MurphyA/hacker-news huggingface dataset:
by mstaoru | 2025-07-10 17:52:03+00:00
> that would be a great idea since everyone hates using Teams, nobody knows where to find files in Sharepoint

Summarizes my experience in my new company, which is a Microsoft shop.

Though RevOps side, I don't see how this would make much difference. If cost cutting got so far, he can also propose switching to Mattermost+Jitsi+Nextcloud, hosted on Hetzner.
by spwa4 | 2025-07-10 17:52:06+00:00
> My life experience says that at least in academic teaching

If you mean financially compensated, just about anywhere. There's only a few places where academic teaching is well compensated and even in those places it doesn't compare to the private sector. Oh and there's an enormous but not very visible industry of having university professors on boards of companies as advisors to make sure there's a very easy transition for them to move to private industry.
by AlotOfReading | 2025-07-10 17:52:17+00:00
A formal proof isn't sufficient on its own for safety-critical applications. You also need to show that your assumptions actually hold in the system and that your proof is the right proof for that system.

I usually tell people that safety-critical processes aren't really about eliminating bugs, that's just a happy side effect. They're about ensuring that the risks for the system are known and reasonably mitigated, with documentation to prove it.
by theamk | 2025-07-10 17:52:25+00:00
the article says:

> the final result “is neither fulfilling the functional requirement nor safe for road users.”

Customers can say all sorts of crazy things, they havo no knowledge of what's a good design or not. It's up to engineers to ensure design is safe. If an engineer knowigly signs-off on the design that is not safe, they deserve all the punishment.
by narush | 2025-07-10 17:52:56+00:00
Hey Simon -- thanks for the detailed read of the paper - I'm a big fan of your OS projects!

Noting a few important points here:

1. Some prior studies that find speedup do so with developers that have similar (or less!) experience with the tools they use. In other words, the "steep learning curve" theory doesn't differentially explain our results vs. other results.

2. Prior to the study, 90+% of developers had reasonable experience prompting LLMs. Before we found slowdown, this was the only concern that most external reviewers had about experience was about prompting -- as prompting was considered the primary skill. In general, the standard wisdom was/is Cursor is very easy to pick up if you're used to VSCode, which most developers used prior to the study.

3. Imagine all these developers had a TON of AI experience. One thing this might do is make them worse programmers when not using AI (relatable, at least for me), which in turn would raise the speedup we find (but not because AI was better, but just because with AI is much worse). In other words, we're sorta in between a rock and a hard place here -- it's just plain hard to figure out what the right baseline should be!

4. We shared information on developer prior experience with expert forecasters. Even with this information, forecasters were still dramatically over-optimistic about speedup.

5. As you say, it's totally possible that there is a long-tail of skills to using these tools -- things you only pick up and realize after hundreds of hours of usage. Our study doesn't really speak to this. I'd be excited for future literature to explore this more.

In general, these results being surprising makes it easy to read the paper, find one factor that resonates, and conclude "ah, this one factor probably just explains slowdown." My guess: there is no one factor -- there's a bunch of factors that contribute to this result -- at least 5 seem likely, and at least 9 we can't rule out (see the factors table on page 11).

I'll also note that one really important takeaway -- that developer self-reports after using AI are overoptimistic to the point of being on the wrong side of speedup/slowdown -- isn't a function of which tool they use. The need for robust, on-the-ground measurements to accurately judge productivity gains is a key takeaway here for me!

(You can see a lot more detail in section C.2.7 of the paper ("Below-average use of AI tools") -- where we explore the points here in more detail.)
by DeveloperErrata | 2025-07-10 17:53:00+00:00
Don't know how Grok is setup, but in earlier models the vision backbone was effectively a separate model that was trained to convert vision inputs into a tokenized output, where the tokenized outputs would be in the form of "soft tokens" that the main model would treat as input and attend to just like it would for text token inputs. Because they're two separate things, you can modify each somewhat independently. Not sure how things are currently setup tho.
by derektank | 2025-07-10 17:53:44+00:00
No? I didn't make the parent comment and I was mostly taking issue with the implication in your comment that US air quality was in some way deficient

But since you asked, while manufacturing solar panels does not itself pose a threat to air quality, environmental and air quality regulations obviously raise the cost of doing business in the manufacturing sector broadly, which makes the US less competitive up and down the supply chain than China. That's obviously not the entire story, but it's certainly part of it.
by ajcp | 2025-07-10 17:54:17+00:00
You're conflating "truthiness" with "correctness". I realize this sounds like an oxymoron when talking about something called ground "truth", but when we're building ground truth to measure how good our model outputs are, it does not matter what is "true", rather what is "correct".

Our ground truth should reflect the "correct" output expected of the model in regards to it's training. So while in many cases "truth" and "correct" should algin, there are many many cases where "truth" is subjective, and so we must settle for "correct".

Case in point: we've trained a model to parse out addresses from a wide-array of forms. Here is an example address as it would appear on the form.

Address: J Smith 123 Example St

City: LA State: CA Zip: 85001

Our ground truth says it should be rendered as such:

Address Line 1: J Smith

Address Line 2: 123 Example St

City: LA

State: CA

ZipCode: 85001

However our model outputs it thusly:

Address Line 1: J Smith 123 Example St

Address Line 2:

City: LA

State: CA

ZipCode: 85001

That may be true, as there is only 1 address line and we have a field for "Address Line 1", but it is not correct. Sure, there may be a problem with our taxonomy, training data, or any other number of other things, but as far as ground truth goes it is not correct.
by ta8645 | 2025-07-10 17:54:36+00:00
Humans are the beneficiaries of millions of years of evolution, and are born with innate pattern matching abilities that we don't need "training" for; essentially our pre-training. Of course, it is superior to the current generation of LLMs, but is it fundamentally different? I don't know one way or the other to be honest, but judging from how amazing LLMs are given all their limitations and paucity of evolution, I wouldn't bet against it.

The other problem with LLMs today, is that they don't persist any learning they do from their everyday inference and interaction with users; at least not in real-time. So it makes them harder to instruct in a useful way.

But it seems inevitable that both their pre-training, and ability to seamlessly continue to learn afterward, should improve over the coming years.
by tstrimple | 2025-07-10 17:55:19+00:00
I feel like this is sort of like reading a book to get better at self defense. Yeah, you'll probably pick up a few interesting things that may be of questionable use. But when you train in martial arts, you often get to go through the motions and put the moves into practice. Even then "real" fights will feel quite different and a lot of the stuff you've learned will likely fly out the window. If you've been in real fights a lot, you've begun to internalize your training and your moves become more like instinct. It's quite difficult to go from book knowledge to instinct without getting beat up a lot in between I think. The real valuable lessons come from building something that breaks and getting to fix it yourself.
by thrance | 2025-07-10 17:55:22+00:00
Oh then nevermind. Grok only went full white supremacist twice after all, so no need to worry. Seriously, when will we be allowed to express concern over Musk's insane conducts? What will it take? Him doing a nazi salute on TV? Oops, already happened.

Also, fuck that "it's just trolling bro" excuse. You don't get to praise Hitler and the Holocaust and then hide behind "shitposting" after. Own it you scummy nazi pieces of shit.
by orwin | 2025-07-10 17:55:24+00:00
I hate software built like this, it's hell and you end up with 4 different designs in your app by the time you hit production.

That's the kind of app that needs internal audit, where some objects are audited, but as the data is never used, the audit in fact only works on a fifth of the project and is never used.

Please say 'no' more often.
by cloverich | 2025-07-10 17:55:26+00:00
Extreme in their lack of interest or tolerance of opposing viewpoints that drowns out meaningful conversation.

The kind of thing that allowed Biden to get so far into the re-election cycle, etc. My favorite recent example is one ive heard from numerous people in real life, is suggesting that the main reason Kamala lost was because she was a woman. Or confusion at all the Texas border counties flipping to Trump. Dems dropped the ball in so many obvious ways and its deeply frustrating that its still difficult to have serious conversations about it. The reality is many of my left leaning friends are still deep in echo chambers they can barely see; its quite different from my right leaning friends who feel a bit closer to delusional on a few particular issues (ie consistently discard and avoid incorporating relatively mundane facts such as low murder rates vs "this is the most dangerous time ever for a child to be outside").
Ask HN: Persisting LLM token streams through a page refresh?
by spruce_tips | 2025-07-10 17:56:00+00:00
Like many of you, I'm streaming responses token by token from LLMs using server sent events (SSEs).

What's the best way to maintain the SSE connection through a page refresh?

I haven't seen a lot of documentation or examples covering this. In most LLM enabled apps I've used, if tokens are currently streaming and the page refreshes/changes, the stream gets interrupted.

One idea I had was writing the streamed tokens into some sort of queue or kafka topic, then connecting my UI to the queue and streaming tokens from there instead. But that seems like a lot of work.

How are most folks doing this?
Show HN: Learn Through Debates
by sillypuddy | 2025-07-10 17:56:49+00:00
I've been frustrated by the quality of our media, it often presents strong arguments for one side, and straw man for the other. So I built a tool where LLMs argue opposite sides of a debate as a way exploring a topic more deeply.

Each side is randomly assigned a model (pro or con), and the idea is to surface the best arguments from both perspectives.

Currently, it uses GPT-4, Gemini 2.5 Flash, and Grok-3. I’d love any reactions or feedback to improve it.

https://www.botbicker.com/
by im3w1l | 2025-07-10 17:56:59+00:00
Just minddumping here but I think that basically high velocity is bad because it leads to high friction and high temperatures. Velocity has two components, downwards and tangential. Tangential velocity is both good and bad. It increases velocity, but it also makes the trajectory more orbitlike, with less downwards acceleration. A gliding plane will both fall slower both due to the above, but also because thats just how gliding works. But if it tumbles it will not glide.
by goodcanadian | 2025-07-10 17:57:01+00:00
Similar concept, I suppose. When I was young, alcohol for consumption at home was generally only available for purchase in government run liquor stores. (This is still the case in some provinces, but no longer true in Alberta.) However, a few licensed premises (bars and restaurants) were permitted to do off-sales and sell alcohol that you were allowed to take with you off the premises.
by mstaoru | 2025-07-10 17:57:05+00:00
You need to work with people who value async communications and uninterrupted work time (or cultivate this culture). I recently moved to EU and it's very very difficult for me to adapt to people insisting on having a call or a meeting for everything. It's such a waste of time and the knowledge is completely lost unless you make notes... in text. Before that I worked in SEA, with teams from across the time zones but in a very "Asian" context. Perhaps since not everybody spoke perfect English, the teams actually preferred typing.

For a while I worked on a 1pm-9pm schedule which gave me a decent overlap with the "let's have a quick call" folks. Having the whole morning to myself was nice.
by kbolino | 2025-07-10 17:57:18+00:00
A hash function cannot create entropy. Let's be clear about that.

A good hash function will preserve entropy, up to the length of its output. If the input X has K bits of entropy and H is an N-bit cryptographic hash function, then the entropy of H(X) is min(K, N). In simpler terms, GIGO.

However, a hash function also scrambles its input, which means the output is indistinguishable from (uniform) random noise. This is the randomizing property I was talking about. It is good enough for hash functions to be used to build stronger primitives, like HMACs, PBKDFs, CSPRNGs, etc. There are many formalizations of this property, but one of the simplest is that given any K bits of the output, you cannot predict the other N-K output bits any better than guessing, even knowing the algorithm.

Of course, if you know the input to a hash function, you can predict the output perfectly. But if you don't know the input, the hash looks like random noise, and for cryptographic hash functions, this is a very strong and fundamental guarantee indeed.
by boroboro4 | 2025-07-10 17:57:49+00:00
The fact the word ends up being 1 token doesn’t mean model can’t track individual characters in it. The model transforms token into vector (of multiple thousands dimensionality), and I’m pretty sure there are dimensions corresponding to things like “if 1st character an ‘a’, 1st is ‘b’, 2nd is ‘a’ etc.

So tokens aren’t as important.
by Merovius | 2025-07-10 17:58:01+00:00
Just to clarify why this is a problem: it’s possible for foo and bar to be defined in different libraries maintained by different people. Potentially several layers deep. And the author of the foo library tests their code and it compiles and all of their tests pass as and everything is great.

But it turns out that’s because they only ever tested it with types for which there is no conflict (obviously the conflicts can be more subtle than my example). And now a user instantiates it with a type that does trigger the conflict. And they get an error message, for code in a library they neither maintain nor even (directly) import. And they are expected to find that code and figure out why it breaks with this type to fix their build.

Or maybe someone changes one of the constraints deep down. In a way that seems backwards compatible to them. And they test everything and it all works fine. But then one of the users upgrades to a new version of the library which is considered compatible, but the build suddenly breaks.

These kind of situations are unacceptable to the Go project. We want to ensure that they categorically can’t happen. If your library code compiles, then the constraints are correct, full stop. As long as you don’t change your external API it doesn’t matter what your dependencies do - if your library builds, so will your users.

This doesn’t have to be important to you. But it is to the Go project and that seems valid too. And it explains a lot of the limitations we added.
by swed420 | 2025-07-10 17:58:14+00:00
It's kind of wild how the west draws this distinction:

If China does it while being responsible and taking into account cost/benefits on overall human well being, it's Creepy Big Brother Communism.

If the US does it and charges money for it (or makes it 'free' by selling your personal info to untrustworthy companies / selling you junk you don't need) it's an innovative futuristic privilege.

Cold War propaganda was incredibly effective through generations.

edit: changed "If China does it for free" to "If China does it" since it had some distracting assumptions
by Xmd5a | 2025-07-10 17:58:34+00:00
Generalized Transformers from Applicative Functors

>Transformers are a machine-learning model at the foundation of many state-of-the-art systems in modern AI, originally proposed in [arXiv:1706.03762]. In this post, we are going to build a generalization of Transformer models that can operate on (almost) arbitrary structures such as functions, graphs, probability distributions, not just matrices and vectors.

>[...]

>This work is part of a series of similar ideas exploring machine learning through abstract diagrammatical means.

https://cybercat.institute/2025/02/12/transformers-applicati...
by smokel | 2025-07-10 17:58:55+00:00
I notice that some people have become more productive thanks to AI tools, while others are not.

My working hypothesis is that people who are fast at scanning lots of text (or code for that matter) have a serious advantage. Being able to dismiss unhelpful suggestions quickly and then iterating to get to helpful assistance is key.

Being fast at scanning code correlates with seniority, but there are also senior developers who can write at a solid pace, but prefer to take their time to read and understand code thoroughly. I wouldn't assume that this kind of developer gains little profit from typical AI coding assistance. There are also juniors who can quickly read text, and possibly these have an advantage.

A similar effect has been around with being able to quickly "Google" something. I wouldn't be surprised if this is the same trait at work.
by tonymet | 2025-07-10 17:59:09+00:00
i see your perspective. Until Musk , it included the scope of publishers that were 1-3 degrees removed from mainstream media and Hollywood.

Post-musk it’s been rebooted into engagement farms and quasi-right-wing influencers.

For the first few months it felt refreshing because all of the once censored (or downranked) spicy content now received visibility.

Now it’s pretty repetitive and lame in its own way.

Every time I open my mute list I resent using the app.
by uludag | 2025-07-10 17:59:13+00:00
In this case though you still wouldn't necessarily know if the AI tools had a positive causal effect. For example, I practically live in Emacs. Take that away and no doubt I would be immensely less effective. That Emacs improves my productivity and without it I am much worse in no way implies that Emacs is better than the alternatives.

I feel like a proper study for this would involve following multiple developers over time, tracking how their contribution patterns and social standing changes. For example, take three cohorts of relatively new developers: instruct one to go all in on agentic development, one to freely use AI tools, and one prohibited from AI tools. Then teach these developers open source (like a course off of this book: https://pragprog.com/titles/a-vbopens/forge-your-future-with...) and have them work for a year to become part of a project of their choosing. Then in the end, track a number of metrics such as leadership position in community, coding/non-coding contributions, emotional connection to project, social connections made with community, knowledge of code base, etc.

Personally, my prior probability is that the no-ai group would likely still be ahead overall.
by trod1234 | 2025-07-10 17:59:24+00:00
My read of this is:

There were parts of the process that check whether its a valid proof, which were previously thought to be equivalent, which were in fact not the case. Computer scientists involved knew of cases where this was not the case but moved ahead anyway because no one would design the systems in the ways the attacks would work.

Attacks only get better.

The original process included the original hash in such systems inputs, but also allowed additional malicious features which could be included to rearrange the output in a way that passes the proof scheme checks despite it being incorrect.

By placing a constraint on input entropy they believe they've mitigated the issue, but it also breaks many applications; with no good alternative.

Imo, This is a weak assertion considering finite fields are used. The title is really misleading.

It should be "Fiat Shamir is broken"; Practical attacks.

These are finite fields so the token output generated doesn't necessarily correspond to the specific path taken.

There may be an infinite many paths, and computation has classical problems of computer science with being able to automatically derive decidable paths from tokens given; potentially leading to the same issues of discovering simple breaks.
by pfdietz | 2025-07-10 18:00:01+00:00
The interesting problem here would be how do you produce a robust parse tree for invalid inputs, in the sense of stably parsing large sections of the text in ways that don't change too much. The tree would have to be an extension of an actual parse tree, with nodes indicating sections that couldn't be fully parsed or had errors. The diff algorithm would have to also be robust in the face of such error nodes.

For the parsing problem, maybe something like Early's algorithm that tries to minimize an error term?

You need this kind of robust parser for languages with preprocessors.
by bluecalm | 2025-07-10 18:00:10+00:00
My point is that it's written in a way that makes malicious compliance possible.

One way to improve it would be to make it clear you can't require any consent before providing your service. It's either necessary or don't ask for it. As it is in Poland you are now served a long form at every opportunity and you have to agree to some part of it or be denied service. Online or when calling you get to listen to a long formula about privacy policy and who administer your data every time you call a bank or most other institutions. It made everyday life worse.

There is no way for me to verify that a given entity is following the contract anyway. GDPR could easily be transparent for consumers/clients. Instead it resulted in additional burden.
by vitaflo | 2025-07-10 18:00:29+00:00
From talking to many parents they want to give them activities so their kids aren’t bored or sitting inside on their phones all day. Sports is one of those things and lets them also be with other kids.

The problem is kids being bored can be a good thing but they are never allowed to be. When I was a kid the internet didn’t even exist let alone cell phones and the only rule was “be home before sundown”. Kids now have way too many distractions and structure and are never given the ability to explore their own world on their own. It’s been manufactured for them.
by FinnKuhn | 2025-07-10 18:00:36+00:00
This doesn't look unsafe though, just inconvenient.

Both in the US[1] and UK[2] you can find bridges with actual 90-degree angles. The one in India[3] is more like 75 degrees.

[1]https://maps.app.goo.gl/3CBqVHbVEtonHjcr9 [2]https://maps.app.goo.gl/8cVB44VDJRPadY6s6 [3]https://maps.app.goo.gl/ikPSmLEGYwVJLqDz7
by onlyrealcuzzo | 2025-07-10 18:00:50+00:00
How were "experienced engineers" defined?

I've found AI to be quite helpful in pointing me in the right direction when navigating an entirely new code-base.

When it's code I already know like the back of my hand, it's not super helpful, other than maybe doing a few automated tasks like refactoring, where there have already been some good tools for a while.