MurphyA/hacker-news huggingface dataset:
by jsnell | 2025-11-03 18:16:28+00:00
It's actually more like 100x their current revenue; they stated last week[0] that they have spending commitments for $1.4T of compute.

Or, well, they stated that the TCO of the compute they have commitments for is $1.4T, which is a somewhat strange phrasing. I assume it's due to it being a mix of self-owned vs. rental compute, and what they mean is the TCO to OpenAI rather than the TCO to the owner of the compute.

[0] https://x.com/sama/status/1983584366547829073
by parineum | 2025-11-03 18:16:34+00:00
Is there something the the SV charter that says companies operating there must do that?

Nobody seems to want to answer the question so I assume nobody wants to confront that a company isn't the bad guy when it fires people it doesn't need. And, if it turns out it did need them, the company will suffer. Usually that doesn't happen though and so I tend to trust that these companies understand their business needs more than I do.

It sucks when people lose their jobs but if there's no job at their company for them anymore, their employer isn't a charity. Just like if a company could no longer afford to pay they employees, the employees shouldn't hang around and work for nothing. You might as well be demonizing people who file for divorce for making their partner single.
by 53p3h2 | 2025-11-03 18:16:51+00:00
Location: Iran/Tehran Remote: Yes Willing to relocate: Yes

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by sheer_health | 2025-11-03 18:16:54+00:00
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by 5bolts | 2025-11-03 18:17:08+00:00
way to much dead space, if i have more horizontal space available in the window don't force it to wrap text. boxes aren't really needed, kind of distracting. Pick a link size (your source and your comment /boxes links should be the same size.

forced padding could go away if you removed the border as well.

and not a fan of the font, is it ALL bold?
by TuningYourCode | 2025-11-03 18:17:09+00:00
Hetzner‘s storage share product line offers a managed Nextcloud instance. I‘m using them as I didn‘t want to care about updating it myself.

The only downside is you can‘t use apps/plugins which require additional local tools (e.g. ocrmypdf) but others can be used just fine.

Calling remotely hosted services works (e.g. if you have elasticsearch on an vps and setup the Nextcloud fulltext search app accordingly)
by juanpabloaj | 2025-11-03 18:17:26+00:00
Location: Chile

Remote: Yes

Willing to relocate: Yes

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Résumé/CV: https://www.linkedin.com/in/juan-pablo-abarz%C3%BAa-jaramill...

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by jelkand | 2025-11-03 18:17:36+00:00
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by zurichisstained | 2025-11-03 18:17:44+00:00
Location: Brooklyn, NY

Remote: Yes, but open to hybrid roles as well

Willing to relocate: No

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by hinkley | 2025-11-03 18:18:11+00:00
First motor I saw in this category was much larger because it had massive mount points to attach it to the car so the torque would go to the wheels and not to destroying the motor.

This one has a narrow ring meant for 3/8” bolts? I guess if you’re buying a 1000 hp motor you can afford titanium carriage bolts.
by maxglute | 2025-11-03 18:18:24+00:00
A few years ago google blocked my youtube red / premium account for spam even though the account was only used to watch videos. Not only did they wipe the account the wiped access to the payment page so I couldn't even cancel membership for months, dealing with robotic messages (you get to appeal every 3 weeks) all while being charged. Oh I also had Google One which promised in person support but they couldnt do shit because YT different team. I ended up cancelling the credit card. Earlier this year, I got a random message that my suspension was reversed and the original suspension was in error.

Just anedotally, I've had my wechat account blocked before and it took less than a day to talk to a person to get it sorted. At least PRC censorship has good customer service.
by beklein | 2025-11-03 18:18:29+00:00
Comparing this blog post to a 500-page book or a multi-hour course and calling it “weaksauce” misses the point. This post is meant as an introduction to the dot product, and it does that really well. The formal definition (6.1) and explanation in Axler’s book wouldn’t make a good starting point for most people, it isn't even a good next step in my opinion. It’s great that you’re passionate about the topic, really, but helping more people discover math means meeting them where they are and appreciating content like this for what it’s trying to do.
by embedding-shape | 2025-11-03 18:18:29+00:00
The definitions of all these words have been going back and forward and never reached any 100% consensus anyways, so what one person understands of "thinking", "conscious", "intelligence" and so on seems to be vastly different from another person.

I guess this is why any discussion around this ends up with huge conversations, everyone is talking from their own perspective and understanding, while others have different ones, and we're all talking past each other.

There is a whole field trying to just nail down what "knowledge" actually is/isn't, and those people haven't agreed with each other for the duration of hundreds of years, I'm not confident we'll suddenly get a lot better at this.

I guess ultimately, regardless of what the LLMs do, does it matter? Would we understand them better/worse depending on what the answer would be?
by zetaben | 2025-11-03 18:18:48+00:00
Konvu | https://konvu.com/ | Software Engineer | FULLTIME | ONSITE | Paris, France

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by naasking | 2025-11-03 18:19:26+00:00
I'm not on the moderation bandwagon to begin with per the above, but if an organization invents a bunch of fake reasons that they find convincing, then any system they come up with is going to have its flaws. Ultimately, the goal is to make cooperation easy and defection costly.

> But how do you know an input is adversarial?

Prompt injection and jailbreaking attempts are pretty clear. I don't think anything else is particularly concerning.

> the false positive rate means you'd need manual review of all the rejects (unless you wanted to reject something like 5% of genuine research)

Not all rejects, just those that submit an appeal. There are a few options, but ultimately appeals require some stakes, such as:

1. Every appeal carries a receipt for a monetary donation to arxiv that's refunded only if the appeal succeeds.

2. Appeal failures trigger the ban hammer with exponentially increasing times, eg. 1 month, 3 months, 9 months, 27 months, etc.

Bad actors either respond to deterrence or get filtered out while funding the review process itself.
by bob1029 | 2025-11-03 18:19:28+00:00
I'm still stuck on whether or not vector search (regardless of vendor) is actually the right way to solve the kinds of problems that everyone seems to believe it's great at.

BM25 with query rewriting & expansion can do a lot of heavy lifting if you invest any time at all in configuring things to match your problem space. The article touches on FTS engines and hybrid approaches, but I would start there. Figure out where lexical techniques actually break down and then reach for the "semantic" technology. I'd argue that an LLM in front of a traditional lexical search engine (i.e., tool use) would generally be more powerful than a sloppy semantic vector space or a fine tuning job. It would also be significantly easier to trace and shape retrieval behavior.

Lucene is often all you need. They've recently added vector search capabilities if you think you really need some kind of hybrid abomination.
by pH14 | 2025-11-03 18:19:34+00:00
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by rayiner | 2025-11-03 18:19:41+00:00
I like universal healthcare in principle, but I don't think the funding mechanism is the problem. We see the same cost inflation in the U.S. in sectors that everyone agrees should be publicly funded, like community colleges or transit. U.S. public spending on tertiary education is about 0.95% of GDP, which is higher than the U.K. (0.5%), Ireland (0.57%), Italy (0.62%), Australia (0.68%), and not that much lower than Germany (1.1%): https://www.oecd.org/en/data/indicators/public-spending-on-e.... Most of those countries have what Americans would call "free college," but we spend just as much as a percentage of GDP--and more in absolute terms--without college being free.

Our colleges simply spend way more money than European colleges. I grew up in Virginia, where George Mason University (GMU) is the second tier public school (behind UVA, William & Mary, and JMU). It's got a budget of $1.4 billion for about 40,000 students.

Compare that to Heidelberg University, which I understand is a renowned university in Germany. Excluding the medical school (which GMU doesn't have), the budget is about 629 million euros, or about $725 million, for about 30,000 students. GMU, which is not a world-famous university, spends almost twice as much money for only 1/3 more students.
by Fripplebubby | 2025-11-03 18:19:41+00:00
I think the tricky thing here is that the specific things I referred to (real time writes and pushing SQL predicates into your similarity search) work fine at small scale in such a way that you might not actually notice that they're going to stop working at scale. When you have 100,000 vectors, you can write these SQL predicates (return the 5 top hits where category = x and feature = y) and they'll work fine up until one day it doesn't work fine anymore because the vector space has gotten large. So, I suppose it is fair to say this isn't YAGNI backfiring, this is me not recognizing the shape of the problem to come and not recognizing that I do, in fact, need it (to me that feels a lot like YAGNI backfiring, because I didn't think I needed it, but suddenly I do)
by creata | 2025-11-03 18:20:01+00:00
But at the very least, surely a dot product explainer should talk about the two main ways of looking at a dot product! This article leaves out the "angle and norms" (||a||·||b||·cos(θ)) interpretation entirely. It's like if someone gave you the formula for complex multiplication, without also showing you how it's all about rotations.

And maybe I'm being a pedant, but the dot product should be between two things that are in the same space. In the Minnesota lottery example, the probabilities should be a row vector instead. It's the exact same calculation, so again, maybe a bit too pedantic.
by khedoros1 | 2025-11-03 18:20:47+00:00
I've got a desktop that I built in 2008 that I only replaced in 2020 because I wanted to play newer games. That 2020 desktop is still going strong. My laptop was produced in 2016. I bought it for about $80 to replace my previous 2016-vintage laptop when its display started to fail. The last computer that I replaced due to performance in everyday tasks was a 2009 netbook.

In the 90s, it was easy to build/buy a machine that would be thoroughly limiting in its capabilities 2-3 years later. Now? I've got a 9-year-old laptop, 5-year-old desktop, and a 2-year-old workstation-class work laptop, and there's very little practical difference in power between them. The newer computer just has some conveniences like USB-C ports+charging.
by sebstefan | 2025-11-03 18:20:51+00:00
```

#define _(e...) ({e;})

#define x(a,e...) _(s x=a;e)

#define $(a,b) if(a)b;else

#define i(n,e) {int $n=n;int i=0;for(;i<$n;++i){e;}}

```

>These are all pretty straight forward, with one subtle caveat I only realized from the annotated code. They're all macros to make common operations more compact: wrapping an expression in a block, defining a variable x and using it, conditional statements, and running an expression n times.

This is war crime territory
by fellowniusmonk | 2025-11-03 18:21:05+00:00
No. As an aside, when you are younger they may elect to put you into a twilight level of sedation.

And that is how I saw the inside of my own beating heart at 10 while I was tied down and essentially naked in front of like 10+ adults.

Oh, and the contrast dye momentarily made me feel like I was being burnt alive from the inside out.
by iaz | 2025-11-03 18:21:09+00:00
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by sbdaman | 2025-11-03 18:21:20+00:00
I've shared this on YN before but I'm a big fan of this piece by Kenneth Taylor (well, an essay pieced together from his lectures).

The Robots Are Coming

https://www.bostonreview.net/articles/kenneth-taylor-robots-...

"However exactly you divide up the AI landscape, it is important to distinguish what I call AI-as-engineering from what I call AI-as-cognitive-science. AI-as-engineering isn’t particularly concerned with mimicking the precise way in which the human mind-brain does distinctively human things. The strategy of engineering machines that do things that are in some sense intelligent, even if they do what they do in their own way, is a perfectly fine way to pursue artificial intelligence. AI-as-cognitive science, on the other hand, takes as its primary goal that of understanding and perhaps reverse engineering the human mind.

[...]

One reason for my own skepticism is the fact that in recent years the AI landscape has come to be progressively more dominated by AI of the newfangled 'deep learning' variety [...] But if it’s really AI-as-cognitive science that you are interested in, it’s important not to lose sight of the fact that it may take a bit more than our cool new deep learning hammer to build a humanlike mind.

[...]

If I am right that there are many mysteries about the human mind that currently dominant approaches to AI are ill-equipped to help us solve, then to the extent that such approaches continue to dominate AI into the future, we are very unlikely to be inundated anytime soon with a race of thinking robots—at least not if we mean by “thinking” that peculiar thing that we humans do, done in precisely the way that we humans do it."
by helicone | 2025-11-03 18:21:40+00:00
Because an automated screening system allows the company to screen many more candidates without interacting with them, which they will do, which will make the majority of these screenings wasted effort.

Let's look at two cases to see why this is: Case 1: company does 10 30 minute in-person technical interviews for a role for equally qualified candidates, doesn't use automated testing. Every candidate knows that because they're talking to a human, so they know they're dealing with a human hiring process that deals with time constraints. They KNOW that they're one of a small group of people selected to move forward. They can reasonably calculate a value for their in-person technical interview as having a 10% chance of success. If they do 7 interviews like this they have a >50% chance of getting hired by someone, which would take them only 3.5 hours of interview time to achieve. Each such hiring process has only take up a combined 5 hours of candidate time.

Contrast this with case 2: company uses your system, and so technically screens 1000 equally qualified candidates in the same period with no human interaction. The candidate now has no idea where they stand in the applicant pool, but they effectively have a 0.1% of getting hired by this company. If they do 666 interviews, they still don't have a 50% chance of getting hired by any company doing interviews like this, and they will have spent two whole weeks of their life not eating or sleeping, just doing interviews. That company will have wasted three weeks of candidate time conducting this round of interviews.

Furthermore, the 10 minute time difference is irrelevant, the candidate already doesn't care when they do the interview, and the pressure in no way lessened. They still have to perform in a 30 minute window, and they will still be nervous. The only difference is the recorded screening is more impersonal, which allows the candidate less opportunity to make a human impression on the hirer.

Your system assumes the applicant's time has no value.
by trhway | 2025-11-03 18:21:44+00:00
>In 2025, after a £12m investment, YASA opened the UK's first axial-flux super factory, in Oxfordshire.

In Bay Area that is small investment in a startup which would be able to lease a small office

>Could lead to significant efficiency gains for EV's, because 1/4 of the motor weight means better power-to-weight ratio...

that would help VTOL a lot. Unfortunately YASA motors are priced for supercars and availability seems to be low. Until some factory in China starts making similar ones, there are not much chances on getting hands on such a motors.
by mbesto | 2025-11-03 18:21:45+00:00
I think the discrepancy is this:

1. We trained it on a fraction of the world's information (e.g. text and media that is explicitly online)

2. It carries all of the biases us humans have and worse the biases that are present in the information we chose to explicitly share online (which may or may not be different to the experiences humans have in every day life)
by embedding-shape | 2025-11-03 18:22:13+00:00
By what definition of "sentience"? Wikipedia claims "Sentience is the ability to experience feelings and sensations" as an opening statement, which I think would be trivial depending again on your definition of "experience" and "sensations". Can a LLM hooked up to sensor events be considered to "experience sensations"? I could see arguments both ways for that.
by dragonwriter | 2025-11-03 18:22:22+00:00
> There are largely three types of cheese in the US: Swiss, American and Cheddar.

All sources I can find have cheddar #1 and mozzarella either #2 or #3 (with cream cheese #2 when moz is #3) in the US. American is behind them and Swiss is way back behind a bunch of other things including Jack and various blends.

Swiss/American/Cheddar might be the big three for a particular sandwich shop, but...
by coryrc | 2025-11-03 18:22:50+00:00
> Direct-drive, wheel-hub motors are not well-regarded right now, specifically because they increase unsprung weight

It will always be lighter to not have the motor in the wheel.

> So instead we backport a bunch of the mechanical infrastructure that transfers power from a traditional ICE engine to the four wheels.

No, we do it because it's smart and efficient for freeway-capable vehicles.

Wheels get banged up in use. They're easy to replace for different applications. They're exposed to 200 kph salt spray at hundreds of RPM. They are not a great place for motors.