Sure, but at that point, why not just Minecraft then?

If this is the only issue, can't this be addressed by normalizing the post-processed data before scoring? (that is, if it really is just a matter of block ordering)

Yes, and deep research was junk for the hard topics that I actually needed to sit down and research. Anything shallower I can usually reach by search engine use and scan; deep research saves me about 15-30 minutes for well-covered topics.

For the hard topics, the solution is still the same as pre-AI - search for popular survey papers, then start crawling through the citation network and keeping notes. The LLM output had no idea of what was actually impactful vs what was a junk paper in the niche topic I was interested in so I had no other alternative than quality time with Google Scholar.

We are a long way from deep research even approaching a well-written survey paper written by grad student sweat and tears.

HIP is something you can put in production and Zluda is a proof-of-concept?

Of libraries that are better than the STL? Or data structures that the STL is missing? Without getting into the weeds of esoteric data structures and algorithms I think there's a few holes that aren't addressed:

https://github.com/martinus/unordered_dense provides better replacements for unordered_map/set.

The STL is missing B-trees and B-heaps, as well as d-heaps.

STL is also missing a radix sort, which is even more sorely missed now that we have std::executor::par_unseq to play with.