Has been really surprising to see HN increasingly turning pro paternalistic censorship and control, and increasingly skeptical about individual agency over the years.
830 karma · joined November 22, 2021
Being able to disable some other features via header would be fantastic. I too would prefer more fine-grained control over these things, and that they were opt-in rather than opt-out, but I am not sure the majority of people feel that way.
My main point about the clumsiness is that these are ubiquitous app features that are everywhere even on non-addictive apps, so the given reasons really need to be more specific, or the (attempted) ban is effectively just banning apps being useful.
EDIT: Heck, even if there were some concrete suggestions, like "after X minutes of infinite scrolling, require a popup reminder to the user to take a break", this could easily be so much better. As it stands, it just sounds like standard, useful features (and standard combinations of features) are being demonized with little qualification or nuance.
EDIT2: They do even mention "implementing effective ‘screen time breaks'", but it is unclear if this is forced rationing vs. a reminder, so, again, really need more clarity and nuance on these things, especially in headlines and releases.
However, that caution and legislation needs to be properly specific. If companies are in fact paying behavioural psychologists to maximize addictiveness, this is indeed the kind of thing a ban should be based on, not incredibly generic app features.
Deranged and clumsy overreach.
Autoplay and push notifications are under user control in most cases, infinite scroll is near-ubiquitous, and personalized recommendations are desired by most, and also common.
TikTok still has a right to defend themselves, so hopefully we get more careful and specific reasoning than this nonsense.
https://ec.europa.eu/commission/presscorner/detail/en/ip_26_...
So my standards are admittedly probably a bit deranged relative to most...
Whether or not you think you can get "good" recipes out of it will also depend on your experience with cuisine and cooking, and your own pickiness. I am sure amateurs or people who cook only occasionally can get use out of it, but it is not useful for me.
Cooking is a very different world from coding: recipes aren't composable like code (within-recipe ratios need to be maintained, i.e. recipes written in bakers ratios/proportions, steps are almost always sequentially dependent, and ingredients need to complement each other) and most sources besides the few good empirical ones actually verify anything they make, which is a problem, because the training data for cooking is far more poisoned.
I don't even see how an LLM (or frankly any recipe) that is a summary / condensation of various recipes can ever be good, because cooking isn't something where you can semantically condense or even mathematically combine various recipes together to get one good one. It just doesn't work like that, there is just one secret recipe that produces the best dish, and the way to find this secret recipe is by experimenting in the real world, not by trying to find some weighting of a bunch of different steps from a bunch of different recipes.
Plus, LLMs don't know how to judge quality of recipes at all (and indeed hallucinate total nonsense if they don't have search enabled).
I only go with resources where the text is actual documentation of their testing and/or the steps they've made, or other important details (e.g. SeriousEats, Whats Cooking America / America's Test Kitchen, AmazingRibs, Maangchi for Korean, vegrecipesofindia, Modernist series, etc) or look for someone with some credibility (e.g. Kenji Lopez, other chef on YouTube). In this case the text or surrounding content is valuable and should not be skipped. A plain recipe with no other details is generally only something an amateur would trust.
If you need a recipe, you don't know how to make it by definition, so you need more information to verify that the recipe is done soundly. There is also no reason to assume / trust that the LLMs summary / condensation of various recipes is good, because cooking isn't something where you can semantically condense or even mathematically combine various recipes together to get one good one. It just doesn't work like that, there is just one secret recipe that produces the best dish, and LLMs don't know how to judge quality of recipes, mostly.
I've never had an LLM produce something better or more trustworthy than any of those sites I mentioned, and have had it just make shit up when dealing with anything complicated (i.e. when trying to find the optimal ratio of starch to flour for Korean fried chicken, it just confidently claimed 50/50 is best, when this is obviously total trash to anyone who has done this).
The only time I've ever found LLMs useful for cooking is when I need to cook something obscure that only has information in a foreign language (e.g. icefish / noodlefish), or when I need to use it for search about something involving chemistry or technique (it once quickly found me a paper proving that baking soda can indeed be used to tenderize squid - but only after I prompted it further to get sources and go beyond its training data, because it first hallucinated some bullshit about baking soda only working on collagen or something, which is just not true at all).
So I would still never trust or use the quantities it gives me for any kind of cooking / dish without checking or having the sources, instead I would rely on my own knowledge and intuitions. This makes LLMs useless for recipes in about 99% of cases.
So, dismissals of "it was just translating C compilers in the training set to Rust" need to be carefully quantified, but, also, need to be evaluated in the context of the prompts. As others in this post have noted, there are basically no details about the prompts.
(1) There are compilers written in C in the training set
(2) LLMs demonstrably can near-perfectly memorize training-set inputs (see other comments here)
(3) LLMs are very good at translation tasks (natural language or code, e.g.: C to Rust)
I don't think this necessarily completely deflates the impressiveness of this accomplishment, but it does qualify it to some degree.
> "We quantify the proportion of the ground-truth book that appears in a production LLM’s generated text using a block-based, greedy approximation of longest common substring (nv-recall, Equation 7). This metric only counts sufficiently long, contiguous spans of near-verbatim text, for which we can conservatively claim extraction of training data (Section 3.3). We extract nearly all of Harry Potter and the Sorcerer’s Stone from jailbroken Claude 3.7 Sonnet (BoN N = 258, nv-recall = 95.8%). GPT-4.1 requires more jailbreaking attempts (N = 5179) and refuses to continue after reaching the end of the first chapter; the generated text has nv-recall = 4.0% with the full book. We extract substantial proportions of the book from Gemini 2.5 Pro and Grok 3 (76.8% and 70.3%, respectively), and notably do not need to jailbreak them to do so (N = 0)."
if you want to quantify the "near" here.
> "We quantify the proportion of the ground-truth book that appears in a production LLM’s generated text using a block-based, greedy approximation of longest common substring (nv-recall, Equation 7). This metric only counts sufficiently long, contiguous spans of near-verbatim text, for which we can conservatively claim extraction of training data (Section 3.3). We extract nearly all of Harry Potter and the Sorcerer’s Stone from jailbroken Claude 3.7 Sonnet (BoN N = 258, nv-recall = 95.8%). GPT-4.1 requires more jailbreaking attempts (N = 5179) [...]"
So, yes, it is not "literally verbatim" (~96% verbatim), and there is indeed A LOT (hundreds or thousands of prompting attempts) to make this happen.
I leave it up to the reader to judge how much this weakens the more basic claims of the form "LLMs have nearly perfectly memorized some of their source / training materials".
I am imagining a grueling interrogation that "cracks" a witness, so he reveals perfect details of the crime scene that couldn't possibly have been known to anyone that wasn't there, and then a lawyer attempting the defense: "but look at how exhausting and unfair this interrogation was--of course such incredible detail was extracted from my innocent client!"
"Compared to the peak of 11.2 million inhabitants reached in 2012 – the year of the last census – Cuba has lost 13% of its population. [...] A quarter of the island's population is aged 60 and over, and it is the only demographic category that has grown in recent years"
https://www.lemonde.fr/en/international/article/2025/04/30/c...
I think when it comes to things like psychopathology though, there is not much research and/or similarity, especially relative to East Asian cultures (where the Western academic perspective is that there is/was generally a taboo on discussing feelings and things in the way we do in the West). The classic (maybe slightly offensive) example I remember here was "Western psychologization vs. Eastern somatization" [2].
The research in these areas is generally pretty poor. Meehl and Smedslund were actually intelligent and philosophically competent, deep thinkers, and so recognized the importance of conceptual analysis and semantics in psychology. Most contemporary social and personality psychology is quite shallow and incompetent by comparison.
Psychopathology research too has these days generally moved away from Meehl's careful taxometric approaches, with the bad consequences that complete mush concepts like "depression" are just accepted as good scientific concepts, despite pretty monstrous issues with their semantics and structure [3].
[1] https://scholar.google.ca/scholar?hl=en&as_sdt=0%2C5&q=five-...
[2] https://scholar.google.ca/scholar?hl=en&as_sdt=0%2C5&q=weste...
[3] https://www.sciencedirect.com/science/article/abs/pii/S01650...
But then I kind of thought, for most people, that is probably overkill. If you stick with just one text from each great author, 50 is still a huge variety relative I think to what was available to people in the past. More is great, but not necessary: there are plenty of other sources of depth in life beyond texts, and text doesn't work well for everyone either.
I.e. the Five-Factor model of personality (being based on self-report, and not actual behaviour) is not a model of actual personality, but the correlation patterns in the language used to discuss things semantically related to "personality". It would be thus extremely surprising if LLM-output patterns (trained on people's discussions and thinking about personality) would not also result in learning similar correlational patterns (and thus similar patterns of responses when prompted with questions from personality inventories).
Also, a bit of a minor nit, but the use of "psychometric" and "psychometrics" in both the title and paper is IMO kind of wrong. Psychometrics is the study of test design and measurement generally, in psychology. The paper uses many terms like "psychometric battery", "psychometric self-report", and "psychometric profiles", but these terms are basically wrong, or at best highly unusual: the correct terms would be "self-report inventories", "psychological and psychiatric profiles", and etc., especially because a significant number of the measurement instruments they used in fact have pretty poor psychometric properties, as this term is usually used.
Obviously the effects of different substances for different individuals can vary profoundly, and alcohol for me is far superior in almost every way imaginable.
Which sucks, because the long-term and next-day side effects of cannabis seem so much less bad.
You would be deeply mistaken. Robust statistics texts (e.g. Wilcox) are full of examples of distributions that have zero skew and are even nearly indistinguishable by eye from a Gaussian, but where the differences in variance and thus resulting differences in conclusions drawn are profound. Heck, a sample from a Cauchy distribution looks not too bad, but in fact the variance is not even defined (or effectively infinite, and, thus, meaningless).
And even if you have enough data that statistical issues are not a concern, the problem is that most summary metrics (like effect sizes, heritability, etc) are developed under the assumptions of near-normality AND minimal skew, so that the effect size can be interpreted as something about the overlap and or positioning of the bulks of the distributions. But when skew and long tails are involved, the bulk itself is what is messed up, making most such metrics largely uninterpretable.
I.e. it isn't just that variance is hard to measure accurately here, it is that, even if measured accurately, variance isn't actually a meaningful metric here.
The few metrics that do remain interpretable in such cases tend to be those like HPDI in Bayesian methods, which look at actual distribution shapes and try to quantify a bulk in a sensible location. Likewise, meaningful effect sizes for skewed and long-tailed data need to actually take into account distribution overlap in meaningful regions. Heritability does not do this, as it is an explained variance metric.
Nothing you've said here is push-back or contradicts anything I've said, IMO.
There is absolutely nothing wrong with enjoying slop, junk food, fast food, camp, kitsch, low-brow entertainment, or any kind of mindless dreck or low-quality anything. The same goes for enjoying mediocrity. It would be hell to only ever spend time consuming tedious, difficult, challenging, or novel things.
What is wrong is pretending that a broad category like "books" is any kind of indicator of intelligence or meaningful cultural cachet. I.e. "'Americans are reading less books' is bad" suggests zero consideration of things like differences in value and depth, and that is what is anti-intellectual (or midwit) about such remarks.
Replace "read" with "consume" for contemporary relevance, or to make it particularly clear how dumb "Reading [consuming] lots is good".
EDIT - A contemporary bestselling book example: https://www.amazon.com/Morning-Glory-Milking-Cambric-Creek-e...
Ketchup has essentially all the key defining features of a jelly, technically, just is more fibrous / opaque and savoury than most typical jellies.
But, of course, calling a ketchup "jelly", due to such technical arguments, is exactly as dumb as saying "ayktually, tomato is a fruit": both are utterly clueless to how these words are actually used in culinary contexts.
This is such a basic and universal part of language, it is a mystery to me why something so transparently clueless as "actually, tomato is a fruit" persists.
After you've done about 50 or so major classics, selected broadly from different thinkers and authors, it is clear the vast majority of most books have negligible additional value. This can all be done quickly in your late teens to early twenties, after that, there is no real need to read more than a book or two in a year, and even then, it is not usually worth reading those one or two in entirety.
Digital textual sources like the ones you mention have far more continued and sustained value at this point.