𝘛𝘦𝘯𝘴𝘰𝘳𝘍𝘭𝘰𝘸 ττ
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𝘛𝘦𝘯𝘴𝘰𝘳𝘍𝘭𝘰𝘸 ττ
@Tensor_Flow_
Decentralized Intelligence | Bittensor ττ | Privacy • Freedom • Community . What they centralize, Bittensor distributes⚡⚡

Proud to share: we co-authored a new paper with @OpenAI on scientific computing in the age of agentic AI. HelixForge, our GPU-native engine for generating synthetic genomes, is featured as the study’s most ambitious and complex system. An entirely new architecture, built from the ground up. Minos is building the infrastructure for the age of AI in genomics.

Bittensor is itself a subnet. And what does a subnet do? It continuously adjusts its incentive mechanism until the interests of participants align with the outcome it is trying to produce. For Bittensor, that outcome is simple: allocate capital as efficiently as possible toward the subnets creating the most value. So when the protocol changes its rules, it doing exactly what every subnet is supposed to do: refining its incentive mechanism as it learns what works, what gets exploited, and what produces the best outcome. You can debate the direction, the speed, or the execution of each change. But (in my view) expecting the rules to remain fixed makes little sense. Asking Bittensor to stop tuning its rules is asking a subnet to stop being a subnet.





we got the full Kimi K3, 2.8T params, running on 80x RTX 5090s. 20 tok/s single stream, day one, untuned. Last week we took GLM-5.2 from 30 to 110 tok/s on this same fleet. This number will climb. A first for open weights: frontier intelligence served with zero HBM, the scarcest silicon in AI. Just GDDR7 gaming cards, plain ethernet, and the official MXFP4 weights, nothing requantized. The most powerful open model on Earth, on the most abundant GPUs on Earth. Any lab, startup, or university can now own it, probe it, fine-tune it, run agents on it. @Kimi_Moonshot



Releasing the model weights and technical report of Kimi K3. Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window. New model architecture: 2.5x the intelligence per unit of compute, not just more params. Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale. Model weights: huggingface.co/moonshotai/Kim… Tech report: github.com/MoonshotAI/Kim… Tech blog: kimi.com/blog/kimi-k3



Releasing the model weights and technical report of Kimi K3. Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window. New model architecture: 2.5x the intelligence per unit of compute, not just more params. Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale. Model weights: huggingface.co/moonshotai/Kim… Tech report: github.com/MoonshotAI/Kim… Tech blog: kimi.com/blog/kimi-k3
















