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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chunk[i] = (offset + i) & 0xFF;
Guar gum, a plant colloid, costs less than agar and is better suited for growing thermophilic bacteria, but is also more difficult to handle, being more viscous and less transparent. The bacterial polysaccharide xanthan is cheaper as well but forms weaker jellies that, as with carrageenan, might result in puncturing its surface. Other colloids, like alginate (from brown seaweed) and gellan gum (from a bacterium), don’t set solely based on temperature and require additives for gelation. These additives might interfere with microbial growth and make the preparation of those jellies less handy than agar plates.