Osaurus brings both local and cloud AI models to your Mac | TechCrunch

"Today, Osaurus can flexibly connect with locally hosted AI models or cloud providers like OpenAI and Anthropic. Users can freely choose which AI models they're using, and keep other aspects of the AI experience on their own hardware, like the models' own memory, or their files and tools. Given that different AI models have different strengths, the advantage of this system is that users can switch to the AI model that best fits their needs."

"Osaurus evolved out of the idea for a desktop AI companion, Dinoki, which Osaurus co-founder Terence Pae described as a sort of "AI-powered Clippy." Dinoki's customers had asked him why they should buy the app if they still had to pay for tokens - the usage units AI companies charge for processing prompts and generating responses."

""That's how Osaurus started," Pae, previously a software engineer at Tesla and Netflix, told TechCrunch over a call. The idea, he explained, was to try to run an AI assistant locally. "You can do pretty much everything on your Mac locally, like browsing your files, accessing your browser, accessing your system configurations. I figured this would be a great way to position Osaurus as a personal AI for individuals.""

"Such a structure makes Osaurus what's called a "harness" - a control layer that connects different AI models, tools, and workflows through a single interface, similar to tools like OpenClaw or Hermes. However, the difference is"

Osaurus is an open source, Apple-only LLM server that lets users switch between different local AI models or cloud providers. The system is designed so users keep their files and tools on their own hardware while other AI components can be run locally or in the cloud. Osaurus originated from a desktop AI companion concept called Dinoki, described as an “AI-powered Clippy,” where customers questioned paying for tokens. That concern led to focusing on running an AI assistant locally. Osaurus connects models through a single interface, functioning as a harness that links models, tools, and workflows, enabling users to select the model that best matches their needs.

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