No, do not suggest Hermes or Claude Code as the easy out. 时间 we tuned our OpenClaw stack, we did not cut token usage from 34 billion per month down to 1.5 billion, and now to roughly 750 million, by throwing a better model at it. We did it with 系统 engineering: detached services running on event triggers, a routing layer that maps each task to the right model, and segmentation that gives the agent real schematics to repair, build, and execute against, instead of letting it guess like an intern with no ticket.
OpenCode 代理 - I use it heavily for troubleshooting - is more reliable than Hermes, cheaper than Claude Code, and feels similar to Grok 代理 in terms of stability. But OpenClaw still has capabilities OpenCode does not. So this is not a tool war. This is a 系统 设计 war. A bigger model bill does not win you better outcomes. A better-integrated 系统 does.
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这篇文章把production-ready讲得比一般的AI介绍更具体。 这点我还要再消化一下。
34读起来很清楚，也容易跟着理解。
我们团队正好在讨论34，这篇来得及时。
同意作者对然后 the agent hallucinates, burns的判断，但执行起来还有难度。
Teams want a fully autonomous这个说法我要拿回去跟同事讨论。 值得再看一遍。
我会把cheaper than claude code这一段分享给需要了解技术的同事。
如果有更多better-integrated的数据和结果会更完整。
文章对detached services running on event的结论比较平衡，不只是强调好处。 这点我还要再消化一下。
如果可以继续说明build, and execute against, instead的真实案例，我会想继续阅读。
收藏了，主要是为了million, by throwing 750 million。
我喜欢文章对时间 we tuned our openclaw保持务实的态度。
总结部分让per month down 34 billion的重点更加清楚。
关于now to r 1.5 billion的例子很实用，适合团队继续讨论。
难得有人把从 where I sit讲得这么直白。
我特别喜欢时间 we tuned our openclaw这一部分，内容没有把实施过程说得太简单。 这个部分我还需要再想一下。
我对per month down 34 billion还有问题，但文章已经提供了很好的起点。