🛰️Application of AIGC in the direction of Web3

Gamefi development engine: The most widely used development engines in traditional games are Unity and Unreal, but in Web3, the game development paradigm may bring about many changes due to AIGC, so a Gamefi development engine using AIGC technology will be required. The characters, scenes, and animations in it are all designed with AIGC. The main program and blockchain part of the game can also be completed through the AIGC code generation function. If these can be realized, designing Gamefi games or metaverse scenes will become very efficient. This set of development engines will be extremely valuable. At present, it is seen that RCT AI is a project that uses artificial intelligence to provide a complete solution for the game industry, but it is still unknown to what extent it uses AIGC technology, but there is already a Gamefi game Mirror World that is developed based on RCT AI Yes, interested friends can learn more.

Developing Gamefi games: the next best thing, if a AIGC fully integrated Gamefi development engine is too far away, then using AIGC tools provided by various manufacturers to develop Gamefi games will also greatly improve efficiency, such as using AIGC to generate game scripts , designing characters, and generating animations will all be realized very quickly, especially once the AI-generated video and 3D scene technology matures, Gamefi game development efficiency will increase by leaps and bounds.

Computing power and data sharing: Training AIGC models requires massive amounts of data and powerful computing power, which leads to huge costs. In order to train its Stable Diffusion model, Stability AI, the AIGC industry leader, runs a computer with more than 4,000 Nvidia A100 GPUs in AWS. Clusters, costing tens of millions of dollars to operate. If tokens can be issued in a decentralized way to encourage users to provide the data required for training models, the copyright issue in AIGC generation can be well resolved. In addition, by issuing Tokens, users can be encouraged to provide a large amount of computing power required for training models, and the cost of computing power can be dispersed to achieve cost sharing and benefit sharing.

Lightweight AIGC public chain platform: It has good scalability, making the public chain network more distributed and highly decentralized. In addition, a content production and privacy ecosystem is built around zero-knowledge proof and AIGC. Its ecological application Snapp can implement specific business logic for some scenarios, such as importing Internet data to the public chain without trust. It is also possible to cooperate with other public chains through the transfer bridge to enhance interoperability and achieve mutual benefit and win-win results.