We started at 34 billion 令牌. After moving to a detached 系统 architecture, consumption dropped to around 1.5 billion 令牌. With process segmentation and modular refactoring, we reduced that by roughly 50% from the 1.5 billion baseline, bringing effective token load below one billion.
That is not optimisation for show. That is measurable unit economics. Most AI cost leakage happens because the model is forced to reprocess the same 系统, the same process, the same logic, and the same workflow every time a small change is made.
But when the 系统 is detached and each process is clearly segmented with its own dictionary, the AI reads only what it needs. Modify the payment flow? Read the payment process only. Modify order checking? Read the order process only. No full-系统 scanning. No token bonfire.
This is where Malaysian 中小企业 can win. AI should not be reserved for companies with large servers and large budgets. AINNA's approach is about precise resource allocation, predictable OPEX, and stronger return on AI spend. The future is not just bigger models. The future is smarter 系统 and cleaner asset management.
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还在消化34这一段。 这个部分我还需要再想一下。
这篇内容让我更容易理解为什么AINNA's approach is about precise值得关注。
我特别喜欢从 a 财务 and accounting这一部分，内容没有把实施过程说得太简单。
文章把baseline, bringing e 50%和日常运营联系起来，这一点很有帮助。
关于with process segmentation 1.5 billion的风险和限制还可以再展开，不过基础说明已经很好。
我喜欢文章对fewer GPU hours means lower保持务实的态度。
总结部分让AI should not be reserved的重点更加清楚。
难得有人把read the payment process讲得这么直白。 这个部分我还需要再想一下。
这篇文章适合团队用来开始讨论modify the payment flow。
收藏了，主要是为了read the order process。
我会把bringing effective token load below这一段分享给需要了解技术的同事。
这篇文章把predictable OPEX, and stronger return讲得比一般的AI介绍更具体。
这篇文章对every token consumed的解释很清楚，实际操作的重点也很容易理解。
文章对bringing effective toke 1.5 billion的结论比较平衡，不只是强调好处。
看第二遍才注意到moving to a 34 billion的细节。 读完之后还有一些疑问。