Interesting approach. I just finished some work for a similar task in a different domain.
One thing that surprised me: tantivy's BM25 search is faster, more expressive, and more scalable than SQLite. If you're just building a local search (or want to optimize for local FTS), I would strongly recommend looking into tantivy.
If you have the resources, it would be very interesting to throw a some models (especially smart-but-context-constrained cheaper ones) at some of the benchmark programming problems and see if this approach can show an effective improvement.
You bet I do. That's an hour of rubber-ducky time working through new architectures with someone who won't get tired of my endless blathering. I've worked through a bunch of bad ideas that way, without embarrassing myself in front of my colleagues.
I also use it to explore topics that I wouldn't spend desktop time on, but that I was curious about. It's like having a buddy who's smarter than me on their special interest, but their special interest is "everything you don't know.". And your buddy's name is Gell-Mann. : - )
You know what's pretty good at cleaning up data that's a total trash fire? _More_ LLM. :-)
I run a web service whose primary purpose is cleaning up messy, SEO-enshittified data from Google, eBay, etc. After years of fine-tuning my own heuristics, I threw a super-cheap LLM at it and it massively out-performed my custom code. It's slower, but the results are well worth it.
The description of the algorithm notes that each irregular pentagon is divided into four sub-pentagons. Eyeballing the maps, I don't see any group of 4 pentagons forming a similar larger pentagon.
I noticed that you had an analog to the H3 landing page on your landing page, allowing zooming in. If you could also steal the next-higher / next-smaller overlay like they did on the H3 landing page, it would make it clearer the relationship between the larger and smaller pentagons.
I've used H3 extensively, and one of the things that always bugged me about it was that each large hexagon was _mostly_ covered by a group of the next smaller ones, but because geometry, the edges have some overlap with the neighbor large hexagons. So I can't just truncate an integer mapping, for example, to get the ID of the next-largest.
Are the examples all actual outputs of the program? It's entirely possible that my understanding of the grammar is off, but it looks like these examples are wrong:
One thing that surprised me: tantivy's BM25 search is faster, more expressive, and more scalable than SQLite. If you're just building a local search (or want to optimize for local FTS), I would strongly recommend looking into tantivy.
If you have the resources, it would be very interesting to throw a some models (especially smart-but-context-constrained cheaper ones) at some of the benchmark programming problems and see if this approach can show an effective improvement.