Open model developers have emerged as key actors in the political economy of artificial intelligence (AI), but we still have a limited understanding of collaborative practices in the open AI ecosystem. This paper responds to this gap with a three-part quantitative analysis of development activity on the Hugging Face (HF) Hub, a popular platform for building, sharing, and demonstrating models. First, various types of activity across 348,181 model, 65,761 dataset, and 156,642 space repositories exhibit right-skewed distributions. Activity is extremely imbalanced between repositories; for example, over 70% of models have 0 downloads, while 1% account for 99% of downloads. Furthermore, licenses matter: there are statistically significant differences in collaboration patterns in model repositories with permissive, restrictive, and no licenses. Second, we analyse a snapshot of the social network structure of collaboration in model repositories, finding that the community has a core-periphery structure, with a core of prolific developers and a majority of isolate developers (89%). Upon removing the isolate developers from the network, collaboration is characterised by high reciprocity regardless of developers' network positions. Third, we examine model adoption through the lens of model usage in spaces, finding that a minority of models, developed by a handful of companies, are widely used on the HF Hub. Overall, activity on the HF Hub is characterised by Pareto distributions, congruent with OSS development patterns on platforms like GitHub. We conclude with recommendations for researchers, companies, and policymakers to advance our understanding of open AI development.

Software process models play a pivotal role in fostering collaboration and communication within software teams, enabling them to tackle intricate development tasks effectively. This paper introduces LCG, a code generation framework inspired by established software engineering practices. LCG leverages multiple Large Language Model (LLM) agents to emulate various software process models, namely LCGWaterfall, LCGTDD, and LCGScrum. Each model assigns LLM agents specific roles such as requirement engineer, architect, developer, tester, and scrum master, mirroring typical development activities and communication patterns. Through collaborative efforts utilizing chain-of-thought and prompt composition techniques, the agents continuously refine themselves to enhance code quality. Utilizing GPT3.5 as the underlying LLM and baseline (GPT), we evaluate LCG across four code generation benchmarks: HumanEval, HumanEval-ET, MBPP, and MBPP-ET. Results indicate LCGScrum outperforms other models, achieving Pass@1 scores of 75.2, 65.5, 82.5, and 56.7 in HumanEval, HumanEval-ET, MBPP, and MBPP-ET, respectively - an average 15% improvement over GPT. Analysis reveals distinct impacts of development activities on generated code, with design and code reviews contributing to enhanced exception handling, while design, testing, and code reviews mitigate code smells. Furthermore, temperature values exhibit negligible influence on Pass@1 across all models. However, variations in Pass@1 are notable for different GPT3.5 model versions, ranging from 5 to over 60 in HumanEval, highlighting the stability of LCG across model versions. This stability underscores the importance of adopting software process models to bolster the quality and consistency of LLM-generated code.

AI ethics has long been a hot-button issue. For some, it's reduced to a debate about whether AI should be making decisions traditionally reserved for humans. There are misconceptions that AI systems will quickly evolve into superhuman intelligence, or that they'll be a silver bullet to solve problems for which we as a society don't yet have answers. As a community, we need to do better. As AI proliferates, it's time to figure out how to operationalize AI ethics.