Focus on the intersection of AI and privacy, talk about fully homomorphic encryption @mindnetwork_xyz Homomorphic encryption allows for direct computation of encrypted data without decrypting it, and the computation result remains encrypted. It has solved a century old problem, which is the contradiction between privacy and practicality. A familiar topic, on chain trading ensures decentralization and transparency, but does not want to expose positions? This requires cryptographic 'magic': As in the example above, in the field of blockchain, its transparency often conflicts with user privacy needs and is a necessary requirement. This demand also exists in the field of AI, for example: hospitals encrypt patients' medical records and hand them over to AI analysis, but usually these patient information involve privacy and need to be kept confidential. It requires AI to obtain diagnostic results without decryption. Mind Network is currently the only project in the industry that focuses on this field. Its goal is to build a "fully encrypted Web3 infrastructure" that addresses industry pain points such as data privacy protection and trusted AI. It mainly consists of three product components: MindChain - FHE Chain Designed for AI Agents FHE Bridge - Cross Chain Protocol Based on FHE Mind Lake - An encrypted database designed for AI agents We will look at it from the perspectives of Crypto and AI respectively Crypto field: For some high-value or sensitive data, it is possible to perform calculations while ensuring privacy, without losing all the characteristics of the blockchain itself, and without worrying about these data being leaked to nodes or other participants on the chain. The easiest example that comes to mind here is in Perps dex, where users want to trade in a decentralized environment while also hiding their position amounts, leverage ratios, or clearing prices, because once this information is exposed, it may be exploited by other traders (such as sniper clearing). FHE can first encrypt these data on the chain, and then calculate the user's position status in an encrypted state, without the need for decryption to trigger smart contract operations. In the field of AI: Protecting input and output data in AI networks through FHE is necessary in both traditional AI and decentralized AI fields, such as the case of medical cases mentioned above. Mind Network supports AI agents to train and reason on encrypted data, thereby protecting private data, which can effectively solve this problem and help AI achieve breakthroughs in certain professional fields or solve problems in special fields. In February of this year, the official announced the integration of DeepSeek and FHE SDK, which allows AI to process data without decryption. The crypto industry is exploring the field of centralized AI, which is highly controversial. Whether we have reached a point where decentralized AI is needed remains to be discussed. Essentially, AI requires a large amount of decentralized data training, and data sharing can easily lead to privacy breaches. Many issues involving sensitive data or users' unwillingness to disclose data can limit the development of decentralized AI. FHE can also solve this problem. At present, FHE has not been widely popularized, mainly due to its high computational cost and high resource consumption. FHE computing is several orders of magnitude slower than ordinary plaintext computing, but this is a direction with very definite requirements, and I am also learning from it. Welcome to exchange and discuss.