MurphyA/hacker-news huggingface dataset:
by dagmx | 2025-06-08 16:15:31+00:00
Meta pays a lot. Most people there don’t work on the shady stuff and don’t pay attention to what else is going on.

That’s generally the case for everyone I know who works there.

Many of them are even quite liberal and will join protests for things that Meta has actively and negatively played a part in, so they’re in effect protesting their own workplace indirectly. But will continue to work there because they can compartmentalize this.
by imiric | 2025-06-08 16:15:32+00:00
> IF you are concerned with only the product, then what’s the difference?

The difference is substantial. If the machine was actually thinking and it understood the meaning of its training data, it would be able to generate correct output based on logic, deduction, and association. We wouldn't need to feed it endless permutations of tokens so that it doesn't trip up when the input data changes slightly. This is the difference between a system with _actual_ knowledge, and a pattern matching system.

The same can somewhat be applied to humans as well. We can all either memorize the answers to specific questions so that we pass an exam, or we can actually do the hard work, study, build out the complex semantic web of ideas in our mind, and acquire actual knowledge. Passing the exam is simply a test of a particular permutation of that knowledge, but the real test is when we apply our thought process to that knowledge and generate results in the real world.

Modern machine learning optimizes for this memorization-like approach, simply because it's relatively easy to implement, and we now have the technical capability where vast amounts of data and compute can produce remarkable results that can fool us into thinking we're dealing with artificial intelligence. We still don't know how to model semantic knowledge that doesn't require extraordinary amounts of resources. I believe classical AI research in the 20th century leaned more towards this direction (knowledge-based / expert systems, etc.), but I'm not well versed in the history.
by andy99 | 2025-06-08 16:17:52+00:00
I wonder if there's every been a study if the air quality in the montreal metro vs comparable cities. Or even within Montreal... does the blue line use tires? (Edit, yes, for some reason I thought one line didn't have them, apparently they all do)

I was told the tires are to reduce noise but I wonder if part of it is to handle some of the steeper sections like Vendome up to Villa Maria.
by jbk | 2025-06-08 16:18:01+00:00
> I haven't and I don't think it is required.

It is required, as soon as you have Android TV support, for example.

https://android-developers.googleblog.com/2022/11/app-bundle...

See https://dev.to/npomepuy/vlc-for-android-updates-on-the-play-...
by jbk | 2025-06-08 16:18:37+00:00
> Since when do you have to hand over your signing keys to Google?

Since it requires App Bundles, which is mandatory, as soon as you have Android TV support, for example.

https://android-developers.googleblog.com/2022/11/app-bundle...

See https://dev.to/npomepuy/vlc-for-android-updates-on-the-play-...
by Workaccount2 | 2025-06-08 16:18:59+00:00
I don't know why software engineers think that LLM coding ability is purpose made for them to use, and because it sort of sucks at it, it therefore useless...

It's like listening to professional translators endlessly lament about translation software and all it's short comings and pitfalls, while totally missing that the software is primarily used for property managers wanting to ask the landscapers to cut the grass lower.

LLMs are excellent at writing code for people who have no idea what a programming language is, but a good idea of what computers can do when someone can speak this code language to them. I don't need an LLM to one-shot Excel.exe so I can track the number of members vs non-members who come to my community craft fair.
by palmfacehn | 2025-06-08 16:19:05+00:00
This part seemed relatively straightforward. I was able to immediately ascertain it as a layman with a simple Google search. The popular desire to misconstrue it as something else seems like a case of motivated reasoning.

> When the President is unable with the regular forces to execute the laws of the United States.

When the protesters interfered with and attack ICE agents, they seemed to trigger this clause. Perhaps professional lawyers will add further depth to the discussion here.

I sincerely hope this situation does not devolve into another summer of destruction.
by CamperBob2 | 2025-06-08 16:19:19+00:00
I don't really buy the education argument. How do you "educate" somebody who lived through the first Trump administration and voted for more of the same? Let's get specific: what exactly did they miss in school that would have driven them towards a different decision?

At some point it's necessary to confront the uncomfortable truth: stupid people are easy for smart, ill-intentioned people to herd, which gives the latter a leg up in any democratic election.

This bug in democracy was there in the beginning. But it's only now, 2500 years later, that it can be exploited effectively enough to invalidate the whole concept.
by ClumsyPilot | 2025-06-08 16:20:20+00:00
Try to transport the following objects - would you rather do it on a bus or tram?

a longtail ebike, a pushchair with/for 2 kids (horizontal arrangement), a dining table for 6 people and 30 kg of cement

I tried it, and with most of em they don’t let you on a bus or you can’t fit but tram is fine
by somethingsome | 2025-06-08 16:20:37+00:00
While I agree overall, LLMs are pattern matching in a complicated way.

You transform your training data in a very strange and high dimensional space. Then when you write an input, you calculate the distance between that input and the closest point in that space.

So, in some sense.. You pattern match your input with the training data. Of course, in a very non intuitive way for humans.

Now, it doesn't necessarily imply things as 'models cannot solve new problems not seen before' we don't know if our problem could get matched to something completely unrelated for us, but in that space it makes sense.

So with your experiments, if the model is able to solve a new puzzle never seen before, you'll never know why, but it doesn't imply either that the new puzzle was not matched in some sense to some previous data in the dataset.
by belter | 2025-06-08 16:21:13+00:00
A human tutor typically misquotes a real source or says “I’m not sure”

An LLM, by contrast, will invent a flawless looking but nonexistent citation. Even a below average teacher doesn’t churn out fresh fabrications every tenth sentence.

Because a teacher usually cites recognizable material, you can check the textbook and recover quickly. With an LLM you first have to discover the source never existed. That verification cost is higher, the more complex task you are trying to achieve.

A LLM will give you a perfect paragraph about the AWS Database Migration service, the list of supported databases, and then include in there a data flow like on-prem to on-prem data that is not supported...Relying on an LLM is like flying with a friendly copilot but who has multiple personality disorder. You dont know which day he will forget to take his meds :-)

Stressful and mentally exhausting in a different kind of way....
by vfunnyline | 2025-06-08 16:21:14+00:00
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by mananaysiempre | 2025-06-08 16:21:25+00:00
Compiler errors won’t cause as many funny consequences with

gcc "$0" -o "$@".out && exec ./"$@".out || exit $? # I'd use ${0%.c} not $@

Love this trick too, but the difference, as far as I understand, is that it only works with a Bourne(-compatible) shell, whereas shebangs or binfmt_misc also work with exec().
by josephcsible | 2025-06-08 16:22:05+00:00
> also actually gets to the actual problem with DoH, which is that the HTTP part has no business being there and increases complexity, which I as a former DNS resolver implementor wholeheartedly agree with!

But that part is wrong too. The HTTP part has a very important reason to be there: because if it weren't, middleboxes would block the traffic.
by catlifeonmars | 2025-06-08 16:22:07+00:00
I think what’s missing here is you should start by reading the RFCs. RFCs tend to be pretty succinct so I’m not really sure what a summarization is buying you there except leaving out important details.

(One thing that might be useful is use the LLM as a search engine to find the relevant RFCs since sometimes it’s hard to find all of the applicable ones if you don’t know the names of them already.)

I really can’t stress this enough: read the RFCs from end to end. Then read through the code of some reference implementations. Draw a sequence diagram. Don’t have the LLM generate one for you, the point is to internalize the design you’re trying to implement against.

By this time you should start spotting bugs or discrepancies between the specs and implementations in the wild. That’s a good sign. It means you’re learning
by svara | 2025-06-08 16:22:12+00:00
> How do you verify when ALL the sources are share the same AI-generated root,

Why would that happen? There's demand for high quality, trustworthy information and that's not going away.

When asking an LLM coding questions, for example, you can ask for sources and it'll point you to documentation. It won't always be the correct link, but you can prod it more and usually get it, or fall back to searching the docs the old fashioned way.
by duncanfwalker | 2025-06-08 16:22:52+00:00
> Comments per PR [...] served as a measure to gauge the depth of collaboration exhibited during the development and review process.

That sounds like a particularly poor measure - it might even be negatively correlated. I'm worked on teams that are highly aligned on principles, style and understanding of the problem domain - they got there by deep collaboration - and have few comments on PRs. I've also seen junior devs go without support and be faced with a deluge of feedback come review time.
by fallingknife | 2025-06-08 16:22:54+00:00
If the plane is able to communicate with ATC it is able to broadcast to the other planes that it is on an emergency landing, so this is not an issue. Even if it has lost all communication this is still not an issue because all the sensors of the other planes can see it. So if the disabled plane immediately goes for an emergency landing, all other planes in the area are able to see its position and that it is not responding to pings, and therefore set safe courses that avoid it. This really isn't a very difficult problem.
by marcosdumay | 2025-06-08 16:24:41+00:00
> For example the EU has its own issues on this front but is modernizing what it does.

The EU has been almost single-handedly designing the international interoperability standards for the kinds of information that the US uses floppies to move around.

I'm not sure any country still remembers the lessons that would be useful for the US on this one situation.
by curious_cat_163 | 2025-06-08 16:25:29+00:00
It is hard to compare models with humans so not sure how to answer it for both. :)

But, for models, this is an interesting finding because a lot of LRMs are LLMs with a _bunch_ of post-training done on top. We know this about DeepSeek R1 (one of the models evaluated in the Apple paper) for sure. They write extensively about how they took DeepSeek-V3-Base and made R1 with it. [1]

If the post-training is resulting in lower performance on simpler tasks then it ought to inspire more research on how to make it so that it doesn't -- i.e., with more training (of any kind), we should be gaining more capabilities. This has been a problem with DNNs historically, btw. We had these issues when fine-tuning text/image classifiers as well. Some weight changes can be destructive. So, it has to be done with a _lot_ of care. And, I am sure folks are working on it, to be honest. Maybe some of them will say something here. :-)

[1] https://github.com/deepseek-ai/DeepSeek-R1
by Workaccount2 | 2025-06-08 16:26:40+00:00
No snowflake feels responsible for the avalanche

- "I didn't write it, I just had the idea"

- "I didn't implement it, I just made the prototype"

- "It wasn't my product, I just fixed some bugs with it"

- "I can't track everything in these implementation updates, I just work with what I am given"

- "I didn't collect the data, I just deal with what is in the dataset"
by SpaghettiCthulu | 2025-06-08 16:26:54+00:00
Because sometimes the user really wants to access those fields, and if the language enforces them being private, the user will either copy-paste your code into their project, or fork your project and make the fields public there. And now they have a lot of extra work to stay up-to-date when compared to just making the necessary changes if those fields ever change had they been public.
by spankibalt | 2025-06-08 16:26:57+00:00
> That’s the literal opposite of my point.

In your proposition, a whole eco system's ("PCs") graphics started only to "look better" than their counterparts, based on your examples anyway, from 1991/92 onwards. Appeals to specific aesthetics, etc. (e. g. only 2D- or only 3D-capabilities) can quickly lead to fallacies of composition. As they so often do in arguments between mostly opinionated men about their favorite toys. :D

> I see you’ve forgotten the part where I mentioned how I ran a side gig as tech support ;)

No, I haven't. Thank you for your service! O7
by zahlman | 2025-06-08 16:27:31+00:00
The README proposes using `pip install futa`, but there is no actual project published on PyPI for this to work - nor any releases in the repository (you'd have to build the wheel yourself). Although given that they once famously took down a "slut" package I have to wonder if they'd actually accept "futa". Although they did accept "fuckit", so.

I did notice that you have a Python interface to ollama listed in requirements.txt (which doesn't really help with packaging anyway) but aren't using it in the code. (In case it's what you were looking for: there aren't currently any ways to describe dependencies that are installed for the system and aren't available as Python packages, but PEP 725 could mean they happen eventually, maybe.) Also, you can use `text=True` (or specify an `encoding` or `errors`) in the arguments for the `subprocess.run` call, to get textual stdout/stderr directly rather than decoding them afterward; and `capture_output=True` is an easier way to get both streams than setting them both to `PIPE`. And you can iterate over a file in text mode directly (which gives a line at a time), rather than reading the whole thing and calling `.splitlines`, simplifying further:

denials = [line.strip() for line in f if line.strip()]

Anyway, I appreciate the commitment to the bit.
by jll29 | 2025-06-08 16:29:00+00:00
The money is not the problem - you want the source code so that if the company disappears, you can still maintain the software.

I would prefer to buy from those commercial players that have a clause in their license saying upon sunsetting the commercial offering or closure of the company the source code becomes open source. In the absence of such a clause, I prefer open source solutions.

[RMS was right saying "Free as in 'freedom' is not about payment." There can be paid-for open source software, and there can be free-of-charge commercial software, but the freedom to edit and recompile is the most important aspect of "being free".]
by exiguus | 2025-06-08 16:30:49+00:00
The Vastaamo data breach, revealed in October 2020, involved a Finnish psychotherapy provider whose patient records were hacked. Attackers demanded ransom from both the company and patients, exposing sensitive data of about 30,000 individuals. The breach highlighted poor security practices, as data was unencrypted and lacked password protection. Vastaamo was fined €608,000 for GDPR violations. The incident led to significant psychological and societal impacts, prompting Finland to strengthen cybersecurity measures.
by yyyk | 2025-06-08 16:33:27+00:00
There's no reason a car should have only 4 wheels, we can even have special wheels deployed only when the breaks are hit at high speed. The AI will be disciplined enough to always use them when necessary (or maybe use an automatic system based on speed alone?).

Or you can have a different wheel count and arrangement normally, as the AI can be trained for this. We don't have to standardize AV cars as much as we have to standardize cars for humans.
by regev3144 | 2025-06-08 16:34:02+00:00
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by fallingknife | 2025-06-08 16:34:56+00:00
We operate cars on the road with not only no centralized system, but also minimally defined and enforced protocol, and yet Waymo has achieved a near zero collision rate inside a swarm of cars that are not running equivalent software. And this is in a situation where cars are only a few feet from each other while operating at top speed. So you can come up with a million objections but they are all solvable. As for automation failure, the rate of that can be easily made lower than the rate of human failure, which currently is fatal to a plane.
by CamperBob2 | 2025-06-08 16:35:18+00:00
Great disassembly job. There's yet another branch of the rabbit hole that Tom almost fell down:

... the multiplier is based on a classic form, having a translinear core, supported by three (X, Y, and Z) linearized voltage-to-current converters, and the load driving output amplifier. I have no clue how the thing works!

The 'translinear' school of design was founded (or at least popularized) by Barrie Gilbert at Analog Devices (https://en.wikipedia.org/wiki/Translinear_circuit , https://en.wikipedia.org/wiki/Barrie_Gilbert ). Gilbert was one of the undisputed GOATs in the analog field and a full-fledged Renaissance man in his own right. It's worth reading more about him if/when you have the time and inclination.
by wolrah | 2025-06-08 16:35:32+00:00
Yeah, Netflix doesn't need the long distance interfaces but that's presumably what the ISPs' equipment is going to be built to interface with, so I would not be expecting them to be able to use such interfaces directly any time soon.

That said, sometimes these interfaces can be split (more common on the switch side than the NIC side) so one 800GbE interface can become 8x100GbE, or as the article mentions a 1.6TbE interface could be 8x200GbE, with a passive breakout cable of some variety.
by rcarmo | 2025-06-08 16:36:39+00:00
The bit about Deep Thought and The Answer has been very much on my mind as we keep building the next generation of compute (now GPU compute) to get more answers out of the cloud.

That and the near-religious worship of said new form, which Adams honed to its pinnacle as the Electric Monk.

(Which is, incidentally, the host name I use for one of my AI inference endpoints…)
by cjauvin | 2025-06-08 16:36:39+00:00
> Emacs is a powerful tool, but it also demands a lot from its user. Eventually I got tired of dealing with the host of plugins and customizations that I needed to keep my system running the way I wanted. I'm at a point in my life where I would rather spend my spare time on hobbies, hanging out with family and friends, and otherwise not messing around with a patchwork of ELisp code snippets that I've cobbled together from various sources. I gradually stopped using Emacs in favor of more modern tools that are less flexible but also less of a hassle.

I don't know how many times I've read a variation on this. It took me a very long time, but now I pretty much made my peace with that: I use Emacs (for certain things), I use VS Code (with Emacs bindings), I use Apple Notes.. I don't find that it's possible or reasonable anymore, the desire to be "pure" and use only ONE tool to rule them all. The same for messaging apps, chatbots, etc.. I now embrace extreme diversity.
by a11r | 2025-06-08 16:36:40+00:00
The system prompt in this experiment limits the solution to always spell out the concrete moves verbally. A human solving the Tower of Hanoi gives up around N=4 and goes off to invent a recursive solution instead. Prompted differently, the LLM would solve these puzzles just fine.

Here is my complete review/analysis of the paper: https://www.linkedin.com/pulse/art-abstraction-human-advanta...

edit: fixed typo
by treetalker | 2025-06-08 16:36:47+00:00
Yes. People seem to be forgetting that, in many contexts, the greater part (or at least a major part) of work's desired outcome is to make changes to the conscious and subconscious minds that enable them to better handle future situations.

I'm all about automated solutions for things — but I find that my desires are typically for unrewarding physical activities. I still don't have a virtually costless robot butler, driver, farmer, chef, and maid to anticipate my needs around the house and home office; to transport my family and me around town safely and on time; to grow microplastic-free food hydroponically; to do my meal planning, shopping, cooking, cleaning, tidying, and laundry with effectively no intervention and supervision on my part. Why not?

Now that would really 10x my productivity.