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Apex (Bittensor’s Subnet 1) Shows a New Path for Decentralized AI Evaluation

One of the hardest problems in decentralized AI is not generation, it is evaluation. As AI systems move into open-ended domains like reasoning, creativity, and agentic behavior, judging quality becomes subjective, expensive, and difficult to verify on-chain. Traditional approaches rely on handcrafted metrics, spot checks, or delayed outcomes. All of them struggle at scale. New…

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Bittensor Brief #17: Sundae Bar – Subnet 121

SUMMARY: The video introduces Sundae Bar (Subnet 121), a Bittensor project building a single generalized, autonomous enterprise AI agent that functions as a true digital worker rather than a chatbot—capable of executing end-to-end business workflows with persistent memory, role awareness, and continuous improvement. After pivoting from multiple vertical agents, Sundae Bar now uses a winner-take-all…

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Mark and Siam Dissect the Bittensor Ecosystem

SUMMARY: Mark Creaser and Siam Kidd explained Bittensor as a decentralized protocol that functions like the “S&P 500 of AI startups,” where 128 independent AI projects (called subnets) compete to solve real-world problems using a proof-of-useful-work incentive mechanism.  They noted that unlike Ethereum, Bittensor has a mandatory value-capture system requiring all subnet ‘$ALPHA’ token purchases…

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Bittensor Partners with HackQuest to Drive Global Subnet Innovation and Onboarding

Bittensor (via Opentensor Foundation – OTF) has partnered with HackQuest, a Web3 education platform, to launch “Build on Bittensor,” a global initiative aimed at onboarding contributors (such as developers and miners) into the Bittensor ecosystem through hands-on learning and subnet experimentation. The program combines Bittensor’s decentralized subnet infrastructure with HackQuest’s education and community network, marking…

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Bittensor Ecosystem Highlights – Jan Week 2

SUBNET UPDATES & ACHIEVEMENTS ➤ @chutes_ai (SN64)Chutes unveiled Fictio, a new character roleplay platform expanding interactive AI use cases within the subnet.Read more: Fictio launch ➤ @tplr_ai (SN3)Templar released a new research paper titled Heterogeneous Low-Bandwidth Pre-Training of LLMs, detailing novel approaches to efficient large model training.Read the paper: Templar research ➤ @babelbit (SN59)Babelbit introduced…

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