bobmarleybiceps

oh that actually helps a lot. I was tying myself in knots trying to think it through.

yeah you're right. I wrote that poorly and seems I can't edit it now.. In the link I shared, he does propose the similar theta and omega notations and provides the existing definition for big O.

dang, that's exactly what it is. I was not expecting someone to recognize it! This also made me smile :-)

Agreed. I generally feel pretty comfortable with letting LLMs implement stuff like individual functions or algorithms. But I feel like it takes some effort to make sure separate things stay separated... (I do think it also helps LLMs if the code to stays nicely abstracted.)

On the other hand, I do some stuff with physics simulations and LLMs will spit out arrays like

static constexpr std::array<int, Q> cx{0, 1, 0, -1, 0, 1, -1, -1, 1};

static constexpr std::array<int, Q> cy{0, 0, 1, 0, -1, 1, 1, -1, -1};

static constexpr std::array<real_t, Q> w{ real_t(4) / 9, real_t(1) / 9, real_t(1) / 9, real_t(1) / 9, real_t(1) / 9, real_t(1) / 36, real_t(1) / 36, real_t(1) / 36, real_t(1) / 36};

static constexpr std::array<int, Q> opp{0, 3, 4, 1, 2, 7, 8, 5, 6};

and it's like ??? I know what these are for, but I have no clue if any of the numbers are right :-)

at least IME, chrome was unusable on Mac because it would regularly use an absurd amount of memory and freeze everything up.

but we only download the giant binary blobs from trusted actors, such as sam Altman and Elon musk!!

I think nature sort of has a reputation for sensationalism / probably overhyped (or outright wrong) stuff in the sciences now anyway... would not surprise me at all that they're desperate for ml stuff :-I

I think eventually companies won't get as much stuff that's usable for marketing, so they'll stop investing so much into ai for math, so eventually cheap and poor graduate students will be able to do relevant work again without worrying about getting scooped by a company with a million GPUs :-/

I feel like ultimately the journal / conference system of "submit pdf and never be able to update it" doesn't make sense in 2026.

- In a lot of fields, the "deliverable" could just be a git repo. - Results could / should be more reasonable to update as possible issues arise. - Peer review is pretty busted in a lot of fields, so a humongous number of papers get through that have pretty glaring issues.

The combination of being incentivized to spew out nonsense papers + academic publishing institutions liking to get more papers to sell to libraries + government funding being partly chosen by academics who are also part of the problem.

I think part of the issue is that there is basically no downside to having bad papers. In ML conferences, I think the only paper I have ever seen retracted was because some of the authors had an affiliation with a Russian institution. There are LOADS of bogus papers at the big ML conferences and none get retracted afaik ... The worst that can happen is someone tweets about it and then everyone forgets about the paper.

never occurred to me to write a comment there, but it actually looks kind of clean imho. Really old C code looked like

void func(var1, var2) int var1, char* var2. { ... }

perhaps some legacy from that? Probably not, but just first thing that popped into my head since it feels similar :shrug:

IMO, yes a lot of the "nvidia pays lots of people to make non-portable, tightly coupled backends to open source projects" is potentially going to be less of a moat?

(Though it could turn into "nvidia pay lots of people to use LLMs to make non-portable, tightly coupled backends to _even more_ open source projects")

A recent review of mine had a LLM-ism at the very end, "would you like me to format this into a formal peer review report?" So they very likely copy-pasted their whole review :). I'm pretty down on academia atm ;-;