This week’s AI Bite: AI in Practice: Claude Code from a Java Developer’s Perspective

Seamless integration with the existing development environment

One of my main concerns before testing AI coding assistants was the need to switch IDEs.

I’ll be honest – VS Code isn’t my go-to editor, and most new tools (like Cursor) are built around it. Claude Code, however, works without forcing me to abandon my current setup, which, for a Java developer, is a huge plus. It lets me leverage AI support while staying fully immersed in my natural development flow.

Quick setup with the init command

My very first interaction with the tool was a pleasant surprise.\ The init command allows you to easily initialize and configure the environment for a specific project. Thanks to that, I was able to quickly set up Claude for our context – including internal tools and libraries.

This feature is especially useful in larger projects, where consistent configuration can make onboarding and day-to-day development significantly smoother.

“Rules” – a step toward team-wide consistency

One of the most interesting features is the concept of rules, which can be defined globally, per project, or locally. This allows teams to establish consistent standards for how they interact with Claude, for example, preferred coding styles, frameworks, or testing approaches.

I haven’t fully explored this feature yet, but I can already see huge potential in building shared practices and improving team alignment.

Everyday experience

Compared to Copilot Agent, working with Claude Code feels much more intuitive. The interface, interaction model, and overall UX are simply better thought out. At first, collaboration with the tool was a bit clunky, but after learning some best practices, communication became smooth and natural.

Claude handles project context well, maintains coherence across files, and suggests meaningful improvements, especially when refactoring or analyzing existing code.

Be careful with tests, AI still needs direction

One area where I noticed some weaknesses is unit testing.\ Claude Code tends to test the implementation rather than the business logic. In one instance, suppressed exceptions led to misleading test suggestions.\ Once those exceptions were restored, the entire test suite started failing.

This example shows that, despite AI’s growing capabilities, developer oversight is still essential. Claude is an excellent assistant, but it can’t (yet) replace critical thinking.

Great potential for Event Sourcing projects

Given our focus on Event Sourcing, I see tremendous potential in using Claude Code for this kind of architecture. It could be particularly helpful for onboarding developers into complex domains, where understanding event flows and business context is key. I haven’t fully tested this scenario yet, but I can already see how it could shorten onboarding time and simplify exploration of existing implementations.

Final thoughts

At this stage, I’m probably using no more than 20% of Claude Code’s potential, but even that has been enough to convince me that it’s one of the most promising AI tools for developers. It doesn’t require switching environments, offers smart configuration options, and naturally integrates into team workflows. From my perspective it’s absolutely worth trying.