Thank you guys for all coming. Uh my name is Harrison. I'm the co-founder CEO of Langchain.We do a bunch of events like these, but this one is a special fireside chat. So we've done more demos in the past, but uh we've been working with Ripling for for a while now and they have a lot of insights around building actual agents. Uh and so I figured it would be much more interesting to do a to do a fireside chat with Anker here.Um, so do you maybe want to do a quick introduction of of yourself and and maybe I'm ...

这两年，写代码这件事变了。 GitHub Copilot 、 Cursor 、 Devin 一路登场，工程师开始习惯 " 打一段话，几千行代码自己长出来 " 。写得出东西，变得前 所未有地容易。但很快大家发现，真正拖住上线节奏的， 不再是「能不能写出来」，而是「敢不敢放上生产环境」 —— 代码量指数级增长，验证、回归、 极端场景覆盖反而被彻底压缩，测试成了 AI 时代新的 " 硬瓶颈 " 。 TestSprite 瞄准的，就是这一条被快速放大的断层： 让 AI 不只负责写代码，还要负责 " 审代码 " 。 它把测试这一步从「工程师下班前随手点两下」升级为 「贯穿开发全链路的自动化基础设施」 —— 既可以通过一个链接，自动帮你把线上产品 " 从头到尾撸一遍 " ，也可以通过 MCP 深度嵌入 Cursor 、 Trae 等 AI IDE ，让 Testing Agent 和 Coding Agent 在幕后互相 " 过招 " ，自动生成测试计划、用例、代码、报告和自愈（ auto-healing ）修正，把验证这件事 做成一个真正可编排、可复用的底层能力。 这套产品背后，是一对典型又不那么 " 典型 " 的技术 ...

Summary of Elastic's Conference Call Company Overview - **Company**: Elastic - **Core Product**: Elasticsearch, a search platform designed for handling unstructured data and making it searchable, which has evolved into areas like observability and security [2][4] Key Points and Arguments AI Impact and Opportunities - **AI Integration**: The rise of AI, particularly large language models (LLMs), is seen as a significant opportunity for Elastic, especially in automating business processes that rely on unstructured data [3][11] - **Search Business Growth**: The search segment is the fastest-growing part of Elastic's business, driven by AI advancements [5][6] - **Security Enhancements**: New AI capabilities, such as Elastic Attack Discovery, automate cybersecurity tasks, enhancing the competitiveness of Elastic's security offerings [6][7] - **Observability Improvements**: AI is also expected to improve observability, making it easier for users to manage and analyze data [8] Financial Performance - **Q1 Performance**: Elastic reported a strong Q1, beating revenue expectations by $18 million, with balanced growth across all geographies [22][21] - **Commitments and Consumption**: Both commitments and consumption levels showed strong year-over-year growth, indicating a healthy business environment [22][23] - **Pricing Strategy**: Elastic has a history of adjusting prices in line with added functionalities, which is a common practice among software companies [25][29] Market Dynamics - **Federal Sector Stability**: The federal market has stabilized, with a focus on efficiency, which is favorable for Elastic's offerings [42][43] - **Competitive Landscape**: Elastic's strengths lie in handling messy data, particularly in security and observability, where competitors may struggle [55][58] - **Vector Databases**: The perception of vector databases is shifting towards being a feature rather than a standalone category, aligning with Elastic's focus on unstructured data [59][61] Strategic Initiatives - **Open Source Strategy**: The adoption of the AGPL open-source license is aimed at increasing developer engagement and top-of-funnel activity, particularly in the vector database space [68][69] - **Sales Strategy Changes**: Recent changes in the sales strategy have led to a focus on enterprise and mid-market accounts, resulting in higher quality and larger deals [76][77] Other Important Content - **Analyst Day**: An upcoming Analyst Day is expected to provide insights into growth and profitability targets, alongside product demonstrations [70][72] - **Long-term Vision**: Elastic aims to embed its platform within emerging AI applications, positioning itself as a core infrastructure provider [17][13] This summary encapsulates the key insights from Elastic's conference call, highlighting the company's strategic focus on AI, financial performance, market dynamics, and future initiatives.

AI Development Strategy - Building successful AI applications requires a sophisticated engineering approach beyond just writing good prompts [1] - The industry emphasizes the importance of evaluations (evals) as a core component of the development process [1] - Evaluations should be intentionally engineered to reflect real-world user feedback and drive product improvements [1] Technical Focus - "Context engineering" is emerging as a new frontier, focusing on optimizing the entire context provided to the model [1] - Context engineering includes tool definitions and their outputs [1] - The industry advocates for a flexible, model-agnostic architecture [1] Adaptability - The architecture should quickly adapt to the rapidly evolving landscape of AI models [1] - Optimize the entire evaluation system, not just the prompts [1]

Core Insights - The article discusses the launch and capabilities of the Comet browser by Perplexity AI, aiming to create an AI operating system that enhances user productivity through automation and integration with various applications [3][9][10]. Group 1: Comet Browser Features - Comet is designed to handle asynchronous and repetitive tasks, providing a seamless user experience by integrating with existing applications like iMessage and email [4][5]. - The browser aims to act as a central hub for managing various digital tasks, allowing users to automate workflows and access information across different platforms [9][10]. - The concept of "context engineering" is introduced, emphasizing the need for AI to autonomously gather and utilize context from various communication tools to enhance user efficiency [5][6]. Group 2: AI and User Interaction - The discussion highlights the importance of achieving a natural and fluid interaction between AI and users, focusing on both intelligence and contextual understanding [6][4]. - The browser is positioned as a next-generation tool that can evolve with advancements in AI models, enhancing its capabilities over time [8][9]. - The potential for AI to automate digital labor is compared to autonomous driving, suggesting that AI can free up time for users by handling complex tasks [4][6]. Group 3: Market Position and User Adoption - Since its launch, Comet has seen a steady increase in user adoption, with a waitlist nearing one million, indicating strong market interest despite its early-stage development [9][10]. - The company aims to create a new category of "agent browsers," differentiating itself from traditional browsers and focusing on building a unique ecosystem [9][10]. - The competitive landscape is discussed, with the expectation that larger players like OpenAI and Google will also enter the agent browser space, further validating the concept [9][10]. Group 4: Challenges and Future Directions - The article addresses the technical challenges of building a robust infrastructure to support the complex interactions required for the Comet browser [28][29]. - There is an emphasis on the need for continuous improvement and adaptation to user feedback, with a focus on maintaining a high-quality user experience [29][34]. - The potential for future hardware development is mentioned, but the primary focus remains on refining the software capabilities of the browser [21][22][25].