洛阳钼业董事长兼首席投资官刘建锋明确表示，“洛阳钼业看好黄金资产的长期市场前景。此次交易是公司落实‘铜金双极’并购战略的重大举措。巴西资源丰富，地缘政治相对稳定，该项目将与洛阳钼业巴西铌磷资产形成良好协同效应，进一步深化公司在南美的资源布局。”
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There’s a secondary pro and con to this pipeline: since the code is compiled, it avoids having to specify as many dependencies in Python itself; in this package’s case, Pillow for image manipulation in Python is optional and the Python package won’t break if Pillow changes its API. The con is that compiling the Rust code into Python wheels is difficult to automate especially for multiple OS targets: fortunately, GitHub provides runner VMs for this pipeline and a little bit of back-and-forth with Opus 4.5 created a GitHub Workflow which runs the build for all target OSes on publish, so there’s no extra effort needed on my end.
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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