As a matter of fact, Nature does regularly publish papers about wetting properties of water. In fact, it just published one last week, from Nature Physics:
Scientists find more or less everything very interesting, even (especially?) things that are supposedly self-evident. You can both make a big splash disproving self-evident things, and much can be learned from it.
What? The basic properties of water are scientifically defined as best we can. There is mathematical proof that 1+1=2. Do you think science starts from nuclear physics or differential equations?
We couldn't build any of this stuff (rockets, LLMs, heart medicine) if the foundation was ill defined.
I think it's the second time I run into you like this, Temporal. I wish HN had a way to classify you as an "AI booster" or equivalent.
> What? The basic properties of water are scientifically defined as best we can. There is mathematical proof that 1+1=2. Do you think science starts from nuclear physics or differential equations?
Yes. There's a lot of interesting things science has to say about water, very specific claims that took a lot of effort to discover, precisely formulate, and reproduce.
We're not talking about those. The whole LLM discussion on HN, as well as in the wider industry, is still stuck at the state where a large (or vocal) group of people refuses to believe water is wet. Yes, there is a similar group that tries to sell water as miracle cure, I'm not denying it - IMO both perspectives are dumb and entirely detached from obvious observational evidence that you can collect for ~free at home in 15 minutes. Example will follow.
There exist the equivalent of foundational, detailed studies on LLMs, at every level of rigor imaginable (with a caveat, it's hard to rigorously prove anything useful in software engineering; it's still largely opinion-driven field). But they're not part of the overall "AI hypers/haters" dynamics.
> I wish HN had a way to classify you as an "AI booster" or equivalent.
You can take any of the LLMs and have it vibecode you a user script in under 5 minutes, than you then can paste into Greasemonkey/Tampermonkey, and voilà, you have me labeled as "AI booster" or filtered out.
In fact, let me help you, I'll time it. I opened chatgpt.com in incognito (to emulate being a rando free user), and put the following prompt in:
> I need a user script I can paste into Tampermonkey on my Firefox that will clearly label user named TeMPOraL with robot emoji and some silly emoji, so I never forget when reading their HackerNews comments that they're an unapologetic AI booster.
This is the promised empirical example. It doesn't prove everything, but it proves something, and it took, end-to-end, a total of 1 minute to perform just now. You can collect many such examples over a single day by just trying. People who keep saying AI is useless and a fad and can't do anything useful, obviously never bother with even that.
FYI: I'm not an AI booster. I like AI, and I find it useful, but I'm not going out of my way to boost it. I just enjoy this topic, but more importantly - and I remain consistent in this - I point out bullshit that doesn't agree with obvious observable reality.
EDIT: try the example yourself, and post whether it works for you too - if it does, it's technically a peer-reviewed, replicated study, but I doubt it'll convince any of the naysayers of anything.
EDIT2: I have plenty of negative things to say about LLM capabilities and how irresponsibly people use them, and I do occasionally write about this (mostly at work, these days), but most HN threads on AI are not on this level - not anymore. They used to be more reasonable back in GPT-4 days.
At the risk of abusing the analogy further: many people aren't refusing to believe it's wet, they're observing that sulfuric acid is also "wet" and can look similar upon visual inspection, and there's a lot of harm coming along with the demonstrated capabilities, in addition to those capabilities themselves being fickle and inconsistent (not a desirable property for a good technology).
This isn't a problem of "doesn't know what AI can do"; yes, some people are misinformed, but you shouldn't dismiss all refusal to use AI as being misinformed. This is a problem of "knows what AI can do, and based on that informed position thinks it's terrible and should have careful guardrails around it".
They're not "3-4 trillion dollars in investments over 5 years" useful, nor "crammed into the throat of every employee on the planet, regardless of their actual job" useful.
The way they are pushed right now will lead to a very hard crash and probably lots of suffering.
Also, you need a more advanced prompt for Firefox on Android :-p
> They're not "3-4 trillion dollars in investments over 5 years" useful
Why not? They're a general-purpose technology, in the same category as "software" or "electricity".
> nor "crammed into the throat of every employee on the planet, regardless of their actual job" useful
They're potentially useful for anything that can be fed into computers (VLMs lifted the "that can be expressed as text" limitation, visual and audio tokens are not a separate category to text tokens anymore). That touches every single job people do in some aspects. Even though LLMs can't do physical work for people, they're still able to help with directing it and teaching it.
"Cramming into the throat of every employee on the planet" was already covered by many comments here, and the article itself - it's about forcing the obstinate holdouts to at least try.
> Also, you need a more advanced prompt for Firefox on Android :-p
No I don't; literally copy-pasted it to Tampermonkey on my Firefox on Android just now, and it works there out-of-the-box too.
Regarding LLMs, they are pushed too hard and too abusively by business people. Employees are being laid off and replaced with chatbots that don't do the job. Frustrating if support for McDonald's, risky if health insurance support. Also the financials don't make sense. AI companies are money pits. Money is ultimately production. We make X amount of stuff yearly, globally. We can't afford to through away 5% of X yearly on technologies that will probably have a proper return in 5 or 10 years. When we mis-allocate resources on scales like these, people die. Look at Communist centralized planning. For $3-4 trillion we could have solved a LOT of actual global problems.
LLMs are fine but they should have matured in the software dev domain for 2-3 more years and then non tech products would have followed.
FYI: I just had three SOTA LLMs + NotebookLM all fail at the simple task of explaining to me where to put a powder detergent in my particular newly bought washing machine, despite having photos of the machine and ability to find the manual (in case of NotebookLM, it literally had the manual as its only source).
After first failure (Gemini 3.5 Flash + NotebookLM), I run the other two (Opus 4.8 on Extra; GPT 5.5 on High) in parallel, and looking at their thought streams, I gave up and dug up the manual and read half of it, before the LLMs finished coming up with - wait for it - wrong answers.
Super frustrating. Doubly so, given that I use them for comparable tasks pretty often and they usually sail through them flawlessly. But this experience happens every now and then. It's only fair to report it, if only so you don't think I'm just AI boosting all the time.
1 is for powder detergent, 2 is for liquid detergent, 3 is for softeners and such.
All three LLMs (Gemini twice, since NotebookLM) insisted I should put the powder detergent into leftmost compartment (2 on the ASCII diagram above). They referred to it by different numbers, but all gave some convincing justification why to put the detergent there. That's despite me posting photos of the compartment drawer, with symbols clearly visible. That's despite demanding they find the manual and cross-ref. I even asked two (Gemini and Claude) to label the actual compartment on the photos I took[0], and both produced some nonsense, with labels in all the wrong places. And they all insisted they're right and issued plenty of warnings about making sure I get this right or else bad things will happen.
BTW. I ended up posting a screenshot of the diagram in the manual to Claude with a passive aggressive comment. Looking at its "thinking summary" and tool calls now, I think at least Claude didn't process the image correctly and only saw:
[ 2 | 3 ]
------------
as those parts are blue, while the bottom is just in the same color as the entire body/frame of the machine. Maybe the contrast was too low for the models. But it was okay for humans, so it's not excusing much, especially that they all claimed to have found the manual, which had a high-contrast diagram.
(Current experience tells me they probably didn't really check the diagram. I noticed recently that all major models seem to have gotten lazy when it comes to reading sources, and are also more than happy to lie about it.)
--
[0] - A method I often use with Claude when I'm not sure if it's dealing with spatial tasks correctly - I have it produce intermediate artifacts that involve modifying "ground truth" inputs - e.g. placing two map pictures on top to verify it solved the coordinate transform, and/or (like here) drawing labels and boxes on top of original photos. I found such requests to be helpful enough I set it as general rule for Claude now.
[1] - Which normally they'd spot, but for some reasons, they didn't.
> I have it produce intermediate artifacts that involve modifying "ground truth" inputs - e.g. placing two map pictures on top to verify it solved the coordinate transform, and/or (like here) drawing labels and boxes on top of original photos.
Like you I use intermediate artifacts all the time, but have never tried with visual elements. But how do you get Claude to modify images? Do you get it to output things to a canvas? html? use an external library?
