What happened
Vlad Mihalcea used GitHub Copilot to build an Optimizer event-history app that would have taken weeks, finishing in two days; Patrick Baumgartner migrated around 900 teaching slides across 15 presentations in roughly eight to ten hours.
Why it matters
Both developers still reviewed plans, set verification gates, and kept responsibility for what shipped, showing that Copilot speeds execution while humans retain ownership of quality and maintainability.
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The three accounts illustrate a common pattern: GitHub Copilot is most valuable when treated as a collaborator rather than an autopilot. Vlad Mihalcea's two-day build of an event-history app for Hypersistence Optimizer shows how AI can clear long-postponed backlog items, but he still reviews generated plans and code. Patrick Baumgartner's slide migration and terminal UI fixes highlight the importance of verification: his team compared screenshots and PDFs to prove the migration preserved content and animations, and they turned a user-reported bug into reproducible tests before submitting an upstream fix. Frank Delporte's Pi4J bug investigation, where Copilot traced a fifth-button failure to virtual-thread pinning on native calls, underscores that AI can accelerate diagnosis while humans remain accountable for what ships. Together, these examples suggest that the productivity gain is real but conditional on disciplined practices such as clear context, explicit verification gates, and human review.
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