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🚨 introducing hobbylm-1b computer use -- fully offline, open weights & we made it 13x faster: 3.4 s → 0.26 s per decision. my 1b model booked a call for sunday 7pm while wi‑fi was off. it found the name in contacts, worked out which sunday, set the time... show more
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14 Kommentare

@hariharanms95 is cooking

@trycua @francedot 👀 this looks promising, gonna run some evals downstream for this haha

@trycua @francedot pls do

Booking a calendar event with the Wi-Fi off, on a MacBook Air, from a 14 MB adapter. That's the future I want: personal, private, on-device Human-Machine Intelligence that runs your own machine with no cloud in the loop. I also like that it says "I can't do that one offline" instead of faking it. Great work, you two.

Love the sealed-test discipline. Each round patched failures from the last one. Have you tried LLM-paraphrased requests or holding out whole phrasing families, to see if it generalizes past the templates rather than chasing them?

Need to put this on a chip with internet and absolutely mogg the home assistant market

Great job would definitely test this out. Probably have to make some changes for windows but a much needed tool

any chance to have this in German? :D

Available via Android app like run anywhere,atomic chat or else?

Can this run in Hermes? How good is it compared to Codex computer use?

two people, 60 h100 minutes, wifi off. the labs can keep their clusters

so damn cool! there's only one thing keeping me from trying to use it everywhere and that's the refusal rate. if it had 0% false positives you could reliably default to Hobby and then upgrade models whenever it fails

Sub-second decision loops on a 1B model running locally on an Air is wild. With OS action spaces, tiny models usually get tripped up by multi-step state verification when a dialog popup changes focus. Does the adapter rely on structured accessibility tree tokens or pure visual coordinate prediction?

Works on windows?
