文 | 万涂幻象 大家好，我是万涂幻象，一名专注商业 AI 智能体开发与企业系统落地的实践者。 长期聚焦定制化业务系统开发、多维表格定制、Prompt定制、智能体设计、自动化工作流构建、 ComfyUI工作流与AI视频制作，致力于为各行各业打造能创造增长、构筑优势的AI落地解决方案。 在这里，持续分享前线实战案例与结构化落地方法，文末还有实用资源推荐，欢迎收藏～ 上周六，我们雷打不动的在腾讯会议里做会员专属共学。 聊着聊着，一位朋友突然在聊天框里抛出了一个问题，当时的截图我还留着： 他问："我看 n8n 好像可以通过 webhook 来定时发送给飞书信息，啥时候专门出一个讲 webhook 的文章 学习一下？" 这个问题，一下子把我心里那个"疙瘩"给勾出来了。 说真的，这玩意儿太常见了。你要做飞书多维表格的数据同步，做 Github 代码提交后的自动化部署， 做 Dify 智能体里的工具调用…基本上，只要你想让两个独立的系统"实时"地通个气，就绕不开它。 但最要命的是，你去网上搜，出来的解释全是"HTTP 回调"、"事件驱动"、"被动通信机制"… 全是这种正确的废话。 这种解释就像一本写满了乐理的说明书，你看 ...

文 | 万涂幻象 国庆中秋双节假期，被一个叫Sora 2的东西给搅黄了。 朋友圈、微信群里，到处都在传一些真假难辨的短片。真正让我放下手头所有事，把全部注意力都投进 去的，不是那些"颠覆世界"的讨论，而是视频本身。 很多片段，已经到了肉眼几乎无法分辨真伪的程度，甚至有些流出来的版本连水印都没有。 这就不是个热闹了，这是个严肃的技术问题。一个能以假乱真的内容生成工具，对我们这些做系统、做 落地的人来说，意味着什么？脑子里一下就塞满了各种可能性和风险。 说实话，东西是真牛。这次的体验门槛也不高，我很快就上手玩了一阵。但新鲜劲儿过去后，一个更现 实的问题浮了上来，也是我们这种做业务系统的人的本能反应： 这东西，怎么用在"生产"里？ 比如，一个做跨境电商的朋友，他们店铺里有几百个SKU，每个都需要一个几秒钟的展示视频。难道让 运营人员一个个手动去网页上生成、下载、再整理上传？ 这效率太低了，而且很容易出错。单个视频做得再酷炫，无法批量化、流程化，那它就只是个"玩具"， 而不是"工具"。 我心里这个疙瘩就结下了。 巧的是，昨天一天之内，OpenAI干了两件大事：凌晨的开发者日，正式放出了Sora 2的官方API；紧接 ...

Market Overview - As of August 18, 2025, the A-share market remains strong, with multiple indices reaching multi-year highs, including the Shanghai Composite Index up 0.85% to 3728.03 points, and the Shenzhen Component Index up 1.73% to 11919.57 points, marking a two-year high [1] - The trading volume for the day was 2.81 trillion yuan, significantly higher than the previous trading day [1] AI Models and Market Predictions - Despite the rapid development of AI, no public large model has successfully predicted the recent market rally, raising questions about the predictive capabilities of these models [1] - Financial large models, such as BloombergGPT, have been developed to analyze historical market data and identify signals of market trends, but they struggle to predict future bull or bear markets accurately [1][2] Development of Financial AI Models - BloombergGPT, launched in 2023, utilizes proprietary financial text data to perform specialized tasks in finance, such as sentiment analysis and entity recognition [2] - The emergence of various open-source and commercial large models in 2024 has lowered the technical barriers for financial model development, yet improvements in predictive capabilities remain limited [5] Challenges in Financial Predictions - The disconnect between technological advancements and financial effectiveness is attributed to the low signal-to-noise ratio in financial data, leading to overfitting in models [5][6] - By 2025, the focus has shifted from unrealistic market predictions to enhancing workflows with AI agents, which can automate complex financial analysis processes [6][7] New Developments in AI Financial Tools - In August 2025, Tsinghua University released an open-source project called Kronos, aimed at predicting financial market trends using time series models [8] - Despite its innovative approach, users have expressed dissatisfaction with the predictive accuracy of Kronos, highlighting a deeper issue of trust in model outputs [9] Alpha Decay in Financial Strategies - The concept of "Alpha decay" explains why many strategies fail to maintain profitability over time, as market participants quickly exploit any discovered patterns [10][12] - Effective trading strategies often rely on unique insights or proprietary data, which are not easily replicated by open-source models [15] Conclusion on Financial AI Tools - The success of models like BloombergGPT lies in their ability to provide high-quality data processing rather than direct trading strategies, emphasizing the importance of proprietary insights in achieving sustainable alpha [15][16]

Core Insights - n8n is evolving from a workflow automation tool to an orchestration layer for AI applications, addressing the need for tools that connect various applications and APIs in a fragmented market [2][3] - The company has experienced rapid growth, with its valuation increasing over eight times in just four months, and revenue rising fourfold in the past eight months [3][9] - n8n aims to empower users by providing a low-code platform that allows both technical and non-technical users to create complex workflows without extensive coding knowledge [5][6] Company Overview - Founded in 2019 by Jan Oberhauser, n8n started as a workflow automation tool and has since pivoted towards AI integration, allowing users to connect various applications and databases visually [5][6] - The company received $1.5 million in seed funding from Sequoia in 2020, marking Sequoia's first seed investment in Germany [2] Recent Developments - n8n is reportedly raising over $100 million in a new funding round led by Accel, with a potential valuation exceeding $2.3 billion [2][3] - The company completed a $60 million Series B funding round in March 2023, achieving a valuation of $270 million at that time [3][62] Business Model - n8n offers two main services: cloud services for individuals and small to medium-sized businesses (SMBs), and enterprise-level services, focusing on the growing demand from SMBs for AI integration [18] - The company has been adopted by various large enterprises and government agencies, with a notable faster adoption rate in the Middle East compared to Europe [18] Community Engagement - n8n has built a strong community by encouraging user contributions and feedback, with over 230,000 active users engaging in forums and creating content on platforms like YouTube [10][56] - The company emphasizes the importance of community support, allowing users to ask questions and receive help regardless of their payment status [56][58] Competitive Landscape - n8n differentiates itself from competitors like Zapier by offering greater flexibility and the ability to handle complex workflows that require multiple conditional steps [30][31] - The platform's architecture, based on Node.js, allows users to easily integrate custom code, making it suitable for a wide range of applications [31] Unique Practices - n8n employs a Fair-Code licensing model, which allows internal use without restrictions but prohibits direct commercialization of the software, aiming to protect the project's value [43][48] - The company has opted for a unique approach to open-source, distinguishing itself from traditional open-source projects by implementing a license that prevents commercial exploitation [44][45] Use Cases - n8n is utilized in various scenarios, including automating customer service processes, internal applications for data analysis, and personal task management [16][30] - The platform's flexibility makes it particularly appealing to organizations with strict data security requirements, as it can be self-hosted [16][17]

Core Viewpoint - Attio is an AI-native CRM designed for marketing teams, aiming to enhance customer relationship management, automate workflows, and provide data-driven reporting. The company recently completed a $52 million Series B funding round, bringing its total funding to $116 million [1]. Group 1: Product Features - Unlike traditional CRMs, Attio integrates AI capabilities from the ground up, addressing pain points such as manual data entry and data silos through automated data ingestion and intelligent workflow automation [2]. - Attio is known for its high customizability and flexibility, allowing users to model data structures according to their business needs and automatically sync data from various integrations [5]. - The platform emphasizes real-time data processing, enabling businesses to manage customer data effectively in a rapidly changing environment [5]. Group 2: Market Position and Growth - Attio has over 5,000 paying customers, including rapidly growing companies like Lovable, Granola, Modal, Replicate, and Public, with expectations of a fourfold increase in annual recurring revenue (ARR) this year [4]. - The target customers are "next-generation companies," particularly those that prioritize efficiency, data-driven decision-making, and are open to adopting new technologies [7]. - The core competitive advantage of Attio lies in its AI-native design, which allows for deeper automation and data understanding compared to traditional CRMs that add AI features later [6].

Key Advantages of Sim - Sim offers an intuitive interface [1] - Sim features a state-of-the-art copilot for faster builds [1] - Sim supports AI-native workflows for intelligent agents [1] Competitive Positioning - Sim outshines n8n [1] Resources - GitHub repo available for Sim [1]

Core Insights - The article emphasizes the challenges faced by ordinary users when trying to adopt AI tools, highlighting the gap between expectations and reality in utilizing these technologies [2][3][34] - It illustrates a real-life experience of a product manager navigating through various AI tools, showcasing the learning curve and frustrations involved in building an AI Agent [5][30] Group 1: AI Adoption Journey - The excitement surrounding AI tools like ChatGPT has led many, including companies, to explore their potential for enhancing business processes [7][10] - The initial curiosity often turns into confusion as users encounter the complexities of setting up AI workflows, which are not as straightforward as advertised [11][24] - The experience of trial and error is common, with users spending significant time troubleshooting and modifying code to achieve desired outcomes [29][30] Group 2: Market Trends and Future Outlook - The global AI market is projected to reach $638.2 billion in 2024, with a compound annual growth rate of 19.1% from 2023 to 2024, indicating robust growth and increasing integration of AI in various sectors [32] - Companies are investing heavily in AI, reminiscent of the early internet era, where some embraced the change while others fell behind, suggesting a critical need for businesses to adapt to AI technologies [32][34] - The article concludes that while AI has limitations, learning to effectively use these tools is essential for navigating the future landscape of technology [34][35]

Core Concept - The article emphasizes the evolution of prompt engineering into "context engineering," highlighting its importance in optimizing large language models (LLMs) for task execution [3][5][19]. Summary by Sections Definition and Importance - Context engineering is described as a critical process that involves adjusting the instructions and relevant background needed for LLMs to perform tasks effectively [3][5]. - The term "context engineering" is preferred as it encompasses the core tasks of prompt engineering while addressing its limitations [5][19]. Practical Application - A specific case study using n8n to develop an AI agent workflow illustrates the practical implementation of context engineering [6][7]. - The workflow includes designing management prompts, debugging instructions, and managing dynamic elements like user input and date/time [7][10]. Key Components of Context Engineering - Effective context engineering requires careful consideration of instructions, user inputs, and structured input/output formats to ensure clarity and efficiency [11][12]. - The article outlines the necessity of defining subtasks with specific parameters such as unique IDs, search queries, source types, and priority levels [12][13]. Tools and Techniques - The use of tools like n8n facilitates the integration of dynamic context, such as current date and time, which is crucial for time-sensitive queries [15][18]. - RAG (Retrieval-Augmented Generation) and memory mechanisms are discussed as methods to enhance workflow efficiency by caching user queries and results [16][17]. Challenges and Future Directions - The article notes that context engineering is complex and requires multiple iterations to refine the process [25][26]. - It anticipates that context engineering will evolve into a core skill for AI developers, with potential for automation in context handling [28][29][30].

Core Insights - Automation is becoming a core value proposition, with companies like Clay and n8n leveraging AI to enhance their offerings and drive significant growth in valuations and revenues [1][11]. Company Overview - Clay recently completed a $100 million funding round, achieving a valuation of $3.1 billion, with its AI-driven advertising product generating $4 million in annual revenue [1]. - n8n is reportedly raising a new funding round led by Accel, with a potential valuation of $1.5 billion, up from $300 million just five months prior after a $60 million funding round [1]. - n8n's annual recurring revenue (ARR) has surpassed $40 million, supported by over 4,400 workflow templates and more than 400 integrated connectors [2]. Growth and Performance - n8n's revenue grew fivefold after integrating AI into its automation workflows, with a doubling of revenue in the first two months of the year [1]. - The platform has seen a 300% increase in AI-related workflow templates in 2024, indicating strong market response to its AI-first strategy [8]. Unique Business Model - n8n was founded by Jan Oberhauser, a former Hollywood visual effects artist, who aimed to create a solution to repetitive tasks, leading to the establishment of an open-source platform with a "Fair-Code" license [3][5]. - This open-source model has fostered a vibrant contributor ecosystem, with over 127,000 stars on GitHub and a wealth of user-generated content, enhancing the platform's value [5]. Competitive Advantage - n8n offers greater flexibility compared to competitors like Zapier and Make, with features such as local deployment options, unlimited workflow execution, and native AI integration [6][7]. - The unique developer community and the resulting network effects create a strong competitive moat for n8n, facilitating rapid adoption and lowering learning costs for enterprise users [10]. Future Vision - The founder envisions a future where AI enhances human capabilities rather than replacing them, emphasizing the combination of AI, code, and human input as the winning formula [11].

Core Insights - The article emphasizes the importance of mindset and practical application in mastering AI and automation tools, particularly for individuals without a technical background [8][10][75] - It highlights the effectiveness of no-code tools like n8n in accelerating learning and creating AI solutions, suggesting that these tools are gaining popularity among business leaders [5][15] Group 1: Learning Methodology - The article outlines a methodology for learning AI and automation that prioritizes practical experience over passive consumption of tutorials [29][33] - It warns against the "tutorial hell" phenomenon, where learners feel they understand concepts without being able to apply them [44][45] - The author stresses the importance of hands-on projects to solidify understanding and build confidence [56][60] Group 2: Key Learning Strategies - The article identifies four core modules for effective learning: proactive learning and practice, solidifying foundational knowledge, avoiding isolation, and adopting other beneficial habits [25][28][74] - It encourages learners to focus on 1-2 tools rather than trying to master everything at once, emphasizing depth over breadth [13][16] - The importance of community and mentorship is highlighted, suggesting that engaging with others can significantly enhance the learning process [66][68] Group 3: Practical Application - The article suggests starting with small, manageable projects to build skills and confidence before tackling larger challenges [59][56] - It emphasizes the need for a solid understanding of basic programming concepts, even when using no-code tools, to facilitate effective automation [60][62] - The use of AI tools like ChatGPT for learning assistance is recommended, positioning them as valuable resources for self-education [63][64]