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Implementations have found ways to optimize transform pipelines by collapsing identity transforms, short-circuiting non-observable paths, deferring buffer allocation, or falling back to native code that does not run JavaScript at all. Deno, Bun, and Cloudflare Workers have all successfully implemented "native path" optimizations that can help eliminate much of the overhead, and Vercel's recent fast-webstreams research is working on similar optimizations for Node.js. But the optimizations themselves add significant complexity and still can't fully escape the inherently push-oriented model that TransformStream uses.
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智能体以LLM为代表的前沿模型作为大脑,通过软件工程令其可以在高阶目标驱动下完成复杂任务。可以说未来大部分的复杂AI应用都会以Agent为载体。事实上,我们在科幻作品中所看到的AI形象,比如《钢铁侠》中的贾维斯或《2001:太空漫游》中的HAL 9000,正是创作者对以Agent为载体的未来AI的直观想象。只是和物理世界交换的AI本身就极为重要和复杂,现在习惯上把这部分单独放在具身智能/机器人领域讨论。
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