Bitcoin News
By James Thorp
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Chamath Jumps In. Chamath Palihapitiya also backed the project during his All-In Podcast appearance.
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How The Tech Works. Bittensor's whole thing centers on distributed model training.
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What Comes Next. March 15 brought news of academic partnerships. Bittensor announced collaborations with several…
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Bittensor just scored big. The decentralized AI project caught attention from Nvidia's Jensen Huang, who basically said it could go mainstream pretty soon.
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Huang talked up Bittensor during a recent company announcement, calling out the project's weird but smart approach to training AI models without central servers.
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And it's not just Nvidia paying attention.
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Chamath Palihapitiya also backed the project during his All-In Podcast appearance. The venture capitalist spent time talking about Bittensor's Covenant-72B model, which is…
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The podcast episode aired March 10, and crypto Twitter went nuts afterward. Bittensor's token price jumped 15% in the hours following Palihapitiya's comments.
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Palihapitiya didn't just mention Bittensor in passing. He spent nearly ten minutes breaking down how their distributed training system works and why it matters for democratizing…
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Bittensor's whole thing centers on distributed model training. Instead of one company running massive server farms to train AI models, they spread the work across a network of…
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It's pretty clever when you think about it. Big tech companies spend millions on AI infrastructure, but Bittensor taps into unused computing power sitting around the world.
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The network has grown fast since launch. Currently, over 5,000 active nodes contribute to model training across Bittensor's subnets.
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But the real innovation isn't just the distributed training. Bittensor uses a consensus mechanism to validate which AI models perform best.
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Technical challenges remain though. Coordinating thousands of nodes to train complex AI models isn't easy.
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March 15 brought news of academic partnerships. Bittensor announced collaborations with several universities to expand research into decentralized AI applications.
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