这个才是最值钱的地方。淘一淘。WY (@akokoi1):国内还在卷OpenClaw的各种喂饭教程，国外已经有人通过出售SOUL、skills变现了。本质上是出售你训练好的OpenClaw，最终交付的内容包括SOUL、MEMORY、skills等文件。两个思路：1️⃣针对某个细分场景打磨一个OpenClaw，比如小红书运营助手，设计师、仓库管理员等等，甚至有人直接克隆了 Dan Koe https://t.co/OE7CW8tIUh ...

Core Viewpoint - The article discusses the rapid evolution of AI, particularly focusing on OpenClaw as a potential "killer application" that could redefine user interaction with AI technology, emphasizing its capabilities and the emerging ecosystem around it [6][10]. Group 1: OpenClaw's Rise - OpenClaw gained significant popularity, achieving over 250,000 stars on GitHub by March 3, making it the top software project on the platform [8][47]. - The emergence of various applications based on OpenClaw has created a large ecosystem referred to as the "lobster family," with major cloud providers and AI companies entering the market to meet rising demand [10][11]. Group 2: AI Evolution Stages - The development of AI assistants can be categorized into four stages, with OpenClaw representing the current phase where AI can perform tasks on behalf of users, resembling human-like capabilities [18][19]. - OpenClaw's functionalities include managing emails, calendars, and other tasks through chat applications, showcasing its potential as a personal assistant [15][21]. Group 3: Skills Community - The ClawHub community has emerged as a platform for sharing and developing skills, with over 15,189 skills available, allowing users to enhance OpenClaw's capabilities [31][43]. - Skills are modular and can be customized for specific tasks, making OpenClaw more effective in various professional contexts [39][40]. Group 4: Security Concerns - Despite its capabilities, OpenClaw faces significant security issues, including risks of data loss and unauthorized actions due to its high level of access [51][54]. - A security audit revealed that OpenClaw's overall safety rate was only 58.9%, indicating potential vulnerabilities in its operation [54][57].

Core Insights - The article discusses the rapid evolution of the MCP protocol from a local tool to an industry standard, highlighting its adoption by major companies like Microsoft, Google, and OpenAI as a de facto standard [2][4][6]. Group 1: MCP Development and Adoption - MCP transitioned from a local desktop tool to a remote server protocol with authentication mechanisms, evolving significantly over the past year [5][6]. - The pivotal moment for MCP's growth occurred around April when key industry leaders publicly endorsed its use, leading to widespread adoption across the sector [4][6]. - The protocol has undergone multiple updates, including the introduction of long-running tasks to support deep research and agent-to-agent interactions [5][10]. Group 2: Technical Challenges and Solutions - Scalability issues arise when multiple instances of MCP handle high request volumes, necessitating shared storage solutions like Redis to maintain state [3][17]. - The initial design allowed too many features to be optional, resulting in many clients not implementing critical capabilities, which diminished the protocol's effectiveness [16][17]. - The evolution of the authentication mechanism was crucial, as the initial version did not adequately address enterprise needs, leading to significant revisions [11][12]. Group 3: Future Directions and Ecosystem - The MCP protocol aims to maintain a balance between simplicity and the ability to support complex interactions, with ongoing discussions about integrating other protocols in the future [6][19]. - The establishment of an official registry for MCP servers is intended to create a centralized ecosystem, allowing for easier discovery and integration of various servers [44][45]. - The article emphasizes the importance of a standardized interface for the registry to facilitate seamless interactions between models and MCP servers [45][46]. Group 4: Use Cases and Applications - Most current use cases for MCP involve data consumption and context management, with a growing interest in using it for more complex workflows and deep research tasks [52][54]. - The introduction of tasks as a primitive aims to address the need for long-running operations, which are increasingly requested by users [54][57]. - The article notes that while many users are currently focused on context-related applications, there is potential for broader use of MCP in various operational scenarios [52][54].

RT Bloomberg New Economy (@BBGNewEconomy).@RishiSunak talks about the skills humans need to adopt in the age of AI, and how managing a “team of agents” will be a new skill set in the job market. #BloombergNewEconomy⏯️ https://t.co/J5lCJTamOX https://t.co/1yeVlWUDOS ...