MurphyA/hacker-news huggingface dataset:
by runako | 2025-08-10 00:13:40+00:00
> What stops the higher ed players from regulatory capture of the state agency in charge of those subsidies and milking that cow for all it's got?

Yes, you are correct that a corrupt state will deliver poor results. A key bulwark against in many places is effective oversight of public assets and administrations. But a corrupt state also could do much worse than $35k for undergraduate tuition. Which suggests priorities are being set to accomplish a different set of goals.

Also keep in mind that the primary mechanism here is not adding regulation. Rather, it's about things like ensuring universities have enough open slots for the children matriculating through their K-12 programs. Think about it more in the way states are generally capable of managing and subsidizing/funding student education at the K-12 level.

Bigger picture: consider why it should even be seen to be such a massive difference in capability for a state to run a public education program for the 4 years after high school vs the first 13 years.
by gopher_space | 2025-08-10 00:15:24+00:00
You need to factor rent increases into your thinking, both commercial and residential. Your breakfast spot is a business that no longer makes financial sense to operate.

Feed the location of a business into a trip planner and note every neighborhood within reasonable commute radius. Calculate the average cost of renting a room in these areas and then multiply by three. That's your de facto minimum wage because you have no applicant pool beneath it.

Adding on to this, your competitors in a better financial position are all paying well above minimum. There's probably a McDonalds across the street starting people at five bucks an hour more than you, and they have that wage plastered on a banner right out front.
by worik | 2025-08-10 00:15:48+00:00
I am against agents. (I will happy to be proved wrong, I want agents, especially agents that could drive my car, but that is another disappointment....)

There is a paradox in the LLM version of AI, I believe.

Firstly it is very significant. I call this a "steam engine" moment. Nothing will ever be the same. Talking in natural language to a computer, and having it answer in natural language is astounding

But! The "killer app" in my experience is the chat interface. So much is possible from there that is so powerful. (For people working with video and audio there are similar interfaces that I am less familiar with). Hallucinations are part of the "magic".

It is not possible to capture the value that LLMs add. The immense valuations of outfits like OpenAI are going to be very hard to justify - the technology will more than add the value, but there is no way to capture it to an organisation.

This "trifecta" is one reason. What use is an agent if it has no access or agency over my personal data? What use is autonomous driving if it could never go wrong and crash the car? It would not drive most of the places I need it to.

There is another more basic reason: The LLMs are unreliable. Carefully craft a prompt on Tuesday, and get a result. Resubmit the exact same prompt on Thursday and there is a different result. It is extortionately difficult to do much useful with that, for it means that every response needs to be evaluated. Each interaction with an LLM is a debate. That is not useful for building an agent. (Or an autonomous vehicle)

There will be niches where value can be extracted (interactions with robots are promising, web search has been revolutionised - made useful again) but trillions of dollars are being invested, in concentrated pools. The returns and benefits are going to be disbursed widely, and there is no reason they will accrue to the originators. (Nvidea tho, what a windfall!)

In the near future (a decade or so) this is going to cause an enormous economic dislocation and rearrangement. So much money poured into abstract mathematical calculations - good grief!
by rainsford | 2025-08-10 00:16:10+00:00
Sure, but I think the point is why do LLM's have a blindspot for performing a task that a basic python script could get right 100% of the time using a tiny fraction of the computing power? I think this is more than just a gotcha. LLMs can produce undeniably impressive results, but the fact that they still struggle with weirdly basic things certainly seems to indicate something isn't quite right under the hood.

I have no idea if such an episode of Star Trek: The Next Generation exists, but I could easily see an episode where getting basic letter counting wrong was used as an early episode indication that Data was going insane or his brain was deteriorating or something. Like he'd get complex astrophysical questions right but then miscount the 'b's in blueberry or whatever and the audience would instantly understand what that meant. Maybe our intuition is wrong here, but maybe not.
by MarkusQ | 2025-08-10 00:17:04+00:00
> Labor reduction in fast food doesn't necessarily look like 'automation'

> It's things like self-ordering, machines that make change (if cash

> handling still matters), conveyor ovens/charbroilers, more centralized

> food prep, self-service and automated beverage dispensing.

Those are things that were previously being done by people that are now being done by machines. In other words, automation.
by PaulHoule | 2025-08-10 00:17:05+00:00
I think what people are missing is that they work sometimes and sometimes they don't work.

People think "Oh, it works better when somebody else does it" or "There must be some model that does better than the one I am using" or "If I knew how to prompt better I'd get better results" or "There must be some other agentic IDE which is better than the one I am using."

All those things might be true but they just change the odds, they don't change the fact that it works sometimes and fails other times.

For instance I asked an agent to write me a screen to display some well-typed data. It came up with something great right away that was missing some fields and had some inconsistent formatting but it fixed all those problems when I mentioned them -- all speaking the language of product managers and end users. The code quality was just great, as good as if I wrote it, maybe better.

Plenty of times it doesn't work out like that.

I was working on some code where I didn't really understand the typescript types and fed it the crazy error messages I was getting and it made a try to understand them and didn't really, I used it as a "rubber duck" over the course of a day or two and working with it I eventually came to understand what was wrong and how to fix and I got into a place that I like and when there is an error I can understand it and it can understand it too.

Sometimes it writes something that doesn't typecheck and I tell it to run tsc and fix the errors and sometimes it does a job I am proud of and other times it adds lame typeguards like

if (x && typeof x === "object") x.someMethod()

Give it essentially the same problem, say writing tests in Java, and it might take very different approaches. One time it will use the same dependency injection framework used in other tests to inject mocks into private fields, other times it will write some a helper method to inject the mocks into private fields with introspection directly.

You might be able to somewhat tame this randomness with better techniques but sometimes it works and sometimes it doesn't and if I just told you about the good times or just told you about the bad times it would be a very different story.
by reactordev | 2025-08-10 00:17:52+00:00
Assume every token is forged. Secure by default. Even if it wastes cpu, validate each and every field. Signatures only work if verified. While you're at it, validate it against your identity database as well. Double check, triple check if you must. This is what I taught my devs.

Tenant, User, Group, Resource - validate it all before allowing it through.
by zahlman | 2025-08-10 00:19:30+00:00
> The number of distinct tokens is tiny compared to the parameter space, and it's not infeasible to store a mapping from token type to character count in those weights.

You seem to suppose that they actually perform addition internally, rather than simply having a model of the concept that humans sometimes do addition and use it to compute results. Why?

> For any finite-length function (like counting letters in a bounded domain), it's just a matter of having a big enough network and figuring out how to train it correctly. They just haven't bothered.

The problem is that the question space grows exponentially in the length of input. If you want a non-coincidentally-correct answer to "how many t's in 'correct horse battery staple'?" then you need to actually add up the per-token counts.
by ehsankia | 2025-08-10 00:19:32+00:00
Analogies are just that, they are meant to put things in perspective. Obviously the LLM doesn't have "senses" in the human way, and it doesn't "see" words, but the point is that the LLM perceives (or whatever other word you want to use here that is less anthropomorphic) the word as a single indivisible thing (a token).

In more machine learning terms, it isn't trained to autocomplete answers based on individual letters in the prompt. What we see as the 9 letters "blueberry", it "sees" as an vector of weights.

> Illusions don't fool our intelligence, they fool our senses

That's exactly why this is a good analogy here. The blueberry question isn't fooling the LLMs intelligence either, it's fooling its ability to know what that "token" (vector of weights) is made out of.

A different analogy could be, imagine a being that had a sense that you "see" magnetic lines, and they showed you an object and asked you where the north pole was. You, not having this "sense", could try to guess based on past knowledge of said object, but it would just be a guess. You can't "see" those magnetic lines the way that being can.
by rainsford | 2025-08-10 00:20:14+00:00
I can't take credit for coming up with this, but LLMs have basically inverted the common Sci-Fi trope of the super intelligent robot that struggles to communicate with humans. It turns out we've created something that sounds credible and smart and mostly human well before we made something with actual artificial intelligence.

I don't know exactly what to make of that inversion, but it's definitely interesting. Maybe it's just evidence that fooling people into thinking you're smart is much easier than actually being smart, which certainly would fit with a lot of events involving actual humans.
by johnfn | 2025-08-10 00:21:18+00:00
Can you enumerate some of these "immeasurable number of failure states"? For me it starts and stops at "can't count letters in a word". That hardly seems catastrophic.

All I have to do is turn on thinking mode and the error goes away. https://chatgpt.com/share/6897e630-77f0-800c-a9bf-30d9c0e271...
by Jimmc414 | 2025-08-10 00:21:42+00:00
Ukraine inherited ~1,900 nuclear warheads when the USSR collapsed but none of the launch codes or operational command systems controlled from Moscow. The warheads required regular replacement of tritium and other components with short half-lives. Post-Chernobyl Ukraine wanted little to do with this responsibility, but in hindsight it seems like a massive strategic mistake for them to give away a massive international bargaining chip in exchange for security assurances that proved worthless when tested in the Budapest Memorandum.
by nis0s | 2025-08-10 00:22:03+00:00
I like the animations, and the site design is wonderful.

I have one nit in general about thinking of evolution as an optimization problem or hypothesis space—it assumes there’s some optimal solution to a given function, but that’s not necessarily the case.

There are optimal thermodynamic states at some scale for an organism, but at another level there are only stochastic processes which are in flux with other parts of a dynamic system. For genes in particular, there’s no optimization per se, but different types of pressures which drive population dynamics depending on the system state.

Maybe this post didn’t mean it this way, and I am reading too much into it. I think the context-dependent agent action described in the post is more in the realm of game theory than evolutionary genetics.

By the way, this may also be of interest https://en.m.wikipedia.org/wiki/Evolutionary_game_theory
by andai | 2025-08-10 00:22:11+00:00
My experience lines up with the article. The agentic stuff only works with the biggest models. (Well, "works"... OpenAI Codex took 200 requests with o4-mini to change like 3 lines of code...)

For simple changes I actually found smaller models better because they're so much faster. So I shifted my focus from "best model" to "stupidest I can get away with".

I've been pushing that idea even further. If you give up on agentic, you can go surgical. At that point even 100x smaller models can handle it. Just tell it what to do and let it give you the diff.

Also I found the "fumble around my filesystem" approach stupid for my scale, where I can mostly fit the whole codebase into the context. So I just dump src/ into the prompt. (Other people's projects are a lot more boilerplatey so I'm testing ultra cheap models like gpt-oss-20b for code search. For that, I think you can go even cheaper...)

Patent pending.
by tylerhou | 2025-08-10 00:22:36+00:00
You should be extremely skeptical of people who claim to have tested IQs above 130 and also believe those tests are not inherently noisy at the top end. Many modern tests lump everyone with 130+ into the same category [1]. An IQ of "easily over 160" is not a clinically valid finding by any standard IQ test that I am aware of.

This is because standard IQ tests are generally designed to measure around the median of the distribution (70-130), and so there is a lot of variance in measurement at the top end. If you happen to have a bad testing day and you make a dumb mistake, your measured IQ might drop by a fairly large number of points -- or, conversely, if you got lucky and guessed right, your measured IQ could be much higher than reality.

For example, the original Raven's Progressive Matrices says [2; page 71]

> For reason's already given, Progressive Matrices (1938) does not differentiate, very clearly between young-children, or between adults of superior intellectual capacity.

where "superior intellectual capacity" is defined as an IQ of ~125 or higher, and (if I am interpreting it correctly), the table on page 79 of [2] says missing a single question could drop a 20-year old from scoring 95 percentile to scoring 90 percentile. That's 5 IQ points on a single question! If you had a bad day, or didn't get enough sleep, you could test significantly worse than your actual "IQ."

Anyone that actually has an IQ of 160 with even a modicum of self awareness should understand that the IQ test they took is inherently noisy at the top end of the scale because sometimes people have off days.

[1] https://en.wikipedia.org/wiki/IQ_classification#IQ_classific...

[2] https://rehabilitationpsychologist.org/resources/SPM%20with%...
by mikert89 | 2025-08-10 00:22:36+00:00
I still think GPT5 is really a cost cutting measure, with a company that is trying to grow to 1 billion users on a product that needs GPUs.

I dont see anyone talking about GPT 5 Pro, which I personally tested against:

- Grok 4 Heavy

- Opus 4.1

It was far better than both of those, and is completely state of the art.

The real story is running these models at true performance max likely could go into the thousands per month per user. And so we are being constrained. OpenAI isnt going for that market segment, they are going for growth to take on Google.

This article doesnt have one reference to the Pro model. Completely invalidates this guys opinion
by stopachka | 2025-08-10 00:23:23+00:00
> By being particularly bad at anything outside of the most popular languages and frameworks, LLMs force you to pick a very mainstream stack if you want to be efficient.

I use clojure for my day-to-day work, and I haven't found this to be true. Opus and GPT-5 are great friends when you start pushing limits on Clojure and the JVM.

> Or 4.1 Opus if you are a millionaire and want to pollute as much possible

I know this was written tongue-in-cheek, but at least in my opinion it's worth it to use the best model if you can. Opus is definitely better on harder programming problems.

> GPT 4.1 and 5 are mostly bad, but are very good at following strict guidelines.

This was interesting. At least in my experience GPT-5 seemed about as good as Opus. I found it to be _less_ good at following strict guidelines though. In one test Opus avoided a bug by strictly following the rules, while GPT-5 missed.
by AndrewKemendo | 2025-08-10 00:24:53+00:00
Can someone remind me of anything Gary has contributed to AI?

Last I saw he hasn’t produced anything but general “pop” books on AI and being associated with MIT, which IMO has zero weight on applied or even at this point theoretical AI, as that is primarily coming out of corporate labs.

No new algorithms, frameworks, datasets, products, insights.

Why is this guy relevant enough to keep giving him attention, his entire ouvre is just anti-whatever is getting attention in the “AI” landscape

I don’t see him commenting on any other papers and he has no lab or anything

Someone make it make sense, or is it as simple as “yeah thing makes me feel bad, me repost, me repeat!”
by mentalgear | 2025-08-10 00:26:19+00:00
The AI community requires more independent experts like Marcus to maintain integrity and transparency, ensuring that the field does not succumb to hyperbole as well as shifting standards such as "internally achieved AGI", etc.

Regardless of personal opinions about his style, Marcus has been proven correct on several fronts, including the diminishing returns of scaling laws and the lack of true reasoning (out of distribution generalizability) in LLM-type AI.

These are issues that the industry initially denied, only to (years) later acknowledge them as their "own recent discoveries" as soon as they had something new to sell (chain-of-thought approach, RL-based LLM, tbc.).
by gnabgib | 2025-08-10 00:26:29+00:00
(2015)

Steve Wozniak used to tip from printed sheets of $2 bills (401 points, 2022, 517 comments) https://news.ycombinator.com/item?id=34566738

Woz Buys $2 Bills in Sheets from the Treasury (25 points, 2012, 12 comments) https://news.ycombinator.com/item?id=4342924
by petetnt | 2025-08-10 00:26:41+00:00
GPT-5 is just OpenAI getting started. Just wait and see what GPT-6 is capable of and imagine that GTP-6 is just OpenAI getting started: if GPT-6 was a high school student, GPT-7 is an expert with masters degree; but GPT-7 is OpenAI getting started
by pests | 2025-08-10 00:27:00+00:00
> “meeting other people in person” as a tiresome chore.

Someone linked the short story The Machine Stops by E. M. Forster the other day where this is an element. A character makes a big deal of having to meet her son in person, opposed of through the machine.

Written in the 20s, gets a lot of things uncannily correct for a society 100 years later. Video calling, silence/do not disturb mode, notifications, air conditioning, people no longer wanting to look at real things with their eyes, etc.
by light_hue_1 | 2025-08-10 00:27:08+00:00
Let me refute that defense. I went to the kinds of schools we're talking about and I had a lab at them for over a decade.

If you went to an elite school and your kid can't get in by competing fairly with other kids, they're subpar and shouldn't get in. Kids of legacies have a massive advantage even if their legacy status is totally ignored.

For example, I'm certain that I can get my kids into MIT (they're a bit young right now). I know exactly what they need to do, how they need to present themselves, what classes to take, courses, extracurriculars, how to stand out, who to ask for letters, who to ask for opportunities like time in a research lab, etc. I've even helped other kids make plans and then get in. Same for my wife for the Ivy League school she attended. Those connections, peers, knowledge, resources, etc. are hard to match. If the kids decide they want to do that.

There's no reason to give these kids (my own included!) any other advantage. They're born with such a massive head start that it's hard to lose, if they put in the work. If they don't, then they shouldn't go to these places.

The only thing legacy admissions do is take away opportunities from students that deserve to be there. Stanford/Harvard/etc. shouldn't get a dime of state or federal funding as long as they continue to do this.
by breadwinner | 2025-08-10 00:27:28+00:00
> It can’t possibly be any more useful than a generic chat bot on a phone. How could it be?

It is the difference between dial-up internet and always-on internet. Can always-on internet do anything dial-up internet couldn't? No... it is the same internet... but being always on makes a big difference.

Same with Alexa. It is always on. No need to pull the phone out of your pocket, find the ChatGPT app, start it, switch it to voice mode, ask the question, then put the phone away and so on. Instead just ask the question and you get the answer. Just like always-on internet, always-on AI makes a big difference.
by oaktowner | 2025-08-10 00:28:31+00:00
> If you remove the people born into privilege, from attending your college, all you succeed is in making your college irrelevant,

I don't think that the Cal Grants program was ever designed to remove those people from the program. It was designed to make sure they didn't get an advantage. In other words, it was prevent universities from letting people who otherwise would not have made the grade in just because their parents made the grade.

Giving alumni's children an advantage isn't giving an advantage to "the smartest, most charismatic, most talented people" -- it's giving an advantage to the luckiest (the ones who happened to be born into it).

And the phrase "it would be ideal if those born into privilege could also clear the SAT" is such a strange one. OF COURSE rich people can "clear the SAT;" in fact, they get the advantage of MUCH better preparation, etc. So this is absolutely about giving an advantage to kids who could not qualify on their own.

To be clear: I don't think Stanford is doing this to keep poor people out (their scholarships have always been very generous). But I do think the administration probably done some basic calculation: they get more in donations from alumni who want legacy admissions for their progeny than they get from Cal Grants.

And Stanford has decided that accepting some kids who just don't make the grade is worth that economic advantage.
by joshuamoyers | 2025-08-10 00:28:35+00:00
Its not at all imo. Franchised businesses are not in the habit of employing low skill workers as a public service. This data is interacting with both covid effects and infrastructure upgrade/rollover - in other words, it takes a while for companies to adopt affordable touch screen ordering systems and its been phased in at a ton of non-fast food (at least in my area) over the same period of time. Local health grocery store has touch screen ordering at their deli, as well as simultaneously going cashless. Most coffee shops too. Look at most international airports - almost all the kiosks have one or no attendants now.
by kawfey | 2025-08-10 00:28:52+00:00
Bluetooth and wifi messengers have been awful over the years, but pretty much every hardware messanger (Gotenna, beartooth, and now meshtastic/meshcore/TTN/lora etc) have been very effective.

Also FRS radios are still a thing.

Though it still would be nice for cell phones to be telecommunicators instead of *cell* communicators.
by Jimmc414 | 2025-08-10 00:29:43+00:00
related papers:

An oat fiber intervention for reducing PFAS body burden: A pilot study in male C57Bl/6 J mice (2025)

https://www.sciencedirect.com/science/article/abs/pii/S00410...

Per- and poly-fluoroalkyl substances (PFAS) in circulation in a Canadian population: their association with serum-liver enzyme biomarkers and piloting a novel method to reduce serum-PFAS (2025)

https://pmc.ncbi.nlm.nih.gov/articles/PMC11909942/