Comp-Sci has an old tradeoff between the time one needs to run an algo, and the space required to hold relevant data for it. Database engineers, and here the term "engineer" may indeed apply, are always pondering how best to index their tables. Last year Ryan Williams researched (famous) polynomial P and PSPACE, and failing at that (like we all do) instead succeeded at time[f(n)] and memoryuse[f(n)] whether P or not. It got to the arxiv February 2025; our man did a seminar last October, and now some autodubber did a youtube so I guess it's serious.
Like experimental physicists don't really grok HEP theory, I'm not pretending I understand more than the basics of what Ryan Williams has done here. All I understand is that he might be able to cut down on RAM usage for any given algo as hasn't already been so "optimised". With the understanding that we're not saving on time.
RAM matters. Prices keep rising, which is trickling into game boxes and further giving the impression of inflation, for the average man[-child]. The engineers buying RAM are doing it for data centers, themselves becoming a political problem for their needs in energy, thermodynamic release thereof (by water, mayhap), and land-area. "Reckless data centers!" has united slopulists from Brandon Herrera to Mary Peltola, neither of whom this blog is endorsing. And the antigrowth crowd including various lawyers and other scum might be keeping compute from launching to NEO space as well.
Data centers solve more the ROM problem than RAM. Those Taylor-series algos require storing (small) arrays, as does the geode rising rather larger. If the numbers can't change, they can be offshored to some shared server. Deterministic random-number results also count as ROM-storable, although in graduation from dev to unit-testing we should be using natural noise (quantum noise seems most-scalable; here's a paper).
RAM would be used where the random-number seed expands dimensionally, to the limitless possibilities of the human imagination. We don't distribute this memory.
I'm considering here how (deterministic) random numbers can give rise to a generated maze for some game. Or to cellular-automated caverns. Fractal landscapes.
I found with some of these algos (including the geode as solution to quintics, since the geode was not in 2025 stored in any offsite server) that storing an array that is too large will, itself, cost time. This is because of the Swap File pattern in just-about any machine with an internal hard drive. If you don't tell the computer otherwise, it will use the drive as an extender to RAM. That is unpleasant for the drive and it wastes your time.
This puts us back to the good ol' days of the TRS-80 when you saved your code on tape and all RAM was hard-limited to, what, 32KB (K for kibi-, these days). Modern bithackers experience those days with stackalloc, after limiting that memory themselves to maybe just one kibi.