MurphyA/hacker-news huggingface dataset:
by PetitPrince | 2025-08-22 11:17:33+00:00
Haven't seen that in Switzerland but most place I see where a shopping cart is really warranted (large stores, Ikea, etc.) have covered parking spots instead of open-air (and/or are smaller than those giant parking lot we can see in the US). My hypothesis is that of the panopticon: since those are smaller space that anti-social behavior is way more noticeable and will not be tolerated by peers.
by Koenvegter | 2025-08-22 11:17:33+00:00
We are excited to share that SowFlow will launch on Product Hunt in 30 days!

SowFlow helps teams capture processes and turn them into clear, repeatable checklists and SOPs. If you want to support us and get early access, you can follow our launch page here: https://www.producthunt.com/products/sowflow-standard-operat...

Does the community here have any tips on how we can make our ProductHunt launch a great success? All tips and resources (next to the producthunt launch guide) are welcome! :)

Regards, Koen
by akoboldfrying | 2025-08-22 11:20:11+00:00
They're different. Most programs can in principle be proven "correct" -- that is, given some spec describing how it's allowed to behave, it can either be proven that the program will conform to the spec every time it is run, or a counterexample can be produced.

(In practice, it's extremely difficult both (a) to write a usefully precise and correct spec for a useful-size program, and (b) to check that the program conforms to it. But small, partial specs like "The program always terminates instead of running forever" can often be checked nowadays on many realistic-size programs.)

I don't know any way to make a similar guarantee regarding what comes out of an LLM as a function of its input (other than in trivial ways, by restricting its sample space -- e.g., you can make an LLM always use words of 4 letters or less simply by filtering out all the other words). That doesn't mean nobody knows -- but anybody who does know could make a trillion dollars quite quickly, but only if they ship before someone else figures it out, so if someone does know then we'd probably be looking at it already.
by tastyface | 2025-08-22 11:21:50+00:00
“The problem is: our billionaires are n—ers. They may be rich. But they're n—er rich. The nature and function of their wealth is profoundly negrous.”

--Curtis Yarvin, https://bsky.app/profile/esqueer.net/post/3lv7lipobrs2a

That should give you a good sense of what kind of man he is, and what it says about those who support him.
by almatia | 2025-08-22 11:23:11+00:00
Couldn't this coherence be obtained with Markov chains fed with your texts? I found this: https://medium.com/@preranabora12/text-generating-with-marko...

For creative writing it's true the "most common word based on your personal corpus for this input" could be useful as a hint input on an llm-powered creative text UI like loom: https://github.com/socketteer/loom
by martin-t | 2025-08-22 11:23:20+00:00
It cannot do anything on its own, it's just a (very complex, probabilistic) mechanical transformation (including interpolation) of training data and a prompt.

Advertising autocomplete as AI was a genius move because people start humanizing it and look for human-centric patterns.

Thinking A"I" can do anything on its own is like seeing faces in rocks on Mars.
by lettucegoblin | 2025-08-22 11:24:06+00:00
I think the coherence might be coming from the filtering, not the fibonacci intervals. my thinking is that the process looks like this: your fibonacci script finds a big list of candidate words -> your grammar rules arrange them -> sentencetransformers aggressively filters this list and picks the single most semantically coherent sentence. so is it possible that sentencetransformers is doing all the heavy lifting for creating coherence? have you ever looked at the raw, unfiltered candidate words/sentences to see if they have any coherence on their own? on the "french toast" example, could this be a case of language pareidolia(e.g. seeing faces in clouds)? the model selects "piney connubial produit" because its math is the closest to the input -> your brain, being a great pattern matcher, creates a story to connect the words. so is the meaning actually being generated by the fibonacci part, or is it just being found by the filter and then interpreted by you? with the punctuation model, i'm guessing it's just learning simple surface-level patterns, right? like, it learns: text contains "but" -> place comma before "but". how does it handle complex sentences that don't follow the simple patterns in your 4mb of training data? does that break down? the comparison to bert seems off because they're solving the problem in fundamentally different ways. tofigure out if the fibonacci part is actually meaningful, have you thought about running some controls? for example, what happens if you replace the fibonacci intervals with random intervals? does the quality of the final, filtered output get worse? what if you just looked at the raw word lists themselves? is a list generated by your fibonacci model measurably more coherent than a list of words just pulled randmly from the text?
by ghaff | 2025-08-22 11:26:16+00:00
Yeah, if you get a sleeper and figure you're saving a night in London or Edinburgh and you don't need to deal with airports, something like the Caledonian Sleeper isn't a bad deal or really consume a lot of extra time. But if you're not in a hurry, it's still a good experience for a lot of people.

But then I've also done a trans-Atlantic crossing but I was semi-retired at the time. Which I would do again at some point but was certainly neither cost nor time efficient.
by nkrisc | 2025-08-22 11:26:29+00:00
Physical property rights are made up too. How can you claim to own something if you aren’t actively defending it nor physically possessing it in that moment.

Do you “own” your house even when you’re not home? Yes, you do, because we all agreed on this made up thing called “property rights” and we pay our tax dollars to have it enforced. Otherwise whoever is in your house “owns” it until you or someone else forcible removes them or convinces them to leave.

All our rules are “made up”.
by lucideer | 2025-08-22 11:26:34+00:00
> Yes we're working on Docker!

That will be nice too, though I was more just referring to simply doing something along the lines of this in your current build:

docker run conanio/gcc11-ubuntu16.04 make clean -C llama.cpp etc etc...

(likely mounting & calling a sh file instead of passing individual commands)

---

Although I do think getting the ggml guys to support Conan (or monkey patching your own llama conanfile in before building) might be an easier route.
by jongjong | 2025-08-22 11:26:38+00:00
A lot of things can happen to break a self-perpetuating cycle. But it's usually some extreme event. The cycle keeps optimizing in a particular direction and this eventually leads to an extreme situation which becomes unsustainable. The natural equilibrium of a self-reinforcing cycle is not static but drifting towards some extreme unstable state. There is usually a point where it breaks down.

But I suspect that being able to figure out causation doesn't matter much from a survival or reproduction perspective because cause and effect are just labels.

Reality in a self-perpetuating cycle is probably like Condition A is 70% responsible and Condition B is 30% responsible for a problem but they feedback and exacerbate each other... You could argue that Condition A is the cause and Condition B is the effect because B < A but that's not quite right IMO. Also, it's not quite right to say that because A happened first, that A is the cause of a severe problem... The problem would never have gotten so bad to such extent without feedback from B.
by matt_kantor | 2025-08-22 11:27:20+00:00
I think they meant:

1. If a process necessarily results in bus factors of zero, that process is flawed.

2. The nature of vibe coding is such that it always produces code with a bus factor of zero (i.e. this is a "fundamental" fact of vibe coding).

I definitely agree with the first point, and I think agree with the second as well (at least if "vibe coding" carries its original implication that you don't even look at/care about the code produced by the LLM).

Did you have a different interpretation? Or do you disagree with one of these points?
by jleyank | 2025-08-22 11:27:22+00:00
Probably trying to expand the regional and age aspects of their customer base. It’s a problem when one’s customer base can age out on you…. Neutralizing the “geographic feel” can also improve survivability in today’s hypersensitive environment.

Ya gotta have reasonable food and service. But that’s expensive and you have to get people looking to spend money in the door first.
by klingon33 | 2025-08-22 11:28:28+00:00
This story of whittling down the product to meet the needs of the user as grepped from several sales calls assumes that a small sample is statistically representative (and it’s not) and assumes simplification always makes for a better product (and it doesn’t).

When I ask 15 users for their input, only 3 will speak up, and their needs and views don’t fully overlap.

Photoshop is a great example of a product that was extremely complicated and difficult to use but dominated the market for many years.

Microsoft has continually made updates to its products over the years, making it basically do the same thing but with unnecessary and frustrating changes, and people keep buying it.

Yes, UX is important, and A/B or similar evolutionary testing is important. Yes, devs tend to overcomplicate things. But, there’s no magic simplification bullet.
by OhMeadhbh | 2025-08-22 11:28:57+00:00
Yup. I noticed a line in Islands in the Net where one of the characters waits until evening to make a phone call when the rates are low. I had a chuckle, but it didn't detract from the rest of the story that was definitely good. Though I should talk to my offspring about making sure they only call me in the evenings so the long distance fees aren't exorbitant, just to see if they try to take away my car keys.
by metalman | 2025-08-22 11:30:42+00:00
localy the equivilant would be if The Coast came back to print.Phones suck for staying in touch with the local sceen.I have noticed a significant increase in postering for events, music and a few more local only type retail stores,and millenials leaning in on traditional trades, craftwork, art, etc. I mean nothing beats a good meme or wacked video on tictok etall, but then it's like time to do something, and the algorythim isn't set up for that.
by jmclnx | 2025-08-22 11:30:45+00:00
>If Ofcom doesn't think this will be enough to prevent significant harm, it can even ask that ISPs be ordered to block UK access.

Well again I guess the UK never heard of VPNs, but they are trying to ban them still, it is like these pols have no clue how the internet works. They never learn these actions are like playing wack-a-mole.
by cyphertechinc | 2025-08-22 11:31:07+00:00
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by softwaredoug | 2025-08-22 11:31:25+00:00
I like Go, but my main annoyance is deciding when to use a pointer or not use a pointer as variable/receiver/argument. And if its an interface variable, it has a pointer to the concrete instance in the interface 'struct'. Some things are canonically passed as pointers like contexts.

It just feels sloppy and I'm worried I'm going to make a mistake.
by dexterlagan | 2025-08-22 11:32:55+00:00
Agreed, but in the case of the lie detector, it seems it's a matter of interpretation. In the case of LLMs, what is it? Is it a matter of saying "It's a next-word calculator that uses stats, matrices and vectors to predict output" instead of "Reasoning simulation made using a neural network"? Is there a better name? I'd say it's "A static neural network that outputs a stream of words after having consumed textual input, and that can be used to simulate, with a high level of accuracy, the internal monologue of a person who would be thinking about and reasoning on the input". Whatever it is, it's not reasoning, but it's not a parrot either.
by ath3nd | 2025-08-22 11:33:18+00:00
> Modern ML is at this hellish intersection of underexplored math, twisted neurobiology and applied demon summoning

Nah, it's just a very very big and fancy autocomplete with probabilistic tokenization and some extra tricks thrown in to minimize the shortcomings of the approach.

> Unfortunately, the nature of intelligence doesn't seem to yield itself to simple, straightforward, human-understandable systems.

LLMs are maybe artificial but they are not intelligence unless you have overloaded the term intelligence to mean something much less and more trivial. A crow and even a cat is intelligent. An LLM is not.
by swader999 | 2025-08-22 11:33:28+00:00
And then what kind of reaction will we have when inference and work is less intensive environment wise for AI to do vs a human? That may even already be the case. See here: https://grok.com/share/c2hhcmQtMw%3D%3D_60f4890d-711b-4331-9... credit: HN user pama
by rzwitserloot | 2025-08-22 11:34:01+00:00
For context, in The Netherlands, supermarkets are in urban areas and have no parking lot at all or a relatively tiny one.

The usual USian aspect of schlepping a shopping cart waay out to your car and then not wanting to schlep it back or to one of those bays is thus far less relevant.

Also, buying all groceries for the next week is rare here. In other words, you just pack your stuff in bags and walk off, no need to bring the shopping cart.

It's a somewhat unique situation (in NL it is not legal to have a store open to the general public except in a commercial zone, i.e. a supermarket cannot open its business in an industrial zone which means the stores kinda have to be in the middle of population centers, which then opens the door to just buying what you need every day. This then in turn means you can go by bicycle and you don't need a massive freezer either).

Interesting how such a relatively innocuous butterfly flap (the zoning laws) result in such an utter change in culture (bicycle, urbanisation, shop-for-the-day instead of for-the-week, etc).
by blixt | 2025-08-22 11:35:29+00:00
I've been using Go more or less in every full-time job I've had since pre-1.0. It's simple for people on the team to pick up the basics, it generally chugs along (I'm rarely worried about updating to latest version of Go), it has most useful things built in, it compiles fast. Concurrency is tricky but if you spend some time with it, it's nice to express data flow in Go. The type system is most of the time very convenient, if sometimes a bit verbose. Just all-around a trusty tool in the belt.

But I can't help but agree with a lot of points in this article. Go was designed by some old-school folks that maybe stuck a bit too hard to their principles, losing sight of the practical conveniences. That said, it's a _feeling_ I have, and maybe Go would be much worse if it had solved all these quirks. To be fair, I see more leniency in fixing quirks in the last few years, like at some point I didn't think we'd ever see generics, or custom iterators, etc.

The points about RAM and portability seem mostly like personal grievances though. If it was better, that would be nice, of course. But the GC in Go is very unlikely to cause issues in most programs even at very large scale, and it's not that hard to debug. And Go runs on most platforms anyone could ever wish to ship their software on.

But yeah the whole error / nil situation still bothers me. I find myself wishing for Result[Ok, Err] and Optional[T] quite often.
by tstactplsignore | 2025-08-22 11:35:37+00:00
Circa 2018-2020, Twitter felt genuinely unique as a social media platform to find intelligent scientific and policy discussion. The quality of discussion in my feed felt much higher than reddit or HN. I could barely imagine leaving it. By 2024, that completely reversed, and it felt much so, so stupider than anywhere else. So many scientists have left. Eventually I left despite having 1000s of followers.

Bluesky has recapitulated or even surpassed peak sci twitter. The signal:noise is excellent. However, it requires some work because there is no algorithm. Aggressively unfollow people with low signal:noise, use the custom feeds that disable reposts and enable replies, use the Quiet Posters feed, and use sill.social. This has created a science feed that for me surpasses even the peak of Twitter, let alone X today which is unusable for scientific discussion.

Finally, the thing that drives me crazy is that Bluesky is literally a popular open-source, nonprofit, Ad-less, algorithm-less, truly free and partially decentralized social media network. It's what we all dreamed about in the 2010s! It's Mastodon but actually popular! But half the tech community have convinced themselves it's a "liberal bubble" (that anyone can join....) and that the website that apparently isn't a bubble is the, err, website run by a billionaire with an algorithm designed to promote certain political content that agrees with that billionaire. Absolutely bizarre situation.
by rbinv | 2025-08-22 11:35:49+00:00
Afaik, there is currently no "GPT-5 Pro". Did you mean o3-pro or o1-pro (via API)?

Currently, GPT-5 sits at $10/1M output tokens, o3-pro at $80, and o1-pro at a whopping $600: https://platform.openai.com/docs/pricing

Of course this is not indicative of actual performance or quality per $ spent, but according to my own testing, their performance does seem to scale in line with their cost.
by ivan_gammel | 2025-08-22 11:36:05+00:00
No, it is not it. The idea is that the living module or RV is still occupied during the travel unlike the motorail, where passengers are traveling in separate passenger cars. Car shuttle trains where passengers stay inside the vehicle also do not offer connection of utilities to RV. And my idea goes further where living module is disconnected from engine/wheels platform when loaded on the train (if the design is standardized similarly to containers, you can get new one at the destination, so the train carries less weight).
by imglorp | 2025-08-22 11:36:07+00:00
Memory unlocked. Around 1980, our local, government mandated, public access program for cable TV would loan out the first over-the-shoulder camera he showed, along with the sorta-portable battery VHS recording rig. White balance was always a challenge with those. AV nerds could go out and tape random events that nobody would watch but it kept us off the street.
by mhh__ | 2025-08-22 11:36:12+00:00
Keep in mind that in industries where people code but aren't really programmers this literally does happen, sometimes very "big" people will be basically scared to share their code because it won't be very good.

But anyway what I mean is that code is us speaking like a computer, LLMs are the other way around, you can see a lot from how someone interacts with the machine.
by torginus | 2025-08-22 11:36:50+00:00
I still don't understand why defer works on function scope, and not lexical scope, and nobody has been able to explain to me the reason for it.

In fact this was so surprising to me is that I only found out about it when I wrote code that processed files in a loop, and it started crashing once the list of files got too big, because defer didnt close the handles until the function returned.

When I asked some other Go programmers, they told me to wrap the loop body in an anonymus func and invoke that.

Other than that (and some other niggles), I find Go a pleasant, compact language, with an efficient syntax, that kind of doesn't really encourage people trying to be cute. I started my Go journey rewriting a fairly substantial C# project, and was surprised to learn that despite it having like 10% of the features of C#, the code ended up being smaller. It also encourages performant defaults, like not forcing GC allocation at every turn, very good and built-in support for codegen for stuff like serialization, and no insistence to 'eat the world' like C# does with stuff like ORMs that showcase you can write C# instead of SQL for RDBMS and doing GRPC by annotating C# objects. In Go, you do SQL by writing SQL, and you od GRPC by writing protobuf specs.
by pshirshov | 2025-08-22 11:37:11+00:00
Yes, it's trivial with the pre-built vllm docker, but I need a declarative way to configure my environment. The lack of prebuilt rocm wheels for vllm is the main hindrance for now but I was shocked to see the sudo apt-get in your code. Ideally, llama.cpp should publish their gguf python library and the conversion script to pypi with every release, so you can just add that stuff as a dependency. vllm should start publishing a rocm wheel, after that unsloth would need to start publishing two versions - a cuda one and a rocm one.
by gield | 2025-08-22 11:37:56+00:00
>in the Netherlands (or neighbouring countries)

Most supermarkets in Belgium use a coin but some supermarkets (notably Colruyt) lock their shopping cart wheels.

Supermarkets that have a step-less escalator (e.g. to go to the parking lot in the basement) also use these locking wheels to make sure the cart never moves on the escalator. I live near an Albert Heijn that has these.
by HarHarVeryFunny | 2025-08-22 11:37:58+00:00
It's a specific architecture. Of course there are (massive amounts) of feedback paths, since that's how we learn - top-down prediction and bottom-up sensory input. There is of course looping too - e.g. thalamo-cortical loop - we are not just as pass-thru reactionary LLM!

Yes, there is a lot more structure to the brain than just the neocortex - there are all the other major components (thalamus, hippocampus, etc) each with their own internal arhitecture, and then specific patterns of interconnect between them...

This all reinforces what I am saying - the brain is not just some random graph - it is a highly specific architecture.
by tomrod | 2025-08-22 11:38:48+00:00
It's a fun rabbit hole.

Classical ML tasks (e.g. classification, regression ), perception (vision, speech) and pattern recognition, generative AI capabilities (text, image, audio generation), knowledge representation and reasoning (symbolic AI, logic), decision-making and planning (including reinforcement learning for sequential decisions), as well as hybrid approaches (e.g. neuro-symbolic methods, fuzzy logic).

The capability areas outside of classical ML have been overlapped now to a degree by GPT architectures as well as deep learning, but these architectures aren't the whole game.