If you would build an in-memory cache, how would you do it?

    It should have good performance and be able to hold many entries. 
    Reads are more common than writes. I know how I would do it already, 
    but I’m curious about your approach.

    Let's assume that keys accessed follow a power law, so some keys get 
    accessed very frequently and we would like them to have the fastest 
    retrieval of all.

Even with that tweak, the hottest keys will still be scattered around memory. Wouldn't it be best if their entries could fit into fewer pages? So, maybe a much smaller "hot" table containing say the 1,000 most accessed keys. We still want a high load factor to maximize the use of cache pages so perhaps perfect hashing?

Negative numbers are sometimes represented with parentheses: (234.58)

Tables sometimes tell you in the description that all numbers in are in 1000's or millions.

The dollar sign is used by many currencies, including in Australia and Canada.

I'd probably look around for some other gotchas. Here's one page on prices in general: https://gist.github.com/rgs/6509585 but interestingly doesn't quite cover the OP's problem or the ones I brought up, though the use cases are slightly different.

EDIT: Here's a better treatment, and it is the case that they give the exact same orderings: https://ajayp.app/posts/2020/05/relationship-between-cosine-...

The paper claims there is literature with more success for LLMs:

   Large language models have been shown to be vulnerable to adversarial
   attacks, in which attackers introduce maliciously crafted token sequences
   into the input prompt to circumvent the model’s safety mechanisms and 
   generate a harmful response [1, 14].