MurphyA/hacker-news huggingface dataset:
by utyop22 | 2025-09-06 21:32:01+00:00
The reality is, language itself does not capture the entirety of what is really going on. And I'd get argue its the poorest way of expressing - but one that enables transmission through various mediums efficiently on a cost basis.

E.g. when I explain a concept, what comes to my mind is not a string of letters and words. There is a mix of imagery and even sounds that I may have acquired from learning about a concept - then I translate that into text so it can be communicated.

Theres a reason why people use native subtitles when watching netflix - text complements imagery and sounds.
by thaumasiotes | 2025-09-06 21:32:45+00:00
The modern Chinese writing system is fully phonetic, just with extremely complex spelling. There is no pretense that characters represent ideas or words. They represent syllables.

Phonetic use of the characters was immediate. The go-to example here is 來, which depicts a stalk of wheat. It is the spelling of the verb "come", and the verb is spelled that way because the character for "wheat" was borrowed with no alterations to represent its own pronunciation, which was shared with the verb.
by johnfn | 2025-09-06 21:33:01+00:00
I am describing a simple eval. This is a strategy that determines how effective an AI system is, they have existed long before LLMs were ever a thing, and they are perfectly happy to deal with non-deterministic behavior. You are acting as if LLMs being non-deterministic means you can't say anything about them, but before LLMs were non-deterministic, traditional ML systems were non-deterministic, and we had no problem building systems to probabilistically evaluate them.
by iLemming | 2025-09-06 21:35:04+00:00
> not found in other lisps that makes it a lot more visually noisy

I disagree. It only feels that way, until you learn how destructuring works in Clojure. Once you grok that, you'd understand why the vector syntax for function arguments was chosen, and not a simple list like in other Lisps. In general, Clojure is highly pragmatic - if something done certain way, most-likely it's for good reasons, and not by accident.

Also, Clojure-maps are just freaking amazing. I wish others, e.g., Elisp had them.

Clojure is extremely and nicely readable, just requires some getting used to it. At some point, I completely replaced my API-testing workflows, switching from JSON to EDN - it's almost twice more compact and much better visually, not to mention that it's incredibly nice to be able to just explore and visualize any given data through the REPL.
by thehappypm | 2025-09-06 21:35:18+00:00
Just some rough physics..

Q = m c ΔT

m = mass of the crust (roughly 10^22 kg)

C = specific heat of crust (roughly 1000 J/kg·K)

ΔT = 1 K

Q = 10^25 joules would be needed to lower the earths crust by 1 degree K

About 10,000 years worth of today’s human energy consumption
by robertclaus | 2025-09-06 21:35:54+00:00
While I get the academic perspective of sharing these insights, this article comes across as corporate justifying/complaining that their model's score is lower than it should be on the leaderboards... by saying the leaderboards are wrong.

Or an even darker take is that its coorporate saying they won't prioritize eliminating hallucinations until the leaderboards reward it.
by throwawaymaths | 2025-09-06 21:36:09+00:00
your point is good and taken but i would amend slightly -- i dont think that "absolute truth" is itself a goal, but rather "how aware is it that it doesn't know something". this negative space is frustratingly hard to capture in the llm architecture (though almost certainly there are signs -- if you had direct access to the logits array, for example)
by sandworm101 | 2025-09-06 21:36:31+00:00
> Why wouldn’t fast home charging scale?

Because current grids are not setup for 10,000-watt chargers in every home and/or parking spot. Just think of trying to provide chargers in an apartment complex, one to each apartment. The power requirements become crazy very quickly. This parallels the issues with rental car companies trying to charge dozens of cars at once, such as at airports. Our grid standards never conceived of such loads becoming commonplace. Whereas today when every EV owner just expects their home charger to be available 24/7, if everyone had an EV some sort of rationing/scheduling would be necessary

Some parts of Canada have already seen this with heat pumps. People were encouraged to switch from natural gas to electric heat pumps. But incentives have been withdrawn as power spikes during cold weather challenge local grids, especially when there is no wind/solar power available locally (think cold winter mornings in the midwest/prairies.)
by kelnos | 2025-09-06 21:36:43+00:00
Seems like it's not that simple. The CHOICE study[0] suggest that some brands do have some good "models" of sunscreen, but some are bad. It's also possible that there's a process issue at the manufacturers, and the quality of different lots can vary:

> Ultra Violette announced it was removing the Lean Screen product from shelves. Across eight different tests, the sunscreen returned SPF data of 4, 10, 21, 26, 33, 60, 61, and 64.

[0] https://www.choice.com.au/health-and-body/beauty-and-persona...
by troupo | 2025-09-06 21:36:57+00:00
> The article says very little.

And that's the crux, isn't it. Because that checklist really is just the tip of the iceberg.

Some people have completely opposite experiences: https://news.ycombinator.com/item?id=45152139

Others question the validity of the approach entirely: https://news.ycombinator.com/item?id=45152668

Oh, don't get me wrong: I like the idea. I would trust LLMs with this idea about as far as I could throw them.
by dotancohen | 2025-09-06 21:37:39+00:00
It's called a RAG, and it's getting very well developed for some niche use cases such as legal, medical, etc. I've been personally working on one for mental health, and please don't let anybody tell you that they're using an LLM as a mental health counselor. I've been working on it for a year and a half, and if we get it to production ready in the next year and a half I will be surprised. In keeping up with the field, I don't think anybody else is any closer than we are.
by IgorPartola | 2025-09-06 21:38:51+00:00
You describe it as challenging but it sounds like realistically it is just badly designed roads. But fixing that aside, nothing really stops you from outfitting secondary roads with transponders in principle. In practice, it is easier to start with freeways because (a) they are much more uniform, (b) the impact of an accident at freeways speeds is much more deadly, (c) no pedestrians, bicycles, etc., and (d) the federal government has control over the freeways (it is a complex relationship but ultimately the feds have a say) which means they can mandate installing the transponders and pay for it. Once the system functions there it can be expanded until every driveway and parking spot is outlined.
by WCSTombs | 2025-09-06 21:39:04+00:00
While this is basically just an advertisement for GitHub Copilot, written by GitHub and published on their official blog, it does make a couple interesting points. Here they are:

1. You can use LLMs to help you learn faster.

2. Review other coders' code to help you learn.

I fully agree with the second one.

I only cautiously agree with the first one, and I'm pretty skeptical of the formula they give in the article. Traditionally, education is accomplished by studying primary and secondary sources, and then by practicing. LLMs don't somehow hack around that core idea, but I think there are ways they can streamline the process. What I propose is that you ask pointed questions to the LLM and have it provide sources in its answer, which you then go and check and read parts of. You still need to evaluate whether each source is trustworthy, but the LLM could save you the initial trouble of tracking down the sources in the first place.

My general advice here though is to tread very cautiously. I think there's a real danger of fooling yourself into thinking you did all of this learning when in reality you're outsourcing a crucial part of the learning process (such as critical thinking) to the LLM, and the really problematic part is that the LLM is pretty much the perfect tool to trap you in that way.
by troupo | 2025-09-06 21:40:27+00:00
A "simple eval" would give you nothing. Anthropoc running out of available compute will affect the output significantly more than a change in the model or "thinking".

> we had no problem building systems to probabilistically evaluate them.

Something tells me the "no problem" in that statement is from Death of Stalin when you get to the actual details of that evaluation: https://youtu.be/kasSSZlBFDs?si=-5kv7LPWi_YStR3C
by tripletao | 2025-09-06 21:40:50+00:00
I believe you're correct. When foreign experts visit temporarily to transfer technology to a new site, there's no bright line between the "non-working" business activities permitted by a B visa or ESTA and those requiring H or L visas. If the DHS thought Hyundai had pushed that line too far, then they could have subpoenaed their records, and perhaps ultimately settled the case with a fine and a promise to comply with the clarified standard in future. That would all have been normal.

They instead chose to lock up random employees "working" in a way their employer had probably assured them (rightly or wrongly) was lawful. That's not normal. The USA is a sovereign country, so it can impose whatever immigration policy it wants. No one should be surprised when foreign multinationals adjust their investment decisions accordingly though, or when American staff abroad face reciprocally unpleasant treatment.
by jmcgough | 2025-09-06 21:41:40+00:00
There's a lot of problems you can point at:

- Through extensive lobbying, the US passed the HMO act of 1973 which requires that all employers offer an HMO plan to employees. HMOs were created to keep costs down, but United really took this to the extreme, making it as hard to use your health insurance as possible, and creating vertical monopolies like OptumRX. United takes so long to pay providers for the work they do that they now offer payday loans to doctors offices, which is crazy.

- The US uses a fee-per-service model that priorities procedures over preventative treatment or patient education. Some other countries have moved towards reimbursement based on health outcomes.

- The Affordable Care Act banned physician owned hospitals, which were growing in popularity and had better outcomes for less fees to patients.

- Private Equity is swallowing up hospital systems, emergency departments, etc. The most common seller is another PE firm, so they try to make a quick return through heavy cuts and then flip it 5 years later.
by Alifatisk | 2025-09-06 21:41:43+00:00
This is huge news, can't believe it hasn't hit the front page yet! It's worth pointing out that this is a non-reasoning model.

> Benchmarks show it beats our previous best, Qwen3-235B-A22B-2507

QWen chat describes the model as following, "Qwen3-Max-Preview is the most advanced language model in the Qwen series, excelling in complex reasoning, instruction following, mathematics, coding, role-playing, creative writing, and more."

> Maximum context length: 262,144 tokens

> Maximum generation length: 32,768 tokens

Compared to Qwen3-235B-A22B-2507 which has

> Maximum context length: 131,072 tokens

> Max summary generation length: 8,192 tokens

So 2x the amount of maximum context on previous model and 4x more max summary generation.

I've been using Qwen3-235B-A22B-2507 as my daily on Qwen chat since it got released and I can say that I've been very satisfied. Especially because Alibabas generous offers on how much I can use their service for free, no timeout, no rate limit, nothing. And for larger content, Qwen2.5-14B-Instruct-1M, amazing model. And they actually benchmark against the beasts, like Opus 4 (non thinking), Kimi K2 and Deepseek V3.1.

Can't wait for the new benchmarks at Artificial analysis to see how it actually compares against the other models, especially Gpt-5, Gemini-2.5-pro and Grok-4

Alibaba has been pushing it lately, incredible work. They deserve way more hype and highlight than what they currently get!
by epolanski | 2025-09-06 21:42:00+00:00
I've always been impressed by professor Paine lucidity when talking about history.

She's the right kind of historian, the bookworm who's gonna read and investigate all of the possible documentation she can find before forming an opinion.

One of the criticism I have towards her, though, is her apparent lack of empathy towards history and its protagonists. She may very well read in history how Mao's genius of involving and empowering women in the communist struggle against the Japanese and Nationalists gave him a crucial advantage. This and other small acts that compound in significant events, that she can find, recognize, trace and expose.

She can clearly recognize how Chinese century of humiliation shapes modern Chinese foreign policy.

Yet, somehow sometimes she cannot see other obvious things.

E.g. Russians and Ukrainians "hate" each other, because they see the other as the bad guy in their biggest trauma. For Ukrainians, whose biggest collective trauma is the Soviet famine of the 20s/30s the Russian is the aggressor. For Russians whose biggest collective trauma is ww2, Ukrainians are those who sided with the Nazi invader.

Both of the previous sentences are equally true and equally...a bit more nuanced and complicated. But they still shape Ukrainians and Russians born 4/5/6 generations after those events.

Yet, professor Paine sometimes cannot see or expose this obviousness.
by skipants | 2025-09-06 21:42:16+00:00
I've already had a pre-screen coding tests judged by AI. It's unsettling.

I also had a CV rejection letter with AI rejection reasons in it as well which was frustrating because none of the reasons matched my CV at all, in my opinion. I am still not sure if the resume was actually reviewed by a human or AI but I am assuming the latter.

I absolutely hated looking for a new job pre-AI and when times were good. Now I'm feeling completely disillusioned with the whole process.
by themaninthedark | 2025-09-06 21:42:56+00:00
The emperor of Japan directly referenced the atomic bombs in his surrender speech:

"We have considered deeply the general trends of the world and the current situation of the Empire, and We have decided to take extraordinary measures to bring the current state of affairs to an end. We hereby inform Our loyal and devoted subjects."

...

"Furthermore, the enemy has begun to employ a new and cruel bomb, causing immense and indiscriminate destruction, the extent of which is beyond all estimation. Should we continue to fight, not only would it result in the ultimate collapse and obliteration of the Japanese nation, but it would also lead to the total extinction of human civilization."

However there were was a major faction that did not want to surrender and had conspired and committed a coup d'état to prevent it: https://en.wikipedia.org/wiki/Ky%C5%ABj%C5%8D_incident
by bigyabai | 2025-09-06 21:43:06+00:00
You're welcome to work hard, and take the 996 positions if they please you. America largely adheres to at-will employment, the more overtime you clock is the less I have to deal with.

After a certain point though, you're laundering the idea of mistreatment through your own identity. For example, maybe 1 in a million Chinese textile workers really does feel like stitching together Disney branded tee shirts is their life's calling. That doesn't mean that everyone else should subsist below the poverty line because they won't step up to meet that person's 996 dedication. Many people will scorn your eagerness to work, especially if you're not producing anything revolutionary or novel with your effort.

It's all about what you have at the end of the day. If you put in 10 years at companies that underpaid you, mistreated you and never gave you significant equity, you were simply taken advantage-of and refuse to admit it. If you really are a 10x engineer then yeah, I'd argue you wasted your time and haphazardly threw away your talent for a zero-net lifestyle.
by socalgal2 | 2025-09-06 21:43:23+00:00
> Sam Altman made a comparison to attorney-client privilege in an interview

Isn't his company, OpenAI, the one that said the monitor all communications and will report anyone they think is a threat to the government?

https://openai.com/index/helping-people-when-they-need-it-mo...

> If human reviewers determine that a case involves an imminent threat of serious physical harm to others, we may refer it to law enforcement.

I get they are trying to do something positive overall. At the same time. I don't want corp owned AI that's monitoring everything I ask it.

IIRC it is illegal for the phone company to monitor and censor communications. The government can ask a judge for permission for police to monitor a line but otherwise it's illegal. But now with AI transcription it won't be long until a company can monitor every call, transcribe it, feed to an LLM to judge and decide which lists you should be on.
by ssl-3 | 2025-09-06 21:43:32+00:00
Strolling down the sidewalk at a park with a friend and chatting with them isn't necessarily a private activity either: We're in a very public space. Anyone within earshot can hear whatever we're talking about. If the sounds of our conversation winds up being incidentally in the background of someone filming the squirrels the tree frogs or something, then there's probably nothing to be done about that.

But (in some states), it seems that it would be a very different can of worms if I were to elect to deliberately record the conversation I have with my friend without their consent. Even in a public space, that would appear to run directly afoul of the applicable laws.
by ninetyninenine | 2025-09-06 21:43:44+00:00
>But an LLM is not answering "what is truth?". It's "answering" "what does an answer to the question "what is truth?" look like?".

You don't actually know this right? You said what I'm saying is theoretically possible so you're contradicting what you're saying.

>You're anthropomorphizing in using terms like "lying to us" or "know the truth". Yes, it's theoretically possible I suppose that they've secretly obtained some form of emergent consciousness and also decided to hide that fact, but there's no evidence that makes that seem probable - to start from that premise would be very questionable scientifically.

Where did I say it's conscious? You hallucinated here thinking I said something I didn't.

Just because you can lie doesn't mean you're conscious. For example, a sign can lie to you. If the speed limit is 60 but there's a sign that says the speed limit is 100 then the sign is lying. Is the sign conscious? No.

Knowing is a different story though. But think about this carefully. How would we determine whether a "human" knows anything? We only can tell whether a "human" "knows" things based on what it Tells us. Just like an LLM. So based off of what the LLM tells us, it's MORE probable that the LLM "knows" because that's the SAME exact reasoning on how we can tell a human "knows". There's no other way we can determine whether or not an LLM or a human "knows" anything.

So really I'm not anthropomorphizing anything. You're the one that's falling for that trap. Knowing and lying are not unique concepts to conciousness or humanity. These are neutral concepts that exist beyond what it means to be human. When I say something, "knows" or something "lies" I'm saying it from a highly unbiased and netural perspective. It is your bias that causes you to anthropomorphize these concepts with the hallucination that these are human centric concepts.

>A lot of people seem to be saying we don't understand what it's doing, but I haven't seen any credible proof that we don't.

Bro. You're out of touch.

https://www.youtube.com/watch?v=qrvK_KuIeJk&t=284s

Hinton, the godfather of modern AI says we don't understand. It's not people saying we don't understand. It's the generally understanding within academia is: we don't understand LLMs. So you're wrong. You don't know what you're talking about and you're highly misinformed.
by Jensson | 2025-09-06 21:43:54+00:00
A weather model is made to predict the weather and used to predict the weather, so there you are right.

A language model is made to predict language, but used to generate code or answers to math questions, that is not the same situation as a weather model. The language model is not made to solve math or generate correct code, if you ask it to predict the weather it wont try to predict the weather, it will just predict the language that is a probable to such a question.

This sort of misunderstanding is what is causing all these debates, many people really struggle understanding what these language models really are.
by sp1982 | 2025-09-06 21:44:17+00:00
This makes sense. I recently did an experiment to test GPT5 on hallucinations on cricket data where there is a lot of statistical pressure. It is far better to say idk than a wrong answer. Most current benchmarks don’t test for that. https://kaamvaam.com/machine-learning-ai/llm-eval-hallucinat...
by didibus | 2025-09-06 21:45:08+00:00
I agree with everything you said except:

> Trying to eliminate cases where a stochastic model the size of an LLM gives “undesirable” or “untrue” responses seems rather odd.

Take it back to what it is like you say, this is a predictive model, and the work of any ML scientist is to iterate on the model to try and get perfect accuracy on unseen data. It makes sense to want to tune the models to lower the rate of predictive errors. And because perfect predictive accuracy is rarely possible, you need to make judgment calls between precision and recall, which, in the case of LLMs, directly affects how often the model will hallucinate versus how often it will stay silent or overly cautious.
by yakshaving_jgt | 2025-09-06 21:46:22+00:00
I've noticed that many programmers believe that parsing is some niche thing that the average programmer likely won't need to contend with, and that it's only applicable in a few specific low-level cases, in which you'll need to reach for a parser combinator library, etc.

But this is wrong. Programmers should be writing parsers all the time!
by geerlingguy | 2025-09-06 21:46:30+00:00
Heh, we didn't always wear sunscreen until I was in my teens... my skin does not thank me.

We do SPF50 or 100 on the kids (and us, of course). I think besides shady products, a lot of them are too hard to apply evenly, so you either spend 10 minutes trying to get it to spread, or you look funny with white smears here and there.
by dvdkon | 2025-09-06 21:46:49+00:00
I, for one, do think the world needs more CLI argument parsers :)

This project looks neat, I've never thought to use parser combinators for something other than left-to-right string/token stream parsing.

And I like how it uses Typescript's metaprogramming to generate types from the parser code. I think that would be much harder (or impossible) in other languages, making the idiomatic design of a similar similar library very different.
by jandrewrogers | 2025-09-06 21:47:35+00:00
The site is part of the largest high-quality geothermal basin in the world. It is larger than most countries, encompassing almost the entirety of Nevada and large parts of adjacent States. The geothermal potential of the region is enormous, even just using classic geothermal technology.

The US has long been the world's leading producer of geothermal power, mostly generated from this basin.