macrozack (ฯ„, ฯ„) ๐Ÿš€ (@macrozack) 's Twitter Profile
macrozack (ฯ„, ฯ„) ๐Ÿš€

@macrozack

Director + communications @MacrocosmosAI
Believer in Bittensor.
๐Ÿงฉ chriszacharia.substack.com
๐Ÿš€ telegram @ t.me/MacrocosmosAI

ID: 1923364983163043840

linkhttps://www.macrocosmos.ai/ calendar_today16-05-2025 13:08:52

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macrozack (ฯ„, ฯ„) ๐Ÿš€ (@macrozack) 's Twitter Profile Photo

๐Ÿ‘๐Ÿ‘ Keith exploring the key problems around dTAO (and actually proposing solutions) ๐Ÿงฉ Root selling at the same rate everyone earns at Seems fair, but it penalises Alpha holders, because when they sell, the token price goes down - and because root has a first mover advantage,

Cryptor โšก๏ธ (@cryptorinweb3) 's Twitter Profile Photo

Thereโ€™s a certain satisfaction in watching centralized AI crackโ€ฆ Especially when youโ€™re invested in the other side named $TAO. I just read this article about the risks of centralized AI and inference by The Defiant, and one line hit hard: โ€œEvery AI query today is an act of

Teng Yan - Championing Crypto AI (@0xprismatic) 's Twitter Profile Photo

Decentralized training in practice: Compute, data, and validation are all done by separate actors, each earning tokens or micropayments. Correctness is often enforced through some form of cryptoeconomics: cheat, and you get slashed. Play fair, and you get paid.

Decentralized training in practice:

Compute, data, and validation are all done by separate actors, each earning tokens or micropayments.

Correctness is often enforced through some form of cryptoeconomics: cheat, and you get slashed. Play fair, and you get paid.
macrozack (ฯ„, ฯ„) ๐Ÿš€ (@macrozack) 's Twitter Profile Photo

Few understand Bittensor deeply, but Kalei's one of them. Glad he's getting more of a platform - our conversations in the Macrocosmos office are ๐Ÿ”ฅ

Ventura Labs (@venturalabs) 's Twitter Profile Photo

Ventura Labs Ep. 50 - Steffen Cruz and Felix Quinque Felix Quinque, AI Lead, and Steffen Cruz (crux), CTO & Co-Founder Macrocosmos (Macrocosmos) Timestamps: 1:24 โ€“ Introduction 2:12 โ€“ Subnet 9โ€™s Transition to IOTA 2:47 โ€“ The Problem: Large Language Model Training 4:51