MurphyA/hacker-news huggingface dataset:
by gpm | 2025-06-01 22:57:42+00:00
> The question is, how is consciousness different from that

A consciousness experiences perceptions, if you don't, I won't be able to describe this to you. If you do, it should be clear what I mean by that.

We have no evidence that either a camera or a GPU executing an LLM experiences perception. Certainly they react to physical stimuli, but so does an atom, physical reaction is not the definition of experience I am referring to when I say perception. We also have no evidence that they do not, except for the lack of evidence to the contrary.

We have some reason to believe that other people do experience perception, in that they spontaneously describe experiencing things that our similar to our experiences, and it's surprising they do that if they don't also experience things*. When I say "we", I really mean "I", but I'm assuming that you have the same experience I do.

> What prevents any of this to be fully mathematically described?

There's nothing that says you can't, in principle, create an entirely accurate mathematical description of perception (in the experiencing and not the reacting sense) where you define that certain abstract variables correspond to certain perceptions and can entirely predict them. The model would still be that, a model that predicts what perceptions occur, not the perceptions themselves. The same way mathematically describing a particle of hydrogen doesn't create a particle of hydrogen. The common concrete example is that mathematically describing what color someone perceives when looking at something, while basically possible, gives absolutely no insight in to what that experience is like (apart from saying "it's similar to <this experience> had by the same consciousness").

* See my other comment in this thread for why this argument does not apply to GPUs running LLMs.
by TuringNYC | 2025-06-01 22:58:08+00:00
>> Did you screw up the font size on some text you just published? Ok, you can fix that in about 5 seconds, and it affects pretty much nothing. Doesn't cost 100x.

Actually, i find these to be worse, because i've been in scrum meetings where 6 people spend 2 minutes talking about this bug, then another 2 minutes talking about the QA of it the next day. Tiny issues are very expensive to fix if you have formulaic team members who arent taking the reigns.
by nkurz | 2025-06-01 22:58:18+00:00
I'm not sure exactly what you are looking for, but Shakhashiri's "Chemical Demonstrations" series includes Nitrogen Triiodide among many other things: https://www.amazon.com/Chemical-Demonstrations-Handbook-Teac...

Edit: It looks like the ScienceMadness page references the exact location: https://www.sciencemadness.org/smwiki/index.php/Nitrogen_tri...
by Daub | 2025-06-01 22:59:14+00:00
You make a very good point, and provide an excellent reference.

It is in human nature to classify, and that is doubly true for an academic. Simply put, without a classification system we can’t point at a thing and say it is different to another thing. Neither can we say that one of those things is greater than the other.

I use libraries a lot and I have been reasonably satisfied with how the Dewey system seems to support common sense differences between ‘things’. Every now and then this common sense fails, but that is the nature of any hermetic system.
by AnotherGoodName | 2025-06-01 22:59:24+00:00
I suspect the make up of this company is going to be the most extreme of extreme mono cultures.

The ‘what marks did you get in high school mathematics’ alone as a screening question (apparently an auto rejection if it’s not top 10%) and the ceo interviewing everyone personally gives a vibe of ‘you must have walked a very similar path in life as the boss to work here.’

Like there doesn’t seem to be any room to be the slightest bit outside of the lane of the ceos personal expectations. What the hell is this company like on the inside? A clone factory i assume?
by thegeomaster | 2025-06-01 22:59:35+00:00
This is kinda frustrating to read. The style is very busy, and it lacks a clear structure. It's basically an information dump without any acknowledgment of what's important or not. Big-O notation is provided for a lot of operations where you wouldn't really care about Big-O (in a system where calls to an LLM dominate, this is most operations). Big picture story about how Claude Code actually works, as in what happens when I type in a prompt (which I'm very much interested, given how much I use it) is lacking. Some diagrams are so nonsensical they become funny. Look at this: https://southbridge-research.notion.site/Prompt-Engineering-... In general, the prompt engineering page, which deserves maybe the most detailed treatment, is just a dump of prompts and LLM bullet point filler.

I don't want to be overly negative, but I think it's only fair given the author hasn't graced us with their own thoughts, instead offloading the actual writing to an LLM.
Show HN: Wikigen.ai – an engaging way to learn [video]
by priorityfill | 2025-06-01 23:00:12+00:00
WikiGen.ai (https://wikigen.ai) is an engaging answering machine with an emphasis on exploration rather than typing / chat. If it feels different from similar apps, ChatGPT, Perplexity and the likes, it's because it is!

Looking at analytics, virtually no one gets past reading the first article and therefore have likely missed the point of this website. But those who did try generally had pretty good feedback! Therefore, I am reposting the Show HN with a 1 minute video that attempts to capture the essence of this MVP.
by numeri | 2025-06-01 23:00:17+00:00
No, it's completely useless, and puts the entire rest of the analysis in a bad light.

LLMs have next to no understanding of their own internal processes. There's a significant amount of research that demonstrates this. All explanations of an internal thought process in an LLM are completely reverse engineered to fit the final answer (interestingly, humans are also prone to this – seen especially in split brain experiments).

In addition, the degree to which the author must have prompted the LLM to get it to anthropomorphize this hard makes the rest of the project suspect. How many of the results are repeated human prompting until the author liked the results, and how many come from actual LLM intelligence/analysis skill?
by Forgeties79 | 2025-06-01 23:00:42+00:00
The episode with the three blind archers guarding the well that grants wishes is a master class in gesticulates at everything

Whenever I think of Tartakovsky I also think of his clone wars micro series he did for Cartoon Network. The episode with the special forces clone troopers that has no dialogue after the first 30-60 seconds or show is just so unbelievably good. So much tension.
by tocs3 | 2025-06-01 23:01:01+00:00
The solutions are NP-Completeeate[1]. Someone may write something to solve the 5x5 nongrams (I think there only 33,554,432) but there is always 6x6 nonograms to work with.

[1]https://medium.com/smith-hcv/solving-hard-instances-of-nonog...
by DavidPiper | 2025-06-01 23:02:02+00:00
The Pirates of the Caribbean trilogy is well-known for being peak CGI Artist overtime and burnout. Don't have the source handy right now, but At World's End in particular was allegedly an absolute meat grinder for the CG folks for years.

That said, I do agree, they made something truly incredible that has stood the test of time. And I wish we could have more of it (but I'll also take better working conditions every day until we figure out how to have both.)
by femiagbabiaka | 2025-06-01 23:02:09+00:00
I see the conflict warning you're referring to, but I can confirm that in my bare minimum repro, those packages can be installed in a system alongside each other, and the warning stays just a warning. I think both packages can be improved by filtering the meson.build file out of the stuff that is exported from each package into the environment. I'll need to investigate that more.

As far as your overall problem goes however, there's likely something else at play. You've nerd sniped me now, so I'll spend some time when I have some next to try to build a test harness for your exact config.
by throwawayffffas | 2025-06-01 23:02:20+00:00
Dead end, I don't think so, but certainly it's playing on hard mode.

I don't know what went wrong on the latest Starship launch, so I won't comment on that.

What I am going to say is that if each rocket engine has 0.001 chance of blowing up, putting 33 engines on your rocket means you have a 3.2% chance your rocket will blow up. If you add another 9 on your second stage you have a 4.1% chance of "rapidly disassembling".

We have seen this play before, the soviets were blowing up rocket after rocket on launchpads while NASA went to the moon and back.
by acquisitionsilk | 2025-06-01 23:02:22+00:00
I'm not saying I want them to run the whole service free of charge, I'm saying I'd consider it a gain for the species if the business closed down. I look forward to alternative ideas (with e.g. no middlemen, and direct payment) being tested out.

The TV era held some promise but steadily declined, and the Youtube era has went similarly. The audiovisual onslaught continues. Technically competent people on here are innoculated against the realities of the average usage on these platforms, which equates to brain-rot of the lowest calibre.
by tester756 | 2025-06-01 23:03:12+00:00
I work in HW industry, but doing SW.

If I deploy a bug despite "unit" tests, then it will probably be caught by 2nd tier tests 1 day later, which will already be more expensive since I'll need to read report, context switch from other thing, fix it, redeploy, wait for CI, wait for 2nd tier tests, etc.

If it will not be caught by 2nd tier, then it probably will be caught by validation engineers, which not only costs their time, but they also have to contact me / fill some bug, I need to get familiar with it, context switch again, fix it, redeploy, wait for tests #1 and #2 and wait for their confirmation

If it will be released to actual customers then there's reputational dmg (can be huge depending on impact), possibly some lost sales / customers, probably a few weeks before it goes back to me thru customer->???->validation->me

So, while 100x more expensive is some extreme case, then usually the cost is very significantly higher the later you find the bug.

But what about HW bugs?

I think those can be really, really expensive.

Imagine catching hardware bug which affects some computation that MUST be fixed at hardware level.

Catching it before release and catching it after selling 100m copies is tens of bilions of dollars difference for top GPUs and CPUs.

Why do you think that those companies are willing to build same product twice so they test eachother? It significantly increases the cost, but reduces the risk.

Think about CrowdStrike's incident: catching that bug at dev level would cost them shitton times less than what actually happened :P

Their stock dropped by like 45%
by twotwotwo | 2025-06-01 23:03:56+00:00
The latest V3 strikes me as a really practical go-to among open-weights models. Lots of tasks don't need the reasoning tokens, and not having to wait for them is nice. (If something does need it you can always switch.) If you're not running it yourself a couple providers have it with full context, 80tps, and a promise not to use your data.

9004 home server is awesome!
by bgwalter | 2025-06-01 23:04:02+00:00
"To OpenAI, these endeavors legitimately contribute to benefiting humanity: building more and more useful AI tools; bringing those tools and the necessary infrastructure to run them to people around the world; drastically increasing the productivity of software engineers."

Why does this lie not go uncontradicted in the article? How does increasing the productivity of software engineers benefit anyone but employers? How do "AI" tools benefit humanity?

"AI" should stay in Academia, where it belongs.
by salamo | 2025-06-01 23:04:44+00:00
Really happy to see additional solutions for on-device ML.

That said, I probably wouldn't use this unless mine was one of the specific use cases supported[0]. I have no idea how hard it would be to add a new model supporting arbitrary inputs and outputs.

For running inference cross-device I have used Onnx, which is low-level enough to support whatever weights I need. For a good number of tasks you can also use transformers.js which wraps onnx and handles things like decoding (unless you really enjoy implementing beam search on your own). I believe an equivalent link to the above would be [1] which is just much more comprehensive.

[0] https://ai.google.dev/edge/mediapipe/solutions/guide

[1] https://github.com/huggingface/transformers.js-examples
by godelski | 2025-06-01 23:05:45+00:00
Nice, I never went to LANL but have a few friends in HPC over there.

You're right, it is theater. But a lot of people think it isn't...

I think it is important to be explicit in why novelty is a crapshoot.

Novelty depends on: - how well you read the work - High level reading means you will think x is actually y - how well read you are - If you're too well read, every x is just y - If you're not well read, everything is novel - how clear the writing was - If it is too clear, it is obvious, therefore not novel

If any process encourages us to be less clear in writing, we should reject it. I've seen a lot of this happening more and more and it is terrible for science. You shouldn't have to mask your contributions, oversell, or mask other related works. Everything is "incremental" and all that novelty is is a measurement of the reader's ego.

What I've just seen is that the old guard lost sight of what was important: communicating. I don't think anyone is malicious here or even had bad intentions. In fact, I think everyone had and still has good intentions. But good intentions don't create good outcomes. They're slow boiled frogs boiled, with slowly increasing dependence on metrics. They can look back and say "it worked for me", blinding them to how things have changed.

> I agree with your point about the journal/conference being the wrong place to do peer review. It is too late to change things by then. The right time is "in the lab", as you say.

I disagree a bit (again, I think you'll agree lol). You're right that some should be happening in the lab. But there is a hierarchy. The next level is outside the lab. Then outside research. Peer review is an ongoing process that never stops. To define it as 3-4 people quickly reading a paper is just laughable. They just have all incentives to reject a work. No one questions you when you reject, but they do when you accept. Acceptance rates sure don't help, and this is the weirdest metric to define "impact" by. I don't even know how one could claim that rejection rate correlates with scientific impact. Maybe only through the confounding variable of prestige and that it is what people target? But then ArXiv should have the highest impact lol.

> Perhaps the only "original" ideas that I have had in my career came from trying to replicate what other people did and finding out something during that process.

Same! I don't think it is a coincidence either. Science requires us to be a bit antiauthoritarian. Trust, but verify is a powerful tool. We need to verify in different environments, with methods that should be similar, and all that. Finding those little holes is critical. A worst, replication makes you come up with ideas. At least if you keep asking "why did they do this?" or "why does that happen?".

I think in a process where we're pushed to quickly publish we do not take time to chase these rabbit holes. Far too often there's a wealth of information down them. But I'm definitely also biased from my poor experience in grad school lol.
Show HN: NextGP – a clean F1 countdown app built with Expo and React Native
by flakii | 2025-06-01 23:07:04+00:00
Hi Hacker News!

I just launched NextGP, an F1 countdown and calendar app built with Expo and React Native. It shows upcoming 2025 F1 sessions with local time conversion and live countdowns.

The iOS version is live, and Android is currently in closed testing. I’d love to get your feedback and hear what you think!

App Store link: https://apps.apple.com/app/nextgp/id6746546202

Thanks!
by axadrn | 2025-06-01 23:07:23+00:00
Yeah, totally fair question.

Originally, I had templUI components available via Go module imports, but there were a few real issues:

- Tailwind v4+ with CSS-only config needs hardcoded paths to `.templ` files. That clashed in teams where Go paths differed (e.g. `~/dev`, `~/code`, etc.).

- It made Git diffs messy – you had `.templ` and `.go` files being imported per component.

- Updating components via module versions often led to unwanted breaking changes – not easy to "just use one component" from a newer version.

- Workarounds like Makefile tricks for Tailwind `src` path felt too hacky.

The CLI avoids all that. It copies clean files into your codebase, so it's portable, version-safe, and no import path magic needed.

That said – I’m open to feedback. If there's a solid reason to bring back module imports (optionally), I’m listening!
by const_cast | 2025-06-01 23:07:28+00:00
Right, it's not a disadvantage because this isn't happening.

If you look at the statistics, black individuals and women make up an extremely small proportion of software developers. White men are not disadvantaged, I'm sorry to say.

For the record, I myself am a white man. The reality is that simply having a white-sounding name makes your likely hood of getting hired go up by over 50%. I'm not going to sit here and act like I'm some sort of victim because some people got together and made an ERG for black engineers. Who cares.
by erklik | 2025-06-01 23:08:07+00:00
I think it makes sense specifically in this case.

Mainly because Islam has jurisprudence around hygiene in a sense. Ritual purification is an actual religious principle.

Islam requires Muslims to pray 5 times a day, and for those prayers, one has to be ritually clean. That involves washing the hands, rinsing the mouth and nose, washing the face, forearms, head, ears, and then the feet. That's effectively all the major parts of the body that are generally not covered by clothes. Your "cleanliness" is invalided if you use the toilet, pass flatulence, vomit, sleep and so on.

More so, for using the toilet, there are rules. You have to find a place that is away from standing water, people's pathways, shade etc ; granted, this generally doesn't apply in today's age. You have to be quiet on the toilet, and not look at anyone. Not allowed to eat any food while defecating. Lastly and most relevant in this case, you have to use water to wash yourself using the left hand, and then afterwards, you need to do the same for washing the front if you've urinated.

The reason why the "bidet spray" thing exists, is largely because of the rules in the religion around that practice. Calling them Arabic wouldn't make any sense because Indonesia, with the largest Muslim population, has similar tools in their facilities. Again, because they're mostly Muslims.

Printing isn't a catholic thing because the religious doctrine didn't emphasis "printing" itself.

Arabic numbers aren't "islamic numbers" because the religious doctrine didn't emphasise the numbers in some way.
by kccqzy | 2025-06-01 23:08:17+00:00
> First of all, a lot of languages don't include arbitrary rounding in their math libraries at all, only having rounding to integers.

You do the simple, obvious and correct thing: multiply by 100, round to int, convert to double, divide by 100. It does not matter whether the final division by 100 results in an exact value. (You might argue this is inefficient but it's not a correctness problem.)

> for example, round(2.675, 2) gives 2.67 instead of the expected 2.68.

You are not going to execute round(2.675, 2) if you follow my advice of rounding after every operation. Because the error will never reach 0.005. Your argument is moot.
by danielhanchen | 2025-06-01 23:08:33+00:00
Oh hey :) Thanks for the kind words - we did provide benchmarks (MMLU, KLD, Perplexity) for Llama 4 Scout, Gemma 3 27B using our methodology - https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs and https://x.com/UnslothAI/status/1915476692786962441

For R1 specifically, we did an internal benchmark on the original model - https://unsloth.ai/blog/deepseekr1-dynamic

For R1-0528 specifically on evals - we're still running them :)) It's quite expensive to run, so we first do "vibe check" on some internal test cases, and they do pretty well!

But we generally stress the bug fixes that we do, which objectively increase performance by +1 to sometimes +10% accuracy - for example Llama 4 bug fixes, Gemma bug fixes - https://news.ycombinator.com/item?id=39671146 etc are much more important :)

We also provide Q8_0 and Q8_K_XL quants, which are mostly equivalent to FP8 - you can also use the magical `-ot ".ffn_.*_exps.=CPU"` incantation to offload MoE layers to RAM!
by gdubs | 2025-06-01 23:08:40+00:00
Been there.

I often talk about how I think people should learn to code not to get a job but for the mental models it gives you.

Similar to how learning a musical instrument enriches your life.

Or art.

And one example that feels particularly apt here is that in my art classes in college, my drawing professors really hammered-in the habit of stepping back a few feet from your canvas.

It might feel like it's applicable specifically to that context, but it's amazing how much that voice comes back even when I'm working on code or 3D modeling: "take a step back."
by mrep | 2025-06-01 23:08:43+00:00
> they just shook up the world's militaries

For the good ones, I doubt it.

Israel today has trophy [0] which can detect if an rpg is going to hit its tank and shoot it out of the sky.

(from wiki): The system allegedly relies heavily on high-speed computational technologies. Upon detection of an incoming projectile, the system automatically computes various parameters, such as the approach vector, nature of the threat, time to impact, and angle of approach. The defensive projectiles are launched by two rotating projectile launchers positioned on the sides of the vehicle. These launchers deploy a number of small EFPs (Explosively Formed Penetrators), forming a precise and closely spaced matrix, targeting an area in front of the anti-tank projectile.

And that's one country with 10 million people and a mere $46.5 billion in military spending. And BTW allies generally share tech (we the US suck right now (sorry)) but Trophy is being integrated with multiple allies [1].

Developing automated drone shooting destroyers I think we can do.

EDIT: To add, I bet those drones can be shot down by 1 minigun or shotgun shell which aren't relatively expensive.

Edit 2, the dutch already have a badass automated minigun too [2, 3].

Edit 3: multiple countries have similar systems but they are all mainly for boats. I think we can adapt the to army bases [4].

[0]: https://en.wikipedia.org/wiki/Trophy_(countermeasure)

[1]: https://en.wikipedia.org/wiki/Trophy_(countermeasure)#Intern...

[2]: https://en.wikipedia.org/wiki/Goalkeeper_CIWS

[3]: https://www.youtube.com/watch?v=-AaUNipuygE

[4]: https://en.wikipedia.org/wiki/Close-in_weapon_system
by storus | 2025-06-01 23:09:02+00:00
I thought that fine-tuning is no longer being done in the industry, instead transformer adapters like LoRA are being used? Having 1000 fine-tune models for each customer seems too heavy when one can have instead 1000 transformer adapters and swap them during the inference for each batch.

I mean there are tricks like Q-GaLore that allow training LLaMA-7B on a single 16GB GPU but LoRA still seems to be better for production to me.