New: VisionPsy-Nano, a 460M vision model that outperforms models twice its size.

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Built with QVAC: a smart security camera that sees and reasons, fully on-device

A real security camera uploads your driveway, your face, and your neighbours to someone else's cloud. To show local AI does not have to, we built a demo (an illustrative example, not a QVAC product) that detects people, vehicles, and bags, judges whether a scene looks normal or high-risk, and raises an alarm, all on your own machine, with no frame ever leaving the device.

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VisionPsy-Nano: state-of-the-art vision AI in its weight class, small enough to run on your phone

Tether AI Research is releasing VisionPsy-Nano, a family of ~460M-parameter vision-language models built to run on the device in your pocket. It leads its weight class on 16 of 17 benchmarks, beats models up to 2.3x its size on ScienceQA, instruction following and hallucination robustness, and the Flash variant reaches the first token in 0.3s on an iPhone 15. Open weights, Apache 2.0.

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Ask your database in plain English, with a local AI that keeps your data on your device

Some data should not go to a cloud chatbot: customer records, patient files, case history, your sensitive data. To show how local AI changes that, we built a demo that turns a plain-English question into SQL and runs it entirely on your own machine, with your rows never leaving the building, allowing you to talk with your database.

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Quick-start: build your first local AI app with your coding agent

Your coding agent can already build almost anything you describe to it. But ask it for an AI feature and it reaches for the same thing every time: a cloud API key, a per-token bill, and your users' data leaving their device for someone else's servers. It does not have to work that way. The AI can run on the device itself, private, offline, and free to run, and your agent can write that code for you. You just have to know how to point it. That is what this guide is for.

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