AI coding tools have transformed the landscape of open-source development, facilitating a swift increase in new code submissions. However, this surge in productivity also brings a significant challenge: an influx of low-quality code that jeopardizes the stability of many projects. As building new features becomes more accessible, maintaining them remains a daunting task, if not more so.

This dichotomy underscores the critical need to reassess the role of AI in open-source development and implement more stringent quality control measures to guarantee the long-term viability of these projects.

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