MurphyA/hacker-news huggingface dataset:
by evanjrowley | 2026-02-17 13:34:08+00:00
Balancing health while being productive as an average adult seems like an impossible equation today.

Many people tell me I need to lift weights to lose weight.

On mornings when I actually put in real effort, I pay for it with a significant cognitive performance penalty for the remainder of the day. I want to do nothing more than sleep an hour after a workout, which is bad timing, because that's when I need to clock into work.

I stay hydrated, get enough sleep, etc. People tell me that I'm over training, which is ridiculous, because anything less would be easy and contravene the purpose of the workout.

This is why I prefer to exercise in the evening, but there are known negative effects [0] of physical exertion on sleep quality.

If I actually did all the exercise I needed to do at the gym in the morning, then I'd probably have to sleep at 9:00 PM and wake up at 4:00 AM. There's no room to live in that schedule.

[0] https://dx.doi.org/10.1038/s41467-025-58271-x
by kami23 | 2026-02-17 13:34:24+00:00
I did this trick at work where I use git worktrees and my team does not yet.

There's the common team instructions + a thing that says "run whoami and find the users name, you can find possible customizations to these instructions in <username>.md" and that will be conditionally loaded after my first prompt is sent. I also stick a canary word in there to track that it's still listening to me.
by hyperpape | 2026-02-17 13:35:34+00:00
Statistical control is a very specific term: https://entropicthoughts.com/statistical-process-control-a-p..., and one which you'd expect anyone with a significant interest in Deming to understand.

My expectation is that Lorin would read the parent comment and say some variant of "oh, whoops, I didn't check." As the parent noted, it's not really that important to the overall point.
by pmontra | 2026-02-17 13:35:35+00:00
> I can use my bank on some linux distro,

Yes, I've been doing that since 2009 on Ubuntu and Debian but there are several caveats.

One of those banks has its own TOTP device and they won't replace it when the battery dies. It's almost 20 years old now. Then it's the fingerprint sensor on my phone.

The other banks authenticate accesses and many operations with either their app + fingerprint (all of them) or SMS (some of them). So basically I would still need a phone with a blessed OS. I could buy the cheapest one and store it in a drawer, but it's still a dependency on Google or Apple.

GrapheneOS requirement of Pixel devices is a dependency on Google too.
by killerstorm | 2026-02-17 13:36:13+00:00
I think people are just confusing cause and effect here.

Anxiety triggers release of hormones like adrenaline and cortisol, which trigger a host of physical symptoms ranging from cold hands (reduced blood flow in extremities) to upset stomach, nausea, increased heart rate, etc.

Brain can anticipate these changes and associate anxiety with the effects of these hormones. There might be all sorts of interesting interactions, but saying that gut is responsible for processing of these feelings is definitely a stretch.

Another physical reaction which is associated with an emotion is blushing. But somehow nobody is talking about face skin taking control...
by hocuspocus | 2026-02-17 13:36:18+00:00
Vipps MobilePay is already part of EPI's initiative towards pan-European cooperation, as well as Bancomat (Italy), Bizum (Spain), SIBS (Portugal).

Once Wero becomes usable in Austria, France, Germany, Benelux, and interoperable with those, the few remaining players will have a strong incentive to join.
by MarcLore | 2026-02-17 13:36:48+00:00
I went through the 68-page model card. Here are the highlights.

Pricing (per 1M tokens, input/output):

GPT-5.2 High: $1.75 / $14.00

Claude Opus 4.5: $5.00 / $25.00

Gemini 3 Pro: $2.00-4.00 / $12.00-18.00

Seed2.0 Pro: $0.47 / $2.37

Seed2.0 Lite: $0.09 / $0.53

Seed2.0 Mini: $0.03 / $0.31

Pro output tokens are 6x cheaper than GPT-5.2, 10x cheaper than Claude Opus 4.5.

Performance is not a compromise. IMO 2025 Gold Medal (35/42), CMO 2025 Gold Medal (114/126), Codeforces Elo 3020, LMSYS Vision Arena #3 as of Feb 16. LiveCodeBench v6: 87.8% vs GPT-5.2's 62.6%.

The most unusual part: they explicitly admit where they fall short. From the model card: "Seed2.0 Series have considerable gaps with Claude in terms of coding, taking SWE-Evo and NL2Repo as examples. Relatively obvious gaps with Gemini in terms of long-tail knowledge, taking SuperGPQA and SimpleQA-Verified as examples." When was the last time you saw that in a model release?

There's also interesting usage data from ByteDance's MaaS platform in China. Frontend dev dominates coding queries at 50%+. Vue.js adoption is 3x React. Most common task is bug fixing, not greenfield development. The internet sector consumes the overwhelming majority of API traffic while manufacturing and automotive are each under 1%.

Three sizes: Pro for complex reasoning and agent tasks, Lite for balanced production use, Mini for high-throughput batch workloads at near-zero cost.

Model card PDF: https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/lj...
by kristopolous | 2026-02-17 13:37:06+00:00
They should actually be science - mostly reproducing/validating things in rigorous methodical ways.

There's far too many adults that don't seem to grasp the basic principles of what the discipline of science is.

At the earliest level instead they should be replaced with kids having to come up with experiments to either show something is or is not real based on an existing demonstration.

For instance, I'll make the claim that each different color of Trix has a unique flavor. And that you can test this because when you eat a certain color you always taste the same flavor.
Then the kids will have to come up with experiments that show that although that is true, the claim that they are flavored differently is false.

Another example. I'll claim that a tall slender glass holds more liquid then a short and narrow one of equal volume and as evidence I'll show that companies charge more for the tall and slender and people pay more for it willingly.

Essentially you start out with something where the response is "that's true but," and then an experiment is constructed from there.

This approach allows for some important lessons.

1. You don't have to successfully explain the phenomena to demonstrate the claim is false.

2. That science is about strongly disbelieving everything until you're forced to accept it as real essentially by exhaustion of objections

3. That poorly constructed tests can reproduce a phenomena that you're trying to isolate, etc.

The change is to not make it open ended or have projects or theme fairs. No posterboards or presentations.

Instead it's a form of debate club with interlocutors where groups are trying to fake and fool the others and separate things

When the general public accuses people of putting their "trust in science" I mean, this will kill that. No, science is all about strong methodological distrust - that's one of the basic premises.
by YeGoblynQueenne | 2026-02-17 13:37:24+00:00
Thanks for the link. Quoting from it:

* The National Physical Laboratory (NPL) tested the algorithm South Wales Police and the Metropolitan Police Service have been using for LFR.

* At the settings police use, the NPL found that for LFR there were no statistically significant differences in performance based on age, gender or ethnicity.

* There was an 89% chance of identifying someone on the specific watchlist of people wanted by the police, and at worst a 1 in 6,000 chance of incorrectly identifying someone on a watchlist with 10,000 images (known as a false alert). In practice, the false alert rate has been far better than this.

It looks like this is the report by the NPL:

science.police.uk/site/assets/files/3396/frt-equitability-study_mar2023.pdf

It's a big report and it'd take some time to go through it but it's clear that laboratory testing of a system deployed in the wild is not going to give accurate results, meaning the "89%" claimed is going to be significantly worse in reality. Anyway there's obvious limitations to the testing e.g. (from the report):

Large demographically balanced datasets: The testing of low error rates in a statistically
significant manner requires large datasets. To achieve the required scale, the
evaluation uses a supplementary reference image dataset of 178,000 face images
(Filler dataset). This is an order of magnitude larger than the typical watchlist size of
an operational Live Facial Recognition deployment. To avoid introducing a
demographic bias due to reference dataset composition, a demographically balanced
reference dataset was used, with equal numbers in each demographic category. For
assessment of equitability under operational settings, the results from the large
dataset are appropriately scaled to the size and composition of watchlist or reference
image database of the operational deployment.

I'd say "uh-oh" to that. Unbalanced classes is a perenial source of error in evaluations. "Equal numbers in each demographic category" is an obvious source of unrealistic bias.

Anyway, I don't have the time to go through that with a fine toothed comb, but just the fact that they report a 100 False Positive Rate for "operator initiated facial recognition" is another big, hot, red flag.

Also, from the UK gov link above:

* The 10 LFR vans rolled out in August 2025 are using the same algorithm that was tested by the NPL.

There's a bit of ambiguity there. The police are using "the same algorithm" tested by the NPL, but are they using the same settings? The report uses specific settings to come up with its conclusions (e.g. a "face match" setting of 0.6 for LFR), but there's nothing to say the police stick to the same. Lots of room for manoeuvering left there, I'd say.

>> The company between Oosto claims 99%.

We can easily dismiss this just by looking at the two digits preceding the "%".
by stego-tech | 2026-02-17 13:37:26+00:00
I’ve been chewing on this for fifteen years, now. There is no pretty or simple or even palatable answer, just a bunch of proposals with tradeoffs.

1) Eliminate the H-1B visa entirely. If a company wants to hire an immigrant, they can just sponsor the Green Card up front, knowing the worker can fuck off once they have it. The net result would be decreased immigration and increased offshoring, which brings me to…

2) Data Sovereignty Schemes. American’s data can only be processed inside American borders by American (or Green Card) workers. It’s absolute protectionism, which means you just shift the negative trends (“credential” mills in particular) onto domestic shores. Rural states and colonies become the new Indias and Philippines for outsourcing companies, depressing labor costs.

3) Unionize the technical trades. This lets the professionals set skill and comp floors, potentially offload benefits burdens to the Union itself rather than the fickleness of the employer, and even undermines the “contractor class” of companies deflating labor through precarious contracts by setting floors industry-wide. The downside is that Unions, like any power structure, can and will corrupt with time and incentive, leading to jams in the marketplace - less an issue in the age of AI, but still one worth noting.

4) Taxation. Companies that do 90% of their business in America but whose workforce (contractors, consultants, and FTEs) aren’t 90% American? No tax breaks for you, pay up. This is a very bad idea on its face, because companies will just shift the transaction offshore to dodge that rule and gum up everything else in the process, but some form of punitive tax scheme for exploiting social safety nets in lieu of fairly compensating workers is sorely needed to stop, if not begin reversing, the current wealth pumps. For-profit business models predicated on shunting workers onto every possible social welfare program as a means of depressing their pay has robbed taxpayers of billions, increased the national debt, and robbed workers of the fruits of their labor. It must be fixed, somehow.

There’s a number of other policies to get into, but that’s the “highlight reel” as it were. The important thing to keep in mind is that the status quo only works for the monied interests, and neither the H-1B workers coming in nor the Americans being shoved onto welfare programs for corporate greed. If a program or system enriches the rich while harming everyone else, it’s a bad system, and needs to be replaced rather than overhauled. Will it be painful? Yes. Will it piss people off? Of course. Will it feel like nobody really won? Ideally, because that means it’s balanced compromise rather than a gift package.
by jordanb | 2026-02-17 13:37:38+00:00
I've never understood this belief. There are many things I struggled with as a child that are very easy for me to understand now. For instance, calculus. I struggled barely passing my classes originally. A few years ago I decided that I really needed to know calculus so I bought a book and worked through all the problems. Not only was it not difficult but the whole thing just made logical sense; it was all straightforward.

I can believe there are some things that would take me longer as an adult to learn than if I were a child (a new language for instance) but origami folding wouldn't be one of them.
by 6LLvveMx2koXfwn | 2026-02-17 13:38:26+00:00
Absolute madness or complete nonsense - I have neither an Apple account or device, nor a Google account or mandated device (e/os on Fairphone 3+) and operate perfectly successfully in the UK with (almost [1.]) zero friction.

1. Revolut app stopped working so I emptied my account and opened a Wise account which is fully administer-able from their website. Revolut has subsequently started working again after a couple of app/OS updates.
by Gormo | 2026-02-17 13:38:31+00:00
Some others that I use:

* NextCloud -- client for personal NextCloud server; this app is used primarily for file sync, with other features accessed with other apps. (https://nextcloud.com/features/?filter=Clients#android-clien...)

* KeePassDX -- password manager, shares DB with KeePassXC on desktop, which is synced via NextCloud. Also functions as a TOTP authenticator. (https://www.keepassdx.com/)

* DAVx5 -- CalDAV and CardDAV client; keeps mobile calendar and contact list synced with private NextCloud server. (https://www.davx5.com/)

* AntennaPod -- excellent FOSS podcatcher. (https://antennapod.org/)

* KDE Connect -- desktop sync tool; allows file/clipboard/keyboard/audio/etc. sharing between phone and a Linux desktop. (https://kdeconnect.kde.org/)

* Kore -- remote control app for a Kodi instance running on your LAN. (https://kodi.wiki/view/Kore)

And I don't see F-Droid itself mentioned -- it's the most popular repository of FOSS software for Android, with an accompanying app: https://f-droid.org.
by jackhackett | 2026-02-17 13:39:01+00:00
I grew bored with Television over 4 years ago, I bought a 86" Tv 4 years ago also and I've never cared enough to take it out of it's box. If I get bored enough I'll watch something using free streaming services. I just buy physical discs of anything I might want to watch. Friends tried to get me to watch football and other sports but it's just too boring. I know the rules but it's just people chasing a ball back and forth. To enjoy sports you have to care who wins, and I don't care, who wins or loses means nothing to me.
by iggori | 2026-02-17 13:39:18+00:00
I wrote a short piece about something I keep seeing in AI‑assisted engineering: we’re moving faster than ever, but a lot of that speed is built on foundations that quietly rot underneath us. The article isn’t anti‑AI — I use these tools every day — but it’s a look at the structural costs we tend to ignore until they blow up in our faces.

If you’ve been working with AI in real codebases (not demos), I’d be curious how this matches your experience.
by HolyLampshade | 2026-02-17 13:39:21+00:00
I know I'm running a bit late to the party here, but maybe someone can provide some color that I (on the slightly older end of the spectrum when it comes to this) don't fully understand.

When people talk about leaving their agents to run overnight, what are those agents actually doing? The limited utility I've had using agent-supported software development requires a significant amount of hand holding, maybe because I'm in an industry with limited externally available examples to build am model off of (though all of the specifications are public, I've yet to see an agent build an appropriate implementation).

So it's much more transactional...I ask, it does something (usually within seconds), I correct, it iterates again...

What sort of tasks are people putting these agents to? How are people running 'multiple' of these agents? What am I missing here?
by agile-gift0262 | 2026-02-17 13:39:37+00:00
I've been using it for more than 2 years, and I can't think of ever going back to a stock OS. I had to send my phone for a screen repair, in the meantime I picked up my old Samsung, and the sheer amount of apps I didn't want, notifications and dark patterns to tricking me into handing over my data made me anxious. I couldn't finish setting the phone up and drove to my parent's home to pick up their old, remotely nerfed by Google, Pixel 4a so I could install GrapheneOS into it and use it while I waited for my repaired Pixel 8.
by harladsinsteden | 2026-02-17 13:39:38+00:00
The sheer amount of what you _might_ need later in life has proven to be simply too much for the time we usually spend for "overall education". I'm completely with you in that we should offer help along the way. But help can only bring you so far and you have to accept it.

In the end that's fine. I have no idea how my car works and if the guy from the repair shop says that I need to pay for a new clutch then that's what I'm gonna do. I am aware that I don't have the knowledge to know whether or not I'm being scammed or not. But I _accept_ that because the alternative (getting to know a lot more details about a car) simply doesn't appeal to me.

If someone wants to use the same approach for everything he does on the internet then that's perfectly fine. But then he needs to accept the consequences as well.
Ask HN: Why does single-node DDP sometimes get slower with more GPUs?
by traceopt-ai | 2026-02-17 13:39:52+00:00
Hi,

I keep running into a frustrating issue with PyTorch DDP on a single node (2–8 GPUs): adding GPUs sometimes makes training slower (not scale proportionally), and it is hard to tell what's actually gating the step.

In practice I see:

one rank silently becomes the “worst rank” and gates every step

step time spikes where GPUs look idle, but it’s unclear if the culprit is dataloader stalls, CPU contention, batch/sequence-length imbalance, or NCCL sync

Questions for folks who run multi-GPU training:

What do you suspect first when scaling regresses on a single node?

What signals do you look at to distinguish data vs compute vs comms/sync?

Any repeatable workflow / checklist that gets you to root cause fast?

Context: I am building a small OSS tool that shows live per-rank step timing + stall attribution (always-on; not a replacement for PyTorch Profiler/Nsight). If you have a workload where DDP scaling is weird and you are willing to run a ~10-minute test, I am happy to help interpret results and prioritize support for your setup.

Repo: https://github.com/traceopt-ai/traceml
by lunias | 2026-02-17 13:40:07+00:00
I'm not sure the people writing these articles actually use AI outside of trivial benchmarks. Sure, it can create "a website" in minutes, but what does that website do? I've found (I'm working on vehicle routing and maintenance scheduling) that you very much have to have a plan in your head and an understanding of what's going on in order to build towards reasonable results effectively. There is no replacement for knowing what you want, how to ask for it, and how to verify the implementation. That takes expertise. You cannot tell AI, "We fired the engineers. Take over their jobs and do them better." That's a non-starter.
by jbonatakis | 2026-02-17 13:40:46+00:00
I built (using AI) a small cli that provides the breakdown of changes in a PR between docs, source, tests, etc

https://github.com/jbonatakis/differ

It helps when there’s a massive AI PR and it’s intimidating…seeing that it’s 70% tests, docs, and generated files can make it a bit more approachable. I’ve been integrating it into my CI pipelines so I get that breakdown as a comment on the PR
by wraptile | 2026-02-17 13:42:13+00:00
> If you aren't a competent musician by the time you're 8 years old it is vanishingly unlikely you are ever going to be a true orchestral soloist. Ditto for something like chess

To me this sounds like an exception to the rule than rule itself. Our society would be perfectly fine to not have this type of entertainer "greatness". I mean, we got rid of castrados because it went too far but the line between cutting kids testicles off vs making them play some useless game 12 hours a day for a decade is quite blury.

I'd argue this extreme specialization of children is fundamentally unethical and should be shunned or even made illegal but it'll take decades if not centuries for our society to realize this because we just value this type "greatness" too highly.
Show HN: Ambient CSS – Physically Based CSS and React Components
by kikkupico | 2026-02-17 13:42:32+00:00
Hello! AmbientCSS is a side project I started 5 years ago because I found the lack of realism in CSS shadows jarring. So, I tried to create a more realistic and consistent shadow system for CSS. It grew too complex and I gave up. Now, thanks to LLMs, I was able to revive it. As such, it's good enough to play with but might not be good enough for production use.
by thaumasiotes | 2026-02-17 13:42:35+00:00
I don't think there was ever a "thought" behind science fairs to be "rethought" now. They're from the "this is something, therefore, we must do it" school of science education.

Most education as delivered is uninterested in whether the recipients learn anything. I remember reading an essay by someone who gave a presentation at an education conference...

He wrote that a schoolteacher came up to him afterwards, excited about his talk. She wanted his opinion on her favorite part of the school year: each of her students gave an oral presentation on a theme that she assigned. She was proud of this assignment, because (a) she enjoyed watching the presentations, and (b) the students liked doing them.

And I was appalled to read this, because I consider those presentations a fancy form of abuse, and they're obviously worthless. But the essay author said:

"I am not going to tell you what I think. Instead, I want to ask you two questions:

First, can you imagine someone knowing the material of your class well, but doing poorly on the presentation?

Second, can you imagine someone doing well on the presentation, but not knowing the material?"

And the teacher grew very quiet.
by nobody9999 | 2026-02-17 13:42:57+00:00
>It's much riskier to hire the wrong employee now. If a bad hire is made, it takes around 2 quarters to build the paper trail needed to be litigation-proof when firing.

Now? That's been the case since at least the 1980s, at least in non-union jobs.

What makes you think things were different before the "now" you reference?
by 1-6 | 2026-02-17 13:43:30+00:00
100% this. There's nothing wrong with focusing on first principles and this is a rediscovery in that matter.

It's just an acknowledgement that the Miura pattern works and this kid kept his focus. To be perfectly honest, he just took something that was already done before (the fold) and applied it. Scientists do this all the time and win prizes--like cellophane tape to create graphene.