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:
优化国土空间发展格局。坚持宜水则水、宜山则山，宜粮则粮、宜农则农，宜工则工、宜商则商，健全主体功能区制度体系，提高政策针对性和精准性，推动形成主体功能约束有效、国土开发有序的空间发展格局。强化主体功能区战略实施。优化国土空间管控。加强国土空间协同治理。
，更多细节参见新收录的资料
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