development
Before diving into the tools, it’s worth mentioning why AI works so well in this field. Software development is structured, logic-driven, and highly data-dependent — exactly the kind of environment where machine learning models can thrive.
Instead of googling the same question ten different ways or manually testing every possible edge case, you can now just ask your AI assistant. And often, the assistant gives you a working example instantly — sometimes better than what you were expecting.
🔧 AI Tools I’ve Tested (And What I Really Think About Them)
1. Amazon Q
Amazon Q is a smart coding assistant built into the AWS environment. It has a chat interface, which lets you ask about your codebase directly. One thing I appreciate is that it tries to understand what your current file or project is doing before it offers suggestions.
For example, if you’re writing an API endpoint, Q might recommend adding test coverage or logging. It also gives multiple variations of a function, so you can pick the one that fits best. It supports multiple languages, including Turkish, which I personally found useful for some localized projects.
A nice surprise was how well it handled documentation suggestions. When I uploaded a somewhat messy backend service, it immediately suggested breaking down the file and adding inline comments. That’s the kind of help that saves hours in a real-world team setting.
2. Cody (Sourcegraph)
Cody feels like a lighter version of Copilot and Amazon Q. It integrates with your codebase and tries to make sense of your repositories. The downside? The free tier is very limited — once you’ve used the chat a few times, it locks features behind a paywall.
I wouldn’t say Cody is bad — in fact, it’s a solid tool if you’re already using Sourcegraph — but in my ranking, it’s definitely below Copilot and Amazon Q.
3. GitHub Copilot
If you haven’t used Copilot yet, start here. It’s deeply integrated with VS Code and works smoothly for most popular languages. What makes Copilot stand out is its access to the massive GitHub codebase. That alone gives it a huge advantage in suggesting real-world code patterns.
What I love about Copilot is how it autocompletes based on context. For instance, if you start writing a React component, it will predict the structure, state logic, and sometimes even the CSS. The suggestions aren’t always perfect — but they’re usually good enough to be a base for your own refinement.
It’s free for many users and has enterprise licensing too, which is a plus for company-wide adoption.
4. Cursor
Cursor is more than just an AI assistant — it’s an entire development environment that’s AI-native. Think of it as a code editor that talks back. You can feed it your entire project, ask it what’s wrong, and it will suggest fixes inline.
It supports all major LLMs in the background — OpenAI, Claude, and more — and gives you a 14-day trial to test everything. What amazed me most is how well it understood complex logic in backend systems. For example, I asked it to refactor a function that involved pagination and error handling across services — and it did it correctly in one go.
Yes, it’s a paid tool, but if you work on large or fast-paced projects, it might be worth it.
5. Bolt.new
Bolt is a web-based IDE that promises big results with minimal prompts — and in my experience, it delivers. It’s especially useful for front-end or full-stack web applications. You type something like “build a to-do app with login”, and it sets up everything: routing, components, styles, and even local storage.
Among the tools I tested, Bolt gave the most complete results with the smallest input. However, the pricing is steeper than the rest. If you’re looking for speed and don’t mind paying more, Bolt is a great option.
6. ChatGPT
Of course, I have to mention ChatGPT. It was one of the first tools I tried — and I still use it almost every day. The conversational style is incredibly useful for step-by-step development. For example, when I say: “I want a login system with passwords,” it follows up with: “Do you want user profiles? Avatars? Editable settings?” — and when you say “yes,” it generates the corresponding code each time.
But there are limits. In long sessions, it sometimes slows down, or fails to fully update previous code. Debugging can get repetitive. Still, as a generalist and brainstorming tool, it’s unbeatable.
7. Claude
Claude is probably the best LLM for code development that I’ve tested. It’s fast, understands context really well, and writes clean code. I used it to refactor a microservice and it not only spotted logical issues but also rewrote documentation and tests — all in one go.
That said, using Claude isn’t always smooth. I faced account restrictions shortly after trying the paid version. Based on other users’ comments online, this seems to be a known issue. Support was not very responsive, which is disappointing for a tool of this quality.
8. Gemini (by Google)
Gemini is still developing, but I found it promising. It’s not as smart as Claude when it comes to deep code logic, but it’s faster and snappier than ChatGPT in some tasks. I like using it for front-end UI suggestions and simple scripts.
And for now — it’s free. That’s a good enough reason to try it and see how it performs for your own needs.
Final Thoughts
AI is no longer just a buzzword — it’s now a real part of our daily work as developers. But it’s not magic either. These tools are assistants, not replacements. They work best when you understand the code yourself and use the AI to speed things up or give you fresh ideas.
For us, the best combination right now is:
✅ GitHub Copilot: for seamless integration,
✅ ChatGPT: for exploration and sequential logic,
✅ Cursor: for deep refactoring and large-scale assistance,
✅Claude: for tackling complex technical tasks and structured reasoning — it shines especially in detailed planning or when breaking down intricate requirements.
If you’re a developer and still hesitant, my advice is: start small. Use one of these tools in a side project, or try to solve a bug with AI help. You might be surprised by how helpful it really is.
About Author
Ozan Akdemir is a computer engineer with over 20 years of experience in software development. He has worked on HMI and UI projects across sectors like textile, marine, aviation, printing, and energy, as well as EU-level communication systems. At macio GmbH, he held roles from developer to software team director, leading industrial software projects. In 2013, he founded Defia Software Engineering in Istanbul. Defia specializes in industrial software — building interfaces, cloud solutions, mobile apps, and test automation tools, mainly for clients in Germany. Today, Ozan focuses on modernizing legacy systems, designing user-friendly interfaces, and supporting digital transformation in industrial environments.
Orhan Şentürk is a senior Java developer with 15+ years of experience in backend development, focusing on finance and enterprise software. He has worked on projects involving legacy modernization, microservices, and high-volume systems, using technologies like Java, Spring Boot, REST APIs, Oracle, and Openshift. Since 2022, he has been part of Defia Software Engineering, contributing to industrial software projects for international clients. He focuses on building scalable backend systems that power complex machine-human interfaces. Currently, Orhan is interested in clean code, performance optimization, and integrating AI tools into development workflows.
