If your AI team is hitting premium-model limits before the 7th day, that is not only a token problem.
It is a unit economics and architecture problem.
Perhaps you should reconsider your model strategy.
In just one month, we built 25 detached 系统, nearly half already app-based, and our development progress now roughly 3× faster.
We also developed our own AI agent with 智能路由 capabilities, plus a web-based 命令行 and Telegram protocol.
总计 AI运营成本? Around RM200/月 sahaja.
And bukan development saja. Our staff use the same stack for daily work, including generating thousands of images every week.
So the interesting part is not only lower AI cost.
It is what happens when model routing, internal agents and detached 系统 start becoming your own infrastructure - instead of depending on one expensive model for everything.
Maybe token tak penat anymore. GPU pula yang fed up tengok kami. He he he.
为合适的任务选用合适的模型. 预留 the strongest model for audit and direction, and turn repeatable intelligence into infrastructure you own.
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我对premium-model还有问题，但文章已经提供了很好的起点。
我会把web-based这一段分享给需要了解技术的同事。
我特别喜欢our staff use这一部分，内容没有把实施过程说得太简单。
这篇文章适合团队用来开始讨论internal agents and detached 系统。
这篇文章把month, we built 25讲得比一般的AI介绍更具体。 值得继续研宄。
关于app-based的例子很实用，适合团队继续讨论。
我们团队正好在讨论25，这篇来得及时。
25这部分我看了几遍，值得再想。 读完之后还有一些疑问。