As intelligent agents begin to "autonomously socialize," does the rise and controversy surrounding Moltbook herald a new stage in the AI × Web3 collaboration? With the rapid evolution of large-scale model applications, the collaborative relationship between humans and AI is undergoing subtle changes. Past application development paradigms were human-centric, with humans playing a decisive role in the application's operational chain. However, the increasing intelligence of models has shaken the foundations of this assumption. Developers and users are beginning to wonder what a product entirely used and dominated by machine intelligence would look like. Moltbook is one such attempt, positioned as a Reddit (similar to Baidu Tieba) specifically for AI agents. The product quickly gained market attention upon its launch, and to date, over 1.6 million autonomous intelligent agents created by humans have flooded the platform. Moltbook's explosive popularity not only demonstrates the advancement of AI technology but also creates opportunities to build large-scale autonomous economic networks of intelligent agents. This is not only an opportunity for AI but also an opportunity for Web3's strength in decentralized economic models. In fact, the rise of Moltbook is not an isolated event; it is rooted in the recently highly regarded open-source agent framework OpenClaw (formerly known as Clawdbot). Unlike traditional AI applications where model capabilities are confined to dialog boxes, OpenClaw breaks the sandbox, granting large models extremely high system privileges. This allows them to freely access user-local data and commonly used applications, and perform human-like actions—essentially giving the large model's "brain" "hands" and "feet." It allows large models to break down complex tasks into workflows through thought, freely combining different tools to complete tasks. It also creates persistent identities and memories for intelligent agents based on the user's hardware. Each intelligent agent's goals, abilities, personality, and values are saved in files and loaded and read before each wake-up call. This approach allows intelligent agents to maintain consistent behavioral patterns over long-term use. Furthermore, OpenClaw allows intelligent agents to proactively intervene periodically without human commands, checking the status of tasks in the current task list and deciding whether to execute them. To empower intelligent agents to perform specific tasks, OpenClaw introduces "Skill" plugins that explain task requirements, execution methods, and necessary tools to the intelligent agents. This plug-and-play open system enables OpenClaw to stably and efficiently complete complex tasks, which is why it has gained significant attention. ✜ The preview section has ended; the remaining hidden core content is here 👇 research.web3caff.com/archives...…