Open source software (OSS) generates trillions of dollars in economic value and has become essential to the technical infrastructures that power organizations worldwide. As these systems increasingly depend on OSS, understanding the evolution of these projects is critical. While existing metrics provide insights into project health, one dimension remains understudied: project resilience, or the ability to return to normal operations after disturbances such as contributor departures,security vulnerabilities and bug report spikes. We hypothesize that stable commit patterns may serve as an indicator of underlying project characteristics such as mature governance, sustained contributors, and robust development processes, factors that existing research associates with resilience. Our findings reveal that only 2% of repositories exhibit daily stability, 29% achieve weekly stability, and 50\% demonstrate monthly stability, while the remaining half are unstable across all levels of granularity. Analysis of the 50 unstable repositories indicate that 86% of activity is concentrated among a few maintainers, with the top 3 contributors accounting for over 50% of commits in the past 5 years. In contrast, the 50 stable repositories distribute work more evenly, with the top 3 contributors representing less than 50% of commits. Our insights thus far indicate the fragile and multi-dimensional nature of OSS project stability, suggesting a need to go beyond commits to understand how our understanding of stability can be enriched with other considerations such as community engagement metrics and issue or pull request churn. Though our efforts only identified two repositories that achieved stability at all three temporal commit granularities, further investigation into their processes and policies can provide insights and foundations for stability-informed risk assessment in practice.

Consistent, diverse, and quality contributions are essential to the sustainability of the open source community. Therefore, it is important that there is infrastructure for effectively onboarding and retaining diverse newcomers to open source software projects. Most often, open source projects rely on onboarding documentation to support newcomers in making their first contributions. Unfortunately, prior studies suggest that information overload from available documentation, along with the predominantly monolingual nature of repositories, can have negative effects on the newcomer experiences and onboarding process. This, coupled with the effort involved in creating and maintaining onboarding documentation, suggest a need for support in creating more accessible documentation. Large language models (LLMs) have shown great potential in providing text transformation support in other domains, and even shown promise in simplifying or generating other kinds of computing artifacts, such as source code and technical documentation. We contend that LLMs can also help make software onboarding documentation more accessible, thereby reducing the potential for information overload. Using ChatGPT (GPT-3.5 Turbo) and Gemini Pro as case studies, we assessed the effectiveness of LLMs for simplifying software onboarding documentation, one method for reducing information overload. We discuss a broader vision for using LLMs to support the creation of more accessible documentation and outline future research directions toward this vision.