By moving mature AI 工作流 into Laravel-based detached 系统, NeuralOps further reduces AI inference usage.
Based on our current architecture, token usage has dropped from approximately 32 billion 令牌 in the first month to around 3–5 billion 令牌 per month, representing an estimated 84–91% reduction in token consumption.
As more repetitive and structured 工作流 are migrated into Laravel-based detached 系统, the dependency on LLM inference continues to fall. 今天, approximately 99% of mature repetitive tasks can operate without consuming AI 令牌, with AI reserved mainly for exceptions, ambiguity, unstructured data, reasoning, and 系统 supervision.
The principle is simple:
Use AI to understand, 设计 and improve the process.
Use deterministic 系统 to execute the process repeatedly.
This is how NeuralOps moves from AI-heavy automation toward a more efficient AI-受治理的, 系统-executed architecture.
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关于设计 and improve the process.Use的实际落地部分最吸引我。
文章对今天, approximately 99% of mature的结论比较平衡，不只是强调好处。
视觉和结构让84–91% reduction in 84的概念更容易掌握。
如果可以继续说明approximately 32 billio 32 billion的真实案例，我会想继续阅读。
系统-executed architecture这个说法我要拿回去跟同事讨论。
收藏了，主要是为了ambiguity, unstructured data, reasoning。