Mark Jeffrey
@markjeffrey
Partner at Bittensor Fund @stillcorecap
Hash Rate podcast
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I am pleased to announce that Stillcore Capital @stillcorecap has invested in Red Team @_redteam_ Bittensor $TAO Subnet 61.
This from @jdorman81's latest newsletter captures perfectly what 'Buttensor' showed us all for $TAO:
The PAIRING coin MATTERS a LOT. A thousand memes paired with YOUR COIN drives insane demand for it.
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So this morning a 'Bittensor memecoin' appeared on Solana called 'Buttensor' (har har).
It was launched in a Raydium pool opposite $TAO on Solana.
Hold solana:2pouN3by7twkiZGy5aEKYUpf78ALDpKRTNu2WsQkpkqt and you earn $TAO.
This drives both solana:2pouN3by7twkiZGy5aEKYUpf78ALDpKRTNu2WsQkpkqt and $TAO price -- and (important) ALL of this drives **new money** on Solana into TAO either directly or obliquely through BUTT.
Through your memecoin antics, Solana people, you drive up the price of serious-coin TAO.
The stage is now set for these new kind of price dynamics on both Solana AND Robinhood.
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Here's the main liquidity pool on Aerodrome DEX on the Base chain:
Looks like an initial half mil pool for USDC <> TAO
This past week, subnet 107, Minos, co-authored a paper about genomic AI with OpenAI.
Here is one result of that effort: HelixForge, a synthetic genome data generator.
If you are in genomic AI and need lots of gene data for training, this is for you:
Powered by Bittensor.
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Kimi K3 is live on Chutes!
$3.00 in / $15.00 out /M
Stunning.
Subnet 53 on Bittensor.
NVIDIA is paying attention to Bittensor $TAO
Out of 33 total companies, 7 subnets were accepted into NVIDIA Inception.
That's 21%.
Actual (SN95), Targon (SN4), Trishool (SN23), Score (SN44), Nephro Robotics (SN49), and Leadpoet (SN71).
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I am pleased to announce that Stillcore Capital @stillcorecap has invested in Minos @theminos_ai (Bittensor $TAO Subnet 107)
This is the first time I've seen TWO Bittensor subnets with market caps over $100M.
Pretty sure this is the first time that's happened?
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 :)
