Bittensor的10个子网尽管相对未知

根据他们的技术，采用和当前的价格行动，专家为每个子网提供了细分，在分散的AI生态系统的不断发展的世界中展示了其独特的用例。

Bittensor's 10 subnets are relatively unknown despite being discussed in the crypto community. An expert offers a breakdown for each subnet, showcasing their unique use cases in the evolving world of the decentralized AI ecosystem.

尽管在加密社区进行了讨论，但Bittensor的10个子网还是相对未知。专家为每个子网提供了细分，在分散的AI生态系统的不断发展的世界中展示了其独特的用例。

The first in the list is the "OG subnet," Templar (SN3), which comes with training powerful models. Targon (SN4) focuses on AI-generated content detection, an emerging field in the digital space.

列表中的第一个是“ OG子网”圣殿骑士（SN3），它带有训练强大的模型。 Targon（SN4）专注于AI生成的内容检测，这是数字空间中的新兴领域。

PTN (SN8) and Zeus (SN18) are another two notable subsets. One provides Bitcoin (BTC) intraday prediction models, demonstrating financial applications of AI. While the other offers powerful multi-modal inference, meaning it can analyze data from diverse sources like text, images, and audio.

PTN（SN8）和宙斯（SN18）是另外两个值得注意的子集。人们提供比特币（BTC）盘中预测模型，证明了AI的财务应用。虽然另一个提供了强大的多模式推断，但这意味着它可以分析来自文本，图像和音频等不同源的数据。

Nineteen (SN19), a subset focusing on practical and scalable AI deployments, has built efficient, high-performance AI inference at scale. On the other hand, Omega (SN21 & SN24) prioritizes large language model, or LLM, fine-tuning and deployment that harnesses the emerging field of generative text AI.

19岁（SN19）是一个专注于实用和可扩展AI部署的子集，它在大规模上建立了有效，高性能的AI推断。另一方面，Omega（SN21＆SN24）优先考虑大型语言模型或LLM，微调和部署，以利用生成文本AI的新兴领域。

Exploring Bittensor: Visual AI and Rewards Focus

探索BITTENSOR：视觉AI和奖励重点

Next in the list is Bitmind (SN34), which specializes in deepfake detection and browser tools, to ramp up on security and user-facing applications. Dojo (SN52), a key subset that emphasizes lightweight, high-speed inference, focusing on efficiency and speed.

探索Bittensor：列表中的接下来的Visual AI和Rewards Focus是BitMind（SN34），它专门研究DeepFake检测和浏览器工具，以加强安全和面向用户的应用程序。 Dojo（SN52）是一个密钥子集，强调轻巧，高速推断，重点是效率和速度。

With a focus on visual AI and an open-source vibe, Gradients (SN56) is responsible for developing image-related AI tasks. The tenth notable subset is Chutes (SN64), which has gained attention for "dominating emissions and rewards." This subset might be responsible for the distribution of TAO, Bittensor's native token, suggesting it's a particularly profitable or active subnet for participants.

侧重于视觉AI和开源氛围，梯度（SN56）负责开发与图像相关的AI任务。第十个值得注意的子集是溜槽（SN64），它引起了“主导排放和奖励”的关注。该子集可能负责Bittensor的本地令牌Tao的分布，这表明它是参与者的特别有利可图或活跃的子网。

The author also revealed their investment, with 10% allocated to these subnets. They noted a strong performance, particularly from Zeus (SN18) and others, bringing their allocation close to 20%. Additionally, the user acknowledges they are still a smaller investor compared to the "OGs" but are bracing themselves and enjoying their journey within the Bittensor ecosystem. In essence, these specific Bittensor subnets showcase the diverse applications and specializations within the decentralized AI network.

作者还透露了他们的投资，分配给这些子网10％。他们注意到表现出色，尤其是来自宙斯（SN18）和其他人的表现，其分配接近20％。此外，用户承认，与“ OGS”相比，他们仍然是一个较小的投资者，但正在努力并享受Bittensor生态系统中的旅程。本质上，这些特定的Bittensor子网展示了分散的AI网络中的不同应用和专业。