MurphyA/hacker-news huggingface dataset:
by didibus | 2025-11-14 20:46:20+00:00
Aren't Californian almonds like 80% of the world's production?

Is the US AI data-centers producing 80% of the world's IT ?

I ask legitimately, I think that would already make it more apples to apples.

Also if you ask me personally, I'd rather have almonds than cloud AI compute. Imagine a future 100 years from now, we killed the almonds, never to be enjoyed ever again by future generations... Or people don't have cloud AI compute. It's personal, but I'd be more sad that I'd never get to experience the taste of an almond and all the cuisine that comes with it.
by tim333 | 2025-11-14 20:46:22+00:00
Well, we'll see. Google have all my info really, including emails and photos and in ~25 years of using them I haven't heard of anything particularly bad happening. They used to have my full location data but offloaded it to my device slightly to my annoyance so they don't get hassled by law enforcement asking where people are. I think people sometimes people worry about the wrong stuff.
by observationist | 2025-11-14 20:47:11+00:00
It's not fair to use prompts tailored to a particular model when doing comparisons like this - one shot results that generalize across a domain demonstrate solid knowledge of the domain. You can use prompting and context hacking to get any particular model to behave pseudo-competently in almost any domain, even the tiny <1B models, for some set of questions. You could include an entire framework and model for rendering clocks and times that allowed all 9 models to perform fairly well.

This experiment, however, clearly states the goal with this prompt: `Create HTML/CSS of an analog clock showing ${time}. Include numbers (or numerals) if you wish, and have a CSS animated second hand. Make it responsive and use a white background. Return ONLY the HTML/CSS code with no markdown formatting.`

An LLM should be able to interpret that, and should be able to perform a wide range of tasks in that same style - countdown timers, clocks, calendars, floating quote bubble cycling through list of 100 pithy quotations, etc. Individual, clearly defined elements should have complex representations in latent space that correspond to the human understanding of those elements. Tasks and operations and goals should likewise align with our understanding. Qwen 2.5 and some others clearly aren't modeling clocks very well, or maybe the html/css rendering latents are broken. If you pick a semantic axis(like analog clocks), you can run a suite of tests to demonstrate their understanding by using limited one-shot interactions.

Reasoning models can adapt on the fly, and are capable of cheating - one shots might have crappy representations for some contexts, but after a lot of repetition and refinement, as long as there's a stable, well represented proxy for quality somewhere in the semantics it understands, it can deconstruct a task to fundamentals and eventually reach high quality output.

These type of tests also allow us to identify mode collapses - you can use complex sophisticated prompting to get most image models to produce accurate analog clocks displaying any time, but in the simple one shot tests, the models tend to only be able to produce the time 10:10, and you'll get wild artifacts and distortions if you try to force any other configuration of hands.

Image models are so bad at hands that they couldn't even get clock hands right, until recently anyway. Nano banana and some other models are much better at avoiding mode collapses, and can traverse complex and sophisticated compositions smoothly. You want that same sort of semantic generalization in text generating models, so hopefully some of the techniques cross over to other modalities.

I keep hoping they'll be able to use SAE or some form of analysis on static weight distributions in order to uncover some sort of structural feature of mode collapse, with a taxonomy of different failure modes and causes, like limited data, or corrupt/poisoned data, and so on. Seems like if you had that, you could deliberately iterate on, correct issues, or generate supporting training material to offset big distortions in a model.
by redwood | 2025-11-14 20:48:40+00:00
In theory but that's where the accounting games come in: how you choose to capitalize the expenses over what time horizon and how you recognize the returns are extremely relevant. While you might point out that there are commonly accepted accounting principles, you'll also note that people use all kinds of different approaches for different types of businesses were where they argue the commonly accepted model is not quite the right fit the shape of the business
by jamilton | 2025-11-14 20:48:44+00:00
I know this has been said many times before, but I wonder why this is such a common outcome. Maybe from negative outcomes being underrepresented in the training data? Maybe that plus being something slightly niche and complex?

The screenshot method not working is unsurprising to me, VLLMs visual reasoning is very bad with details because they (as far as I understand) do not really have access to those details, just the image embedding and maybe an OCR'd transcript.
by teddyh | 2025-11-14 20:49:23+00:00
> OK, but according to ChatGPT

Shut up.

If that’s the best argument you have, then you’ve got nothing.

I, like most people, don’t come here to see AI slop opinions. We can get that approximately everywhere else. We don’t need HN to be filled with AI confabulations, and if it were to happen, we would leave.
by ghm2180 | 2025-11-14 20:49:52+00:00
I would offer a stronger more pointed observation, ofen the problem in building a good classifier is having good negative examples. More generally how a classifier identify good negatives is a function of:

1. Data collection technique.

2. Data annotation(labelling).

3. Classfier can learn on your "good" negatives — quantitaively depending on the machine residuals/margin/contrastive/triplet losses — i.e. learn the difference between a negative and positive for a classifier at train time and the optimization minima is higher than at test time.

4. Calibration/Reranking and other Post Processing.

My guess is that they hit a sweet spot with the first 3 techniques.
by sklarsa | 2025-11-14 20:50:06+00:00
I've been using Incus containers (not VMs) for running tests against a "real" OS and it's been an absolute game changer for me. It's granted me the ability to simultaneously spin up-and-down a plethora of fresh OSes on my local dev machine, which I then use as testing targets for components of my codebase that require Docker or systemd. With traditional containers, it's tricky to mimic those capabilities as they would exist on a normal VM.

Because both my project and Incus are written in Go, orchestrating Incus resources in my test code has been pretty seamless. And with "ephemeral" containers, if things start to get out of hand, I just need to stop the container to clean it up. Much easier than a 2-step process like it usually is.

Looking forward to seeing what's to come in IncusOS!
by chemotaxis | 2025-11-14 20:50:11+00:00
I wonder if it's because we mean different things by generalization.

For text, "generalization" is still "generate text that conforms to all the usual rules of the language". For images of 13-hour clock faces, we're explicitly asking the LLM to violate the inferred rules of the universe.

I think a good analogy would be asking an LLM to write in English, except the word "the" now means "purple". They will struggle to adhere to this prompt in a conversation.
by esbranson | 2025-11-14 20:50:29+00:00
As here, saying the Islamic State was a criminal organization may have been true, but once they were declared to be organized armed groups participating in an non-international armed conflict, they were subject to lawful killing. Should they give clear indication that they have placed themselves hors de combat (surrendered), they are subject to life imprisonment. Though it'd probably be safer to set their own boat on fire and jump off before heading out to sea.
by bglusman | 2025-11-14 20:50:45+00:00
We can't know how much is about the prompt though and how much is just stochastic randomness in the behavior of that model on that prompt, right? I mean, even given identical prompts, even at temp 0, models don't always behave identically.... at least, as far as I know? Some of the reasons why are I think still a research question, but I think its a fact nonetheless.
by eszed | 2025-11-14 20:50:45+00:00
Because it becomes harder to "hide" things - like, the provisions being bargained for, or politicians' actual convictions about particular measures. There are items which now get passed in omnibus bills, bargained for behind closed doors by leadership, which couldn't (whip votes as ye may) be passed in up-or-down votes on their own merits. Those are, in my opinion, corrupt bargains, and shouldn't happen. I like legislative horse-trading - it's an important part of the democratic process - but I'd like it to be open and above board.
by simoncion | 2025-11-14 20:51:20+00:00
> There is no hope of excessive weight gain here.

Uh. We can pretty confidently say that ancient civilizations had fat folks, too.

Also, butter, processed animal fats (such as lard), fatty meats... none of these are recent inventions, and they're all good at helping you to grow fat. I feel very confident in claiming that they (or things functionally just like them) have been around for a thousand years, and I expect that they've been around for several thousand.
by thewebguyd | 2025-11-14 20:52:03+00:00
> where they don't see any way out but to keep going and hope that it works out for them somehow, for some reason.

That's the core issue. No one wants to fail early or fail fast anymore. It's "lets stick to our guns and push this thing hard and far until it actually starts working for us."

Sometimes the time just isn't right for a particular technology. You put it out there, try for a little bit, and if it fails, it fails. Move on.

You don't keep investing in your failure while telling your users "You think you don't want this, but trust us, you actually do."
by theevilsharpie | 2025-11-14 20:52:14+00:00
Dial-up modems can transfer a 4K HDR video file, or any other arbitrary data.

It obviously wouldn't have the bandwidth to do so in a way that would make a real-time stream feasible, but it doesn't involve any leap of logic to conclude that a higher bandwidth link means being able to transfer more data within a given period of time, which would eventually enable use cases that weren't feasible before.

In contrast, you could throw an essentially unlimited amount of hardware at LLMs, and that still wouldn't mean that they would be able to achieve AGI, because there's no clear mechanism for how they would do so.
by Scubabear68 | 2025-11-14 20:52:30+00:00
For me, the big miss with Postgres Aurora RDS was costs. We had some queries that did a fair amount of I/O in a way that would not normally be a problem, but in the Aurora Postgres RDS world that I/O was crazy expensive. A couple of fuzzy queries blew costs up to over $3,000/month for a database that should have cost maybe $50-$100/month. And this was for a dataset of only about 15 million rows without anything crazy in them.
by thunky | 2025-11-14 20:52:41+00:00
> The very fact that you merely see this as "a new productivity source" support my sense of the disconnect I mentioned.

Help me understand since my "disconnect" seems to be ruffling your feathers...

What is the correct way to refer to a new tool that is being used to increase productivity?

Or maybe you don't have a problem with the term I used but at the suggestion that someone might find the tool to be useful?

Or is it that I'm suggesting that humans are often unreliable?

I'm having a hard time understanding what is controversial about this.

Machines are better than humans at some things. Humans are better than machines at some things.

Hope you don't find that too offensive.
by didibus | 2025-11-14 20:52:56+00:00
The issue is how you can also easily imagine a lot of negative.

Neither those imagining positives or those imagining negatives can really prove to the other that they're right. This becomes the source of the conflict.

If you predicted the negative outcomes, you'd also oppose it. If you believe the outcomes will be positive, you'd be a proponent.

Now it also happens that a lot of those predicting positive outcomes are currently making money off the development of AI and so have an investment in its continued enablement. Which muddies things even more, because it's an obvious bias.
by AgentME | 2025-11-14 20:53:44+00:00
The reported case about water wells running dry had to do with issues in construction rather than anything about the data center's regular operation:

> But the reason their taps ran dry (which the article itself says) was entirely because of sediment buildup in groundwater from construction. It had nothing to do with the data center’s normal operations (it hadn’t begun operating yet, and doesn’t even draw from local groundwater). The residents were wronged by Meta here and deserve compensation, but this is not an example of a data center’s water demand harming a local population.

https://andymasley.substack.com/p/the-ai-water-issue-is-fake...
by ticulatedspline | 2025-11-14 20:53:45+00:00
This is cool, interesting to see how consistent some models are (both in success and failure)

I tried gpt-oss-20b (my go-to local) and it looks ok though not very accurate. It decided to omit numbers. It also took 4500 tokens while thinking.

I'd be interested in seeing it with some more token leeway as well as comparing two or more similar prompts. like using "current time" instead of "${time}" and being more prescriptive about including numbers
by hbn | 2025-11-14 20:53:49+00:00
They really need to work on making an easier transition from Chrome if they care about converting users. Add an out of the box (preferably default) option to make the tabs look normal and not ugly like they are now. Change the private window shortcut to Cmd(Ctrl)+Shift+N like every browser has standardized on. Also I swear they override the normal text input behaviour, specifically if I'm using option+left/right arrows to jump between tokens in the URL bar, it jumps my cursor to unexpected places, like at the wrong side of periods.

Tangentially related but I also find their devtools very lacking compared to Chrome's. They should straight up rip off everything Chrome does in that department.
by Animats | 2025-11-14 20:53:55+00:00
Article doesn't say much. Nor does the Anthropic article.

AI as a power tool for attackers does provide additional attack power. Even if it can't do anything new, it can do a lot of existing stuff and follow up on what's working. Which is often enough to get the job done. Plus, like all programs, it's fast, patient, and can be parallelized. "Agentic AI" provided with a set of hacking tools it can run is somewhat scary.
by dash2 | 2025-11-14 20:53:59+00:00
Cool story. Got evidence? I googled for a minute and found this official document:

"Thus, as shown in Figure 3, the modal share of rail has oscillated between 7 and 10% for almost 30 years, with a low point reached in 1995 following several years of crisis, and a maximum reached in 2011."

https://www.autorite-transports.fr/wp-content/uploads/2018/0...

Here's Figure 3. https://share.google/images/58s3sPR3bQ1LbQEHT . Where's the increase?
by bloak | 2025-11-14 20:54:04+00:00
But perhaps all languages have a countably infinite number of words, in which case that proof doesn't work. (In English we have: legless, leglessness, leglessnessless, leglessnesslessness, ... It's not a great example, but it's good enough.)

Even if the number of words in a language were finite we wouldn't have a reasonable way of counting them. There are too many kinds of fuzziness involved in deciding what counts as a "word" and you can't ignore the borderline cases because the borderline cases vastly outnumber the straightforward cases.
by jimbokun | 2025-11-14 20:54:24+00:00
Indeed.

Occasionally I will see posted the beautiful school lunches given to children in many European countries. Nutritious, appetizing, made from scratch.

These lunch ladies are the ones fighting to be allowed to do the same things for the children in their communities in the USA. But getting ham strung by the whims of federal politics and the crippling fear that someone somewhere might be given something for free they could have paid for themselves.

More power to the Lunch Ladies.
by d3Xt3r | 2025-11-14 20:54:41+00:00
I can't speak for the Intel ones but I hear the AMD Zen 4 and later ones have been solid. I have a UM780 XTX that I've been using in a homelab setup and it's been rock solid. Zero issues with Linux (Arch btw), zero hardware issues after nearly 2 years of 24x7 operation.
by phba | 2025-11-14 20:54:58+00:00
Adding to that from https://en.wikipedia.org/wiki/Thorn_(organization):

> Thorn works with a group of technology partners who serve the organization as members of the Technology Task Force. The goal of the program includes developing technological barriers and initiatives to ensure the safety of children online and deter sexual predators on the Internet.

> Various corporate members of the task force include Facebook, Google, Irdeto, Microsoft, Mozilla, Palantir, Salesforce Foundation, Symantec, and Twitter.

Apparently Thorn scratched that list from their current website, but the Wiki page has an archive link.
by tstrimple | 2025-11-14 20:55:26+00:00
You're not wrong. We routinely ignore things like this: https://www.theguardian.com/us-news/2017/sep/05/hookworm-low...

> The average income is just $18,046 (£13,850) a year, and almost a third of the population live below the official US poverty line. The most elementary waste disposal infrastructure is often non-existent.

> Some 73% of residents included in the Baylor survey reported that they had been exposed to raw sewage washing back into their homes as a result of faulty septic tanks or waste pipes becoming overwhelmed in torrential rains.

...

> An eight-year-old child was sitting on the stoop of one of the trailers. Below him a white pipe ran from his house, across the yard just a few feet away from a basketball hoop, and into a copse of pine and sweet gum trees.

> The pipe was cracked in several places and stopped just inside the copse, barely 30ft from the house, dripping ooze into a viscous pool the color of oil. Directly above the sewage pool, a separate narrow-gauge pipe ran up to the house, which turned out to be the main channel carrying drinking water to the residents.

> The open sewer was festooned with mosquitoes, and a long cordon of ants could be seen trailing along the waste pipe from the house. At the end of the pool nearest the house the treacly fluid was glistening in the dappled sunlight – a closer look revealed that it was actually moving, its human effluence heaving and churning with thousands of worms.
by aljgz | 2025-11-14 20:55:34+00:00
This is going to be disliked, but it's my opinion:

I believe being hardline on the organizations and products that actually respect users and choices leads to much worse outcome.

I'm using Firefox, Edge, and Chrome on phone and desktop. My main browser is Firefox on both, and I use the two others only when needed. I trust Mozilla to be more aligned with my needs than any company that creates a free tool to keep users in their ecosystem. Those companies are doing what they are supposed to do, and as a person, it's my responsibility to use what aligns with my values. But it's important to understand that I belong to one of the many nieche types of users, and if I expect Mozilla to only target my nieche, the userbase will shrink so much that it will be unsustainable, sounds familiar?

So, as a long-time user of Firefox, I generally and cautiosly trust Mozilla, I support them when they try new features while keeping the user in control, and I don't think the evolution of products have to be stopped because some of us are too stongly attached to the old ways.
by elcritch | 2025-11-14 20:55:38+00:00
Exactly when a group like Hamas controls your home and brutally executes any dissidents or even suspected dissidents in public on a routine basis it’s difficult for any individual to fight back [1], almost worse is torture and maiming of anyone who even hints at “disloyalty” [2].

In such situations though encrypted messaging becomes crucial, but it’d be hard to hide.

Note: links are rather disturbing.

1: https://www.cnn.com/2025/10/15/world/video/hamas-killings-ga... 2: https://x.com/afalkhatib/status/1979752415973834965
by poszlem | 2025-11-14 20:55:45+00:00
I’m not sure if it's just me, but I've also noticed Claude becoming even more lazy. For example, I've asked it several times to fix my tests. It'll fix four or five of them, then start struggling with the next couple, and suddenly declare something like: "All done, fixed 5 out of 10 tests. I can’t fix the remaining ones", followed by a long, convoluted explanation about why that’s actually a good thing.
by hdheidby | 2025-11-14 20:56:27+00:00
Flint has all these problems all because of the politicians. The city is insanely polluted. The waters are polluted, the air polluted, the trains aged and polluting, the works. Guess that's what happens when you have a country run by uneducated democrats and republicans who also have a hand in the extreme exploitation of the land. For those not in the know, the bureaucracy controls all businesses through trusts overregulation, which overexploit water sources for financialization use (ai, bitcoin, fracking, etc.) and manufacturing and industry. Heck, blackrock is the largest and controls almost everything in america.
by dietrichepp | 2025-11-14 20:56:28+00:00
Somebody wanted to set breakpoints in their C code by marking them with a comment (note “d” for “debugger”):

//d

You can get a list of them with a single Awk line.

awk -F'//d[[:space:]]*' 'NF > 1 {print FILENAME ":" FNR " " $2}' source/*.c

You can even create a GDB script, pretty easily.

(IMO, easier still to configure your editor to support breakpoints, but I’m not the one who chose to do it this way.)
by porphyra | 2025-11-14 20:58:07+00:00
LLMs can't "look" at the rendered HTML output to see if what they generated makes sense or not. But there ought to be a way to do that right? To let the model iterate until what it generates looks right.

Currently, at work, I'm using Cursor for something that has an OpenGL visualization program. It's incredibly frustrating trying to describe bugs to the AI because it is completely blind. Like I just wanna tell it "there's no line connecting these two points but there ought to be one!" or "your polygon is obviously malformed as it is missing a bunch of points and intersects itself" but it's impossible. I end up having to make the AI add debug prints to, say, print out the position of each vertex, in order to convince it that it has a bug. Very high friction and annoying!!!
by cholantesh | 2025-11-14 20:58:37+00:00
This was a great read, and the previous post in the series. I see a lot of very convincing arguments here (https://maximullaris.com/awk.html#why) but for me one of the biggest points in favour of python (and I say this is someone who, for learning, will always just reach for C++ because of my muscle memory) is its eminent readability. If I'm writing a script, quite a lot of the time, it's meant not just for myself but for my peers to use with some degree of regularity. I feel pretty confident that there would be much more operational overhead and a lot of time spent explaining internals with awk than with python.