我们在当时的文章里写道：「如果说英伟达的 H 系显卡是 AI 开发的金字塔尖，那么 Mac Studio 正在成为中小团队手中的瑞士军刀。」
图④：在湖北恩施土家族苗族自治州巴东县沿渡河镇小神农架村骄顶寨高山苹果种植基地，无人机在空中吊运采摘的苹果。
。搜狗输入法对此有专业解读
他補充說：「春天時一個漢堡500萬里亞爾（3.81美元），現在要1200 萬（9.13美元）。這些數字根本無法理解。」
I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.