How 3 Java Champions are using GitHub Copilot for increased productivity

Three experienced Java developers describe GitHub Copilot as a productivity tool that can accelerate implementation, investigation, migration, prototyping, documentation, and code review, while emphasizing that developers retain responsibility for requirements, quality, architecture, and shipped results. Vlad Mihalcea uses Copilot in IntelliJ to speed feature development and analysis, creating reusable skills that preserve his writing style and existing code conventions while selecting models according to task complexity. Patrick Baumgartner favors agentic workflows for tasks ranging from slide-deck migrations and cross-platform bug fixes to a privacy-focused mathematics learning app, stressing upfront discovery, explicit plans, incremental delivery, task-specific verification evidence, reusable agent skills, and deterministic quality tools such as static analysis and architectural checks. Frank Delporte shows how targeted context and AI-assisted investigation helped diagnose a Raspberry Pi GPIO bug caused by virtual-thread pinning during native calls, while advocating tests, explanations, human review, and maintainable code. Across the examples, the central recommendation is to use AI as a well-informed sparring partner and execution assistant rather than an autonomous substitute for engineering judgment, domain knowledge, and human feedback.

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