"It's no different with machine learning and large language models. If anything, the open source ecosystem has grown richer and more complex, because now there are open source models to complement the open source code. For article, we've pulled together some of the most intriguing and useful projects for AI and machine learning. Many of these are foundation projects, nurturing their own niche ecology of open source plugins and extensions."
"Most of these projects offer demonstration code, so you can start up a running version that already tackles a basic task. Additionally, the companies that build and maintain these projects often sell a service alongside them. In some cases, they'll deploy the code for you and save you the hassle of keeping it running. The code itself is still open, so there's no vendor lock in."
Open source software continues to drive innovation in machine learning and large language models by combining open source code with open source models. A diverse set of projects supports fine-tuning models, building agentic frameworks, and adding extensible plugins and tools. Many projects provide demonstration code to run basic tasks immediately, while companies offer optional deployment, maintenance, and custom add-ons as paid services. The underlying code remains open to prevent vendor lock-in, enabling organizations to adopt, modify, and extend projects. Examples include pre-coded Agent Skills for coding agents and curated directories of LLM applications and tools.
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