In this blog post, the authors explore the issue of fake GitHub stars and how to identify them. They use a combination of machine learning and heuristics to detect suspicious accounts and star patterns on GitHub repositories. The authors purchased fake GitHub stars from various vendors to gather data for their analysis and developed a simple heuristic to identify obvious fake accounts based on limited activity. However, they found that sophisticated fake accounts were more challenging to identify using this approach alone. To improve the detection, they used unsupervised clustering techniques to group accounts with similar behavior and identified suspicious repositories. The authors share their findings and provide an open-source solution in Python and dbt for others to analyze GitHub repositories and detect fake stars. They also discuss the importance of detecting fake accounts to maintain trust on GitHub and highlight that while building models for 100% accuracy is hard, techniques with high precision and recall can be developed, and simple heuristics can still provide valuable insights.