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Discussion (10 Comments)Read Original on HackerNews
The market is telling us that through increased hardware prices.
LLMs being very powerful means that we need to start being smarter about allocating resources. Should chat apps really eat up gigabytes of RAM and be entitled to cores, when we could use that for inference?
I’d just store a table of records with the leaf, associated with the seed. A good fuzzer is entirely deterministic. So you should be able to regenerate the entire run from simply knowing the seed. Just store a table of {leaf, seed}. Then gather all the seeds which generated the leaf you’re interested in and rerun the fuzzer for those seeds at query time to figure out what choices were made.
> Skiplists to the rescue! Or rather, a weird thing we invented called a “skiptree”…
I can't help but wonder. The article makes no mention of b-trees if any kind. To me, this sounded like the obvious first step.
If their main requirement was to do sequential access to load data, and their problem was how to speed up tree traversal on an ad-hoc tree data structure that was too deep, then I wonder if their problem was simply having tree nodes with too few children. A B+ tree specifically sounds to be right on the nose for the task.