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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在理解了 Wire 如何生成实体类并进行编解码后，我们会遇到一个跨平台开发的常见问题：KMP 共享模块生成的 Kotlin ByteArray 数据需要被 Android 和 iOS 主工程使用，Android 可以直接使用，而 iOS 使用的是 NSData 类型，这里有两种实现策略：
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