How can you easily grasp the current market trends, technological developments, ecosystem progress, and governance dynamics in the Web3 industry? Web3Caff Research's "Market Pulse Analysis" column delves into the front lines to identify and select current hot topics, providing valuable interpretations, commentary, and fundamental analysis. See through the phenomena to the essence, and follow us to quickly capture the forefront of the Web3 market.
Author: Hendrix , Researcher at Web3Caff Research
Cover: Typography by Web3Caff Research
Word count: 3600+ words in total
With the rapid evolution of large-scale model applications, the collaborative relationship between humans and AI has begun to subtly change. Past application development paradigms were human-centric, with humans playing a decisive role in the operational chain of application use. However, the increasing intelligence of models has shaken the foundations of this assumption, and 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 success not only demonstrates the progress 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 the decentralized economic models that Web3 excels at to flourish.
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 limitation, granting large models extremely high system privileges. This allows them to freely access user's local data and commonly used applications, and to perform human-like actions, essentially giving the large model "hands" and "feet" on top of its "brain." It allows large models to break down complex tasks into workflows through thought and freely combine 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 enable intelligent agents to perform special tasks, OpenClaw also introduced "Skill (model context file)" plugins to explain to intelligent agents the requirements of the task, the execution method, and the tools to be used. This plug-and-play open system enables OpenClaw to complete complex tasks stably and with high quality, which is why OpenClaw has gained a lot of attention.
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