MurphyA/hacker-news huggingface dataset:
by wongarsu | 2025-06-14 22:01:06+00:00
A big part of the difficulty of such an attempt is that we don't know the ground truth. A model is fair or unbiased if its performance is equally good for all groups. Meaning e.g. if 90% of cases of Arabs committing fraud are flagged as fraud, then 90% of cases of Danish people committing fraud should be flagged as fraud. The paper agrees on this.

The issue is that we don't know how many Danish commit fraud, and we don't know how many Arabs commit fraud, because we don't trust the old process to be unbiased. So how are we supposed to judge if the new model is unbiased? This seems fundamentally impossible without improving our ground truth in some way.

The project presented here instead tries to do some mental gymnastics to define a version of "fair" that doesn't require that better ground truth. They were able to evaluate their results on the false-positive rate by investigating the flagged cases, but they were completely in the dark about the false-negative rate.

In the end, the new model was just as biased, but in the other direction, and performance was simply worse:

> In addition to the reappearance of biases, the model’s performance in the pilot also deteriorated. Crucially, the model was meant to lead to fewer investigations and more rejections. What happened instead was mostly an increase in investigations , while the likelihood to find investigation worthy applications barely changed in comparison to the analogue process. In late November 2023, the city announced that it would shelve the pilot.
by knallfrosch | 2025-06-14 22:02:18+00:00
Through hyperbole, you imply that he's overpaid by market forces. If he manages 20.000 people and increases productivity by just 10%, he'd be worth 2.000 "normal guy" salaries. So a 100 factor doesn't seem far fetched to me.

What strategy would you suggest to arrive at a better price for his work?
by flutas | 2025-06-14 22:02:32+00:00
not op but cleanliness would be my first expectation

I've seen many reports of dirty waymos on reddit recently for example.

second I'd assume they would start charging you for point 3, "loading delay fee" when you take too long to load, after all that's missed profit from other rides.

after that point 1 and 2, with you getting either a Jag (nice car), a Zeekr (unknown to me, Chinese company), or a Ioniq 5 (much cheaper feeling car than a Jag, with hard plastic everywhere). You want the jag? Expect to pay for it. So suddenly all cars aren't the same, and only some are comparable to Uber Black.

To summarize:

Point 4, followed by 3, followed by 2 and 1 (which imo are just one point). 5 I don't expect to change unless they have to start cost-cutting on compute and sensors, but I HIGHLY doubt that.
by TMWNN | 2025-06-14 22:02:41+00:00
I presume that the lack of an Ultimax-style bypass is why, as I understand it, there are no freezer cartridges for Atari 8-bit. (The piracy problem was just as bad, however, because of Happy Computers <https://en.wikipedia.org/wiki/Happy_drives>.)

On the other hand, Atari 8-bit's design allows for FujiNet to work without the workarounds/disadvantages a Commodore equivalent would have. <https://news.ycombinator.com/item?id=37424773>
by aucisson_masque | 2025-06-14 22:03:42+00:00
That’s the opposite of reasoning tho. Ai bros want to make people believe LLM are smart but they’re not capable of intelligence and reasoning.

Reasoning mean you can take on a problem you’ve never seen before and think of innovative ways to solve it.

LLM can only replicate what is in its data, it can in no way think or guess or estimate what will likely be the best solution, it can only output a solution based on a probability calculation made on how frequent it has seen this solution linked to this problem.
by AlotOfReading | 2025-06-14 22:03:48+00:00
Musk doesn't need autonomous vehicles to derail public transit. Hyperloop predated FSD, to use your example. Moreover, the objection applies equally to taxis and Uber/Lyft.

It's also not an actionable objection. Let's say we go and ban autonomous vehicles. Why wouldn't the same billionaires simply continue lobbying against public transit improvements and for the repeal of the ban? They have the money to do both.

We haven't failed to invest sufficiently in public transit for 50+ years solely because of billionaire lobbying. That's not the blocker.
by jwitthuhn | 2025-06-14 22:06:18+00:00
Pytorch also runs great on apple silicon, though it is hard to directly compare because Apple's high end GPUs can't compute anywhere near as much as nvidia's high end stuff.

e: I'll also add that pytorch does still have one oddity on apple silicon which is that it considers each tensor to be 'owned' by a particular device, either a cpu or gpu. Macs have unified memory but pytorch will still do a full copy when you 'move' data between the cpu and gpu because it just wasn't built for unified memory.
by gametorch | 2025-06-14 22:06:50+00:00
My experience is the opposite.

I have an extensive, strong traditional CS background. I built and shipped a production grade SaaS in 2 months that has paying users. I've built things in day that would have taken me 3+ days manually. Through all of that, I hardly wrote a single line of code. It was all GPT-4.1 and o3.

Granted, I think you need quite a lot of knowledge and experience to know how to come up with coherent prompts and to be able to do the surgery necessary to get yourself out of a jam. But LLMs have easily 3x'd my productivity by very quantifiable metrics, like number of features shipped, for example.

I've noticed people who actually build stuff agree with me. That's because it's such a tremendous addition of value to our lives. Armchair speculators seem to see only the negative side.
by nradov | 2025-06-14 22:06:58+00:00
Cost was absolutely a factor. The Panavia Tornado was previously certified to carry US nuclear bombs. The same could have been done with the Eurofighter but Germany would have had to pay 100% of the program cost because no other NATO member was interested.

I can't see any scenario where France would agree to share nuclear weapons with Germany. The only approach that might work would be to forward deploy French nuclear forces on German territory, with Germany picking up the cost.
by teekert | 2025-06-14 22:07:11+00:00
I know a cop, they do public searchings for weapons or drugs. Our law dictates fairness. So every now and then they search an elderly couple. You know how this goes and what the results are.

Any model would be unfair, age-wise but also ethnically.

To be most effective the model would have to be unfair. It would suck to be a law abiding young specific ethnic minority.

But does it help to search elderly couples?

I’m Genuinely curious what would be fair and effective here. You can’t be a Bayesian.
by andrewmcwatters | 2025-06-14 22:09:39+00:00
It's definitely not true in any meaningful sense. There are plenty of us practitioners in software engineering wishing it was true, because if it was, we'd all have genius interns working for us on Mac Studios at home.

It's not true. It's plainly not true. Go have any of these models, paid, or local try to build you novel solutions to hard, existing problems despite being, in some cases, trained on literally the entire compendium of open knowledge in not just one, but multiple adjacent fields. Not to mention the fact that being able to abstract general knowledge would mean it would be able to reason.

They. Cannot. Do it.

I have no idea what you people are talking about because you cannot be working on anything with real substance that hasn't been perfectly line fit to your abundantly worked on problems, but no, these models are obviously not reasoning.

I built a digital employee and gave it menial tasks that compare to current cloud solutions who also claim to be able to provide you paid cloud AI employees and these things are stupider than fresh college grads.
by rockemsockem | 2025-06-14 22:09:48+00:00
I believe this is the main piece

> with minimal dependencies

I haven't tried running SD 3.5 specifically, but it's built on hugging face libraries which I personally always find to be a mess of dependencies that make it really hard to setup without the exact configuration the original developers used (which is often not provided in enough detail to actually work). This makes it pretty hard to run certain models especially if it's a few months/years after the original release.

For example this appears to be the requirements for the stability AI reference implementation for SD3.5 and there are no versions specified and it includes "transformers" which is just an enormous library.

https://github.com/Stability-AI/sd3.5/blob/main/requirements...
by wongarsu | 2025-06-14 22:11:27+00:00
A lot of the best internet services came around in the decade after the dot-com crash. There is a chance Anthropic or OpenAI may not survive when funding suddenly dries up, but existing open weight models won't be majorly impacted. There will always be someone willing to host DeepSeek for you if you're willing to pay.

And while it will be sad to see model improvements slow down when the bubble bursts there is a lot of untapped potential in the models we already have. Especially as they become cheaper and easier to run
by cableshaft | 2025-06-14 22:11:44+00:00
If/when we get to the point where my job is automated out of existence, then so will a large enough categories of jobs that there's going to have to be some sort of universal basic income system in place, so hopefully I'll just be on that and work on one of my creative passions, like writing or music or board game design/development, and supplement that with what I've managed to sock away so far.

I could see myself doing teaching also. Or just become a barista or cook, but I'm not sure I can handle standing for that long at a time anymore, the front of one of my thighs starts tingling and bothering me (when walking it doesn't really have a problem, it's mainly when I stand mostly in place, like when I'm cooking).

I wouldn't mind doing some sort of research but I've don't have any experience with that, outside of research projects in college a couple of decades ago.
by 1024core | 2025-06-14 22:12:36+00:00
The other day I almost got ran over by an old lady in her old Volvo wagon at a stop sign. She seemed to have gotten confused a little and was turning left but couldn't figure out the right move to make. People behind her honked and she decided to just go for it. I happened to be in the crosswalk and just happened to look over at the honking, and saw her coming, so managed to jump out of her way.

She was easily over 90, if not over 95.

People like her could really benefit from a personal Waymo. Just sell a car with FSD built in, at the level of a Waymo, and bam! That would make so many senior citizens' lives easier!
by TandemApp | 2025-06-14 22:12:49+00:00
I built Tandem to help remote managers have more thoughtful 1-on-1s with their team. I used to wing my meetings: forgetting follow-ups, scrambling for topics, and missing chances to support people properly.

If you're managing a remote team, I'd love for you to try it and see if it makes your conversations smoother, deeper, and easier to run. Feedback very welcome and I'm happy to answer any questions!
by akchro | 2025-06-14 22:13:07+00:00
All the components needed to be big enough for eye tracking "selection" to consistently work. It's frustrating when you are trying to look at a button to press, but the eye tracking thinks you are looking elsewhere. If the screen were any small, the components wouldn't fit. I believe the dimensions should work on most computer screen sizes and just bar out mobile devices. This was also intended since the eye tracking does not work on mobile.
by 3abiton | 2025-06-14 22:14:23+00:00
To hammer one point though, you have to understand that researcher are desensitized to minor novel improvement that translate to great value products. While obviously studying and assessing the limitations of AI is crucial, to the general public its capabilities are just so amazing, they can't fathom why we should think about limitations. Optimizing what we have is bette than rethinking the whole process.
by ikrima | 2025-06-14 22:14:39+00:00
Yup, it's 100% generated by an LLM. I thought that was intentionally clear? (I'm recovering from a TBI so I'm still adjusting to figuring out how to relearn typing; I use the LLMs as my voice mediated interface to typing out thoughts).

I'm not sure there's an argument I'm hearing here other than you seem to have triggered some internal heuristic of "this was written by an LLM" x "It contains math words I don't understand" => "this is bullshit"

which you wouldn't be wrong but I am making a specific constructionist modal logic here using infinity-groupoids from category theory. infinite dimensional categories are a thing and that's what these transfinite numbers represent

you have hyperreal constructionists of the reals as well which follows nonstandard analysis. you can also use the Weil cohomology which IIRC gets us most of calculus without the axiom of choice but someone check me on that.

so....again, not sure what your specific critique is?
by VagabundoP | 2025-06-14 22:16:29+00:00
Having had someone close go through it with Endo, the pain she'd deal with meant she could barely walk 100m and lost 20kg from being bed bound and unable to eat. Eventually Endo nearly killed this person it was so debilitating and only a hysterectomy managed to restore her life back to her, which obviously brought on early menopause.

Not all Endo suffers get that bad. But there's just no way of knowing if you're going to be one of the unlucky ones.

For QOL if you diagnosed, make some plans. If you want kids ever then make that your top priority, because it can effect fertility, and being pregnant can help.

Even when its seems like its gone, it might come back.

If your country doesn't have free public health insurance, get it and get good coverage, you don't want to be left hanging here.

The operations can really help for a time, but it depends on how good the surgeon is. Find a good one that listens to you.

Endo can gum up things and the scar tissue from ops can gum up your insides, and its a real messy treatment(laser ablation). Ovaries can get calcified etc.

If it gets bad bad - and you'll know what I'm talking about if it happens to you or someone you care about- do not put off getting a hysterectomy. There is no point in suffering in pain month after month and not having a life. If I had to do the last twenty years over again I would have counselled her to get the hysterectomy when she couldn't walk down the stairs without pain, after multiple hormone treatments and ops.
by bdavbdav | 2025-06-14 22:17:01+00:00
But that’s not seen at the sensor - at least not at once - look at the sun and then look immediately at the dark sky moon (if it were possible) - the only reason you get the detail on the moon is the aperture in front. You couldn’t see the same detail if they were next to each other. The precision is the most dark in the scene next to the most bright, as opposed to the most dark possible next to the most bright. That’s the difference.
by labrador | 2025-06-14 22:18:37+00:00
You're assuming we're saying LLMs can't reason. That's not what we're saying. They can execute reasoning-like processes when they've seen similar patterns, but this breaks down when true novel reasoning is required. Most people do the same thing. Some poeple can come up with novel solutions to new problems, but LLMs will choke. Here's an example:

Prompt: "Let's try a reasoning test. Estimate how many pianos there are at the bottom of the sea."

I tried this on three advanced AIs* and they all choked on it without further hints from me. Claude then said:

Roughly 3 million shipwrecks on ocean floors globally Maybe 1 in 1000 ships historically carried a piano (passenger ships, luxury vessels) So ~3,000 ships with pianos sunk Average maybe 0.5 pianos per ship (not all passenger areas had them) Estimate: ~1,500 pianos

*Claude Sonnet 4, Google Gemini 2.5 and GPT 4o
by georgemcbay | 2025-06-14 22:19:57+00:00
AI, in the form of LLMs, can be a useful tool.

It is still being vastly overhyped, though, by people attempting to sell the idea that we are actually close to an AGI "singularity".

Such overhype is usually easy to handwave away as like not my problem. Like, if investors get fooled into thinking this is anything like AGI, well, a fool and his money and all that. But investors aside this AI hype is likely to have some very bad real world consequences based on the same hype-men selling people on the idea that we need to generate 2-4 times more power than we currently do to power this godlike AI they are claiming is imminent.

And even right now there's massive real world impact in the form of say, how much grok is polluting Georgia.
by ikrima | 2025-06-14 22:20:01+00:00
lol, it's a sketch of a proof covering a large swath of unexplored math. the other poster wasn't wrong when he said I smashed 10y+ of graduate math in one sentence.

Aleph numbers = these are cardinals sizes of infinity; depending on your choice of axioms, ZFC or not, you have the continuum hypothesis of aleph0 = naturals, aleph1= 2^N = Continuum

Beth numbers are transfinite ordinals => they generalize infinitesimals like the 1st, 2nd, 3rd. so you can think of them as a dual or co-algebra (I'm hand waving here, it's been twenty years since real analysis).

Betti numbers are for persistent cohomology; they track holes similar to genus

I mean there's a lot to cover between tropical geometry, differential geometry, and algebraic analysis. So sometimes alarm bells are false alarms and your random internet commenter knows what he's talking about but is admittedly too sloppy but it's 5 pm on a Saturday and I wrote that in the morning while making breakfast eggs, not for submission to the annals of Mathematics!

Thank you for coming to my TED Stand Up Talk.

More math at the GitHub: http://github.com/ikrima/topos.noether

Also, if you're really that uptight, most of this is actually to teach algebraic topology to my autistic nonverbal nephew because I'm gonna gamify it as a magic spell system

So it'll be open source and that begs the question, if you use it to learn something, did that mean I just zero-proof zero-knowledge something out of you that I didn't even need to know by making a self referential statement across both space & time?

peace out my ninja!
by dcrazy | 2025-06-14 22:20:02+00:00
I’m 14 minutes into this 2 hour 15 minute presentation that hinges on precision in terminology, and Yedlin is already making oversimplifications that hamper delivery of his point. First of all, he conflates the actual RGB triplets with the colorspace coordinates they represent. He chooses a floating point representation where each value of the triplet corresponds to a coordinate on the normalized axes of the colorspace, but there are other equally valid encodings of the same coordinates. Integers are very common.

Secondly, Rec. 2100 defines more than just a colorspace. A coordinate triple in the Rec. 2100 colorspace does not dictate both luminance and chromaticity. You need to also specify a _transfer function_, of which Rec. 2100 defines two: PQ and HLG. They have different nominal maximum luminance: 10,000 nits for PQ and 1,000 nits for HLG. Without specifying a transfer function, a coordinate triple merely identifies chromaticity. This is true of _all_ color spaces.

On the other hand his feet/meters analogy is excellent and I’m going to steal it next time I need to explain colorspace conversion to someone.
by ApolloFortyNine | 2025-06-14 22:20:13+00:00
I mean... It's crazy to read this in a thread complaining about the quality of ubers. I guess you didn't start the thread but your here all the same. It's this behavior that enables it.

The last terrible Lyft I had had a 4.9, yet the car literally rattled and you could 'hear' the suspension (hard to explain, whatever the hell it was wasn't right).

Guessing by the odometer being 220k and the sticker over the check engine light, it had likely been like that for a while.
by refulgentis | 2025-06-14 22:20:20+00:00
Ah, tyvm, that maps well onto my knowledge set, I have a ONNX inference wrapper written in Dart. However, I have never been able to leverage transformers.js ONNX demo code, i.e. have a reference to port to Dart.

IIRC it is written in an abstraction layer that supports a transformers-like API surface. This also makes it opaque to figure out what you're actually passing to the model, adding a Python dep mess on top of that...woo boy.
by doctorpangloss | 2025-06-14 22:21:36+00:00
The simpler explanation is that Google mismanages its pay-per-use consumer-facing products. Consider Google One and YouTube Premium are also overpriced, and everyone tells them so.

It's obviously a mistake to charge more than Uber or Lyft, it's crazy obvious, like mind meltingly obvious. Sometimes it's just the obvious thing. Google's problem is that its management is so bad, it doesn't understand: just because something happens (paying more for rides) doesn't mean it makes sense. After all taxis are more expensive sometimes, and people pay for them, and where's the article that litigates all the dumb reasons people give for doing that?
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by yuvrajbuilds | 2025-06-14 22:21:52+00:00
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