MurphyA/hacker-news huggingface dataset:
by photonthug | 2025-06-15 02:53:46+00:00
This is a typical critique that sort of assumes you have to be right about everything to be right about anything. Maybe a useful point of comparison is aether theories in physics. Wrong, sure, but useless? People might argue whether it was always pure distraction or a useful building block, but I wonder what Maxwell or Einstein would say. If nothing else, one needs something to ground one's thinking and to argue against, which is why replacement theories usually need to acknowledge or address what came before. And typically we try to fix bad theories rather than simply discarding them, especially if there's no alternative available. What are the other available "grand unifying theories" of consciousness? To quote the end of Aaronson's rebutal:

> In my opinion, the fact that Integrated Information Theory is wrong—demonstrably wrong, for reasons that go to its core—puts it in something like the top 2% of all mathematical theories of consciousness ever proposed. Almost all competing theories of consciousness, it seems to me, have been so vague, fluffy, and malleable that they can only aspire to wrongness.

Of course, it's not on Aaronson to rescue the theory he's trying to disprove, but notice that he is out to disprove it and spends his time on that, rather than imagining what axioms might be added or replaced, etc. Proving that having a large Φ-value is not a sufficient condition for consciousness hardly seems devastating "to the core", because finding better descriptions of necessary conditions would still represent significant progress.

Similarly a critique like

> He thinks that because in CPUs the physical transistors don't have as many connections as neurons in the brain, that it's fundamentally limited and cannot be conscious.

seems a little bit narrow. I do agree it seems to misunderstand universality, but on the other hand, maybe it's just distracted by counting IO pins on chips, and what it should focus on more is counting nodes/edges in neural net layers, and whether connection-counting in hardware-vs-software might need to have a weighting-coeffecients, etc. HN loves to celebrate things like the bitter lesson, the rise of LLMs and ML, and the failure of classical logic and rule-based reasoning and NLP. Is all of that same stuff not soft-evidence for the relevance if not the completeness of IIT?
by firesteelrain | 2025-06-15 02:54:37+00:00
It’s just a truth table. I had a hunch that it was a truth table and then I asked AI how it figured it out and it confirmed it built a truth table. Still impressive either way

* Goal: Pick (Group ∧ Method) such that Group can plimf ∧ Method is a type of plimfing

* Only one group (Plyzers) passes the "can plimf" test

* Only one method (Quoning) is definitely plimfing

Therefore, the only valid (Group ∧ Method) combo is: → (Plyzer ∧ Quoning)

Source: ChatGPT
by tylerhou | 2025-06-15 03:01:06+00:00
> Sure, if you make a bunch of assumptions and manually implement how you think a regex will compile your code, allowing the optimizer to take your compile time implementation and make a finely tuned algorithm specifically for one use case,

But that's not what is happening. The only thing that the optimizer does is unrolls the loop that advances the state. Otherwise, the sequence of instructions is standard: two loads and some pointer arithmetic. There is no finely tuned algorithm. https://godbolt.org/z/fqdb5bssc

> But if you use an actual regex the way people actually use them, using either the one provided by their standard library or one that is readily available then regex is pretty slow.

Yes, most regex implementations are not designed to be efficient, they are designed for ergonomics (including supporting exponential-time features like lookaround). (Both boost::regex and PCRE2 support exponential-time features; std::regex is just not optimized at all.) This is well-known. https://swtch.com/~rsc/regexp/regexp1.html. That's why I said "well-optimized regex." Most regexes, in practice, are not well-optimized!

Maybe this is a semantic distinction between what you and I consider a "regex." I interpreted "regex" as how people in CS theory understand it; i.e., a finite automata that decides whether a string is in a given language. I think that's perfectly reasonable here, as the given problem is finding the fastest algorithm that decides whether a string is in the language generated by ASCII characters without the vowels. I think it's reasonable for me to use a (not hand-optimized) implementation that obeys that definition in my comparison.
by martinpw | 2025-06-15 03:01:11+00:00
Important caveat to that study from right at the end of the article:

the data set used for the study, while massive, was limited to 2017 data. [...] Uber only added a tipping function to its app in 2017

So the study was either before you could even tip in the app or soon after and when it was still new.

A more recent study would interesting.
by kelnos | 2025-06-15 03:01:33+00:00
When I first set up an account at a new org or whatever, I don't think about the possibility of rotation later, but once I get my first "your password has expired and needs to be reset" message, I just add a counter to the end of the password that I increment each time I'm required to change it. Successive passwords have no less entropy than the original password, anyway.

Fortunately, I haven't encountered a system that does a similarity check when changing the password.
by Brystephor | 2025-06-15 03:03:00+00:00
Forcing reasoning is analogous to requiring a student to show their work when solving a problem if im understanding the paper correctly.

> you’d have to either memorize the entire answer before speaking or come up with a simple pattern you could do while reciting that takes significantly less brainpower

This part i dont understand. Why would coming up with an algorithm (e.g. a simple pattern) and reciting it be impossible? The paper doesnt mention the models coming up with the algorithm at all AFAIK. If the model was able to come up with the pattern required to solve the puzzles and then also execute (e.g. recite) the pattern, then that'd show understanding. However the models didn't. So if the model can answer the same question for small inputs, but not for big inputs, then doesnt that imply the model is not finding a pattern for solving the answer but is more likely pulling from memory? Like, if the model could tell you fibbonaci numbers when n=5 but not when n=10, that'd imply the numbers are memorized and the pattern for generation of numbers is not understood.
by FabHK | 2025-06-15 03:03:06+00:00
Wow, USD 8 to 11 per km.

Hong Kong taxis cost USD 3.5 for the first 2 km, then USD 1.4/km, and less than a dollar per km above USD 13.

https://www.td.gov.hk/en/transport_in_hong_kong/public_trans...
by wpm | 2025-06-15 03:07:28+00:00
One small cut, but one edging so so close to the line of death.

Who the *fuck* does Apple think they are forcing app icons provided by the developer into a specific shape? Or applying some weird ugly "glass" filter on it? Without permission from the user?

Hey Alan Dye, take a chill pill man. It's not your computer once I buy it.
by ryandv | 2025-06-15 03:07:44+00:00
> If you want to define "god" as a definition for abstract concepts, I can't stop you, although I doubt many would agree.

This is extremely ignorant. God has been likened to an intellect since the days of the Neoplatonists and Aristotle himself as a "nous" or universal mind. Cosmological arguments necessitating the existence of this god as a "first cause" to avoid fallacies of infinite regress ended up rooting much of medieval theology and mainstream religious scholarship.

> The difference is that things like mathematics and identities are useful.

Civilizations have centered around the mental and social construct of gods since the era of Mesopotamian god-kings that built and organized society.
Show HN: I created a multiple JSON queries tool
by anlac96_it | 2025-06-15 03:07:54+00:00
Tired of searching through JSON data field by field, so I created a JSON multi-queries tool.

First, I pass the JSON to the tool. Then I extract all the paths I'm interested in (for debugging, testing, or demoing to a co-worker). Next time, I just pass in new JSON data and instantly see those fields.

I'm too lazy to copy and paste, so the tool supports extracting JSON paths by clicking on the JSON key.

It's a nice-to-have tool - not essential - but it helps me reduce some clicks and makes working with JSON a bit smoother.

If you are interested, you can give it a try here: https://app.postpilot.dev

Any feedback is appreciated.
by yesco | 2025-06-15 03:09:02+00:00
I'm broadly speculating here, but I tend to view most mechanisms like this as evolution's "desire" for a well tuned, but imperfect, CRC check.

Ignoring whether or not it would even be possible, a perfect CRC is antithetical to evolution itself, wiggle room for mutation must always be possible, but too much mutation gives you cancer and systemic malfunction. So you end up with these bizarre processes that allow just the right amount of imperfection.

With sperm specifically it ends up closer to a signature check than just a CRC, if the sperm doesn't exhibit behavior that falls under a certain umbrella of expected behaviors, it's rejected by the surrounding environment. The difficulty to comprehend it could even be a feature of the process in many respects, especially when you consider everything in this realm risks getting "hacked" if precautions are not put into place.

So when I see huge numbers like this, I see it as an indirect measure of the precision of the overall process. To put it another way, it's like brute forcing a password you don't know, but happen to have a lot of hints to (since obviously, we are all still the same species at the end of the day).
by krackers | 2025-06-15 03:09:21+00:00
If anything you'd think that the neurosymbolic people would be pleased that the LLMs do in fact reason by learning circuits representing boolean logic and truth tables. In a way they were right, it's just that starting with logic and then feeding in knowledge grounded in that logic (like Cyc) seems less scalable than feeding in knowledge and letting the model infer the underlying logic.
by smelendez | 2025-06-15 03:09:32+00:00
Yeah, Walgreens (US drugstore chain for those who aren’t familiar) often has their cashiers say “hi, welcome to Walgreens!” when customers come in.

It just doesn’t work. Customers are wearing headphones, or on the phone, or sick with a respiratory infection and wearing a mask and trying not to talk unnecessarily, or don’t speak English very well, or maybe they’re just trying to remember everything they need, and it quickly gets awkward for everyone.

The same store often has the credit card terminal ask customers to donate a dollar to various causes, which I’ve seen completely stump foreign tourists and generally slowing down the line.
by consumer451 | 2025-06-15 03:10:32+00:00
> Where is the will?

The memory of what happens when hate takes control died, literally. The people who survived WWII are mostly gone. Now, without their knowledge, without their shame, we are swinging back to the other side.

My concern is that the swing happens to go the wrong way, at the same time that we happen to have an incredibly asymmetric ability to destroy each other.

We might be 1 or 5 swings away from that being a truly end-of-times situation. At this point, that seems to me like it would be when a pissed off kid, whose parents were killed as "collateral damage," could create a novel pathogen with only a $10k lab.

If tech and hate keeps advancing at the current rate, what is that, 100 years max? We really need to get our shit together.
by ethbr1 | 2025-06-15 03:11:00+00:00
The problem is inherent in any business that scales revenue by volume.

For many goods (short term rental spaces, rideshares), there's a finite supply of quality at a given price point.

Both AirBnb and Uber/Lyft tried to over-scale their supply, to drive revenue, and quality suffered.

Now they're fishing for the Amazon-ian point where they maximize revenue without making people so unhappy they stop using the service.
by Andrex | 2025-06-15 03:11:50+00:00
I mean, why did we go to Mars without any intention of colonizing it? The Moon?

Exploration is what humans do. If Alpha Centauri is only 5 years away, you can bet we're sending people there no matter what. Permanent colonization only becomes a priority in an existential event, and if we eventually achieve ~1.0C travel then that implies our tech is at a level we'd be colonizing Mars and a few moons long before we think about leaving the solar system.

Even getting to 0.9C is going to be very very hard for humanity.
by bscphil | 2025-06-15 03:16:17+00:00
> Why would you want to continue using a service that is ripping you off?

For the same reason that I'm going to continue using Uber despite them ripping other people off, as described in this very thread. People systematically overweight their own negative experiences and underweight those of others; I believe that every single negative story about Lyft and Uber I've read in this thread is likely to be true. In other words, they do sometimes rip people off. On the other hand, am I likely enough to be ripped off the next time I use Uber that it doesn't make sense to use it? (And do what instead, walk?) No. It's unfortunate, and I support social solutions to the problem like better regulation of businesses, but if I personally dropped every company I think sometimes rips people off, I would do business with no one ever.
by Daub | 2025-06-15 03:18:57+00:00
I will reserve judgement on this for the simple fact is that att conservation has been responsible for a huge amount of Art vandalism.

The 'restoration' of the cistine chapel ceiling was funded by a Japanese tv company. The cheapest approach was chosen which assumed that michelangelo made absolutely no corrections to his fresco using applied paint. It is perfectly obvious that this was a mistaken assumption, in the process removing many of the artists original work. I can upload some slides later if anyone is interested.

In victorian times many classic sculptures were scrubbed of their original paint and their stonework bleached, just in order to serve the tastes of the time.

And let us not forget that modern conservators will add or remove elements according to the clients taste. Eg change the flag of a ship from British to American.
by spockz | 2025-06-15 03:18:59+00:00
IME, the suffering of bad performing code is mostly secondary. It increases compute costs. Mostly because requiring more beefy VMs than strictly required which is still benign and possibly more cost-efficient than spending more engineering effort. Sometimes because of the lack of performance now more scaling and orchestration is required which comes additional complexity and therefore compute and staffing cost. This is rare to get noticed and fixed due to organisational momentum.

The worst is when the performance is so bad it starts to prevent onboarding new features or customers.
by NoahZuniga | 2025-06-15 03:19:16+00:00
This article doesn't mention what the yoy return is (bizarrely), but just buying the S&P 500 outperforms human investors, so just buying stocks randomly, which would have about the same expected return as buying the S&P would beat human investors. So as far as I can tell from the article, it might as well be buying randomly.
by b0a04gl | 2025-06-15 03:19:22+00:00
got it, thanks. makes sense that it depends on the projection target. SHACL+SPARQL seems like the strongest runtime check layer then. for projections like graphql or java where enforcement is weaker, is there any way to inject runtime guards or contract tests as part of the generated code? or is the idea to keep enforcement external and just let uda define the schema canonically?
by zer00eyz | 2025-06-15 03:20:01+00:00
No, they do not. No LLM is ever going to be self aware.

It's a system that is trained, that only does what you build into. If you run an LLM for 10 years it's not going to "learn" anything new.

The whole industry needs to quit with the emergent thinking, reasoning, hallucination anthropomorphizing.

We have an amazing set of tools in LLM's, that have the potential to unlock another massive upswing in productivity, but the hype and snake oil are getting old.
by kulahan | 2025-06-15 03:21:42+00:00
Threatening someone for being a complete asshole is always okay, and even cool.

I really do not care how uncomfortable it makes the driver to move a family a few extra blocks to somewhere vaguely safe. I’d similarly threaten him if he tried to drop my family off in a forest, or on the side of a highway, even if that’s what the GPS, God’s Position System, tells them to do.

If your job ends in a way that someone who was your customer is now in danger, you absolutely deserve to be threatened.
by serbuvlad | 2025-06-15 03:22:02+00:00
The human mind is an estimator too.

The fact that the human mind can think in concepts, images AND words, and then compresses that into words for transmission, wheras LLMs think directly in words, is no object.

If you watch someone reach a ledge, your mind will generate, based on past experience, a probabilistic image of that person falling. Then it will tie that to the concept of problem (self-attention) and start generating solutions, such as warning them or pulling them back etc.

LLMs can do all this too, but only in words.
by globnomulous | 2025-06-15 03:22:25+00:00
What you're describing sounds to me like absolute hell on earth.

I'm not interested in reaching the finish line with maximum speed and bypassing the hard work of struggling with and solving problems myself.

Partly this is because working this way has real benefits that are difficult to quantify. One example: I've recently dumped an enormous amount of time into investigating performance problems in the tools my team use. I've spent more time making dumb mistakes than actually improving anything. I've also learned a tremendous amount, to the point that I was able to diagnose in seconds the cause of a serialization error in one of the tools we use for testing. Others were convinced that these crashes were expected. I was able to show them that, and why, this was wrong. I've likely saved multiple people on my team days' worth of confusion and struggling, because they were trying to solve the wrong problems. If they'd charged ahead with their intended fix, I suspect the result would have been an outage in a global service that has stringent requirements for availability.

An LLM may have been able to tell me in seconds how to solve the performance problem that started my investigations and dumb mistakes. But I'd have learned basically nothing.

If your goal is to make something specific and code is both the obstacle and the means of reaching that goal, sure, great, I'm glad LLMs work so well for you.

I just want to program. I want to solve problems, understand, and become better at working with programming languages, software, and systems. I haven't seen any evidence that LLMs will help me do this. As far as I can tell, they'd do the opposite. They strike me as a layer of awful, chipper bureaucracy between me and what I actually want to work on. I call this meeting-based programming -- and if that's what software engineering becomes, I'd rather leave the field than adopt that style of workong. And maybe that's a good thing. Maybe LLMs will enable more people to make better stuff faster, and maybe that'll be better for everyonr.

I suspect it won't though. I think it would be a dangerous Faustian bargain, and I'm pretty sure I'd rather die than cede intellectual work -- the thing I love most -- to a machine.
by olinelson | 2025-06-15 03:22:29+00:00
First principles over "Best practice" Ability over Credentials Evidence over Experience

Human Programmers is a community and unDirectory of hand-picked software professionals.

Digital Renaissance men and women with taste, purpose, and opinions. Humans dedicated to the craft of building things that serve a purpose, with care.

We're launching soon with a list of only 50 hand-picked software professionals.

What say you?
by whoisyc | 2025-06-15 03:23:10+00:00
1950: cars give you the freedom to go anywhere you want. Artificial fertilizer puts an end to hunger in industrialized world. Yeehaw!

2000: you are a second class citizen who can’t even get a job in many places if you do not have a car. Also the median person is overweight. But here is this new internet thing that lets you get everything you need in life sorted out with no need for human interactions. Yeehaw!

2025: the average person can no longer hold a conversation with a stranger for five seconds without having an anxiety attack. Oops!
by qarl | 2025-06-15 03:24:05+00:00
> The paper doesnt mention the models coming up with the algorithm at all AFAIK.

And that's because they specifically hamstrung their tests so that the LLMs were not "allowed" to generate algorithms.

If you simply type "Give me the solution for Towers of Hanoi for 12 disks" into chatGPT it will happily give you the answer. It will write program to solve it, and then run that program to produce the answer.

But according to the skeptical community - that is "cheating" because it's using tools. Nevermind that it is the most effective way to solve the problem.

https://chatgpt.com/share/6845f0f2-ea14-800d-9f30-115a3b644e...
by adgjlsfhk1 | 2025-06-15 03:26:26+00:00
For pretty much any graphics, you shouldn't be using a capped display space at all. You should be doing all of your math with 32 bit floats (or integers) in a linear color space (wrt photons or human color perception depending on what type of effect you're performing). You should only be converting to a display color space at the end.
by acyou | 2025-06-15 03:26:59+00:00
In what way is endometriosis related to a) hormonal birth control (estrogen and progestin), b) sexually transmitted disease, c) age, d) pregnancy, e) obesity? The article didn't seem to discuss these much, I don't know much about endometriosis, but I feel like I have heard these come up and was surprised not to see them discussed.
by diimdeep | 2025-06-15 03:27:58+00:00
Perhaps in the coming years, when Make reaches its 50th anniversary, people will finally begin to question the status quo regarding need of different separate language to build their programs in some particular language, e.g. C language.

https://oneofus.la/have-emacs-will-hack/files/1978_Feldman_M...

Some already do

https://github.com/tsoding/nob.h

https://youtu.be/D1bsg8wkZzo?t=97

https://youtu.be/mLUhSPD4F2k?t=73

https://youtu.be/eRt7vhosgKE?list=PLpM-Dvs8t0Va1sCJpPFjs2lKz...