「Jinqiu Select」 跨越语言与时差，传递科技圈最值得被听到的声音。 < Overview > Harrison Chase 作为 LangChain 的联合创始人 兼 CEO，是 AI Agent（AI 智能体）基础设施领域最具影响力的工程实践者之一。 自 2022 年开源 LangChain 以来，他 亲历了从早期 GPT-3.5 的简单链式调用，到如今 Claude Code、Deep Research 等长程 Agent 爆发的完整技术周期。 Chatbot 已经不适合 作为新一代 AI 产品的主流形态了。 这不是模型能力的问题，而是产品形态的问题。 Chatbot 的本质是"即时响应"，追求的是低延迟和高流畅度。 但真正有价值的一些日常工作从来不是这样运转的。一份优质的研究报告需要反复检索、交叉验证、结构化整理；一个可靠的代码 PR 需要理解上下文、规划方 案、测试验证、处理边界情况。 这些任务的共同特点是：它们需要时间，需要多步骤的自主决策，需要在过程中不断调整策略。换句话说， 它们需要的不是一个"即时响应者"，而是一个"长程执 行者"。 这正是 Long-horizon Agent 崛 ...

当老板要求分析竞品的Deep Research功能时，如何系统性地完成一份有价值的报告？本文通过真实案例拆解竞品分析的完整流程，让你掌握 一套让战略洞察落地为可执行建议的方法论。 前面三篇讲了为什么做、评什么、怎么选竞品。这篇我用一个真实案例，把整个流程串起来走一遍。 案例背景就是开头提到的那个场景：老板说"现在其他产品都出了Deep Research功能，去帮我分析一下他们做的效果"。 拿到这个需求，我会怎么做？ 第一步：先别急着动手，把需求拆清楚 很多人拿到需求就开始打开竞品截图，这是不对的。我会先问自己几个问题： 1.1 这份报告是给谁看的？ 老板让做的，那主要受众就是老板和管理层。他们关心的是战略层面的东西：竞品在押注什么方向？我们应该怎么跟进？有没有风险？ 这意味着我的报告要结论前置，先说判断再给证据，不要铺垫太多背景。 1.2 Deep Research到底是什么？ 在分析竞品之前，我得先搞清楚这个功能本身是什么。 Deep Research是OpenAI在2025年2月首次推出的功能，核心特点是：AI能自主进行网络搜索、整合多个来源的信息、深度分析数据，最后生成一份完整的 研究报告。 和普通的A ...

Core Insights - The article emphasizes that competitive product analysis has become an essential skill for AI product managers due to the rapid iteration and evolution of AI products [1][3] - It highlights the unique characteristics of the AI industry that necessitate continuous competitive analysis to maintain market relevance and identify differentiation opportunities [3][4][5] Importance of Competitive Analysis - AI products evolve at a much faster pace than traditional software, making previous conclusions potentially obsolete within weeks [3] - The AI market is crowded yet distinctly segmented, requiring thorough research to identify unique positioning and differentiation strategies [4] - The low switching costs for users mean that they can easily migrate to competitors if new features are introduced, underscoring the need for constant vigilance in competitive analysis [5] Value of Competitive Analysis - The first layer of value is understanding the market landscape and identifying the company's position among competitors [6][7] - The second layer involves recognizing strengths and weaknesses to guide product iterations, ensuring that the company meets or exceeds industry standards [8] - The third layer aids in strategic decision-making by predicting competitive risks and industry trends, which is crucial for resource allocation and strategic planning [9][10] Tailoring Reports for Different Roles - Different stakeholders require different insights from competitive analysis reports, necessitating tailored presentations for executives, product teams, development teams, and marketing teams [9][10] - Executives focus on strategic positioning and opportunities, while product teams seek actionable insights for feature improvements [9] - Development teams look for technical implementation details, and marketing teams are interested in pricing strategies and promotional channels [10] Conclusion - The core purpose of competitive analysis is to gain a comprehensive understanding of the market landscape, identify product strengths and weaknesses, and formulate precise product strategies and differentiation [11]

Core Insights - Meta announced the acquisition of Manus, an AI Agent startup, for $2 billion, highlighting its urgent need for a capable team and technology in the AI space [1][4][8] - The acquisition reflects a broader trend among tech giants to address anxiety over AI capabilities and market competition, with many companies resorting to buying talent and technology [9][10] Group 1: Acquisition Details - Manus, founded by Chinese entrepreneurs, achieved an annualized revenue of $125 million within eight months of its product launch [1] - The deal was characterized by a quick negotiation process, with Meta's CEO Mark Zuckerberg agreeing to the founder's asking price without hesitation [1] - Following the announcement, Meta's stock price fell for two consecutive trading days, indicating skepticism from the market regarding the potential impact of the acquisition [1] Group 2: Meta's Challenges - Meta's previous AI model, Llama 4, faced significant criticism for underperformance despite initial high rankings, leading to concerns about the company's AI capabilities [2][3] - The company has struggled to produce a competitive foundational model, while rivals like Anthropic and Google continue to excel in the AI space [3][11] - Meta's revenue model is heavily reliant on advertising, which is threatened by the rise of AI Agents that change user engagement dynamics [5][11] Group 3: Market Dynamics - The acquisition of Manus is seen as a strategic move to mitigate Meta's vulnerabilities in AI, as the company faces competition from both established players and emerging startups [6][9] - Manus's reliance on third-party models for its product experience introduces risks related to cost variability and supply chain stability [5][6] - The acquisition reflects a broader pattern of tech companies seeking to secure their positions in the rapidly evolving AI landscape, often driven by fear of falling behind [9][10] Group 4: Future Considerations - The integration of Manus into Meta's ecosystem could provide opportunities for deeper product integration across platforms like Facebook and Instagram [7] - However, concerns remain about whether the acquisition will effectively address Meta's underlying issues, particularly regarding organizational culture and integration challenges [14][15] - Historical examples of successful acquisitions in the tech industry suggest that simply buying technology may not resolve deeper organizational deficiencies [12][16][18]

Core Viewpoint - The article discusses the perception of job-hopping among high-value talent versus ordinary workers, highlighting that the former is often viewed positively while the latter may face negative judgments regarding stability and capability [5][6][7]. Group 1 - High-value talent, such as former OpenAI engineer Yao Shunyu, is seen as making career moves that reflect ambition and a desire for growth, rather than instability [4][5]. - Ordinary workers, on the other hand, may be labeled as lacking stability or capability when they change jobs frequently, which can lead to negative consequences during resume screenings by employers [6][8]. - The article suggests that the rules governing perceptions of job-hopping are often biased against ordinary workers, while high achievers are not bound by the same standards [7][8].

深度研究（Deep Research），让人工智能（AI）系统从"生成文本"进化为"发现知识"，进而完成复杂的开放式任务。 目前，Deep Research 已被广泛应用于文本生成、科研、软件工程等领域中，帮助任何人完成诸如学术综述、会议海报、演示文稿（PPT）生成，科研选题 生成、实验执行和学术写作，以及地球观测、软件库补全等工作。 然而，这一领域尚处早期阶段，业内缺少关于 Deep Research 路线图、基础组件、实践技术、关键挑战及未来方向的系统描述。 日前，来自山东大学、中国人民大学和清华大学的研究团队及其合作者，在一篇最新综述中详细梳理了 Deep Research 的前世、今生和未来，为 Agentic 研究范式提供了理论基础。 Deep Research 是什么？ Deep Research 赋予了 LLM 一个端到端的研究工作流程，使其能够作为智能代理，在最少的人工干预下生成连贯且有明确来源依据的研究报告。在实际操 作中，Deep Research 系统的 LLM 代理会先规划研究问题，从多种异构来源中获取并筛选相关信息，维护并更新工作记忆，最终输出经过验证、并标注准 确来源的答案。 研究 ...

Investment Rating - The report does not explicitly state an investment rating for the industry or specific companies [2]. Core Insights - The AI application activity has seen a significant rebound, particularly with Gemini showing notable growth, while domestic applications remain stable. OpenAI has released the GPT-5.2 series, focusing on optimizing agent workflows [2][7]. - Oracle reported a total revenue of $16.1 billion for Q3 2025, marking a 13% year-over-year increase, with cloud revenue reaching $8 billion, up 33% [2][13]. Summary by Sections AI Applications - OpenAI launched multiple updates, including GPT-5.2, while Google expanded its applications significantly, enhancing productivity features for enterprise users [7][12]. - The active usage of chat assistant applications has increased, with Gemini leading the growth, while other applications like Claude and ChatGPT also saw slight recoveries [9][12]. Oracle's Performance - Oracle's cloud business continues to grow, with cloud infrastructure revenue increasing by 66% year-over-year, and GPU-related revenue soaring by 177% [13][14]. - The company's remaining performance obligations (RPO) reached $523.3 billion, a staggering 433% increase year-over-year, indicating strong future revenue potential [14][17]. - Despite robust revenue growth, Oracle faces cash flow pressures, with a free cash flow of -$10 billion due to significant capital expenditures [17][18].

Google’s NotebookLM adds ‘Deep Research’ tool, support for more file types https://t.co/WnuTsPGt0y ...

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Product Offering - Messari's Deep Research 提供定制的分析师级别研究报告，一键生成 [1] - 企业用户可享受 20% 的折扣，使用代码 DEEPRESEARCH20 [1] Promotion - 提供 20% 的企业版折扣，促销代码为 DEEPRESEARCH20 [1]