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Before string caching, the code would dynamically generate this banner based on your current terminal dimensions on every frame. But that’s wasteful! Now, we pre-compute every banner size (accounting for any amount of shutdown time remaining) ahead of time and slam that pre-computed banner into a byte buffer, skipping the intermediate allocation.
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Фото: Patrick Pleul/dpa (Photo by Patrick Pleul/ Picture alliance via Getty Images
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.
治水安邦，兴水利民。新征程上，既需要国家水网纵横神州、跨域调度的“大手笔”，也离不开每一滴水精打细算、每一条河精准施策的“绣花功夫”。“节水优先、空间均衡、系统治理、两手发力”的治水思路，正让清水高效利用、碧波重焕生机，最终汇聚成实现高质量发展的磅礴力量。