Please
I beg you
On my hands and knees, I beg you
STOP. USING. GENERATIVE. AI. TO. ANSWER. LEGAL. QUESTIONS. OR. GET. LEGAL. ADVICE.
Buck Borasky, Frontier Programmer {he/him}
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Retired scientific applications programmer: algorithmic composition, digital sound synthesis, #WNBA analytics
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Please
I beg you
On my hands and knees, I beg you
STOP. USING. GENERATIVE. AI. TO. ANSWER. LEGAL. QUESTIONS. OR. GET. LEGAL. ADVICE.
here’s an interesting paper (that I’ve only skimmed and can’t really claim to have understood) that does a few interesting things.
Back when I was young and mentally agile, using parallel computers to solve Ising models was a thing, and it maybe sold a few instances of exotic machines.
This paper is part of a trend that reverses the process: here, they’re using physical instantiations of Ising models to solve parallel computations of the NP-hard variety.
That was interesting thing one.
Interesting thing two is using telecom-grade DSPs to condition the input into the system and to equalize the output, compensating for known distortions in the optical path.
Interesting thing three is what the DSPs on the output end are doing — there’s compensation for known distortions that comes out of the system, but they’re also *leaving in some of the noise*. The noise acts as “simulated”annealing (it’s more like *real* annealing).
So: they’ve gotten a lot of mileage by looking at several problems “in reverse”, which I guess is interesting thing number four.
https://arxiv.org/pdf/2509.09581
University press release with link to an article in Nature:
https://www.queensu.ca/gazette/stories/using-light-based-computing-tackle-complex-challenges
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