MurphyA/hacker-news huggingface dataset:
by StopDisinfo910 | 2025-06-09 12:12:34+00:00
I mean, ok, but why?

The point of lowering application permission is not to prevent you from doing things. It’s to prevent the application to do things you don’t want.

That’s why people try to give apps as little permission as possible and only grant them when they are required.

Technically you are one vulnerability away from irremediably losing everything after opening a seemingly innocent file. I am actually convinced the sole reason it doesn’t happen is because it doesn’t make sense to target people doing that because they virtually don’t exist.
by hypercube33 | 2025-06-09 12:13:05+00:00
My guess on some of this has to do with a few things. Hormone levels vary throughout the day along with immune system activity; My allergies are always worse in the morning than the day. I'm sure time is a huge component in a lot of medical things but I haven't personally seen any studies on this.

Most people also fast at night (sleeping) and are less physically active etc etc.
by jcelerier | 2025-06-09 12:13:11+00:00
> After this experience, I consider it an absolute requirement to have a real "private" struct member facility in a language.

I think that's the wrong take to have. Life is much easier when you accept the reality of a world where people will do whatever they want with what you give them.

C++ has private, and so what? I've seen #define private public or even -Dprivate=public, I've seen classes with private implementation detail reimplemented with another name and all fields public & then casted, I've seen accessing types as char arrays and binary operations to circumvent this, I've seen accessing the process raw memory pages. If someone other than you can call the code, it's not yours anymore to decide what can be done with it.

What you don't owe anyone is the guarantee of things working if people stray from the happy path you outline - they want help after going astray, give them your hourly rate on fixing their mistakes.
by jefflinwood | 2025-06-09 12:14:12+00:00
This was a great article!

I saw "To Fly!" for the first time at the Smithsonian Air and Space Dulles location (Steven F. Udvar-Hazy Center) on their IMAX screen two years ago. Definitely a film of its moment, and I can see how that influenced future science film documentaries.

My dad worked for Spitz doing Omnimax installations and planetariums, but I don't know any of the details. I would assume this was probably the late 70s or early 80s.
by gavinray | 2025-06-09 12:14:15+00:00
I have faith that ML can approximate calories from images under the following conditions:

- The food is inside containers of a standard size, which the model can use as a volumetric reference

- Optimally, the containers are clear plastic, for depth approximation

- The food is homogenous, or mostly-homogenous. Dishes containing a lot of mixed ingredients seem like an intractable problem to me

Now, whether or not abilities like this are useful to you would seem heavily dependent on your diet.
by rmah | 2025-06-09 12:15:04+00:00
War. It's called war. In ye olden days, military forces pillaged farms (for food) and sacked cities (for loot). Rarely, there have been short eras of formalized military v military conflicts within various cultures, but this was not the norm. In more modern times, military forces destroys enemy infrastructure to degrade their ability to make war directly target civilians to reduce the # of enemies and hurt morale (supposedly). While sad, one of the facts of life is that war leads to civilian deaths. Usually far more than direct military deaths.
by LorenDB | 2025-06-09 12:15:27+00:00
> you can only bend the higher of the two notes in a hole

This only holds true if you don't count overbending.

Overbending works by forcing the "wrong" reed to play, e.g. forcing the draw reed to sound when you blow while the blow reed remains silent. Overbends nicely complement normal bends, but interestingly, overbends are a half-step higher than the highest reed in the hole.
by friendzis | 2025-06-09 12:15:29+00:00
> It's not the same as a Federal system where there is one way and the rest are derivatives of it.

The general framework is still based on the same core principles and regulations, therefore cores most of the systems are much more alike than different. The differences come in additional details, where data models can be extended from common core and local workflows and local integrations, where custom development is required anyway. If those custom developments are built on top of common open core, the know-how translates laterally across whole EU and expertise can at the very least be shared.
by ksec | 2025-06-09 12:15:41+00:00
I am a little lost.

>The first issue I have with the paper is that Tower of Hanoi is a worse test case for reasoning than math and coding. If you’re worried that math and coding benchmarks suffer from contamination, why would you pick well-known puzzles for which we know the solutions exist in the training data?

Isn't that exactly what is wrong? It is in the training data and it cant complete it.

It simply isn't reasoning, it is second guessing a lot of things as though it is reasoning.
by JKCalhoun | 2025-06-09 12:15:45+00:00
Almost proves it is not an oracle then, not perceived as one.

Rephrasing: LLMs are the modern day oracle that we disregard when it appears to be hallucinating, embrace when it appears to be correct.

The popularity of LLMs may not be that we see them as mystical, but rather that they're right more often than they're wrong.

“That is not what I meant at all;

That is not it, at all.”

— T.S. Eliot
by latexr | 2025-06-09 12:15:45+00:00
> Cringing at the thought of expressing yourself isn't healthy.

That’s not what I said, and if you engage with my comment until the end in good faith you might realise that. I specifically said it is a positive for the author to share what makes them happy.

But “cringe” was definitely the wrong word and weakened what I was attempting to express. I had just came from that subreddit and failed to identify the correct feeling. This is not an excuse, the error there was mine.

> There is no extracting the objective from the emotional when the very topic is the emotions it makes you feel.

Then be honest about it and say you’re sharing your experience, don’t pretend to be making a list to “contribute to the discussion” or “help others make up their mind”. That’s my point. I was genuinely interested in the article until I realised it was just personal gushing. Which again, is fine, just be honest about it.

In other words, I completely agree with you. But what you’re defending the author for is not what they did.
by poncho_romero | 2025-06-09 12:15:57+00:00
But healthcare is a captive market. People will pay whatever is necessary for life saving treatment. That’s not the case with, say, buying a new couch. If couches are too expensive, people will find alternative places to sit or forgo having a seat altogether. No diabetic can say no to insulin, so the market forces that produce sane prices elsewhere in the economy vanish in healthcare
by EMM_386 | 2025-06-09 12:16:11+00:00
Because I provided evidence?

And made the relevant point that I need know what you mean by "understanding"?

The only 2 things in the universe that know that 6 is the maximum white flag emojis for jokes, and then might be modified by plus signs is ...

My brain, and that digital instance of Claude AI, in that context.

That's it - 2. And I didn't teach it, it picked it up.

So if that's not "understanding" what is it?

That's why I asked that first, example second.

I don't see how laying out logically like this makes me the "Reddit Expert", sort of the opposite.

It's not about knowing the internals of a transformer, this is a question that relates to a word that means something to humans ... but what is their interpretation?
by lo_zamoyski | 2025-06-09 12:16:14+00:00
The latter wouldn’t be valid, as baptism also involves an intentional act. Merely touching holy water doesn’t baptize you. In fact, you don’t even need holy water for baptism, strictly speaking. Any Christian can validly baptize any non-Christian by merely pouring any water over someone’s head and stating that they baptize this person in the name of the Father, the Son, and the Holy Spirit. (Whether it is a licit baptism is another question.)

As for the former, that could very well be an example of the absurdities that Pharisaical thinking can lead to. On the one hand, there is the prudential application of the law. On the other, there is the semantic manipulation of the law even to the point where its observance is rendered comically vacuous and nonexistent.
by delusional | 2025-06-09 12:17:40+00:00
This comes down to the interpretation of the Turing test. Turing's original test actually pitted the two "unknowns" against each other. Put simply, both the human and the computer would try to make you believe they were the person. The objective of the game was to be seen as human, not to be indistinguishable from human.

This is obviously not quite what people understand the Turing test as anymore, and I think that interpretation confusion actually ends up weakening the linked paper. Your thought aptly describes a problem with the paper, but that problem is not present in the Turing test by its original formulation.
by reaperducer | 2025-06-09 12:18:02+00:00
Why would investors care about your product or users? They care about returns.

You can do two, or even all three of those things. Human beings are not boolean greed machines.

The HN bubble likes to reduce everything to a numbers game. Real life isn't like that, as demonstrated by the many tens of thousands of companies that aren't run like a dystopian Silicon Valley comic book.
by bob1029 | 2025-06-09 12:18:33+00:00
For B2B scenarios, I was able to wiggle my way out of learning the iOS ecosystem by using PWAs in Safari.

If you have access to a mobile device management system, it's trivial to push "apps" to the Home Screen of each device using this technique.

For B2C, I don't have any good alternative. You're going to need to be in the App Store.
by zzbzq | 2025-06-09 12:18:59+00:00
The LLMs do have "latent knowledge," indisputably, the latent knowledge is beyond reproach. Because what we do know about the "black box" is that inside it, is a database of not just facts, but understanding, and we know the model "understands" nearly every topic better than any human. Where the doubt-worthy part happens is the generative step, since it is tasked with producing a new "understanding" that didn't already exist, the mathematical domain of the generative function exceeds the domain of reality. And, second of all, because the reasoning faculties are far less proven than the understanding faculties, and many queries require reasoning about existing understandings to derive a good, new one.
by incomingpain | 2025-06-09 12:19:57+00:00
I do have a tab open. Over a week since my last query to perplexity. It still has the best layout of all AI ive tried.

It has never been my go-to AI.

Claude I have never used directly, it's always secondary like Pycharm.

My LLM rankings:

1. Chatgpt and grok

3. Deepseek R1 Qwen3 8b hybrid.

4. meta ai in messenger

5. IBM granite 3.2 8b

6. Google Gemma 9B

7. Mixtral

I need a better video card :)

Right now I am monkeying around with Deepseek r1 qwen3 235B with a context window of 20,000. 1 eternity later...
by akoboldfrying | 2025-06-09 12:21:38+00:00
Sure, a non-human's performance "should" be capped at ~50% for a large sample size. I think seeing a much higher percentage, like 73%, indicates systematic error in the interrogator. This -- the fact that humans are not good at detecting genuine human behaviour -- is really a problem in the Turing test itself, but I don't see a good way to solve it.

LLaMa 3.1 with the same prompt "only" managed to be judged human 56% of the time, so perhaps it's actually closer to real human behaviour.
by adrian_b | 2025-06-09 12:22:28+00:00
You are right and almost all methods of numerical integration (in any case all those that are useful and I am aware of) are equivalent to approximating the target function with another simpler function, which is then integrated using an exact formula.

So the estimation error is introduced at the step where a function is approximated with another function, which is usually chosen as either a polynomial or a polynomial spline (composed of straight line segments for the simplest trapezoidal integration), not at the actual integration.

Fortunately, for well-behaved functions, when they are approximated by a suitable simpler function, the errors that this approximation introduces in the values of function integrals are smaller than the errors in the interpolated values of the function, which are in turn smaller than the errors in the values estimated at some point for the derivative of the function (using the same approximating simpler function).
by danielvaughn | 2025-06-09 12:23:06+00:00
Just want to point out that nowhere in this article (nor in others like it) is the actual thing that was created.

I’m not a skeptic, I know that AI is super powerful, because I use it every day.

But I also have been trying to get it to produce quality UI for a long time, and have never succeeded.

So, I’m gonna need to see some proof.
by clejack | 2025-06-09 12:23:17+00:00
Are people still experiencing llms getting stuck in knowledge and comprehension loops? I used them but not excessively, and I'm not heavily tracking their performance either.

For example, if you ask an llm a question, and it produces a hallucination then you try to correct it or explain to it that it is incorrect; and it produces a near identical hallucination while implying that it has produced a new, correct result, this suggests that it does not understand its own understanding (or pseudo-understanding if you like).

Without this level of introspection, directing any notion of true understanding, intelligence, or anything similar seems premature.

Llms need to be able to consistently and accurately say, some variation on the phrase "I don't know," or "I'm uncertain." This indicates knowledge of self. It's like a mirror test for minds.
by antisol | 2025-06-09 12:25:18+00:00
> petulant and entitled

Some people think it comes off as petulant and entitled when you create a new thing with no regard to being compatible with the old thing, and then demand that the entire world adapts to you and starts supporting your thing.

> I hope you'll invest some effort in supporting this new group of people prolonging its life.

If they can show some tangible progress and improvements I almost certainly will! :)
by martindbp | 2025-06-09 12:25:19+00:00
Many people who claim that people don't understand how AI works often have a very simplified view of the short comings of LLMs themselves, e.g. "it's just predicting the next token", "it's just statistics", "stochastic parrot" and seems to be grounded in what AI was 2-3 years ago. Rarely have they actually read the recent research on interpretability. It's clear LLMs are doing more than just pattern matching. They may not think like humans or as well, but it's not k-NN with interpolation.
by aplanas | 2025-06-09 12:25:54+00:00
That is a key area too. I know that most of the distros are working in that direction[1], and in the case of Fedora, openSUSE or Debian they are reaching high level of reproducibility.

Those distributions are making huge efforts in keeping a core that is 100% reproducible, working upstream to fix issues, and providing reporting and tests tools to detect regressions (for example [2])

This is why a fork is usually a bad approach.

[1] https://reproducible-builds.org/who/projects/ [2] https://en.opensuse.org/openSUSE:Reproducible_Builds
by redrblackr | 2025-06-09 12:25:56+00:00
They launched those live stats in 2023[[1](https://help.kagi.com/kagi/company/history.html)], it shows all data since they built the logger.

Not zero indexing is misleading if you are comparing discrete things like GPU performance, not in the case of plotting a timeline graph. Their published stats could be seen as misleading if they only displayed a short and/or a specific timeline (excluding the latest data for example).
by vjulian | 2025-06-09 12:26:13+00:00
I use ChatGPT (4o model) to track my daily macros, specifically calories, protein, fat, and carbs. I have a prompt containing my macro targets and a list of the foods I typically eat or have at home. Throughout the day, I report my meals, for example five large eggs, two slices of sprouted wheat toast, and a cup of spinach with a pat of butter. It calculates the macros for each meal and keeps a running macros total so I can see how I’m tracking. It’s so easy—I don’t even type, I use (the overall excellent) ChatGPT’s TTS function in the iOS app.

If I eat something unexpected, I have it recalculate what I need for the rest of the day. If I were planning on 8 ounces of chicken for dinner but had an unplanned snack, it might tell me that 6 ounces will suffice now, and I now have room for more fat, so I could add some cheese. I find it quite accurate in this.

Where it loses accuracy is over time. Even when focused on just a single day, the cumulative totals can be wrong when the context window gets too large. I catch this by mentally adding up the numbers it spits out. If the numbers seem off, I open a new chat. Also, it’s not reliable for looking back over multiple days or trying to track long-term patterns.

When I take a photo of a meal (like when eating out), it performs very well. I ask it to list the contents of the plate, then I correct any assumptions. Once corrected, the resulting macros are usually within 5% accuracy. I also tell it the quality of the ingredients and restaurant overall, which can help a bit.
by rhubarbtree | 2025-06-09 12:26:22+00:00
If there is one cliche that I’d like to outlaw, it is this one. It’s a vacuous tautology and yet I hear so many people parrot it all the time.

There are many reasons to believe LLMs in particular are not going anywhere fast.

We need major breakthroughs now, and “chain of thought” is not one.
by Terretta | 2025-06-09 12:26:52+00:00
> "I want my phone to work until the hardware fails... But apple explicitly tries to make the environment around the old phone obsolete."

As of December 31, 2022, U.S. telcos* decommed the 3G tech phones of the mid-2000s used.

As long as your iPhone can still use the telco network, it still does the functions it shipped with just fine.

A phone's environment depends on a lot more than its hardware maker.

(FWIW, thanks to its different incentives than other handset makers, Apple continues to offer the longest average OS support of the hardware and OS giants. Google's Pixel 8 is the first to bump Google's 3 years to match Apple's 7, and Samsung's S24 also finally matches 7 years. So on current devices, they stepped up, but on devices before 2023, Apple's support is more than double theirs. Arguably, these two finally stepping up were in response to the "environment" of Apple's incentives and EU regs.)

* Verizon was the last. AT&T turned off its 3G network on February 22, 2022. T-Mobile dismantled Sprint's 3G CDMA network on March 31, 2022, and its own 3G UMTS network on July 1, 2022.
by coltoneakins | 2025-06-09 12:27:35+00:00
100% agree it is about re-educating yourself about calories in food. I wasn't happy with my weight but it was never 'obese': I am 5' 8" and weighed 187 lbs.

Back in November I started tracking calories in the app Cronometer. I lost 35+ lbs down to 151 lbs as of this morning.

Even as a 'relatively healthy' dude, I realized just how bad my perception of calories and macros in food was. So, I totally agree with this.
by rsynnott | 2025-06-09 12:27:44+00:00
This is kind of the classic story of attempts to cut government 'waste'; new person in goes in gung-ho having apparently never read about any previous attempts, discovers that actually most of the spending is on untouchable stuff, screws around a bit saving almost no money but causing severe disruption which will cost far more in the long run than any savings, declares victory. Repeat in ten to twenty years, when everyone's forgotten.
by lordnacho | 2025-06-09 12:27:47+00:00
This is maybe the best response thus far. We can say that there's no real modelling capability inside these LLMs, and that thinking is the ability to build these models and generate predictions from them, reject wrong models, and so on.

But then we must come up with something other than opening up the LLM to look for the "model generating structure" or whatever you want to call it. There must be some sort of experiment that shows you externally that the thing doesn't behave like a modelling machine might.
by Mordagh | 2025-06-09 12:28:30+00:00
> I do use network transparency (ssh -Y) quite often and it's not there in Wayland.

There's waypipe (https://gitlab.freedesktop.org/mstoeckl/waypipe), which works well in my experience. Although I must say I don't use network transparency (be it X or Wayland) much these days.
by dijksterhuis | 2025-06-09 12:28:38+00:00
i think you might have misunderstood the meaning of “producing text” in the parent comment.

the kind of people the parent comment was talking about tend to believe they can send three emails and make millions of pounds suddenly appear in business value (i’m being hyperbolic and grossly unfair but the premise is there).

they think the idea is far more valuable than the implementation - the idea is their bit (or the bit they’ve decided is their bit) and everyone else is there to make their fantastic idea magically appear out of thin air.

they aren’t looking at tests and don’t have a clue what a linter is (they probably think it’s some fancy device to keep lint off their expensive suits).
by shagie | 2025-06-09 12:29:05+00:00
If you are not familiar with the term, it can be hard to search for it.

Google doesn't give you the answer (unless you're reading the AI summaries - then it's a question of which one you trust more). Instead it provides links to

https://www.scienceofbirds.com/blog/the-parts-of-a-feather-and-how-feathers-work https://www.birdsoutsidemywindow.org/2010/07/02/anatomy-parts-of-a-feather/ https://en.wikipedia.org/wiki/Feather https://www.researchgate.net/figure/Feather-structure-a-feather-shaft-rachis-and-the-feather-vane-barbs-and-barbules_fig3_303095497

These then require an additional parsing of the text to see if it has what you are after. Arguably, one could read the Wiki article first and see if it has, but it's faster to ask ChatGPT and then verify - rather than search, scan, and parse.
by Zak | 2025-06-09 12:30:12+00:00
This seems like the sort of question that's very likely to produce a hallucinated answer.

Interestingly, I asked Perplexity the same thing and it said that clothianidin is not commonly recommended for carpet beetles, and suggested other insecticides and growth regulators. I had to ask a follow-up before it concluded clothianidin probably will kill carpet beetles.
by sbarre | 2025-06-09 12:30:58+00:00
AI and vibe coding lets you get that rough prototype up and running so much faster than before, and so creates that illusion of momentum and completeness more than ever.

How many people here have been subjected to that "looks good, put it in production!" directive after showing off a quick POC for something? And then you have to explain how far away from being production-ready things are, etc...

There's a reason wireframing tools intentionally use messy lines, and why most UX people know better than to put brand colours in wireframes.
by lofaszvanitt | 2025-06-09 12:31:41+00:00
You push sha512 hashes of things in a githup repo and a short sentence:

x8 version: still shit . . x15 version: we are closing, but overall a shit experience :D

this way they won't know what to improve upon. of course they can buy access. ;P

when they finally solve your problem you can reveal what was the benchmark.