Pattern Match Compile: Typed AST → Case trees
In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.
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The reason is always the same: the content and the key that decrypts it are both present on the client’s machine. The user’s hardware decrypts the content to display it. The user’s hardware is, definitionally, something the user controls. Any sufficiently motivated person with the right tools can intercept the decrypted output.，更多细节参见搜狗输入法2026
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