这种复合效应能否实现——商家最终将Managerbot视为可信赖的守护者还是精巧的推销工具——将决定Block的未来走向。这家公司已将企业身份、人员规模和华尔街叙事都押注于一个信念：AI代理能在减少人力介入的情况下创造更大价值。Managerbot是首个承载这份厚重承诺的产品。而那些依靠Square终端维持营业、在餐巾纸上手写排班表、因时间不够而放弃营销的小企业主，从未要求成为硅谷最大胆AI理论的试验品。但从今天起，他们已是这场变革的核心。
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This is a good heuristic for most cases, but with open source ML infrastructure, you need to throw this advice out the window. There might be features that appear to be supported but are not. If you're suspicious about an operation or stage that's taking a long time, it may be implemented in a way that's efficient enough…for an 8B model, not a 1T+ one. HuggingFace is good, but it's not always correct. Libraries have dependencies, and problems can hide several layers down the stack. Even Pytorch isn't ground truth.