目前Lexi支持Facebook、Instagram广告投放，不限国家、语言和行业。已服务超过 10 万家中小企业，覆盖北美市场与东南亚、非洲等多个新兴市场超90 多个国家，日注册用户稳定维持在四位数以上，服务对象涵盖初创企业主、线上商家、自雇创作者及本地服务商，体现出其强大的通用性与市场刚需。 Lexi采用SaaS订阅制，用户首周可低门槛试用，后续以每周计费的方式持续订阅，换算月均费用约为 199 美元。 锦秋基金表示："锦秋基金作为12 年期的 AI Fund，始终以长期主义为核心投资理念，积极寻找那些具有突破性技术和创新商业模式的通用人工智能初创 企业。" 投资界7月17日消息，AI创新科技公司「Sandwich Lab」宣布完成数百万美元首轮融资，由锦秋基金与汇量科技联合领投，澜松资本担任长期独家财务顾 问。本轮融资主要用于核心AI能力研发、全球市场拓展与国际化人才团队建设。 Sandwich Lab 的创始团队由来自阿里巴巴达摩院、苹果、微软、百度、Giorgio Armani 等全球顶尖公司成员组成，覆盖 AI 算法、产品设计、品牌创意与 海外市场多个维度。其创始人郭振宇博士本科毕业于浙江大学，博 ...

BigBear.ai (BBAI 16.01%) saw its stock close at $8.22 on Thursday, July 17, marking a significant 15.5% increase. The intraday trading showed notable volatility, ranging between a low of $7.25 and a high of $8.38.In the context of broader market movements, BigBear.ai's performance outstripped that of key indices. The S&P 500 saw a 0.54% increase, while the Nasdaq Composite rose by 0.74%, indicating that the stock's robust rise was primarily driven by company-specific excitement rather than macroeconomic fac ...

Core Insights - The article discusses the competitive landscape of AI large models, highlighting the recent release of xAI's Grok-4 and Kimi's K2 model, which have sparked a new wave of advancements in the field [1][4]. Model Performance Summary - Grok-4 achieved a significant score increase from 42.6 to 65.0 in the ScienceQA evaluation, marking a 50% improvement and surpassing OpenAI's o3 model to become the state-of-the-art (SOTA) model [4][8]. - Kimi K2, a non-thinking model, scored 49.6, placing it in the top ten, with a BoN (N=5) score of 73.0, indicating strong performance in multi-step reasoning tasks [11][24]. - OpenAI's o3-pro model scored 59.6, showing improvement over its predecessor, but with increased response time and API costs [11][25]. Cost and Efficiency Analysis - Grok-4 is noted for its competitive pricing at $15 per million tokens, significantly lower than o3-pro's $80, while maintaining high performance [15][21]. - Doubao-Seed-1.6 demonstrated a cost-effective model with a score of 56.6 and an output price of $1.1, making it one of the best value models [15][18]. - The analysis indicates a trend where longer reasoning times correlate with higher scores, with Grok-4 having the longest average response time of 227 seconds [17]. Model Innovations - Grok-4 incorporates advanced features such as real-time web retrieval and multi-agent collaboration for enhanced reasoning capabilities [23]. - Kimi K2 is recognized for its innovative training techniques, including the MuonClip optimizer and a comprehensive agent simulation pipeline, which contribute to its large parameter count and performance [24]. - OpenAI's o3-pro model has been optimized for scientific and programming tasks, showcasing improved reliability and reasoning capabilities [25]. Leaderboard Updates - The leaderboard reflects updates from 16 companies with 43 different model versions, maintaining a consistent ranking for major players like OpenAI, Google, and ByteDance [5][8]. - The leaderboard will continue to evolve with monthly updates, providing ongoing insights into model performance and capabilities [1][5].

机器之心报道 机器之心编辑部 与以往的基础大模型升级不同，通用 Agent 可以自动利用多种工具进行规划，帮助人们完成复杂的任务，包括自动浏览用户日历，生成可编辑的 PPT，运 行代码等等。Agent 能够连接你的 Gmail、GitHub 网站获取信息并解决问题，使用 API 来访问各种应用。Agent 加持的 AI 智能有了大幅提升 —— 基于 ChatGPT Agent 的模型在 HLE 基准上拿到了 41.6% 的分数，是 o3 和 o4-mini 的几乎两倍。 ChatGPT Agent 目前已向 OpenAI Pro、Plus 和 Team 计划的订阅用户开放。 想要使用的用户在 ChatGPT 的工具下拉菜单中选择「Agent 模式」即 可。 OpenAI 表示，企业版和教育版用户预计将于夏季晚些时候获得新功能。在正式发布时，Pro 用户每月通常最多可使用 400 次 Agent 提示，其他付费用户 则最多可使用 40 次。目前尚不清楚该功能何时会面向 ChatGPT 免费用户推出。 这是 OpenAI 迄今为止最为大胆的一次新产品发布，从此以后 ChatGPT 成为了一款能够为人们采取行动和分 ...

Core Viewpoint - Mistral AI aims to position itself as a European counterpart to OpenAI, focusing on developing advanced AI models and applications to compete in the AI landscape [1][3]. Group 1: Product Developments - Mistral AI has released several open-source models, including a highly regarded OCR model, a multimodal model comparable to Claude, and the first reasoning large model named Magistral [2][4]. - The company recently upgraded its Le Chat application, enhancing its capabilities to compete directly with ChatGPT [4][23]. - New features of Le Chat include a research mode that can generate structured reports on complex topics, a voice mode powered by the Voxtral model for natural speech interaction, and advanced image editing capabilities [6][9][13][16]. Group 2: Voice Recognition Model - Mistral AI launched the Voxtral model, touted as the "best open-source" speech recognition model, which surpasses existing models like Whisper large-v3 and GPT-4o mini Transcribe [27][29]. - Voxtral supports long context understanding with a maximum of 32k tokens and can transcribe audio up to 30 minutes long, showcasing its advanced capabilities [30]. - The model features built-in question-answering and summarization functions, automatic language recognition, and the ability to trigger backend functions directly from voice commands [30]. Group 3: Market Position and Community Response - Mistral AI's recent advancements indicate a strong momentum in the European large model sector, generating excitement among users and industry observers [24]. - Users have reported positive experiences with Le Chat's image editing capabilities, claiming it performs better than OpenAI's offerings [17][18].

白交 雷刚 发自 纽凹非寺 量子位 | 公众号 QbitAI 实用，太实用了！这才是OpenAI Agent该有的样子。 就在刚刚，OpenAI最新发布来了， ChatGPT Agent 正式对外亮相。 这是一个把 "想" 和 "干" 统一了的智能体，之前 深度研究 的思考和分析能力， Operator 的操作执行能力，在ChatGPT Agent实现了统 一。 而且ChatGPT Agent还可以接管你的整个电脑——这几乎就是全新的 操作系统 了。 能做什么？ 工作场景 里，安排和改期会议、生成PPT、制定出差和外出议程、自动提交报销……几乎就是大厂高管才能配置的 助理 的核心工作。 生活场景 下，你个人的旅游行程规划设计、重大活动如婚礼晚宴安排……一些定期需要手动更新的认证证明……差不多也是董事长CEO们 个 人秘书 实现的能力。 但现在，ChatGPT Agent一夜之间人人都可拥有。OpenAI还专门配备了 专用模型 ，创造了全新的SOTA，刷新了模型能力新纪录。 之前，通用Agent们只敢自称"实习生"，但OpenAI在自研底层模型能力的底气下，几乎就把"实习生"变成了"大秘书"。 之前一个创业赛道 ...

Core Viewpoint - The article discusses the launch of OpenAI's new Agent mode, which signifies a shift from AI merely responding to queries to actively performing tasks, marking the beginning of an era where AI can "do" rather than just "talk" [3][5]. Summary by Sections 1. Introduction to Agent Mode - OpenAI introduced the Agent mode, allowing users to directly request tasks from ChatGPT, such as purchasing items or generating presentations, with the AI autonomously executing these tasks in a virtual environment [4][5]. 2. Capabilities of Agent Mode - The Agent mode can utilize three tools: text browser, visual browser, and terminal, enabling it to perform complex tasks efficiently [8][10]. - In demonstrations, the AI successfully completed tasks like planning a wedding and ordering custom stickers, showcasing its ability to interact with various online services and generate detailed reports [9][10]. 3. Integration of Tools - The Agent mode is a combination of two previously launched tools, Operator and Deep Research, which were merged to enhance functionality and efficiency in task execution [11][12]. - This integration allows the AI to perform tasks that require both browsing and deep analysis, improving the overall user experience [13]. 4. Performance Metrics - The new Agent mode achieved a score of 42% in the "Humanities Last Exam," indicating a significant improvement in performance compared to previous models [15]. - The model's ability to perform web operations is approaching human levels, demonstrating the potential for further advancements in AI capabilities [19][20]. 5. Challenges and Considerations - Despite the advancements, users may experience longer task completion times and occasional errors, highlighting the need for further refinement [22]. - The introduction of Agent mode raises concerns about privacy and security, particularly regarding the handling of personal information during automated tasks [24]. 6. Future Implications - The rise of Agent mode signifies a new phase in AI development, prompting questions about the evolving relationship between humans and AI, particularly in the workplace [25][26]. - As AI takes on more responsibilities, the impact on job roles and the nature of work will need to be addressed, indicating a transformative shift in various industries [26][27].

Core Insights - Thinking Machines Lab, founded by former OpenAI CTO Mira Murati, has raised $2 billion in seed funding led by a16z, achieving a valuation of $12 billion, marking it as the largest seed funding round in tech history [1][2] - The initial funding target was $1 billion with a valuation of $9 billion, but the final amount increased significantly over a few months [1] - The company currently lacks specific product offerings and revenue, with only a high-profile founding team and vague technological direction publicly available [1] Company Overview - Mira Murati has been with OpenAI since 2016, serving as CTO and leading the development of groundbreaking technologies like GPT-3, GPT-4, DALL-E, and ChatGPT [2] - The founding team includes notable AI experts such as John Schulman, Barret Zoph, Bob McGrew, Alec Radford, Alexander Kirillov, Jonathan Lachman, and Lilian Weng, all of whom have significant contributions to AI advancements [4][5][7][9][12][13][15] Talent Acquisition in AI Industry - The competition for top AI talent has intensified, with companies like Anthropic, Safe Superintelligence, and Thinking Machines Lab emerging as key players, all led by elite AI researchers [17] - The trend indicates that talent is becoming the most critical factor in the AI industry, surpassing computational power and data [17] - Major tech companies are aggressively acquiring talent, as seen in Meta's recruitment efforts, which include significant investments and hiring from various AI firms [18][19][20] Future Product Development - Thinking Machines Lab plans to release its first product within months, focusing on open-source components and AI solutions tailored to business KPIs, referred to as "reinforcement learning for businesses" [16] - The company emphasizes multimodal capabilities and effective safety measures for AI systems, aligning with industry trends towards responsible AI development [16]

Core Viewpoint - The AI Agent industry is expected to mature significantly by 2025, with the potential to create a complete commercial ecosystem around AI applications, driven by advancements in large models and the development of AI Agents [1]. Group 1: Technology and Product Development - The AI Agent technology framework is becoming clearer, consisting of foundational large models, various tools, and supporting infrastructure [4][12]. - The core components of AI Agents are the underlying large models and tools, which enable the execution of complex tasks [12]. - The current AI Agent products are still evolving, but a basic framework for future general-purpose AI Agents is forming, with 2025 being identified as the "Year of the Agent" [9][20]. Group 2: Market Segmentation - C-end Agents focus on general intelligence and user needs, aiming for standardized products that can reach a broad audience [4][36]. - B-end Agents emphasize integration with specific business scenarios, with companies like Microsoft and Salesforce leading the way in commercializing these solutions [5][37]. Group 3: Commercialization Trends - The commercialization of C-end Agents is more about establishing user engagement and market presence, while B-end Agents are seeing gradual adoption in specific enterprise applications [39][44]. - The global commercialization of AI Agents is progressing faster in overseas markets compared to domestic ones, with significant revenue growth observed in companies like OpenAI and Anthropic [43][52]. Group 4: Future Outlook - The AI Agent industry is anticipated to reach a tipping point as general-purpose products emerge, unlocking long-term market potential [45][59]. - The increasing complexity and length of tasks that AI Agents can handle indicate a trend towards more sophisticated applications, potentially leading to self-generating ecosystems in the future [32][59].

Group 1 - Huang Renxun, CEO of Nvidia, predicts that the next wave of artificial intelligence will be Physic AI, which uses fundamental principles to replace human coding and algorithm description for result prediction [1] - The shift from current text/image models to industrial simulation and biomedicine indicates a potential transformation in investment logic within the industry [1] - If Physic AI achieves autonomous derivation of physical laws, it could disrupt traditional simulation tools and advance industries like autonomous driving and material research [1] Group 2 - The launch of the domestically produced "Pinyuan AI Integrated Machine" series in Shenzhen marks a significant breakthrough in China's AI hardware, achieving full domestic control over key software and hardware technologies [2] - The Pinyuan AI Integrated Machine features 16 domestically produced Jiangyuan D10 AI inference acceleration cards, enhancing model inference efficiency by 300% in text generation and image recognition scenarios [2] - This development is expected to strengthen national technology security and reduce reliance on foreign technologies, while promoting AI applications in enterprises and government [2] Group 3 - The recent registration of the "Fengyu" large model by SF Technology and other AI services indicates a speeding up of industry standardization, with a total of 439 generative AI services registered as of June 30, 2025 [3] - The entry of traditional giants like SF into the generative AI space signifies the expansion of large models from the internet into logistics and telecommunications sectors [3] - Companies are now required to demonstrate that their technologies can effectively reduce costs and improve efficiency, moving beyond mere concepts [3]