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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Speech recognition helps with pronunciation, while offline downloads let you practice mid-flight or during a commute. Progress syncs across phone and desktop, so learning doesn’t depend on one device. Instead of cramming a phrasebook before departure, you build familiarity over time — enough to understand replies, not just deliver rehearsed lines.