HackerNews中文版

去年十月，我开发了一个网络分析工具。它有两个显著特点：便宜，并且每周通过人工智能发送邮件摘要，这样你就不必查看仪表板了。结果表明，便宜并没有带来什么优势，但为数不多的用户真的很喜欢邮件摘要。如今世界变化很快，我突然开始尝试使用智能体，并且在 Claude Code 中完成所有工作，所以我决定推翻一切，将该工具重建为 lodd.dev，一个面向智能体的无头网络分析工具。它拥有 42 个 MCP 工具和一个完整的 API，允许智能体调用和处理分析数据，作为其流程的一部分。我尝试针对智能体的使用进行了优化，因此它具有高效的响应（一个快照大约需要 60 个 token），一个 llms.txt 文件来指导智能体处理设置，只读权限以简化身份验证，以及一个托管的 oAuth（用于桌面和移动应用程序）和使用 API 密钥的 stdio 版本（用于终端使用）。使用电子邮件中的 OTP 进行人工介入身份验证。明确要求智能体使用它效果很好，我特别喜欢将其与其他上下文来源（如 GSC 或提交历史记录）结合使用，但让智能体在没有提示的情况下调用它一直是一个小小的挑战。不过，在 Claude.md 文件的开头添加几行代码已经有所不同。我很想知道，你认为在与代码（或其他自动化工作流程）相同的对话中获取分析数据有什么价值（如果有的话）？

查看原文

Back in October, I built a web analytics tool. It had two distinguishing features: it was cheap, and it had a weekly AI email summary so you wouldn't have to check your dashboards. Turned out that cheap made no difference, but the few users I had really liked the email digest.The world moves fast these days, and suddenly I found myself experimenting with agents, and doing all my work in Claude Code, so I decided to throw out everything and rebuild the tool as lodd.dev, a headless web analytics tool for agents. 42 MCP tools and a full API that lets agents call and act on analytics data as part of their processes.I've tried to optimise it for agent usage, so efficient responses (a snapshot is 60ish tokens), an llms.txt to guide the agent to handle setup, read only for simplified auth, and both a hosted oAuth (for desktop and mobile apps) and stdio version using an API key for terminal use. Human in the loop authentication using OTP in an email.Explicitly asking the agent to use it works really well, and I particularly like combining with other context sources like GSC or commit history, but it's been a little bit of a challenge having the agent call it unprompted. A few lines at the top of a Claude.md file has made a difference though.I'd love to hear what value, if any, you see from being able to get analytics data in the same conversation as your code (or other automatic workflows)?

Show HN：我扔掉了我的数据分析看板，取而代之的是 42 个 MCP 工具