MurphyA/hacker-news huggingface dataset:
by btilly | 2025-06-14 23:05:38+00:00
You are being unnecessariky rude. I have not forgotten what it says.

There are pairs of physical properties, connected by a Fourier transform, that cannot both be known at once. Therefore each property is, in general, only known to be in a range. And therefore we cannot always compare these properties between different particles.

As for measurement, in the Everett interpretation the values are not known after measurement either. All that is knowable are correlations due to entanglement.

But now we are far afield. You made an a priori statement about physics. Physics is not required to oblige you. My understanding of physics says that it does not. But even if I am wrong, and it currently does oblige you, it is still not required to.

A priori assertions do not have a good history. For an example, look at Kant's assertion that reality must be described by Euclidean geometry. He was not in a position to understand the ways in which he would prove to be wrong.
by Levitating | 2025-06-14 23:06:28+00:00
> One variety used rose-colored lenses, as the coloring was thought to prevent a chicken wearing them from recognizing blood on other chickens

So that's only relevant tot the rose-colored variant.

I think the answer lies in this quote right above it:

> They differ from blinders in that they allow the bird to see forward, whereas blinders do not.

Where "blinders" is a hyperlink to an article concerning blinders for chickens.

That article has a piece comparing blinders to spectacles:

> Blinders work by reducing the accuracy of pecking at the feathers or body of another bird, rather than spectacles which have coloured lenses and allow the bird to see forwards but alter the perceived colour, particularly of blood.

But this again only refers to the coloured lenses, which in the article was said to be a variant.

So my understanding is that both blinders and spectacles work by restricting the vision of the bird but the spectactles additionally had a rose-colored variant.
by wongarsu | 2025-06-14 23:07:57+00:00
And there seem to be concrete results that would allow you to improve the LLM prompt to make these interactions more successful. Apparently giving the human 2-3 possible options and letting the human have the final choice was a big contributor to the bad results. Their recommendations go the route of "the model should explain it better" but maybe the best results would be achieved if the model was prompted to narrow it down until there is only one likely diagnosis left. This is more or less how doctors operate after all.
by zer00eyz | 2025-06-14 23:08:11+00:00
> seems more philosophical than scientific

I think this is a fair assessment but reason, and intelligence dont really have an established control or control group. If you build a test and say "Its not intelligent because it can't..." and someone goes out and add's that feature in is it suddenly now intelligent?

If we make a physics break through tomorrow is there any LLM that is going to retain that knowledge permanently as part of its core or will they all need to be re-trained? Can we make a model that is as smart as a 5th grader without shoving the whole corpus of human knowledge into it, folding it over twice and then training it back out?

The current crop of tech doesn't get us to AGI. And the focus to make it "better" is for the most part a fools errand. The real winners in this race are going to be those who hold the keys to optimization: short retraining times, smaller models (with less upfront data), optimized for lower performance systems.
by robocat | 2025-06-14 23:08:28+00:00
> Self-driving cars are cool but I'd rather have good public transit

False dichotomy.

Good public transport would be self driving cars as a feeder network to mass transit once the self driving tech is cheap enough.

It could only work well as work habits change to stop having peak hours (peak usage for low-utilization self-driving cars doesn't seem likely to be economical).
by ratorx | 2025-06-14 23:08:50+00:00
Being pedantic, even if you have to pay for a type of car, you still have no variance to expectation when you know what you are getting. I think that point was more about the variance in driving, driver etc rather than car type.

Re: enshittification in general. I think the incentives are better aligned for self-driving. Eg. charging people who create trash etc can also make the company money whilst improving overall experience.

With non self-driving, you have to rely on user ratings etc to penalise a specific driver, which seems inherently more fuzzy. The company has conflicting goals of keeping enough drivers (drives costs down etc), whilst guaranteeing a certain experience. It is difficult to create a system for drivers to “improve” (eg. Clean their car) and for a company to directly encourage that, whereas it’s easier to just charge people who litter more etc in a fully automated system.
by ElectricalUnion | 2025-06-14 23:09:14+00:00
Do you pull the entire toolchain inside the makefile if it doesn't exist in the host? Or you're just using it as a glorified alias over the (rather arcane) podman container run/docker run calls?

Also, how do you solve the problem of actually bootstrapping over different versions of docker/podman/make? Do you have some sort of ./makew like how Maven uses to bootstrap itself?
by dhash | 2025-06-14 23:10:47+00:00
I love this kind of research since it correctly identifies some issues with the way the public interacts with LLM’s. Thank you for the evening reading!

I’d love to see future work investigating - how does this compare to expert users (doctors/llm magicians using LLM’s to self diagnose)

- LLM’s often provide answers faster than doctors, and often with less hassle (what’s your insurance?), to what extent does latency impact healthcare outcomes

- do study participants exhibit similar follow on behavior (upcoding, seeking a second opinion, doctors) to others in the same professional discipline
by jeffbee | 2025-06-14 23:10:50+00:00
The reason I hedged and said "... or some automatic system ..." was because they use a machine-learned forecast of the memory requirements of every container and use that as the soft limit for the container when it starts. You can read about that at [1]. But what I was getting at is that using less than the configured amount of memory does not lead to more containers able to be scheduled on a given machine, nor does it lead to lower economic chargeback. Machines are scheduled and operators are charged by the configured limit, not the usage.

Giving memory back to the operating system is antithetical to the nature of caching allocators ("caching" is right there in the name of "tcmalloc"). The whole point of a caching allocator is that if you needed the memory once, you'll probably need it again, and most likely right now. At most what these allocators will do unless you configure them differently is to release memory to the system very, very slowly, and only if an entirely empty huge page — a contiguous area of several megabytes — surfaces. You can read how grudgingly the tcmalloc authors allow releasing at [2]. jemalloc was once pretty aggressive about releasing to the OS, but these days it is not. I think this reflects its evolution to suit Meta internal workloads, and increased understanding of the costs of releasing memory from a huge-page-aware allocator.

1: https://dl.acm.org/doi/pdf/10.1145/3342195.3387524 2: https://github.com/google/tcmalloc/blob/master/docs/tuning.m...
by fluidcruft | 2025-06-14 23:10:50+00:00
Sure, nobody has to tip anyone. But I do tip taxis and etc, typically about 30%, and it factors into my overall price perception.

I'm just saying $15 that I will add a tip to vs $20 that I have no intention or inclination to tip isn't anything more than I don't have any expectations or empathy about tipping a machine. It doesn't seem particularly complex an issue about why Waymo can charge the same amount that I am willing to pay anyway.
by Fripplebubby | 2025-06-14 23:12:57+00:00
Interesting quote from the venturebeat article linked:

> “There is also a reason why clinicians who deal with patients on the front line are trained to ask questions in a certain way and a certain repetitiveness,” Volkheimer goes on. Patients omit information because they don’t know what’s relevant, or at worst, lie because they’re embarrassed or ashamed.

In order for an LLM to really do this task the right way (comparable to a physician), they need to not only use what the human gives them but be effective at extracting the right information from the human, the human might not know what is important or they might be disinclined to share, and physicians can learn to overcome this. However, in this study, this isn't actually what happened - the participants were looking to diagnose a made-up scenario, where the symptoms were clearly presented to them, and they had no incentive to lie or withhold embarrassing symptoms since they weren't actually happening to them, it was all made up - and yet, it still seemed to happen, that the participants did not effectively communicate all the necessary information.
by koito17 | 2025-06-14 23:14:24+00:00
Automatic toolchain switching does not trigger compilation of later Go toolchains; it only attempts fetching a blob (if available) and using that to perform builds[1]. If a system supports Go 1.21 but not 1.23.1 (e.g. Windows 7, mentioned in a sibling comment), then this project will fail to build on said system. Likewise, if a user on Go 1.21 has disabled automatic toolchain switching, the Go CLI refuses to build the project at all.

Overall, I would say the minimum required Go version is indeed the Go version declared in the go.mod, since that is the declared minimum required toolchain.

[1] https://go.dev/doc/toolchain#select
by bravesoul2 | 2025-06-14 23:14:27+00:00
Spitballing. Another way to deal with the problem is like what would you do if you had billions of pieces of unstructured data (except for maybe the data being somewhat XML like) and you don't control any of it but you need to make sense of it as (ignoring rounding errors) your only business concern. That company is Google of course.

Maybe let the business units be loose but make the sense making central. Any individual unit can eventually tidy things up (SEO!) but everything will work regardless. The UX effect might be you can't find something decent to watch but that is an entirely different problem solved by not using Netflix and going to the theatre!
by em-bee | 2025-06-14 23:14:52+00:00
My lack of skill would be more burdensome than helpful for such teams. I'm not new either. I've been at this game for over a decade now.

seems like you are also suffering from imposter syndrome. if you have been doing this for over a decade you should have learned something.

I lack the skills to better my situation

practice. work on a side project. follow and implement tutorials. contribute to FOSS projects. (contact me if you want some suggestions. see my profile)
by crazygringo | 2025-06-14 23:14:58+00:00
That's actually a really interesting question. Because it's not necessarily about handling most usage, but also about handling peak usage. Is it worth the cost to keep everything 4-seater if that means they can all enter "carpool mode" whenever required at times of peak demand?

Because once they become ubiquitous, I suspect the vast majority will be operating in carpool mode at rush hour. Most people won't be willing to pay 4x to get a private vehicle if they're by themselves. Especially since the more vehicles there are, the more efficient carpool mode becomes for everyone.
by BobbyTables2 | 2025-06-14 23:15:28+00:00
Indeed.

Imagine if asking a specialist the time meant they had 3 broken clocks on the wall and picked one.

That’s pretty much how initial medical diagnoses are done.

Insurance companies then limit what’s types of care/investigation can be done for various conditions.

Doctor may know that medicine X will work best but insurance demands that Y and Z be first tried before covering X. Same with tests.

I’m tired of it.
by thrance | 2025-06-14 23:17:35+00:00
I'm wary of any classification that puts humans in a special category of their own, as the crown jewel of the tree of life (many such cases).

> The ability to model the internal dialogues of others.

It feels like someone spent a lot of time searching for something only humans can do, and landed on something related to language (ignoring animals that communicate with sounds too). How is this ability any different than the "Theory of mind"? And why is it so important that it requires a new category of its own?
by kristopolous | 2025-06-14 23:17:52+00:00
> Started off as a way to pay people less, especially for odd jobs.

Kind of. American tipping came out of the post-slavery south as a form of exploitation where people weren't guaranteed a wage.

This is why tipping was common in historically black jobs like hospitality, food service workers and railroad porters.

There still a federal "tipped" minimum wage at $2.13 - which some states still abide by, roughly corresponding to the historic south https://www.epi.org/publication/waiting-for-change-tipped-mi...

These also seem to be some of the worst tipping states according to most sources, https://www.lyft.com/blog/posts/the-united-states-of-tipping...

Which kind of makes sense - if people in those states invented tipping to pay people less, then those states paying tipped people less isn't that surprising ...

Cultural behavior patterns last decades, which is why there's some dissipation 150 years later.

These things can be weird. For instance coat check (person who holds on to expensive coat) and car valet (person who holds on to expensive car) is functionally equivalent with a 100 year separation so the tip culture sticks.

Same goes for the shoe shiner and car washer; the person who makes your mode of transportation more presentable.

Maybe this sounds like crazy free association, but the pattern seems to hold. Take porters and food delivery drivers, for instance, not that different.

Anyway, when you start scratching at weird american anomalies like tipping and the electoral college, usually you find something to do with slavery's long tail.
by metalman | 2025-06-14 23:19:04+00:00
lots of sci-fi had "data runners" operative word is "had", because it was an edgy smuggling thing, but now here we are specing the whole thing out and given the technology to get data links from any moving vehicle on earth to space and back down to any other moving vehicle somewhere else on earth, this is a temporary measure untill China gets it's LEO sattelite swarm up to full density, like space x has now. not that tape/hard drive smuggling will go away, bit it will be just for ambitious second rung operations
Show HN: ETL System to Extract Product Data from Websites and Upload to Shopify
by gustavofortti | 2025-06-14 23:19:18+00:00
About a year ago, I built an ETL system to extract and process product data from e-commerce websites, mainly supplements. It maps product structures, automates data collection, cleans and classifies the information, and uploads it directly to Shopify through API integration.

The system uses Python and Selenium for extraction, with transformation steps focused on cleaning, enrichment, and product classification. It was designed to be modular and easy to extend to new websites.

I paused development but I'm considering reviving it. Feedback or suggestions are welcome.

Repo: https://github.com/GustavoFortti/products-crawler
by kelnos | 2025-06-14 23:19:28+00:00
Uber Black provides a similar experience, or at least it used to (haven't used it in many years). I think it's a matter of having professional drivers, where there's an expectation that their customer/client isn't there to socialize, and their job is to drive, and that's it. It's more of a "high class" thing, for lack of a better term.

(IIRC when Uber first started, before UberX, their driver pool was essentially just this sort: people who drove for private car services.)
by reddalo | 2025-06-14 23:19:31+00:00
Speaking of small costs, some time ago I paid 2 euros with my credit card in order to enter the central train station toilets in Milan, Italy.

The toilets were awfully dirty, there was no toilet paper and no soap. I took some pictures just in case, then I filed a chargeback with my bank. After some weeks, they gave me my 2 euros back, and the company that manages the toilets probably paid a small fine to MasterCard or whatever.

Was it a waste of time, for just 2 euros? Sure. But if nobody starts complaining, nothing will ever be fixed.
by bigyabai | 2025-06-14 23:20:55+00:00
The first comment is correct. "Don't do that" is the correct answer, and everything else is inconvenient tripe. If you must give an LLM control of your computer, make it a virtual container. No amount of sandboxing can ever make this safe, case closed.

Plus, many people are already using isolation tech like Bubblewrap when they use AI apps in a Flatpak. Presumably not to avert anything as disastrous as LLMs controlling their PC, but sandboxed all the same.
by usehand | 2025-06-14 23:20:56+00:00
Charges for goods not delivered as agreed falls under the protection of the Fair Credit Billing Act. If you made a good faith attempt to resolve with the merchant (which you did) you should use your credit card to charge back the amount (some let you request a partial charge back, but if not you can request a full one and explain in the extra info that you want a partial one).

This might not seem worth it for $3, but if they get a lot of these the credit cards/banks might start giving them a hard time about it, so I think it's worth the minor hassle (everything can be done via the credit card app usually)
by kelnos | 2025-06-14 23:22:17+00:00
I would expect it to happen much less often. And each instance of it will likely only be for a single customer's ride, as they'll report the issue in the app, and the car will be taken out of service until it's cleaned.

For an Uber/Lyft driver, if they're even (made) aware of the problem, they'll probably not take care of the issue until they've finished their day of driving.
by fmobus | 2025-06-14 23:23:47+00:00
My neighbors are already hustling at 6:30, consistently. By 7:15am there's already a steady stream of students going to a nearby school three blocks from my place.

I once had to leave super early in winter and saw several office buildings with lights on at 6:45 with people working inside (no, not janitorial staff).

Germans get started quite early compared to every country I've ever worked/lived.
by em-bee | 2025-06-14 23:24:01+00:00
erm, where i have been living for the past 15-20 years, each large grocery run takes at least 3-4 hours. 1-2 of which are simply the time to get to the supermarket and back. and then one hour to top up every other day, mostly fresh vegetables. so in a week, shopping takes me 6 to 8 hours.

and i have no idea how i am supposed to be cooking for a whole week in two hours unless i eat the same thing every day. that doesn't sound desirable at all
by drtgh | 2025-06-14 23:24:48+00:00
I don't need it at the moment (European), so I'm not updated. But awaiting someone else's answer, it is not difficult to get to grips with the matter, I suggest start with a "XRay vs Trojan" search, they are proxy servers (Trojan is GFW or GO) and visit all the links about the matter (any language, Chinese included, under several search engines), paying attention to the user's comments about the used protocols ( VMess, VLESS, XTLS, REALITY, VISION, and so on), different tools, clients, and searching again about them within a deep loop search.

> How can you trust one over another?

Even though those protocols and tools are open source, I would take it easy, read the code, search for analyses about it, trying make sure that nothing is happening in the background (my sincere apologies to the authors), and after this compile it my self. I would use a dedicated machine for it, not my main computer, and monitor the traffic. I mean, I would not install/use them without minimal checking first.
by godelski | 2025-06-14 23:25:22+00:00
self.q = nn.Linear(embed_size, embed_size, bias = False) self.k = nn.Linear(embed_size, embed_size, bias = False) self.v = nn.Linear(embed_size, embed_size, bias = False)

Try

self.qkv = nn.Linear(embed_size, 3*embed_size, bias = False) def forward(...): ... qkv = self.qkv(x)
by toast0 | 2025-06-14 23:25:35+00:00
I don't think government influence/mandate is required to earn the name 'censorship'.

Certainly, government censorship is more worrisome than private censorship. I would be upset if a government imposed a list of disallowed topics on a forum such as HN; however, I do not wish to participate on a forum without a list of disallowed topics. That the admins (and the users, through flagging norms) prohibit certain topics is clear, and I think it qualifies as censorship, especially where admins are actively supressing certain topics (which they do).
by liuliu | 2025-06-14 23:26:33+00:00
Congrats on finding a bug!

However, the keyword here is training / inference divergence. Unfortunately, nobody is going to spend multi-million to retrain a model, so our reimplementation needs to be bug-to-bug correct to use the trained weights properly. That's why the reference implementations are essential because it is from the original model trainers so you have the best "bet" on matching the training code properly.

To give you some concrete example of bugs we needs to maintain:

1. In SDXL, they use OpenClipG for text encoding, but wrongfully uses 0 as padding tokens (corresponding to symbol "!") whereas even for OpenClipG its own training, the endoftext token was used as padding token. However, if you switching SDXL to use endoftext token as padding token, due to training / inference divergence, you get subpar generated images.

2. In FLUX, we mainly use T5 as text encoder. However, T5 usually used as encoder with mask to exactly the same input length, to avoid extended impact of padding tokens. In FLUX, we don't apply mask for T5 text encoding, hence intuitively causing padding token to take more effect than it should. Again, "fixing" this bug without retraining you will get subpar generated images.

There are many examples like this, some are easier to fix some are not (HiDream uses a different ODE solver that is different than what we usually do for rectified flow, hence you need to negate its prediction to be compatible with existing samplers, but this is "easier to fix").

TL;DR: Yes, there are bugs in software, but we better to maintain bug-to-bug compatibility than trying to "fix" it, hence highlight the importance of a "done" reference implementation, rather than a usual "active" implementations in software industry otherwise.

(I maintain the most complete reimplementation of SoTA media generation models in Swift: https://github.com/drawthingsai/draw-things-community/tree/m.... So I tend to think that I know one or two about "reimplementation from scratch".)
by jhanschoo | 2025-06-14 23:27:27+00:00
I think that "solving world problems with novel solutions" is a strawman for an ability to reason well. We cannot solve world problems with reasoning, because pure reasoning has no relation to reality. We lack data and models about the world to confirm and deny our hypotheses about the world. That is why the empirical sciences do experiments instead of sit in an armchair and mull all day.
by conradev | 2025-06-14 23:28:13+00:00
Looks like he actually tried it:

But some ideas cannot be crushed by bankruptcy and the dream of providing lenses to all of America’s hens was carried on by the son of one of Vision Control Inc.’s founders, a young Mr. Randall Wise. Wise, a Harvard Business school graduate and former nautical shipping consultant, used the millions he made from selling his software company to establish Animalens, Inc. Instead of pecking at each other (success!), the hens were now pecking at the air, rubbing their eyes repeatedly on their wings, and suffering from corneal ulcers and ruptured eyes.

https://www.vice.com/en/article/chickens-wore-sunglasses-ind...