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:
// 桶内用插入排序（小数组效率高）
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"There's the urine, the droppings," he says. "One time there was a carpet leading up to the altar that became absolutely sodden with urine and droppings and had to be thrown away. They also destroyed the brass work."
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