At first glance, the benchmarks and their construction looked good (i.e. no cheating) and are much faster than working with UMAP in Python. To further test, I asked the agents to implement additional different useful machine learning algorithms such as HDBSCAN as individual projects, with each repo starting with this 8 prompt plan in sequence:
从目前芯片厂商的产能分配来看,资源向AI 相关业务倾斜已成必然。三星、SK 海力士、美光等三大存储芯片巨头,2025 年用于数据中心级存储芯片的产能占比已提升至 40% 以上,较 2023 年增长了 15-20 个百分点。在台积电、三星晶圆代工业务中,为 AI 芯片(如 GPU、ASIC)预留的产能也在持续增加,这挤压了消费级芯片的生产空间。
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