Bittensor的10個子網儘管相對未知

根據他們的技術，採用和當前的價格行動，專家為每個子網提供了細分，在分散的AI生態系統的不斷發展的世界中展示了其獨特的用例。

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.

儘管在加密社區進行了討論，但Bittensor的10個子網還是相對未知。專家為每個子網提供了細分，在分散的AI生態系統的不斷發展的世界中展示了其獨特的用例。

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.

列表中的第一個是“ OG子網”聖殿騎士（SN3），它帶有訓練強大的模型。 Targon（SN4）專注於AI生成的內容檢測，這是數字空間中的新興領域。

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）是另外兩個值得注意的子集。人們提供比特幣（BTC）盤中預測模型，證明了AI的財務應用。雖然另一個提供了強大的多模式推斷，但這意味著它可以分析來自文本，圖像和音頻等不同源的數據。

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歲（SN19）是一個專注於實用和可擴展AI部署的子集，它在大規模上建立了有效，高性能的AI推斷。另一方面，Omega（SN21＆SN24）優先考慮大型語言模型或LLM，微調和部署，以利用生成文本AI的新興領域。

Exploring Bittensor: Visual AI and Rewards Focus

探索BITTENSOR：視覺AI和獎勵重點

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：列表中的接下來的Visual AI和Rewards Focus是BitMind（SN34），它專門研究DeepFake檢測和瀏覽器工具，以加強安全和麵向用戶的應用程序。 Dojo（SN52）是一個密鑰子集，強調輕巧，高速推斷，重點是效率和速度。

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.

側重於視覺AI和開源氛圍，梯度（SN56）負責開發與圖像相關的AI任務。第十個值得注意的子集是溜槽（SN64），它引起了“主導排放和獎勵”的關注。該子集可能負責Bittensor的本地令牌Tao的分佈，這表明它是參與者的特別有利可圖或活躍的子網。

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.

作者還透露了他們的投資，分配給這些子網10％。他們注意到表現出色，尤其是來自宙斯（SN18）和其他人的表現，其分配接近20％。此外，用戶承認，與“ OGS”相比，他們仍然是一個較小的投資者，但正在努力並享受Bittensor生態系統中的旅程。本質上，這些特定的Bittensor子網展示了分散的AI網絡中的不同應用和專業。