MurphyA/hacker-news huggingface dataset:
by somenameforme | 2025-06-11 04:10:58+00:00
There's endless studies looking at the relationship between exercise and just about everything. It can do everything from substantially reduce your risk of cancer [1] to dramatically reducing your risk of getting a cold [2] and resulting in equally dramatically less severe symptoms if you do catch one.

And nobody really knows why this is, though there are plentiful hypotheses. And exercise is just one aspect of living healthy, though a very important one. You find similar strong associations between 'clean' eating and all other sorts of aspects of a living a healthy life.

Not only does it have effects but rather dramatic ones. I'd think most people would probably see this in their daily lives as healthfulness has dramatic effects on both physical and psychological wellbeing.

[1] - https://www.cancer.gov/about-cancer/causes-prevention/risk/o...

[2] - https://www.bbc.com/news/health-11664660
by Compzilla | 2025-06-11 04:11:12+00:00
Well, you are entitled to your opinion. That said, I’m not trying to shame individual buyers. It is more just documenting sales that reflect a broken or speculative market. I think that is a valid "reason."

All the addresses I mention are public and already on Zillow/Redfin, along with all the other details. I’m not listing out bidder names or license plates... just citing public sales. It’s less about the buyer (who is not mentioned) and more about how far from fundamentals pricing has drifted.
by gojomo | 2025-06-11 04:11:22+00:00
The training sets can already include direct data series about the world, where the "work of human beings" is just setting up the the collection devices. So models can absolutely "experience the world".

But I'm not suggesting they'll advance much, in the near term, without any human-authored training data.

I'm just pointing out the cold hard fact that lots of recent breakthroughs came via training on synthetic data - text prompted by, generated by, & selected by other AI models.

That practice has now generated a bunch of notable wins in model capabilities – contra the upthread post's sweeping & confident wrongness alleging "Ai generated content is inherently a regression to the mean and harms both training and human utility".
by PaulShin | 2025-06-11 04:13:15+00:00
A fascinating thought exercise. It mirrors a question we obsess over every day at my startup: "In 15 years, what will an 'office' visit look like?"

My bet for the gas station: they become high-density "Service Hubs." The main product won't be selling gas, but selling time and convenience back to the driver. Think ultra-fast EV charging bays, automated Amazon package return kiosks, a great coffee subscription service, and maybe even quick biometric health check pods. They'll be data-driven, hyper-efficient service points.

I believe the traditional office is facing the same existential shift. Its core "product" a desk to sit at from 9 to 5 is becoming as obsolete as the gasoline pump.

The "office" of the future isn't a physical place; it's a system. A Workspace OS where the most important flow isn't people commuting to a building, but information flowing seamlessly from conversation → to idea → to action. It's a space where an AI teammate handles the repetitive work (summarizing meetings, tracking tasks), freeing up humans to do the deep, creative work that truly matters.

So, in 2040, perhaps the best gas stations and the best offices will have one thing in common: you'll barely notice they're there. They'll just be a seamless, intelligent system that helps you get where you need to go whether that's across the country, or from a great idea to a finished product.
by rybosome | 2025-06-11 04:13:20+00:00
I think the point the author misses is that many applications of fine-tuning are to get a model to do a single task. This is what I have done in my current role at my company.

We’ve fine-tuned open weight models for knowledge-injection, among other things, and get a model that’s better than OpenAI models at exactly one hyper specific task for our use case, which is hardware verification. Or, fine-tuned the OAI models and get significantly better OAI models at this task, and then only use them for this task.

The point is that a network of hyper-specific fine-tuned models is how a lot of stuff is implemented. So I disagree from direct experience with the premise that fine-tuning is a waste of time because it is destructive.

I don’t care if I “damage” Llama so that it can’t write poetry, give me advice on cooking, or translate to German. In this instance I’m only ever going to prompt it with: “Does this design implement the AXA protocol? <list of ports and parameters>”
by Mathnerd314 | 2025-06-11 04:14:34+00:00
OK, so this intuition is actually a bit hard to unpack, I got it from bits and pieces. So this is this post https://www.fast.ai/posts/2023-09-04-learning-jumps/. Essentially, a single pass over the training data is enough for the LLM to significantly "learn" the material. In fact if you read the LLM training papers, for the large-large models, they generally explicitly say that they only did 1 pass over the training corpus, and sometimes not even the full corpus, only like 80% of it or whatever. The other relevant information is the loss curves - models like Llama 3 are not trained until the loss on the training data is minimized, like typical ML models. Rather they use these approximate estimates of FLOPS / tokens vs. performance on benchmarks. But it is pretty much guaranteed that if you continued to train on the training data it would continue to improve its fit - 1 pass over the training data is by no means enough to adequately learn all of the patterns. So from a compression standpoint, the paper I linked previously says that an LLM is a great compressor - but it's not even fully tuned, hence "not trained to saturation".

Now as far as how fine-tuning affects model performance, it is pretty simple: improves fit on the fine-tuning data, decreases fit on original training corpus. Beyond that, yeah, it is hard to say if fine-tuning will help you solve your problem. My experience has been that it always hurts generalization, so if you aren't getting reasonable results with a base or chat-tuned model, then fine-tuning further will not help, but if you are getting results then fine-tuning will make it more consistent.
by uxcolumbo | 2025-06-11 04:15:02+00:00
Out of interest:

How has Trump improved our lives and country in his first term?

And how is he improving our lives now?

Trying to understand how you as an HNer, who I’m assuming applies logical / critical thinking, considers facts and evidence as important, can vote for someone who a) is a convicted criminal b) absolutely not interested in improving our lives.

What’s the appeal?
by rolandog | 2025-06-11 04:16:29+00:00
> The immigration crackdowns are very popular across the entire (R) electorate, regardless of their wealth.

Sure. The rich (R) electorate gladly watch how the non-rich (R) electorate has taken the bait and is happy chasing after the newly othered.

They won't be happy if the non-rich republicans start taking their aim at lobbying, unfair tax-breaks for the rich, or any form of taxation that prevents them from keeping their ill-gotten wealth.
by refulgentis | 2025-06-11 04:16:37+00:00
John, do you understand that the thing you're quoting says "We reserve the right to pull things", not "We reserve the right to swap in a new service"?

Do you understand that even if it did say that, that wasn't true either? It was some weird undocumentable half-beast?

I have exactly your attitude about their cavalier use of preview for all things Gemini, and even people's use of the preview models.

But I've also been on this site for 15 years and am a bit wow'd by your interlocution style here -- it's quite rare to see someone flip "the 3P provider swapped the service on us!" into "well they said they could turn it off, of course you should expect it to be swapped for the first time ever!" insert dull sneer about the quality of other engineers
Show HN: Toward Nirvana with Optional Parameters
by owlstuffing | 2025-06-11 04:17:49+00:00
Hey HN!

Oracle refuses to add optional parameters (and named args) to Java. So I did via a javac plugin and a smidge of hacking to modify the AST. The result is a pretty comprehensive implementation of the feature, without breaking binary compatibility. Here's a short summary.

---

The manifold-params compiler plugin adds support for optional parameters and named arguments in Java methods, constructors, and records -- offering a simpler, more expressive alternative to method overloading and builder patterns.

public String valueOf(char[] data, int offset = 0, int count = data.length - offset) {...} valueOf(array) // use defaults for offset and count valueOf(array, 2) // use default for count valueOf(array, count:20) // use default for offset by naming count

This plugin supports JDK versions 8 - 21+ and integrates seamlessly with IntelliJ IDEA and Android Studio.

Key features:

- Optional parameters -- Define default values directly in methods, constructors, and records

- Named arguments -- Call methods using parameter names for clarity and flexibility

- Flexible defaults -- Use expressions, reference earlier parameters, and access local methods and fields

- Customizable behavior -- Override default values in subclasses or other contexts

- Safe API evolution -- Add parameters or change defaults without breaking binary or source compatibility

- Eliminates overloads and builders -- Collapse boilerplate into a single, expressive method or constructor

- IDE-friendly -- Fully supported in IntelliJ IDEA and Android Studio

Learn more: https://github.com/manifold-systems/manifold/blob/master/man...
by gojomo | 2025-06-11 04:18:13+00:00
No, new more-capable and/or efficient models have been forged using bulk outputs of other models as training data.

These inproved models do some valuable things better & cheaper than the models, or ensembles of models, that generated their training data. So you could not "just ask" the upstream models. The benefits emerge from further bulk training on well-selected synthetic data from the upstream models.

Yes, it's counterintuitive! That's why it's worth paying attention to, & describing accurately, rather than remaining stuck repeating obsolete folk misunderstandings.
by mark_l_watson | 2025-06-11 04:18:33+00:00
I am still not willing to upgrade to a Pro account. I pay $20 a month for both Gemini and ChatGPT, and for what I need this is currently enough.

I have dreamed of having powerful AI ever since I read Bertram Raphael's great book Mind Inside Matter around 1978, getting hooked on AI research and sometimes practical applications for my life since then.

I can easily afford $200 for a Pro account but I get this nagging feeling that LLMs are not the final path to the powerful AI I have always dreamed of and I don't want to support this level of hype.

I have lived through a few AI winters and I worry that accountants will tally up the costs, environmental and money, versus the benefits and that we collectively have an 'oh shit' moment.
by PaulShin | 2025-06-11 04:19:12+00:00
"Am I a dinosaur?" and "Is anyone else struggling with this?" I think you're asking the most honest questions in our industry. Thank you for this thread.

My skill that often feels "out of reach" is building a truly great Go-to-Market machine.

I'm a product-obsessed founder. I can spend all day architecting our 'Workspace OS' or fine-tuning our AI, MAKi. But when it comes to sales funnels, marketing channels, and building a repeatable sales process, my brain just fogs over. I truly feel like I wasn't born with the "sales gene."

After 10 years, multiple pivots, and learning to survive like a cockroach, here's the only method I've found for tackling any skill that feels too big: Make it small.

1. Don't read 20 books; talk to one person. I find one person who is great at it and just ask: "What is the very first, smallest thing you would do?"

2. Don't build a complex system; do one dumb, simple action. I don't try to build the perfect marketing funnel. I write one LinkedIn post. I send one cold email. The goal isn't to succeed, it's just to start and get a single data point.

3. Don't guess what the market wants; ask one customer. I ask, "How did you really find us?" Their answer is always more valuable than a market research report.

4. Don't try to master it in a week; just survive to the next day. The goal is just to learn one thing today, so I can be slightly less clueless tomorrow.

I don't think any of us have the "right brain" for everything. But cockroaches don't have big brains either. They just keep moving, adapting, and refuse to die.

We just need to find the next small, tangible step. That's how we get unstuck.
by lacker | 2025-06-11 04:19:20+00:00
I already do understand set theory and the construction of the real numbers. There are plenty of great books on that, or really nowadays you can simply ask ChatGPT. I am interested in learning about how Cantor, Weierstrass, the ancient Greeks, and others felt, as they grappled with the challenge of making rigorous mathematics handle the infinite. That's what this book is good at.
by pier25 | 2025-06-11 04:19:58+00:00
Raph, are you familiar with Rive[1]?

Their vector rendering implementation is much faster than Skia[2].

How does Vellum compares to Rive in terms of performance?

[1] https://rive.app/

[2] https://x.com/gordonphayes/status/1654107954268782595
by tstactplsignore | 2025-06-11 04:20:02+00:00
Sorry, you are also incorrect.

I don't think we should exaggerate - "pausing meaningful work indefinitely" is an exaggeration - but the Trump administration has already definitely decimated biological research country-wide in a manner we have not seen in our lifetime. This is the biggest story among scientists in the country right now.

The upcoming 2026 budget will be its own battle, but what has already happened in 2025 is devastating. The budget is there, as you point out, but it is not being spent. It's worth pointing out this is all being sued in at least a dozen or so lawsuits, and is likely to be found highly illegal in most forms, but the damage has already been done, and the administration will find new creative ways to get around court orders.

Please see: https://jeremymberg.github.io/jeremyberg.github.io/index.htm... https://grant-watch.us/reports.html https://www.theatlantic.com/health/archive/2025/03/trump-nih... https://apnews.com/article/trump-science-medicine-research-c... https://pepfar.impactcounter.com/ https://www.nytimes.com/interactive/2025/06/04/health/trump-... https://www.nytimes.com/interactive/2025/05/22/upshot/nsf-gr... https://airtable.com/appjhyo9NTvJLocRy/shrNto1NNp9eJlgpA?Ffj... https://www.nytimes.com/2025/02/14/health/national-institute... https://www.statnews.com/2025/05/08/trump-nih-cuts-jama-stud... https://www.nytimes.com/2025/02/27/health/usaid-contract-ter... https://www.nytimes.com/interactive/2025/03/15/opinion/forei... https://www.nytimes.com/2025/03/07/health/usaid-funding-dise...
by irjustin | 2025-06-11 04:20:19+00:00
I think out of the "serious magicians" there's also the general "fraud" style. Who were in fact truly angry.

Make a space shuttle disappear[0], or hide an elephant. The TV show revealed all those to be fake and were in fact - paid audiences. My little brain trusted that they weren't paid and wanted to know so bad how the space shuttle disappeared.

Overall the TV and the internet helped push magic to exactly where it is today. Amazing, talented folks who even when you know how it's done, it's so good - it's magic.

[0] https://www.youtube.com/watch?v=1lxX3rqhZtw
by mjevans | 2025-06-11 04:20:56+00:00
They'd play it for free* but how many would pay? Would that number go down if DRM were involved?

Personally, Steam's about where I draw the line. They've given me enough value as an ecosystem and their native 'DRM' doesn't seem that obtrusive, so they've got a lot of my money over the years.

I've regretted every single time I happened to buy a game, which turned out to have other DRM or any form of 'anti cheat' client side. They just never work in the long run.
by mmanfrin | 2025-06-11 04:21:11+00:00
I built a protein comparison site (aggregating nutrition, ingredients, electrolytes, prices) and I expected a little bit of traffic, but I'm getting less than a single visitor every 2 days from google. It's absolutely dead.

A year ago I threw together a tiny little site with some datamined assets from a game (deadlock) that randomly got indexed and saw a couple hundred visits a month from google.
by toomuchtodo | 2025-06-11 04:22:17+00:00
60 percent of Americans can’t afford a basic quality of life. People are having less kids because of their economics globally. The existing system is the forcing function.

https://www.unfpa.org/news/fertility-fallacy-five-things-you...

https://www.bbc.com/news/articles/clynq459wxgo

This is before climate impact accelerates.

https://en.wikipedia.org/wiki/Planetary_boundaries

https://www.weforum.org/press/2024/01/wef24-climate-crisis-h...

Living standards won’t increase because demographic dividends from growing working age populations is over, and total fertility rates are rapidly declining globally.

https://www.mckinsey.com/mgi/our-research/dependency-and-dep...

https://www.cato.org/cato-journal/spring/summer-2018/demogra...
by physicalrobot | 2025-06-11 04:22:49+00:00
We did two separate studies, one of accuracy and cost the other of accuracy and latency. Most of our studies used cost as the definition is pretty clear and less sensitive to environmental conditions (e.g. LLM provider changes) or definition (e.g. typical vs. worst case). But latency is a concern for many usecases, so we did want to investigate that problem as well.

Note we used Random LLM which had a 0.84 correlation with human labeling rather than 0.90 from only using gpt-4o-mini.
by gdiamos | 2025-06-11 04:23:15+00:00
Finetuning is deep learning training. It's pretty difficult to get right.

Andrej's 2019 blog laments on some of the reasons why it is hard and I can relate to a lot of this - https://karpathy.github.io/2019/04/25/recipe

The biggest mistake I see people making is this quote from the blog: "a 'fast and furious' approach to training neural networks does not work and only leads to suffering"

I'll probably write more about it in a few months...
by refulgentis | 2025-06-11 04:23:44+00:00
Well, no. Well, sure. You're done, but we're not going in circles. It'd just do too much damage to you to have to answer the simple question "Where does the legalese say they can swap in a new service?", so you have to pretend this is circular and just all-so-confusing, de facto, we have to pretend it is confusing and/or obviously wrong to use any Gemini 2+ at all.

It's a cute argument, as I noted, I'm emotionally sympathetic to it even, it's my favorite "get off my lawn." However, I've also been on the Internet long enough to know you write back, at length, when people try anti-intellectualism and why-are-we-even-talking-about-this as interaction.
by ActorNightly | 2025-06-11 04:24:25+00:00
Its really fascinating how your brain works.

Again, you either lack basic comprehension of written text, unable to imagine the conversation taking place with a context in mind, and understand that just because the message wasn't communicated the way you would have preferred doesn't mean the message is wrong or contradictory.

Or you understand all that, but you have intimately tied your ideological beliefs into your identity to the point where admitting you are wrong is basically saying to yourself that you are shell of a person.

One last question: suppose we took random people off the street, and then gave them that transcript and asked them to interpret what Fauci was saying, and the vast majority of them agreed that nothing in that message was contradictory - would you change your mind? Or would you think that you are more "enlightened" than all of them?
by davydm | 2025-06-11 04:25:03+00:00
Now they just need to make them not suck. I jumped to team red believing the opensource driver hype and regretted it. I could (fairly) consistently cause driver crashes by opening the map in DRG. And the driver doesn't properly recover like it does on windows - screen artifacts remain until reboot.

When the 1-year-old xfx suddenly decided that fans shouldn't spin, I gave it away and moved back to team green.

Would I like Nvidia to also go opensource? For sure! But functionality over form, I say. Opensourcing isn't a magic bullet that makes code better.
by andsoitis | 2025-06-11 04:26:44+00:00
> data series about the world, where the "work of human beings" is just setting up the the collection devices. So models can absolutely "experience the world"

But not experience it the way humans do.

We don’t experience a data series; we experience sensory input in a complicated, nuanced way, modified by prior experiences and emotions, etc. remember that qualia is subjective, with a biological underpinning.
by andreareina | 2025-06-11 04:26:57+00:00
> Get a colonoscopy at 45. We are seeing a big increase in younger people.

N.B. this is not just an increase in detection (as could happen with more/better screening where the true rate isn't changing), but mortality:

> In people under 55, however, death rates have been increasing about 1% per year since the mid-2000s. https://www.cancer.org/cancer/types/colon-rectal-cancer/abou...
by Caelus9 | 2025-06-11 04:27:13+00:00
My attitude towards AI is one of balance not overly dependent, but not completely rejecting either. After all, AI is already playing an important role in many fields, and I believe the future will be a world where humans and AI coexist and collaborate. There are many things humans cannot do, but AI can help us achieve them. We provide the ideas, and AI can turn those ideas into actions, helping us accomplish tasks.
by refulgentis | 2025-06-11 04:28:14+00:00
I don't know what you're talking about, partially because of poor grammar ("you knowledge cut-off does appear") and "presumption" (this was front and center on their API page at r1 release, and its in the r1 update notes). I sort of stopped reading after there because I realized you might be referring to me having a "knowledge cut-off", which is bizarre and also hard to understand, and it's unlikely to be particularly interesting conversation given that and the last volley relied on lots of stuff about tool calling being, inter alia, niche.
by ericrallen | 2025-06-11 04:30:06+00:00
Interestingly, the Kagi Assistant managed to find this thread while researching the question, but every model I tested (without access to the higher quality Ultimate plan models) was unable to retrieve the correct answer.

Here’s an example with Gemini Flash 2.5 Preview: https://kagi.com/assistant/9f638099-73cb-4d58-872e-d7760b3ce...

It will be interesting to see if/when this information gets picked up by models.
by hn_throwaway_99 | 2025-06-11 04:33:46+00:00
The issue with things like cologuard is they really only detect cancer. Colonoscopies can prevent cancer by having polyps removed before they turn into cancer.

Colonoscopy is a more invasive procedure obviously, but complication rates are very low. It's worth it to find a great gastroenterologist to do the procedure.
by margalabargala | 2025-06-11 04:34:42+00:00
It's certainly the case that colorectal cancer is becoming more common, and among younger people. Plenty of possible reasons; diet, plastics, PFAS, all of the above, take your pick.

However, the fact that mortality has been decreasing above the age of frequent screenings, and increasing below that age, tells us that whatever the problem, the symptom (cancer) could be addressed with better screening, leading to earlier detection and treatment.

Mortality is increasing in young people because they don't get screened, so when it does happened no one catches anything until it's too late.

If the age for recommended regular colonoscopies was 40 or 45, we would see the same mortality reduction above that cutoff.