Cara Membeli Mantis (SN123) di LBank

Mantis, often identified by the ticker or subnet tag sn123, is a decentralized financial forecasting project built within the Bittensor ecosystem. It operates as a specialized artificial intelligence network known as Subnet 123, designed to generate high quality market prediction signals. The primary objective of the project is to create an incentivized environment where participants collaborate to predict short term movements in various financial assets, including major cryptocurrencies like Bitcoin and traditional forex pairs. The technical architecture of the network relies on a competitive interaction between two main roles: miners and validators. Miners are responsible for submitting encoded data, known as embeddings, which represent their predictions for future price changes over specific periods, such as one hour. Unlike traditional prediction models that might operate in isolation, Mantis uses a cooperative approach where multiple inputs are combined to improve the overall accuracy of the network. Validators act as the objective referees of the system. They use information theoretic methods to measure exactly how much value a specific miner’s contribution adds to the collective prediction. Instead of simply looking at whether a price went up or down, the system evaluates the mathematical quality of the information provided. This rigorous scoring mechanism ensures that only participants who offer genuine predictive insight, or alpha, receive rewards. By focusing on time series data and machine learning, Mantis aims to function as a decentralized signal machine. The project is often described as the first of its kind to successfully implement a cooperative, incentivized prediction market for asset prices on a blockchain. Its long term goals involve scaling these predictive capabilities to cover a wider range of financial instruments and making the resulting signals verifiable and useful for decentralized finance applications. The project emphasizes scientific rigor and resistance to manipulation, ensuring that the network remains focused on extracting real predictive value from its distributed participants. Pelajari Lebih Lanjut