MurphyA/hacker-news huggingface dataset:
by therealpygon | 2025-08-22 04:23:33+00:00
I would consider reading the actual report more closely rather than an article of questionable accuracy. For example:

> “For instance, an employee can adjust based on new instructions, previous mistakes, and situational needs. A generative AI model cannot carry that memory across tasks unless retrained.”

This is factually false; that is exactly what memory, knowledge, and context can do with no retraining. Not having completely solved self adjustment is not a barrier, merely a hurdle already currently in research. Imagine if, like the human brain, an LLM were to apply training cases identified throughout the day while it “slept”; the author seems to think this would be a massive undertaking of “retraining”. And sorry, if you’ve worked with many of the same types of employees I have over there years, you’d already know that the suggestion employees are more easily adaptable, will remember across tasks, and are good at adjusting to situational needs, can be laughable and even detrimental to think, depending on the person.

The statement seems to be based more on the complaint of a lawyer who has no actual AI technical expertise; hardly the best source for what things AI can and cannot do “currently”. It’s useful to consider that almost all of the subjective opinions expressed in this report come from, effectively, 300 or so (maybe less) individuals, and that it isn’t all that easy to distinguish between the findings that are truly fact-based or opinion-based, especially with the linked post.

It is also important to note that this report seems to focus more on the feedback and data from CEOs who look at P&L, not intrinsic or unquantified values. How do you directly quantify a developer fixing 3 bugs instead of 1 in your internal tool? Unless there are layoffs attributed to this specifically, and not “market changes” or general “reorganizations”, how is this quantified? There are a million things AI might do in the future that may not have a massive, or any, clear return on investment. If I buy a better shovel that saves me an hour on digging a trench in my own backyard, how much money did that save me?

GDP is 29.2t, of which an additional google would find that U.S. labor accounts for an estimated 18.5t. 2.2% of 18.5t, or 29.2t, is still not 61m. In most cases, if the simple part of the math doesn’t fit, there are potentially some bigger logic mistakes at play.

Best of luck on your understanding. As I said, I’d suggest maybe starting with direct statements from factual sources and the report rather than those the author (or you) interpreted.
by steve_adams_86 | 2025-08-22 04:24:20+00:00
I wrote so many sophisticated, nearly magical Selenium helpers in PHP for massive test suites for a fairly prominent website in the early 2000s. I remember simultaneously having a sense of great accomplishment and deep shame and frustration, haha. It was so hard to build good testing tools. I think we did alright, though.

These days I write automated UI tests with barely a second thought. It has gotten so much easier.

It turns out it came out in 2004. I had no idea I was working with cutting edge testing software at the time. That also explains why it was so rough on the edges and there were so few resources to draw on to get it working better in edge cases. Although it was kind of brutal, I think selenium taught me a ton about asynchronicity and concurrency. That was probably good for my career
by h4ck_th3_pl4n3t | 2025-08-22 04:25:51+00:00
Regarding SSL: just make sure you are using macOS 10.9.3, earlier versions (all of them, and I mean, all since the 80s) never had a working SSL implementation.

For more, read about the GOTO FAIL bug: https://nvd.nist.gov/vuln/detail/cve-2014-1266
by raggi | 2025-08-22 04:26:31+00:00
"an LLM" could imply an LLM of any size, for sufficiently small or focused training sets an LLM may not be transformative. There is some scale at which the volume and diversity of training data and intricacy of abstraction moves away from something you could reasonably consider solely memorization - there's a separate issue of reproduction though.

"novel" here depends on what you mean. Could an LLM produce output that is unique that both it and no one else has seen before, possibly yes. Could that output have perceived or emotional value to people, sure. Related challenge: Is a random encryption key generated by a csprng novel?

In the case of the US copyright office, if there wasn't sufficient human involvement in the production then the output is not copyrightable and how "novel" it is does not matter - but that doesn't necessarily impact a prior production by a human that is (whether a copy or not). Novel also only matters in a subset of the many fractured areas of copyright laws affecting the space of this form of digital replication. The copyright office wrote: https://www.copyright.gov/ai/Copyright-and-Artificial-Intell....

Where I imagine this approximately ends up is some set of tests that are oriented around how relevant to the whole the "copy" is, that is, it may not matter whether the method of production involved "copying", but may more matter if the whole works in which it is included are at large a copy, or, if the area contested as a copy, if it could be replaced with something novel, and it is a small enough piece of the whole, then it may not be able to meet some bar of material value to the whole to be relevant - that there is no harmful infringement, or similarly could cross into some notion of fair use.

I don't see much sanity in a world where small snippets become an issue. I think if models were regularly producing thousands of tokens of exactly duplicate content that's probably an issue.

I've not seen evidence of the latter outside of research that very deliberately performs active search for high probability cases (such as building suffix tree indices over training sets then searching for outputs based on guidance from the index). That's very different from arbitrary work prompts doing the same, and the models have various defensive trainings and wrappings attempting to further minimize reproductive behavior. On the one hand you have research metrics like 3.6 bits per parameter of recoverable input, on the other hand that represents a very small slice of the training set, and many such reproductions requiring strongly crafted and long prompts - meaning that for arbitrary real world interaction the chance of large scale overlap is small.
by simoncion | 2025-08-22 04:27:06+00:00
> [GCP] can't compete with Azure...

Based on my professional experience, when we ignore cost [0] my ranking of the big three for "plain old computing" (that is, just compute, networking, and storage workloads) is AWS, GCP, Azure.

Azure is very, very flaky. Things often break for no clear reason, or things are changed in unexpected ways without warning, and then quietly reverted back. [1] I used to say that the only consistently good thing about Azure was that you could throw an entire Subscription in the trash and have everything in there be destroyed... but even that has become intermittently unreliable!

Given how godawful Azure is, I expect that companies use it either because they think they need it for its AD integration, or because they get very deep discounts on Windows licenses for VMs running on Azure.

[0] Both because I never had to pay the bills and (I assume) our cost estimators were always full of lies because the tooling (and I) had no idea what discounts we had negotiated.

[1] (Don't) ask me about the time Azure added in some sort of multi-minute "cooldown" time for reuse of a statically-assigned IP address that was assigned to a VM that Azure reported was completely destroyed, and we were attempting to assign to a brand new VM. Creation of the new VM kept failing with some wacky error. Azure support was clueless, and the problem vanished and came back several times over the course of a year.
Ask HN: Would an AI chatbot that searches guides/wikis in-game be useful?
by weizhenchu | 2025-08-22 04:29:01+00:00
would an in-game AI chatbot that (1) answers quick guide questions (best gear, boss tactics) and (2) opens/pins wiki pages on top of the game help you look up info while playing?

I feel like it could save time compared to watching multiple YouTube videos, but I haven't seen a tool that combines both functions widely used by players yet.

when I was playing Monster Hunter I asked ChatGPT for a gear recommendation — some of its suggestions differed from what I saw in videos. That made me wonder how much people would trust an AI versus curated wiki/video sources.

A few things I'm curious about:

• Would you trust an AI's build/gear suggestions, or prefer direct wiki citations? (Would video citations help?)

• Would you want an AI companion that cheers you the first time you take down a boss, or just chats while you play — or would that get annoying?
by RossBencina | 2025-08-22 04:30:13+00:00
Thanks for pointing that out. This is news to me. uv has been on my radar for a while and was considering switching to it as a better dependency manager. I didn't realise that it had ambitions beyond being "a better pip." At face value this is a real turn-off. Definitely violates the "do one thing and do it well" principle and puts it squarely in the "does complicated things that I want to avoid" (like poetry) category.
by dang | 2025-08-22 04:31:15+00:00
By generic tangent I just meant we ended up arguing about Napoleon of all things! and the flamebait part was the sarcastic/snarky bit.

But I totally get how the GP comment landed the way you describe, but that's why we have guidelines like these:

"Please don't pick the most provocative thing in an article or post to complain about in the thread. Find something interesting to respond to instead."

and (repeating this one) "Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith."

Applying those to the GP comment (https://news.ycombinator.com/item?id=44974675), while it's true that the first sentence could sound like chest-beating, the rest of the comment was making an interesting point about risk tolerance.

The 'strongest plausible interpretation' might go something like this: "Even if the article is correct that 95% of companies are seeing zero return on AI spend so far, that by no means proves that they're on the wrong track. With a major technical wave like AI, it's to be expected that early efforts will involve a lot of losses. Long-term success may require taking early risk, and those with lesser risk tolerance, who aren't willing to sustain the losses associated with these pathfinding efforts, may find themselves losing out in the long run."

I have no idea whether that's right or not but it would make for a more interesting and less hostile conversation! which is basically what we're shooting for here.
by apimade | 2025-08-22 04:31:20+00:00
How is this any different from outsourcing attestation to a third-party?

Considering most regulations require the party reliant on validating the data to store the source or proof, it seems like the only plausible mass-deployment of this approach would be US state-based ID systems specifically for drinking/anonymous patrons. That excludes driving, gambling, restricted entertainment, medicine etc.

Countries wouldn’t adopt it nationally or federally, they’d copy it.

Maybe online games or communities that want to keep age of users above a set amount? I don’t see them paying for this though.

Am I missing something?
by runarberg | 2025-08-22 04:31:41+00:00
In my circles there is a saying: If you are at a party, and somebody brings a Nazi to the party, and nobody kicks the Nazi out of the party, then you are at a Nazi party.

Sequoia Industries were made aware that one of their partners was a racist Islamophobe, they opted not to do anything about it, and allowed him to continue being a racist Islamophobe partner with Sequoia, one can only assume that Sequoia Industries is an Islamophobic investor. I personally see people knowingly accepting money from racist Islamophobes as being a problem, and I would rather nobody did that.
by tomhow | 2025-08-22 04:32:33+00:00
Your comment in the other thread got some downvoting and heated replies, I think just because it's considered impolite - rightly or wrongly in your case - to promote your own product in someone else's thread.

We're always happy to showcase cool projects on HN, and your project seems cool. You can email us - hn@ycombinator.com – and we'll work with you to come up with a Show HN post that gives your project the best chance at being seen and appreciated by the community.
by shkkmo | 2025-08-22 04:33:04+00:00
The actual article is really interesting and informative.

I've yet to see a single comment here that referrences any of the facts or ideas presented in the article. It is entirely composed of people responding to the headline.

I found the section on MERS particularly interesting. Minimal training on how to asses inconsistent assements between countries, complete failure to mention massive laundering scandals at banks in the asses countries... And this is the primary method of international oversight to see if these systems work and are implemented consistently.
by Imustaskforhelp | 2025-08-22 04:33:10+00:00
Hey man, I was seeing your comments and you do seem to respond to each and everyone nicely regarding this sudo shenanigan.

I think that you have removed sudo so this is nice, my suggestion is pretty similar to that of pxc (basically determine different distros and use them as that)

I wonder if we will ever get a working universal package manager in linux, to me flatpak genuinely makes the most sense even sometimes for cli but flatpak isn't built for cli unlike snap which both support cli and gui but snap is proprietory.
by olyjohn | 2025-08-22 04:35:19+00:00
Maybe he can work on making Facebook not be such a piece of shit. I feel like he got his one lucky break and should just give up on trying to make more money. He already has billions. Is he proud of Facebook as a product? Because as a user it feels sluggish, buggy, inconsistent, and just full of low quality trash. I would be embarrassed if I was him.
by ahazred8ta | 2025-08-22 04:36:07+00:00
Raymond Smullyan had many logic puzzles, including the idol that always lies and the idol that always tells the truth. https://duckduckgo.com/?q=raymond+smullyan+puzzles&ia=web

https://www.explainxkcd.com/wiki/index.php/246:_Labyrinth_Pu...

There is a funny story about the idols. https://astralcodexten.substack.com/p/idol-words
by lossolo | 2025-08-22 04:37:24+00:00
Without a provable hold out, claim that "large models do fine on unseen patterns" is unfalsifiable. In controlled from scratch training, CoT performance collapses under modest distribution shift, even with plausible chains. If you have results where the transformation family is provably excluded from training and a large model still shows robust CoT, please share them. Otherwise this paper’s claim stands for the regime it tests.
by recursivecaveat | 2025-08-22 04:37:46+00:00
Read it like: "any amount of gambling may lose you money". The damage is proportional (and to some extent probabilistic) to the amount. As opposed to say: "any amount of horizontal speed may put you into orbit", which is not true ofc, there's a floor below which you will come right back down. Its a distinction between something which has non-zero levels that have no effect and something where the effect scales all the way from 0. It's a statement about populations obviously, diagnostics have finite precision etc etc, but it holds true as far as we know.
by wahnfrieden | 2025-08-22 04:39:00+00:00
Can't do that with state of the art LLMs and no sign of that changing (as they like to retain control over model behaviors). I would not want to use or contribute to a project that embraces LLMs yet disallows leading models.

Besides, the output of an LLM is not really any more trustworthy (even if reproducible) than the contribution of an anonymous actor. Both require review of outputs. Reproducibility of output from prompt doesn't mean that the output followed a traceable logic such that you can skip a full manual code review as with your mass renaming example. LLMs produce antagonistic output from innocuous prompting from time to time, too.
by alexey-salmin | 2025-08-22 04:42:21+00:00
> People pay their babysitters or gardeners under the table, or transact with friends and family without reporting income, and this is a huge amount of lost tax revenue.

This money was already taxed when the individual who pays the babysitter received it. It's questionable whether the society as a whole benefits from taxing babysitters.

> Heck, in the era of ZIRP, many economists were seriously talking about negative interest rates. You can't do that if a person has the option of taking out cash and hiding it under the mattress.

I'm not sure you'll gain much support for bespoke policies like that. Just reading this passage made me feel an urge to hide some cash under the mattress.
Show HN: BetterAEO – Measure AI search readiness and get AI recommendations
by mukul767 | 2025-08-22 04:44:58+00:00
Hi HN,

I’m launching betterAEO, a tool we’ve been building to help websites understand and improve how they appear in AI-generated answers from tools like ChatGPT, Google AI Overviews, Perplexity, and Claude.

The motivation came from noticing that traditional SEO metrics don’t capture how AI engines select and summarize content. Sites with strong SEO can still be invisible in AI summaries, meaning users get answers that favor competitors even when your content is high quality.

betterAEO lets you:

- Measure your site’s AI-readiness with a single AEO Score - Identify gaps in schema, snippets, trust signals, topical authority, and answer formatting - Get AI-powered recommendations to fix or improve issues and boost visibility - Track progress over time with historical scoring and reporting

You can run single-page scans or full-site crawls. No signup is required for testing, and you can see results instantly: https://betteraeo.com

Background & Approach:

betterAEO was created based on extensive research, articles, and publicly available guidelines on Answer Engine Optimization (AEO) as of today. It is constantly evolving, adapting to new AI signals, trends, and best practices as they emerge. The system evaluates multiple signals, including structured data, content alignment with search intent, technical crawlability, answer presentation, and more, and provides actionable recommendations powered by AI.

We built this while working with SEO teams and content marketers frustrated that AI engines increasingly control visibility, but there was no structured framework to measure or optimize for it.

I’d love feedback from the HN community, especially on the usefulness of the AEO score, the AI recommendations, and any additional signals you’d want to see measured.
by tomhow | 2025-08-22 04:45:08+00:00
We (and you) can never be sure why people downvote things, and whilst I think the parent commenter was well-intentioned, I think perhaps the downvotes were due to the perception of intruding on someone else's comment/subthread with promotion of one's own project.

As for your own comments, you seem to have a campaign against HN going on. Yes the HN audience can be hard to understand sometimes, but on the whole it seems easy enough to do well here if you bring a generosity of spirit, and not so well if you bring combativeness and hostility.

The guidelines [1] make it clear what we're expecting, and the first two words of the "In Comments" section are "Be kind". If you start with that then you'll be on solid ground.

Regarding your username, we can't have usernames like this, because it effectively trolls the community every time you comment. So I've banned the account for now. You're welcome to register a new account with a normal username, or email us (hn@ycombinator.com ) asking us to change the username. We'd certainly be very happy to have you post interesting comments from your experience as a retired aerospace engineer or any other life experience you no doubt have.

[1] https://news.ycombinator.com/newsguidelines.html
by Imustaskforhelp | 2025-08-22 04:45:35+00:00
Yup which is why I think that the bubble is going to burst but what's surprising to me is that a lot of normal folks might be hurted by this too in the sense that s&p 500 has really concentrated its holding into companies believing into AI and the hype train seems to be coming to an end and the bubble coming near its burst.
by your_fin | 2025-08-22 04:46:53+00:00
Very roughly, Zig is much closer to Go in terms of philosophy of language design, where as Rust is much closer to Go in terms of the kind of software people write with it.

Go and Zig are both aggressively small and portable languages, and e.g. prefer explicit `defer` where Rust would handle cleanup implicitly. Unlike Zig, Go and Rust are both widely used and mature ~tier-1 languages with a deep bench of native libraries. However, there's a deep gulf between their philosophies. Go values a shallow learning curve and making code easy to write, whereas Rust values expressivness and making it hard to write bad code. This is not to say that you can't write Good Software in Go or make money with Rust code. However, the languages tend to attract distinct groups of programmers, and there's a reason flamewars crop up between their proponents with unusual regularity.

Zig is notable for targeting C's niches of having lots of structural flexibility in memory management, being good at calling C code, and building C programs. C is the native language of most truly powerful interfaces (operating systems, graphics, cryptography, databases, other programming languages, device drivers, low-level networking, etc.), and programs primarily concerned with interfacing with such things tend to be better off written in C. Working with C code is in the happy path for Zig, while other mainstream programming languages demand significant specialized expertise or wrapper libraries (1). Wrapper libraries aren't always available, are almost always less useful than the original, and only save you /some/ build headaches. This makes Zig a candidate in many situations where C - and /maybe/ C++ - are the only real competition.

In short:

If your interest is learning new ways to think about writing programs, Rust will give you a more distinct perspective than Zig. Like Go, there will be lots of libraries and guides to help you out.

If your interest is in learning to write a kind of program you couldn't in Go and think "closer to the metal", Zig is likely better suited to task.

(1): This isn't nearly as true for C++, but there's still enough impeadence mismatch between idiomatic C and C++ to invite interfacing bugs and wrapper libraries.
by DrewADesign | 2025-08-22 04:47:10+00:00
This video has some great footage of typical modern break-ins:

https://m.youtube.com/watch?v=iWWWyG5ZwG8

The lockpicking lawyer’s skillset is not what’s required for that task. 30 seconds would be an eternity for a typical thief to just be able to open a car door. Most of these thieves take maybe 15 seconds from approach to escape. Stealing a whole car is a little bit different but nobody is going to sit there with their adrenaline pumping, wearing gloves, in the middle of the night trying to pick a car door lock that won’t even disable the alarm.
by yuliyp | 2025-08-22 04:47:51+00:00
I think that simulation claim that pick-2 can send 2.5x as much traffic to most loaded vs least loaded is a bit misleading: if the load metric is completely random then that might happen. The more correlation to load the better. Also, rather than looking at the ratio of most loaded to least loaded, it might be better to look at the ratio of most loaded to average: that is, how much extra work did we send to a poor server. In that, pick-2 has an absolute cap of 2xing the load on a server.