海外媒体随即跟进，复述了 Anthropic 的话术。然而这套叙事逻辑很快就翻车了：毕竟「蒸馏」这件事美国 AI 公司训练的时候也会做，更何况 Anthropic 自己也有类似行为：
Gamma 的在线编辑功能最为强大，生成过程也展示了其内置的详细工作流。但遗憾的是，最终成片的质量欠佳，配图审美更是「一言难尽」。或许 Gamma 的定位偏向专业用户，存在一定的学习门槛，在本次追求「下限」的测试中，它未能发挥出真正的实力。
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She gives the example of a previous client where one co-CEO worked more closely with the marketing and product departments, and the other mainly with finance, government regulatory bodies and legal.
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The common pattern across all of these seems to be filesystem and network ACLs enforced by the OS, not a separate kernel or hardware boundary. A determined attacker who already has code execution on your machine could potentially bypass Seatbelt or Landlock restrictions through privilege escalation. But that is not the threat model. The threat is an AI agent that is mostly helpful but occasionally careless or confused, and you want guardrails that catch the common failure modes - reading credentials it should not see, making network calls it should not make, writing to paths outside the project.
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