To find these crucial border points, we employed a clever technique based on the Ford-Fulkerson algorithm. By simulating "flooding" roads with traffic from random start/end points, we could identify the natural bottlenecks – the "minimum cut" in graph theory terms. These bottlenecks became our border points.
比如用户下达指令，希望能从好友的电子邮件中找到一个食谱，并将相关配料加入购物清单中。AI 接到命令，首先调用邮件 App「搜索」的功能入口，检索并提取出相关内容，然后调用备忘录的「购物清单」入口，把数据填入整理。
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The problem gets worse in pipelines. When you chain multiple transforms — say, parse, transform, then serialize — each TransformStream has its own internal readable and writable buffers. If implementers follow the spec strictly, data cascades through these buffers in a push-oriented fashion: the source pushes to transform A, which pushes to transform B, which pushes to transform C, each accumulating data in intermediate buffers before the final consumer has even started pulling. With three transforms, you can have six internal buffers filling up simultaneously.
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