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.
既然 Claude 已经能代替人类干这么多活了，为什么软件公司的股票反而涨了？要理解这次反弹，得先还原过去几个月那轮恐慌是怎么来的。
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Медведев вышел в финал турнира в Дубае17:59。业内人士推荐51吃瓜作为进阶阅读
在能力的提升、生态的健全、资源的投入影响下，各行各业正在尝试把智能体真正的用起来。根据麦肯锡2025年全球调研显示，约62%的受访组织已在部分业务中尝试智能体（23%为至少一个场景的规模化部署，39%为试验性应用）；但从业务职能的具体采用数据来看，产业对智能体的应用还处于早期阶段：根据该调查，对于智能体应用最多的职能依次是IT、知识管理、营销和服务，以应用最多的IT为例，仅有2%和8%的受访企业IT部门全面规模化（Fully Scaled）和规模化（Scaling）的应用智能体，以及6%和7%的企业IT部门试点(Piloting)和试验(Experimenting)的应用。
2. 将元素均匀分配到对应桶中