Core Viewpoint - The release of Kimi K2 marks a significant turning point for the company, indicating a shift from a reliance on scaling laws to a more innovative approach in AI model development and strategy [2][4][22]. Group 1: Kimi K2 Release and Its Impact - Kimi K2 achieved a global fifth ranking in the LMArena leaderboard and first among open-source models, surpassing competitors like Claude 4 and DeepSeek-R1-0528 [2]. - The release is seen as more than just a temporary success; it represents a deeper strategic shift for the company and the industry [4][22]. - Kimi K2 introduces two major advancements: an expansion of model parameters to over 1 trillion and the concept of "model as agent," allowing for tool utilization [23][35]. Group 2: Challenges Faced by Kimi - Kimi's previous strategy relied heavily on scaling laws, believing that larger models and more data would lead to better performance, but this approach faced challenges as high-quality data became scarce [8][13][14]. - The company's user growth strategy was questioned after competitors like DeepSeek demonstrated significant user acquisition without marketing spend, highlighting the need for a more effective product [18][54]. - Kimi's marketing budget reached approximately 900 million RMB in 2024, yet user engagement declined, indicating a disconnect between spending and user retention [17]. Group 3: Strategic Transformation - The company has shifted its focus from aggressive marketing to enhancing model performance and embracing open-source collaboration, reflecting a significant cultural change [55]. - Kimi's team has decided to halt all marketing activities and concentrate resources on foundational algorithms and the K2 model, emphasizing the importance of product quality over quantity [55]. - The strategic pivot is seen as a response to the success of DeepSeek, which has prompted Kimi to adopt more effective architectural choices and prioritize technical research [55][56].

AI Engineering Resources - A free illustrated guidebook on MCP fundamentals is available [1] - The guidebook contains over 75 pages [1] - The guidebook includes 11 hands-on projects for AI engineers with code [1] MCP Fundamentals - The guidebook visually explains MCP fundamentals [1] - The approach is 100% hands-on [1]

Open Source LLM Framework - A framework connects any LLM to any MCP server (open-source) [1] - The framework enables building custom MCP Agents without closed-source apps [1] - Compatible with Ollama, LangChain, etc [1] - Allows building 100% local MCP clients [1]

Agentic Economy & MCP Standard - The agentic economy is emerging, where AI agents can interact, find counterparts, and purchase services from other agents, businesses, or tools [4] - MCP (Message Communication Protocol) is becoming a standard for agentic interaction, dominating the space compared to Open API and Google's A2A [8][9] - Tool discovery, a key feature of MCP, allows agents to dynamically discover and use tools based on the workflow, differentiating it from Open API [7][8] - A centralized marketplace of MCP services, like APIFY, can provide access to various services with a single API token, enabling rapid scaling of the ecosystem [12] APIFY's Role & Marketplace - APIFY is a marketplace of 5,000 tools (actors), primarily data extraction tools, with a community of creators who monetize their tools [4] - Actors are self-contained software units with defined input and output, facilitating easy integration with other systems [4][5] - APIFY has integrations with workflow automation tools and MCP, enabling AI agents to call actors from the marketplace [6][7] - APIFY enables publishing and monetization of tools or agents, providing access to a broad ecosystem of developers and visibility [23][24] Challenges & Future - Agents currently rely on human developers for access to tools and services, hindering their ability to autonomously find and purchase services [10][11] - Trust between agents and tools is a key open question, as is the overall value and reliability of autonomous tool discovery [25][26][27] - The company paid out over $4 million to creators last month, with actors generating over $500,000 per month, indicating rapid ecosystem growth [23]

MCP Overview & Architecture - MCP (Micro Control Plane) is defined as a pluggable architecture for agents, contextualized within an enterprise cloud service [5][6] - Sentry's MCP server was initially built as a fun project and is biased towards Sentry's application monitoring services [4][5] - The industry views MCP as a potential solution for integrating services into various agents, enabling bug fixes and workflow enhancements within editors [7][8][25] Implementation & Challenges - Implementing MCP involves complexities around OAUTH 21%, requiring solutions like Cloudflare Shim for proxying OAUTH 2 API [16][17] - A key challenge is that MCP cannot simply sit on top of Open API; systems need to be designed around how agents and models react to provided context [19][20][21] - Current client support for native authentication is still evolving, with some clients like Cursor experiencing breakage [22] Security & Best Practices - Security is a major concern, particularly with the standard IO interface, and random MCP tools should not be allowed within organizations [27] - For B2B SaaS companies, focusing on OAUTH with remote environments is crucial for integrating services into agents [25] - Companies should avoid simply proxying Open API and exposing it as tools, as this yields poor results; intentional design and context provision are necessary [30] Agent-Centric Approach - The industry should focus on building agents, viewing MCP as a plug-in architecture to leverage the value of LLMs [39][40] - Exposing agents through the MCP architecture, particularly in B2B settings, is seen as a significant value unlock [42] - Optimizing for context in workflows and understanding data is crucial when designing agents, with a focus on providing structured information like Markdown for language models [31][50]

Core Viewpoint - The article discusses the significant changes in Baidu's search engine, marking it as the largest revision in a decade, emphasizing the integration of AI technologies into search functionalities and the potential implications for investment and AI entrepreneurship [2][3]. Group 1: Changes in Search Engine - Baidu has established itself as synonymous with "search" in the Chinese market, achieving daily search volumes in the billions [2]. - The traditional search engine business model heavily relied on online marketing services, particularly advertising revenue, which accounted for over half of its income [3][4]. - The advent of AI transforms search capabilities, allowing for more natural language processing and user intent understanding, moving beyond simple keyword matching [5][8]. Group 2: New Features and Capabilities - The updated search interface allows for longer, more conversational queries, accommodating a wider range of user inputs [10]. - Multi-modal input methods have been introduced, including voice search and image recognition, enhancing user interaction [15][19]. - Baidu's AI search now generates results that include text, images, and videos, with a focus on providing credible sources for the information presented [23][26]. Group 3: Ecosystem and Collaboration - The introduction of the MCP (Model Capability Protocol) facilitates collaboration between various AI applications, positioning Baidu as a leader in integrating AI capabilities across platforms [26]. - Baidu's search platform has integrated with 18,000 MCPs and over 220 AI applications, creating a diverse and open ecosystem [26]. Group 4: Video Generation and AI Creativity - Baidu has launched the MuseSteamer video generation model, which has gained recognition for its performance in generating high-quality videos from images [31]. - The model supports the creation of videos with sound, enhancing the creative possibilities for content creators [31]. Group 5: Future Implications - The changes in Baidu's search engine represent a significant shift towards a more integrated AI ecosystem, with the potential to redefine user experience and engagement [33]. - The competition among major tech companies to dominate the AI landscape is intensifying, presenting both opportunities and challenges for investors and entrepreneurs [32][33].

RT Avi Chawla (@_avichawla)After MCP, A2A, & AG-UI, there's another Agent protocol (open-source).ACP (Agent Communication Protocol) is a standardized, RESTful interface for Agents to discover and coordinate with other Agents, regardless of their framework (CrewAI, LangChain, etc.).Here's how it works:- Build your Agents and host them on ACP servers.- The ACP server will receive requests from the ACP Client and forward them to the Agent.- ACP Client itself can be an Agent to intelligently route requests to t ...

Agent & MCP Server Development - Cloudflare and Work OS are collaborating to promote the idea that agents acting on behalf of users need the same credentials and authorization as user-facing projects [1] - The industry is moving towards more fine-grained authorization for AI agents, potentially authorizing per-line changes, per-tool changes, or even network connections [20] - Cloudflare offers a free tier for Durable Objects, which can be used for persistent storage in agents [3] Cloudflare's Offerings - Cloudflare provides compute cloud workers, AI model hosting, vectorized inference, vector database, SQL database, durable objects, video streaming, and image optimization [2] - Cloudflare workers have bindings that allow interaction with other Cloudflare products and other companies' products [3] - Cloudflare's agents framework includes an OAuth framework for setting up authorization, enabling easy identification of the worker or agent acting on behalf of a user [5] MCP Server Demo & Use Case - A basic MCP server was built using Cloudflare and Work OS, which is available for users to check out and run [6] - The demo showcases ordering a shirt via an agent, demonstrating how agents can act on behalf of users with proper authorization [9][10][11] - The demo uses Cloudflare's key-value storage to save order data, accessible through the interface [12] - Durable Objects can store data directly on the context associated with a worker object, unique for each user [14][16] Security & Authorization - The industry emphasizes the importance of audit trails with OAuth tools to track agent interactions, including reasons for interaction, the user on whose behalf it acted, and the outcome [21] - The industry needs to consider users as deputies who have access to tools and can potentially misuse them [21]

Agentic Applications - Agentic applications require both Agent-to-Agent communication (A2A) and Machine Control Protocol (MCP) [1] Agent Collaboration - MCP equips agents with tool access [2] - A2A enables agents to connect and collaborate in teams [2]

随着大模型能力的突破，"可调用工具的智能体"已经迅速从实验室概念走向应用落地，成为继大模型之后的又一爆发点。与此同时，围绕 Agent 构建的 开发框架和基础设施在迅速演进，从最早的 LangChain、AutoGPT，到后面崛起的 OpenAgents、CrewAI、MetaGPT、Autogen 等，新一代 Agent 框 架不仅追求更强的自主性和协同性，也在探索深度融合进业务的可能。 框架之争的背后，实则是新一轮开发范式和商业模型的重构起点。清华 MEM 工程管理硕士、SeamLessAI 创始人王政联合清华大模型团队 LeapLab 发 布了一款面向 Agent 协作的开源框架 Cooragent，参与到了 Agent 框架生态中。Cooragent 的最重要的特点之一就是用户只需一句话描述需求，即可生 成专属智能体，且智能体间可自动协作完成复杂任务。王政团队分别发布了开源版本和企业版本，进行社区和商业化建设。其中，开源版本已获得 1.9k stars。 本次访谈中，王政向 InfoQ 分享了其对 Agent 发展的洞察，以及 Cooragent 的设计思路背后对行业现状和未来发展的思考。 王政指出， ...