Max

@DrocksAlex2 @bittensor what’s their subnet?

$TAO in one picture

@RvCrypto good morning my friend

Good morning $TAO fam

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@markjeffrey @chamath TEMPLAR MENTIONED @DistStateAndMe - This is mainstream now

Bittensor peeps: check out 31:44 - Templar sn3 discussed. @chamath -- they've achieved a *72* billion parameter model with decentralized training, not a 4 billion parameter model :)

Ready to build?

is this AGI?

miners are coooooking go and check by yourself: console.scorevision.io/elements/manak…

watch out when using autoreseach models often overfit the parameters and will show you amazing results just to make you happy

custom vision ai dynamic models coming to you soon on @manakoai

S-TILL-CORE thanks @markjeffrey and @rob_svrn, glad to be working with you onwards!

@TaikiMaeda2 Imagine you had the potential to solve any problem with #Bitcoin That's $TAO

open router for vision? lovable for vision? replit for vision? don’t know, but what i know: just from one prompt, you’ll get: -> and end to end computer vision pipeline + real time deployment -> your app code -> a dynamic link to sn44 -> an endpoint / sdk that you can implement seamlessly you’ll always get the best vision ai bricks, working together for you vision vibe coding season is near

@LeadpoetAI well done. happy to be part of the early adopters

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@mil_max19342 github.com/unconst/Arbos

@MaxScore What s Arno ? Send the git repo plz 🙏

me to everyone since i've forked arbos & autoresearch

i really should go to bed now... arbos is learning about the different class ids sn44 gets from the miner.py file (the model is not doing extremely well atm but i've just started building this earlier today on the eurostar...lol) the left sidebar will be connected to @Polymarket for live execution @sire_agent will finally get its in-play edge generation machine powered by @webuildscore + @manakoai long journey still...but we'll get there i think i'll ask arbos to do some pre or post training to increase consistency after we get the basic stuff set up

we recently solved this issue, sharing a bit of alpha here. with @manakoai v1 you'll be able to download a desktop app, or load up a raspberry pie with your custom model (most of our distilled models weigh 20MB compared to GB from VLMs), that would scan cameras that are live and connected to your network and then start working based on your initial prompt automatically. it would then run the inference locally and only send "evidences", the game report + highlights or in your case count points...

@MaxScore Had a really frustrating game of Padel yesterday. A guy on the other team was the biggest cheater I've seen. Any hard to call shots, he'd always say it was out. I swear he misrepresented the score at least once every 5 minutes and at crucial times too. Total loser. Anyway, driving home, I thought of Manako. How hard would it be to have locally installed Manako on a raspberry pie that was connected to a digital scoreboard and a camera? And the camera/Manako would just watch the game and automatically keep track of the score?

@arnod3f @tseutseutao @webuildscore

@tseutseutao @webuildscore maybe @MaxScore just bought a new car and: “you’re not ready for this one” it’s “bigger” “even bigger than that”

$TAO #SN44 - @arnod3f is hyping all the @webuildscore holders and they don’t even know it.