In the past two months, the overall quality of Coin Safety has also dropped a lot due to the lowering of the threshold, but it also killed the valuation of many projects, and squeezed out a first level foam to yield profits to the second level, so the probability of gold mining in it is relatively high. Among them, the FHE of Mind Network is a hard core thing with good quality. Since it became the only FHE framework officially integrated by DeepSeek the day before yesterday, the currency price has also risen all the way. Mind Network is also the fastest to complete the fast route from wallet to alpha to contract within 48 hours, and there is a shortage of cash at present, and Mind Network is Coin Safety itself So the next round of voting will probably have it. @Mindnetwork_xyz is a fully homomorphic encryption technology that belongs to the field of privacy computing. Essentially, it is an AI race track, and its core implementation allows AI to complete the processing process without directly accessing user or other agent/enterprise data. Unlike other PPT projects, it has already launched the FHE SDK based on Rust, which is also the first FHE project to be launched on the main network. It is currently the only FHE integrated by DeepSeek official website. The link is as follows https://((github.com))/deepseek-ai/awesome-deepseek-integration There are three main schools of thought in the field of privacy computing, but their implementation ideas and purposes are completely different 1. The familiar implementation of ZK proves the correctness of the results without exposing the original data. MPC, on the other hand, can complete multi-party calculations to obtain answers without providing all the raw data to others. For example, Nillion uses MPC technology, which can be reviewed in https:// (x.com)/jason_chen998/status/1851851883918295337 3. The third one is FHE, which is relatively the most technically difficult and is hailed by Portal Ventures as the holy grail of encryption. Its implementation effect is to perform calculations on already encrypted data without the need for decryption. The application scenarios of FHE, whether it is Web2 or Web3, are very extensive. For example, many hospitals, schools and other organizations now have the need to cooperate with AI companies to train models. However, the data of these organizations must be encrypted according to regulations before they can be released. However, encrypted data itself does not have readability, and when given to AI companies, they do not know what this means and cannot be trained. FHE uses a specific, irreversible algorithm to encrypt the original data, which can then be used for addition and multiplication based on the ciphertext to construct any Boolean circuit for complex operations. The results obtained are consistent with those obtained by performing the same operation directly on plaintext. Mind Network has also introduced the AgenticWorld narrative concept, a world dominated by autonomous AI agents on the chain, where agents independently perform various tasks, collaborate, and make decisions. However, the problem faced in this narrative is that the chain is completely transparent but involves a large amount of assets and data that require agents to make decisions and process. If agents can collaborate and communicate with each other, how can we ensure the interests of each agent and avoid becoming a dark forest of savage plunder. So FHE is needed to protect the core privacy data of AI agents without interfering with the computation and collaboration processes based on this data. For specific implementation, please refer to this document at https://// ((github.com))/mind-network/build-agentic-world-with-mind/blob/main/README-ZH.md