In a recent development, two AI systems successfully implemented a requested UI feature. However, the true value of the project lay not in the feature itself but in the regression bug that emerged. After the change, a critical contextual action failed silently, a dangerous flaw in production software.

This bug surfaced because the AI systems, focused on new functionality, overlooked existing behaviors that weren't explicitly asserted or tested. The implementations differed significantly: one prioritized user experience and localization, while the other emphasized modular code and safety. Only one approach included comprehensive unit tests, revealing the regression.

This experience underscores the need to evaluate AI coding tools in real projects rather than demos. It's crucial to ask how these tools protect existing behaviors and handle implicit assumptions. As AI continues to play a larger role in coding, understanding its limitations and strengths becomes vital for real-world applications.