Context: Continuous integration (CI) is a software engineering technique that proclaims a set of frequent activities to assure the health of the software product. Researchers and practitioners mention several benefits related to CI. However, no systematic study surveys state of the art regarding such benefits or cons. Objective: This study aims to identify and interpret empirical evidence regarding how CI impacts software development. Method: Through a Systematic Literature Review, we search for studies in six digital libraries. Starting from 479 studies, we select 101 empirical studies that evaluate CI for any software development activity (e.g., testing). We thoroughly read and extract information regarding (i) CI environment, (ii) findings related to effects of CI, and (iii) the employed methodology. We apply a thematic synthesis to group and summarize the findings. Results: Existing research has explored the positive effects of CI, such as better cooperation, or negative effects, such as adding technical and process challenges. From our thematic synthesis, we identify six themes: development activities, software process, quality assurance, integration patterns, issues & defects, and build patterns. Conclusions: Empirical research in CI has been increasing over recent years. We found that much of the existing research reveals that CI brings positive effects to the software development phenomena. However, CI may also bring technical challenges to software development teams. Despite the overall positive outlook regarding CI, we still find room for improvements in the existing empirical research that evaluates the effects of CI.

Recent developments in Large Language Models (LLMs) have significantly expanded their applications across various domains. However, the effectiveness of LLMs is often constrained when operating individually in complex environments. This paper introduces a transformative approach by organizing LLMs into community-based structures, aimed at enhancing their collective intelligence and problem-solving capabilities. We investigate different organizational models-hierarchical, flat, dynamic, and federated-each presenting unique benefits and challenges for collaborative AI systems. Within these structured communities, LLMs are designed to specialize in distinct cognitive tasks, employ advanced interaction mechanisms such as direct communication, voting systems, and market-based approaches, and dynamically adjust their governance structures to meet changing demands. The implementation of such communities holds substantial promise for improve problem-solving capabilities in AI, prompting an in-depth examination of their ethical considerations, management strategies, and scalability potential. This position paper seeks to lay the groundwork for future research, advocating a paradigm shift from isolated to synergistic operational frameworks in AI research and application.

Two-dimensional (2D) carbon allotropes have received considerable attention due to their unique properties and potential applications in several fields, including electronics, catalysis, energy storage, and sensing. Following the experimental realization of graphene, numerous other 2D carbon structures have been proposed and, in some cases, successfully synthesized. This work presents a concise review of the recently experimentally realized 2D carbon allotropes, including graphynes, biphenylene-based networks, fullerene networks, and monolayer amorphous carbon. For each class, we discuss structural characteristics, theoretical predictions, and synthesis methods, with emphasis on the interplay between theory and experiment. We also highlight instances where experimental studies overlooked relevant theoretical contributions. Finally, we identify theoretically predicted structures that remain unexplored experimentally, suggesting opportunities for synthesis-driven investigations.