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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:
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西方评论界普遍认为，奥斯汀的成就远在勃朗特之上。因为奥斯汀深知自己的局限——身为女性，她的视野被限制在“三四个乡绅家庭”的客厅里。但她没有试图突破这种局限去写自己不熟悉的工人运动（像勃朗特在《谢利》中那样遭遇滑铁卢），而是在有限的半径内，挖掘出了无限的深度。