MurphyA/hacker-news huggingface dataset:
by edwardbernays | 2025-06-14 21:19:26+00:00
I wasn't aware people could act so credulously in the face of obvious hyperbole.

Do you really, truly, believe this poster to be making a positive claim about when Protestants did or did not exist? If so, I weep for your social comprehension skills.

Christian moralism is an antiquated, self-contradicting, socially regressive belief system that continues to plague humanity and politics to this day. If people want to believe in Sky Daddy on their own time then that's fine, we all need a way to cope our way through the horrors of living with other humans. Your capacity to believe in Sky Daddy ends where it impinges on my freedoms, which are not granted by any imaginary deity. We should keep Yahweh (and every other unfalsifiable, immaterial, intelligence) out of the politics that govern mortal, material, extant humans. An omnipotent presence could surely argue for his own agenda in politics. It's baffling that so many narcissists believe themselves to be charged with the responsibility for advocating his agenda instead.
by senko | 2025-06-14 21:19:43+00:00
Gary Marcus isn't about "getting real", it's making a name for himself as a contrarian to the popular AI narrative.

This article may seem reasonable, but here he's defending a paper that in his previous article he called "A knockout blow for LLMs".

Many of his articles seem reasonable (if a bit off) until you read a couple dozen a spot a trend.
by hunglee2 | 2025-06-14 21:20:59+00:00
of course there is censorship on HN but this is true in any space where people can publicly communicate with each other. It is a fantasy to believe free speech actually exists in any place, what is different is which topics are taboo, and the manner in which such taboos are enforced (de-ranked, shadow banned, public ban, post removal, de-platforming, gulag)
by npinsker | 2025-06-14 21:22:29+00:00
It doesn't invalidate your ideas, but it severely harms customer trust before you've even had the chance to build up any. It makes me worry that your backend code is also written by AI, without enough oversight from humans. What if your analytics code silently doesn't work also?

You can do a quick Google search to see what works for your competitors. From mine, https://www.blaze.ai/ has a creative and effective homepage.
by 1659447091 | 2025-06-14 21:23:12+00:00
I think the point is that the rest of the country will soon catch up to what border states have known for decades. Undocumented immigrants are too important to the economy.[0] But excellent for scaring people into voting a certain way. It's why Texas refuses to require businesses to check work status and do audits[1][2], instead they put on a song and dance at the border.

[0] hhttps://www.texastribune.org/2024/12/06/texas-undocumented-i...

[1] https://www.texastribune.org/2025/06/05/texas-e-verify-requi...

[2] https://news.ycombinator.com/item?id=44250270
by TeaVMFan | 2025-06-14 21:25:55+00:00
Flavour 0.3.1 is now live on Maven Central. It includes these enhancements:

* Ticket #11 is resolved: Spaces are allowed in path parameters

* Ticket #3 is resolved: Fixed vague error message for missing setter in html:bidir-value

* Fixed archetype to hide static message after startup

For more information on Flavour:

* Flavour Book: https://frequal.com/Flavour/book.html

* Flavour Home Page: https://flavour.sf.net/
by tomp | 2025-06-14 21:26:07+00:00
Key point:

The model is considered fair if its performance is equal across these groups.

One can immediately see why this is problematic, easily by considering equivalent example in less controversial (i.e. emotionally charged) situations.

Should basketball performance be equal across racial, or sex groups? How about marathon performance?

It’s not unusual that relevant features are correlated with protected features. In the specific example above, being an immigrant is likely correlated with not knowing the local language, therefore being underemployed and hence more likely to apply for benefits.
by labrador | 2025-06-14 21:26:10+00:00
The key insight is that LLMs can 'reason' when they've seen similar solutions in training data, but this breaks down on truly novel problems. This isn't reasoning exactly, but close enough to be useful in many circumstances. Repeating solutions on demand can be handy, just like repeating facts on demand is handy. Marcus gets this right technically but focuses too much on emotional arguments rather than clear explanation.
by troupo | 2025-06-14 21:27:24+00:00
Ask women how many of their common maladies still get untreated or misdiagnosed.

White[1] males are extremely over represented in medical literature and research. And I'm saying it as a white male myself.

[1] This actually has ramifications beyond just gender. See e.g. https://www.statnews.com/2020/07/21/dermatology-faces-reckon...
by Mordisquitos | 2025-06-14 21:27:41+00:00
It's interesting how American cultural expectations of forced social interaction may be having the effect of promoting automated systems as a reaction.

As someone who lives in Spain and has lived in the UK, the idea of choosing self-checkout at a supermarket to avoid small talk with a cashier sounds alien to me; we simply don't do that here. While cashiers will certainly chat with certain customers while scanning their items, it's either that they know each other or it was initiated by the customer. I always choose staffed checkout over self-checkout because it's literally less effort for me, but I could imagine American social expectations at checkout —"How are you doing today?", "Oh these apples look amazing!", "Having a party are we?"— absolutely tipping the balance of effort and pushing me to self-checkout.
by perrygeo | 2025-06-14 21:28:42+00:00
True but you're missing the part where your salvation lies in a named technique: daith piercing.

You believed in that. And it worked. The placebo effect is still an empirically measured effect. Vitally, it requires the consent of the one being "placebo'd".

You healed your RSI with your mind bro. Stop fighting it.
by Jabrov | 2025-06-14 21:29:00+00:00
I’m so tired of hearing this be repeated, like the whole “LLMs are _just_ parrots” thing.

It’s patently obvious to me that LLMs can reason and solve novel problems not in their training data. You can test this out in so many ways, and there’s so many examples out there.

______________

Edit for responders, instead of replying to each:

We obviously have to define what we mean by "reasoning" and "solving novel problems". From my point of view, reasoning != general intelligence. I also consider reasoning to be a spectrum. Just because it cannot solve the hardest problem you can think of does not mean it cannot reason at all. Do note, I think LLMs are generally pretty bad at reasoning. But I disagree with the point that LLMs cannot reason at all or never solve any novel problems.

In terms of some backing points/examples:

1) Next token prediction can itself be argued to be a task that requires reasoning

2) You can construct a variety of language translation tasks, with completely made up languages, that LLMs can complete successfully. There's tons of research about in-context learning and zero-shot performance.

3) Tons of people have created all kinds of challenges/games/puzzles to prove that LLMs can't reason. One by one, they invariably get solved (eg. https://gist.github.com/VictorTaelin/8ec1d8a0a3c87af31c25224..., https://ahmorse.medium.com/llms-and-reasoning-part-i-the-mon...) -- sometimes even when the cutoff date for the LLM is before the puzzle was published.

4) Lots of examples of research about out-of-context reasoning (eg. https://arxiv.org/abs/2406.14546)

In terms of specific rebuttals to the post:

1) Even though they start to fail at some complexity threshold, it's incredibly impressive that LLMs can solve any of these difficult puzzles at all! GPT3.5 couldn't do that. We're making incremental progress in terms of reasoning. Bigger, smarter models get better at zero-shot tasks, and I think that correlates with reasoning.

2) Regarding point 4 ("Bigger models might to do better"): I think this is very dismissive. The paper itself shows a huge variance in the performance of different models. For example, in figure 8, we see Claude 3.7 significantly outperforming DeepSeek and maintaining stable solutions for a much longer sequence length. Figure 5 also shows that better models and more tokens improve performance at "medium" difficulty problems. Just because it cannot solve the "hard" problems does not mean it cannot reason at all, nor does it necessarily mean it will never get there. Many people were saying we'd never be able to solve problems like the medium ones a few years ago, but now the goal posts have just shifted.
by preommr | 2025-06-14 21:29:25+00:00
Started off as a way to pay people less, especially for odd jobs.

Grew to a point where it's disconnected from the actual value of the service, so people like waiters make way more than if it was priced according to market price, but people pay anyways because it's not about the service, but about not feeling guilty for being cheap. The ecosystem has now found a balance that hurts the consumer, which they're willing to put up with because it's socially ingrained. The people providing a service make more, the business owner doesn't really care, and can't get rid of tips because it's a cutthroat industry and they wouldn't get workers, and higher wages would cause sticker shock, so they too have no incentive to make any changes. The customers group is too big, and don't have enough structure to organize any meaningful change. So it is what it is.

You can see it now, people complain about how tipping is everywhere, including for walk-ins where no table service is provided, but eventually this too will be normalized.

My personal hope is that one day we start tipping our doctors, our dentists, our programmers, to see how big and stupid this dumpster fire can grow.
by neepi | 2025-06-14 21:29:25+00:00
Indeed. I completely agree with this.

My objection to the whole thing is the AI hype bros, which is really the funding solicitation facade over everything rather the truth, only has one outcome and that is that it cannot be sustained. At that point all investor confidence disappears, the money is gone and everyone loses access to the tools that they suddenly built all their dependencies on because it's all proprietary service model based.

Which is why I am not poking it with a 10 foot long shitty stick any time in the near future. The failure mode scares me, not the technology which arguably does have some use in non-idiot hands.
by mtmail | 2025-06-14 21:29:50+00:00
> if anyone else is surprised by the lack of coverage on the front page with regards to the recent wars

HN user for 10(?) years. It's not surprising at all to me. It's off-topic here. Plenty of other websites cover the stories, TV covers it. I come here to escape the 24/7 news cycle. Celebrity deaths hardly make the frontpage, if the inventor of an operating system dies you'll top spot and 2000 comments. It's not that users don't care, it's more the expectation that people get their news from multiple sources (websites, aggregator, forums) and not every forum needs to cover all topics.
by FilosofumRex | 2025-06-14 21:30:24+00:00
In most MILP domains, the underlying engineering know-how is more critical than mathematical formulations or CS coding: (that's why most OR groups operate independently of math or CS departments).

OSS never took off among professional engineers because they've have "skin in the game", unlike math and CS folks who just reboot, and pretend nothing is wrong.
by foldr | 2025-06-14 21:30:58+00:00
This sort of omission would not be considered scientific malpractice even in a journal article, let alone a blog post. A rebuttal of a position that fails to address the strongest arguments for it is a bad rebuttal, but it’s not scientific malpractice to write a bad paper — let alone a bad blog post.

I don’t think I agree with you that GM isn’t addressing the points in the paper you link. But in any case, you’re not doing your argument any favors by throwing in wild accusations of malpractice.
by lvl155 | 2025-06-14 21:31:03+00:00
Aren’t you PE? Just look up the lawsuits bro. On a serious note, I don’t think Google or Tesla will take on that liability once we get to scale. That basically defeats the purpose of autonomous. Their legal and ops team will do everything to push an alternate business model similar to lease. This is why I really think autonomous has to be at least two orders of magnitude “safer” to be viable at scale (more than 10% on the road).
by MBCook | 2025-06-14 21:31:05+00:00
There’s something to be said for being able to not be forced to deal with a person, but I see something different personally.

I’m “old” (40s) so I didn’t grow up with Uber. Maybe that colors my take.

I don’t want to hire random Joes. If I wanted to buy a lift from a random person, I’d expect it to be very cheap.

If I’m hiring someone to drive me from A to B I want a professional service. I want professional drivers in a fleet of maintained cars.

With Uber/Lift you don’t know. Many drives do a great job and treat their cars/passengers like they’re professionals. Others don’t.

The taxi industry sucked. They had no competition and could get lazy and do a terrible job and people still had to use them anyway. That needed fixing.

But I don’t think the lesson we should learn is “taxis bad” but “bad service is bad”. And Uber/Lyft being so variable is not a plus at their prices.
by Geee | 2025-06-14 21:31:36+00:00
Obviously the opposite. Competition keeps prices low and quality high.

"Publicly-owned" would be expensive and low quality, and would make the people running the operation filthy rich. Non-profit would mean that whoever is running it would increase their salaries until there's no profit. There would be no reason to lower prices or increase quality, if competition is non-existent.
by bandrami | 2025-06-14 21:32:15+00:00
How actually useful are they though? We've had more than a year now of saying these things 10X knowledge workers and creatives, so.... where is the output? Is there a new office suite I can try? 10 times as many mobile apps? A huge new library of ebooks? Is this actually in practice producing things beyond Ghibli memes and RETVRN nostalgia slop?
by ummonk | 2025-06-14 21:32:24+00:00
Most of the objections and their counterarguments seem like either poor objections (e.g. ad hominem against the first listed author) or seem to be subsumed under point 5. It’s annoying that most of this post focuses so much effort on discussing most of the other objections when the important discussion is the one to be had in point 5:

I.e. to what extent are LLMs able to reliably make use of writing code or using logic systems, and to what extent does hallucinating / providing faulty answers in the absence of such tool access demonstrate an inability to truly reason (I’d expect a smart human to just say “that’s too much” or “that’s beyond my abilities” rather than do a best effort faulty answer)?
by andy99 | 2025-06-14 21:32:25+00:00
The better definition of equal performance would obviously be that the metrics for the detector - accuracy or false positive rate etc would be the same for all groups.

I won't comment on why it's defined the way that it is.

Edit: it looks like they define several metrics, including ones like I mention above that consider performance and at least one based on what number or percentage is flagged in each group.
by throwawayqqq11 | 2025-06-14 21:33:25+00:00
Being flagged as potential fraud based on eg. ethnicity is what you want to eliminate, so you have to start with the assumption of an even distristribution.

From the article:

> Deciding which definition of fairness to optimize for is a question of values and context.

This optimization is the human feedback required to not have the model stagnate in a local optimum.
by krackers | 2025-06-14 21:33:41+00:00
Hasn't the symbolic vs statistical split in AI existed for a long time? With things like Cyc growing out of the former. I'm not too familiar with linguistics but maybe this extends there too, since I think Chomsky was heavy on formal grammars over probabilistic models [1].

Must be some sort of cognitive sunk cost fallacy, after dedicating your life to one sect, it must be emotionally hard to see the other "keep winning". Of course you'd root for them to fall.

[1] https://norvig.com/chomsky.html
by oblio | 2025-06-14 21:33:49+00:00
People funding autonomous driving will obviously lobby against increased funding for public transit and they will also fund demonizing public transit.

Look at Musk and Vegas. The vast majority of mass transportation in Vegas should be handled by actual public transit, most likely high speed rail from LA and light rail along the Strip to downtown Vegas and a few other places.

Instead Vegas has a silly monorail, a few buses that don't even get dedicated bus lanes on 8+ lane stroads and something stupid like, dunno, 20 daily flights from LA. Plus Musk setting up tunnels or hyperloops or other stupidities.
by labrador | 2025-06-14 21:33:54+00:00
I've done this excercise dozens of times because people keep saying it, but I can't find an example where this is true. I wish it was. I'd be solving world problems with novel solutions right now.

People make a common mistake by conflating "solving problems with novel surface features" with "reasoning outside training data." This is exactly the kind of binary thinking I mentioned earlier.
by steamrolled | 2025-06-14 21:34:10+00:00
> Gary Marcus isn't about "getting real", it's making a name for himself as a contrarian to the popular AI narrative.

That's an odd standard. Not wanting to be wrong is a universal human instinct. By that logic, every person who ever took any position on LLMs is automatically untrustworthy. After all, they made a name for themselves by being pro- or con-. Or maybe a centrist - that's a position too.

Either he makes good points or he doesn't. Unless he has a track record of distorting facts, his ideological leanings should be irrelevant.
by skhameneh | 2025-06-14 21:34:16+00:00
Anecdotally, LLMs as a whole haven't made my life noticeably any better. I see some great use cases and some impressive demos, but they are just that. I look at how many things that LLMs have noticeably made worse and by my own impression it outweighs improvements.

- I asked when a software EOL will be, the LLM response (incorrectly) provided past tense for an event yet to happen. - The replacement of Google Assistant with Gemini broke using my phone while locked and the home automation is noticeably less reliable. - I asked an LLM about whether a device "phones home" and the answer was wrong. - I asked an LLM to generate some boiler plate code with very specific instructions and the generated code was unusable. - I gave critical feedback to a company that works with LLMs regarding a poor experience (along with some suggestions) and they seemed to have no interest in making adjustments. - I've seen LLM note takers with incorrect notes, often skipping important or nuanced details.

I have had good experiences with LLMs and other ML models, but most of those experiences were years ago before LLMs were being unnecessarily shoved into every possible scenario. At the end of the day, it doesn't matter if the experience is powered by an LLM, it matters whether the experience is effective overall (by many different measures).
by FranzFerdiNaN | 2025-06-14 21:34:36+00:00
I don’t need a tool that’s right maybe 70% of the time (and that’s me being optimistic). It needs to be right all the time or at least tell you when it doesn’t know for sure, instead of just making up something. Comparing it to going out in the streets and asking random people random questions is not a good comparison.
by herbst | 2025-06-14 21:34:42+00:00
- can't create AI campaigns, nothing happens when I click the button

- AI content is very generic, images even more so. Storyline, Strategy, ... All sound very ai'ish and barely focused.

- it just feels undone, and like a test thing with rough corners

I am definitely not your target audience. For basic AI marketing I just built that into my projects myself.

However I feel very lost in your tool, it just generates semi random, semi relevant boilerplate texts.

Also the point where you ask for my social media passwords to 'save them in your vault' i have red flags waving.
by dwohnitmok | 2025-06-14 21:36:10+00:00
> PA proves that if it can prove everything that it proves that it can prove, then PA must have a proof of one of the two previous statements.

I don't believe this is true. I don't know what result you're using here, but I think you're mixing up "provable" and "true".

In particular your line of reasoning violates Lob's Theorem, which is a corollary of the second incompleteness theorem.
by klelatti | 2025-06-14 21:36:30+00:00
> He was named "Horatio" after his godfather Horatio Walpole, 1st Earl of Orford (third creation), (1723–1809), the first cousin of his maternal great-grandmother Anne Turner (1691–1768). Horatio Walpole was a nephew of Robert Walpole, 1st Earl of Orford, (second creation) the de facto first prime minister of Great Britain. [1]

Horatio Nelson

[1] https://en.wikipedia.org/wiki/Horatio_Nelson,_1st_Viscount_N...
by dangus | 2025-06-14 21:36:46+00:00
To expand on this, and maybe this point is obvious, but accelerators and VCs purposefully fund a lot of companies they assume will not succeed. Something like 10% success is an acceptable target.

I wonder if all this focus on AI companies is actually denying opportunity to founders who have better ideas? Every AI agent company in YC and other accelerators is one that could have been taken by someone else.

But that’s the thing, AI is the highest risk highest reward opportunity, which fits the VC model. A boring CRUD app that solves an actual problem might be more likely to succeed but less likely to explode into a unicorn.
by sansseriff | 2025-06-14 21:36:56+00:00
An excellent video. I've admired Yedlin's past work in debunking the need for film cameras over digital when you're going after a 'film look'

I wish he shared his code though. Part of the problem is he can't operate like a normal scientist when all the best color grading tools are proprietary.

I think it would be really cool to make an open source color grading software that simulates the best film looks. But there isn't enough information on Yedlin's website to exactly reproduce all the research he's done with open source tools.
by lossolo | 2025-06-14 21:37:11+00:00
They can't create anything novel and it's patently obvious if you understand how they're implemented. But I'm just some anonymous guy on HN, so maybe this time I will just cite the opinion of the DeepMind CEO, who said in a recent interview with The Verge (available on YouTube) that LLMs based on transformers can't create anything truly novel.
by ttoinou | 2025-06-14 21:39:04+00:00
Oh that's normal then. There are mandatory steps of dynamic range reduction in the video editing / color grading pipeline (like a compressor in audio production). So the whole information is not lost but the precision / details can be yes. But that's a weird definition, there are so many photons in daylight capture that you could easily say we really need minimum 21 bits per channel minimum (light intensity of sun / light intensity of moon)
by dangus | 2025-06-14 21:40:11+00:00
Have they done that? In my opinion they’ve built an AI IDE consisting of 90% forked open source code that has an identical experience to competitors with less of a chance to integrate with big tech Google/Microsoft/Apple-style ecosystem products.

The “step one: get user data, step two: ???, step 3: profit” business model is well established, and it definitely doesn’t always work.