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Monday, April 21, 2025

What GitHub can tell us about the future of open source



Crucially, these and other cloud infrastructure, containerization, and pipeline automation projects benefit from strong enterprise involvement. Developers from Google, Red Hat, AWS, and VMware contribute code to Kubernetes and related projects, ensuring these tools meet basic enterprise requirements. But it’s more than that. We’re seeing an increasing propensity by competitors to collaborate on common infrastructure platforms, allowing organizations to focus on higher-level innovation.

Machine learning and AI: where the magic happens

Such infrastructure projects make it possible for enterprises to embrace AI. And oh, have they embraced it, thanks to a wealth of open source projects. Open source machine learning libraries and new AI projects experienced unprecedented growth in both usage and community size. Although established frameworks like TensorFlow and PyTorch remained extremely popular, the real story was the advent of generative AI and large language model projects in open source. In 2023 alone, the number of generative AI projects on GitHub jumped 248% year over year, while the count of individual contributors to these projects grew by 148%, according to GitHub data.

Several standout projects exemplify this AI dominance in open source. Hugging Face Transformers, a library unifying state-of-the-art models, soared to over 140,000 stars by 2025, becoming a central tool for natural language processing and model sharing. Newcomer projects saw some of the fastest star-growth ever recorded: LangChain, an AI framework introduced in late 2022 for building applications with language models, rocketed to over 100,000 stars within about a year. Likewise, AutoGPT, an experimental autonomous AI agent, gained more than 174,000 stars, putting it among GitHub’s top projects virtually overnight. Even AI applications like Stable Diffusion (for image generation) and its popular web UI accumulated huge communities. Of course, GitHub stars don’t translate into revenue and are not a hard metric for adoption, but they do indicate just how much AI has captivated the attention of developers.

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