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， ...

作者 | 华卫 "选择合成数据赛道的底层逻辑其实很简单，AI 的快速爆发带来了数据需求，这个 Gap 要靠合成数据 去填。" 光轮智能联合创始人兼总裁杨海波表示，在大语言模型领域不存在外部合成数据的发展机会，因为其 自身就具备强大的数据生成能力，能够利用自身模型结合专家标注自我服务。然而，随着人工智能向 物理世界拓展，给外部公司带来了供应合成数据的商机。 光轮智能正在做的事就是，提供帮助 AI 进入物理世界的 3D 合成数据。具体来说，光轮智能为具身 智能行业提供拥有足够真实的物理交互能力的、人类示范在环的、场景足够丰富的仿真合成数据。 现在，光轮智能几乎服务了所有的国内外头部的具身智能企业和主机厂， 包括英伟达、Figure AI、 DeepMind、Wayve、智元机器人、银河通用、比亚迪、博世等数十家公司。 在这背后，是一支年轻化的技术团队，成员以 90 后、00 后为主力，不仅吸纳了来自英伟达的仿真 专家、阿里最年轻的算法人才，还招募了众多应届生。在创业初期短短几个月内，光轮智能的核心班 底便基本就位，其中不乏因看好行业需求主动加入的成员。 成立几个月就赚钱了 这家成立仅数月的企业，在合成数据尚未成为 ...

整理 | 褚杏娟 当地时间 6 月 26 日，在上个月的 Google I/O 上首次亮相预览后，谷歌如今正式发布了 Gemma 3n 完整版，可以直接在本地硬件上运行。 "迫不及待地想看看这些 Android 的性能！"正式发布后有开发者说道。 Gemma 系列是谷歌推出的一组开源大模型。与 Gemini 不同：Gemma 面向开发者，可供下载和修 改，而 Gemini 是谷歌的封闭专有模型，更注重性能与商业化。 据悉，此次正是发布的 Gemma 3n 现已具备输入图像、音频和视频的能力，支持文本输出，还能在 最低 2GB 内存的设备上运行，在编程与推理等任务上据称表现更佳。具体看，主要更新亮点包括： 至于基准测试，Gemma 3n 的 E4B 模型成为首个在参数规模低于 10 B 的前提下，LMArena 测评得 分突破 1300 的模型，表现优于 Llama 4 Maverick 17 B、GPT 4.1-nano、Phi-4。 效果好不好？ 天生多模态设计：原生支持图像、音频、视频和文本的输入，以及文本输出。 端侧优化设计：Gemma 3n 着眼于运行效率，提供两种基于"有效参数"的尺寸：E2B 和 ...

Core Insights - The article discusses the challenges and requirements faced by Infra engineers in the context of AI model training and deployment, emphasizing the importance of robust infrastructure to support large model systems [1][3][4]. Group 1: Event Overview - The AICon Global Artificial Intelligence Development and Application Conference will be held in Beijing on June 27-28, focusing on AI infrastructure and ecosystem building [2]. Group 2: Common Issues in Model Engineering - Infra engineers frequently encounter issues such as training interruptions and performance inconsistencies, particularly in large-scale GPU clusters [4][5]. - The need for effective performance profiling and monitoring systems is highlighted, as manual troubleshooting is inefficient [3][12]. Group 3: Performance and Stability Challenges - Common problems during online training include hardware errors, algorithmic flaws, and configuration issues, which can lead to task failures [4][6]. - The importance of collaboration between Infra engineers and business engineers is emphasized to address complex issues like abnormal loss spikes and runtime errors [5][7]. Group 4: Resource Management and Optimization - Efficient resource scheduling and job tuning are critical for optimizing AI model performance, with a focus on the compatibility of parallel strategies [8][9]. - The integration of new features often requires careful management to avoid conflicts with existing functionalities, necessitating iterative development processes [10][11]. Group 5: Cost Reduction Strategies - Strategies for reducing the cost of large model inference include optimizing caching strategies and improving GPU utilization [14][15][16]. - The design of model architectures should consider deployment performance from the outset to ensure cost efficiency [15]. Group 6: Open Source Challenges - The article discusses the challenges of managing open-source projects, including community engagement and user feedback [19][20]. - Building a sustainable open-source community requires balancing company commitments with community contributions [21][22]. Group 7: GPU Virtualization Trends - The discussion includes insights on GPU virtualization technologies, highlighting the importance of vendor support for effective implementation [22][23]. - The evolution of heterogeneous deployment strategies is noted, with a focus on optimizing resource allocation across different hardware types [24][25].

Core Insights - Google has officially launched Gemini CLI, an AI assistant for terminal environments, offering generous free usage quotas of 60 calls per minute and 1,000 calls per day [1][4][6] - The introduction of Gemini CLI marks a significant development in the competitive landscape of AI coding tools, with developers previously spending hundreds to thousands of dollars on similar tools [3][6] - Gemini CLI is open-source and has gained significant attention, achieving 15.1k stars on GitHub within a day of its release [8] Pricing and Accessibility - Users can access Gemini Code Assist for free by logging in with a personal Google account, unlocking the Gemini 2.5 Pro model and a million token context window [4] - The free usage model is seen as a strategic move to increase competition, particularly against Claude Code [6] Features and Capabilities - Gemini CLI supports various functionalities including code writing, debugging, project management, document querying, and code explanation, while also connecting to the MCP (Model Context Protocol) server for enhanced capabilities [10][15] - The tool is compatible with Mac, Linux, and Windows platforms, allowing for high efficiency and customization through a simple text file [10] Competitive Landscape - The launch of Gemini CLI has intensified competition in the AI coding tool market, with developers noting its superior performance compared to Claude Code in various coding tasks [18][20] - Feedback indicates that Gemini 2.5 Pro has significantly improved code generation and understanding capabilities, leading to faster bug fixes and higher completion rates in programming tasks [20][21] Development Philosophy - Google emphasizes a generalist model with Gemini 2.5 Pro, which is not specifically trained for coding tasks but rather designed to understand broader contexts and user needs [16][17] - The development team is focusing on integrating various capabilities rather than solely enhancing coding skills, aiming for a more holistic approach to software development [17][23] Future Outlook - The positive reception of Gemini CLI suggests a potential shift in the AI programming landscape, with indications that Google may be regaining ground in this competitive field [24]

整理｜冬梅 6 月 23 日，中国证监会网站显示，国产 GPU 龙头之一的沐曦集成电路（上海）股份有限公司（以 下简称"沐曦"）IPO 辅导状态已变更为"辅导工作完成"，其上市辅导机构为华泰联合证券。这也意味 着沐曦的科创板 IPO 之旅又迈出了重要一步，接下来会提交上市申报材料，准备在 A 股上市。 | | | 公开发行辅导公示 | | 辅导对象 | 辅导机构 | 备案时间 辅导状态 | 派出机构 | 报告类型 报告标题 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | 北京 | - | 天津 | 上海百英生物科技股份有限 公司 | 国泰海通证券股份有限公司 | 2025-02-10 辅导工作完成 | 上海证监局 | 辅导工作完成报告 关于上海百英生物 ... | | - | 河北 | - | mg | | | | | | | - | 内蒙古 | = | 辽宁 | > 上海奉天电子股份有限公司 | 东方证券股份有限公司 | 2022-09- 辅导工作完成 05 | 上海证监局 | 辅导工作完成报告 关于上海奉天电子 ... | | - ...

Core Insights - The article discusses the challenges and advancements in deploying large models on edge devices, emphasizing the need for optimization in architecture, systems, and algorithms to meet the high demands of mobile, automotive, and IoT applications [1][3][4] Group 1: Engineering Challenges - Edge devices face significant resource limitations in terms of computing power and bandwidth compared to cloud environments, necessitating low-bit quantization of models for deployment [3][4] - The rapid evolution of large models complicates commercial deployment, as updates and improvements can lag on edge devices due to user-driven update mechanisms [4][5] - The current state of large models is still in a "technology accumulation" phase, with future deployment contingent on advancements in edge computing capabilities and model stability [4][14] Group 2: Performance Optimization - The team developed a self-researched inference framework achieving over 180 tokens/s in real-time inference, utilizing strategies like dynamic input support and speculative decoding to enhance performance [1][6][7] - Techniques such as low-bit quantization and instruction-level optimizations are employed to maximize efficiency on resource-constrained devices [7][12] - The framework supports a shared base model architecture, allowing multiple business applications to utilize a single model while maintaining performance through LoRA modules [10][11] Group 3: Future Directions - Future breakthroughs in edge model deployment are expected to hinge on hardware advancements and the evolution of model architectures, such as Linear Attention, which could alleviate resource constraints [14][16][17] - The emergence of next-generation chips designed for large models is anticipated to significantly enhance the capabilities of edge devices [15][17] - The exploration of new model architectures that reduce memory usage while maintaining performance is crucial, especially for applications requiring long context inputs [16][17]

Core Insights - The article discusses the establishment of the Agent2Agent (A2A) project by the Linux Foundation in collaboration with major tech companies like AWS, Google, and Microsoft, aimed at creating an open standard for communication between AI agents [1][3][7] - A2A is positioned as a higher-level protocol compared to the Model Context Protocol (MCP), facilitating seamless interaction among multiple AI agents, while MCP focuses on integrating large models with external tools [6][7][11] - The article highlights the importance of these protocols in enhancing the reliability and functionality of AI systems, particularly in complex workflows involving multiple AI agents [14][15][18] Summary by Sections A2A Project Announcement - The A2A project was announced at the North America Open Source Summit on June 23, with initial contributions from Google, including the A2A protocol specification and related SDKs [1] - The A2A protocol aims to address the "island" problem of AI by enabling communication and collaboration between different AI systems [1] Comparison with MCP - MCP has rapidly expanded, growing from 500 servers in February to over 4000 servers currently, indicating its swift adoption [4] - A2A operates at a higher level than MCP, focusing on inter-agent communication, while MCP standardizes communication between large models and external tools [6][7] Developer Perspectives - Developers express uncertainty about how A2A and MCP will coexist, with some suggesting that A2A needs to demonstrate unique capabilities to stand out [11] - A2A's HTTP-based communication model may offer easier integration compared to MCP, which has been noted for its complexity [11][12] Protocol Necessity and ROI - The necessity of adopting these protocols is questioned, with some industry leaders suggesting that they should only be used when genuinely needed [13] - The article emphasizes the challenges in measuring ROI for AI applications, highlighting that only about 5% of generative AI projects have turned into profitable products [18] Security and Monitoring Concerns - There are concerns regarding the security and complexity of both protocols, particularly in terms of identity verification and authorization [17] - The monitoring and evaluation mechanisms for agent-driven systems are still in early stages, indicating a need for further development in this area [17]

Core Viewpoint - Baidu's Comate AI IDE represents a significant advancement in AI coding tools, enabling efficient, intelligent, and user-friendly coding experiences for developers and businesses, with over 43% of new code generated by this tool daily [1]. Group 1: Product Features - Comate AI IDE integrates four key aspects: intelligence, expansion, collaboration, and inspiration, providing comprehensive capabilities for AI-assisted coding, multi-agent collaboration, and enhanced multi-modal functionalities [2]. - The IDE features the programming agent Zulu, which can autonomously think and make decisions, allowing developers to complete complex tasks simply by voice commands [2]. - Multi-modal capabilities include converting design drafts to code (F2C), images to code, and natural language to code, achieving high fidelity in code generation and significantly reducing repetitive labor by 80% [3]. Group 2: Competitive Advantages - Comate AI IDE includes over ten built-in development tools and supports integration with external tools and data, making it adaptable to various development scenarios [3]. - Compared to competitors like Cursor, Comate AI IDE excels in real-time code preview, proactive requirement refinement, and intelligent page debugging, particularly enhancing natural language understanding for Chinese developers [3]. Group 3: Market Outlook - The AI coding market is expected to experience explosive growth by 2025, with self-developed independent IDEs seen as the next generation of advanced intelligent coding assistants [1].

最终目标不再是仅仅"完成编码"，而是利用 AI 构建 自适应、可观测、韧性更强 的系统。AI 帮助开发 者从繁琐的、重复性的工作中解放出来，将精力投入到更高阶的系统设计、创新性功能开发以及核心 业务逻辑的实现上。 还记得 GitHub Copilot 刚出现时，我们惊叹于它能补全一行代码。但今天，AI 在软件开发中的角色 正经历一场 质的飞跃 。前不久，GitHub CEO Thomas Dohmke 指出，真正的变革不在于"AI 取代写 代码"，而在于它正在 重构软件开发的起点、过程与目的本身 。 AI 不再是工具， 而是"共创者"与"驱动者" 起点重构：从需求到架构雏形 大模型能基于自然语言描述，生成初步的需求文档、API 设计草图甚至数据库 Schema。这大大加速 了项目启动和原型验证。想象一下，对 AI 说："我需要一个能处理高并发订单、支持优惠券和库存管 理的电商微服务 API"，它就能给出结构化的设计建议。这是一个多么美妙的体验！ 过程重构：从"氛围编程"到"智能体驱动交付" "Vibe Coding" (氛围编程)： AI 作为强大的"上下文感知"助手，深度融入开发环境（如 IDE）。 它能理 ...