Longevity of the device (r1) and relief of the infrastructure

Longevity and reducing the burden on infrastructure should be a high priority for every innovative AI company, as should privacy and data security, not just that of the company, of course, but also that of r1 users. Therefore, with this topic, I would like to urgently encourage the developers and decision-makers at Rabbit to think about future strategies, or rather, ideally, not just think about them, but in the best case scenario, for all of us, to start implementing them at some point. To process as many processes and data processing as possible not in the cloud, but if it is technically possible, for example depending on the battery life of r1, to shift them to the device (r1) as much as possible.

In the long term, this would certainly reduce costs and reduce the burden on the environment.

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+1

Another idea in the same vein; pointing r1 at our self hosted AI. Yes, that wouldn’t be capable of LAM-things (unless it was open sourced at some point), but it could lighten the load and cost for many of the in-between steps.

Now that I think about it… I suppose this would be indirectly possible through the teaching mode and pointing that at a self hosted URL/IP as one of the steps :thinking:

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