Recently, a lot of people around me have been bragging about AI projects, Each one is more exquisite than a PPT, but when it comes to running, it either relies on centralized computing power or closed source algorithms. After seeing so many siege lions, only @ Talus_Labs impressed me. It is not about creating "AI tools", but building a system that allows AI to play, verify, and make money on the chain. Each agent can make their own decisions, engage in battles, and even be audited. Others are still talking about AI being able to 'help you', but Talus is already teaching AI to 'live on its own'. Recently, I came across a detail about @ Talus_Labs that struck my mind: the Walrus team announced in a post that they will be responsible for Talus' "historical memory/state data/context storage", while the Sui layer will be responsible for agent execution and coordination. This is not a simple division of roles, but an architectural philosophy: splitting the data layer and execution layer. The siege lion is understood as follows: If you put everything on the chain: inference, storage, state management... then the cost and latency are almost unbearable. Talus' design is to give agents long-term memory, but the memory is not on the main chain, and the main chain is only responsible for coordination and verification. This is the only way to support a real scale AI agent ecosystem. The key next is not 'who builds the model first', but 'who can make the workflow, tool calls, and state evolution of the agent into composable modules'. Talus' Nexus architecture provides direction: tool plugin, process disassembly, and agent ability to call external interfaces. This means that agents are no longer isolated black boxes, but have "ecological interfaces". So now I want to say: to write Talus, you don't need to start from common points such as financing/Testnet, but use this "data layer decoupling+agent modularization" to tell the story. That is a perspective that others may not pay much attention to, but it is informative enough.