MurphyA/hacker-news huggingface dataset:
by muzani | 2026-02-19 10:17:10+00:00
Useful graph from Jane: https://www.threads.com/@wongmjane/post/DUzvR7_ESDa/i-came-u...

Malaysians call it CNY. Part of it is because Korean, Mongolian, etc are more of a minority here. I think another part of it is because the Islamic year is also lunar, which makes it more confusing. Especially this year, where CNY coincides with Ramadan.
by lgvld | 2026-02-19 10:17:26+00:00
> Instead of distributing Chromium, By default Electrobun uses your system's native WebView (WebKit on macOS, Edge WebView2 on Windows, WebKitGTK on Linux).

still:

> Optional CEF: bundle CEF (Chromium) when cross-platform consistency matters most.

from: https://blackboard.sh/electrobun/docs/guides/what-is-electro...
by 9dev | 2026-02-19 10:17:34+00:00
> No, you CANNOT found a company like that. It’s an absolute fabrication.

I'm a German. I have done this myself. I assure you it's true.

> You also seem to somehow justify spending 25k on an endeavor you don’t know will succeed upfront, when every other country on the planet allows you to open one with orders of magnitude smaller amounts of capital.

If you are planning a serious endeavour where you intend to limit your liability, you'll have a business plan. And that allows multiple options, from a bank loan of a local bank that will very likely be granted on good conditions, to one of the various municipal, regional, or federal programs that offer grants for newly founded companies. My current company secured 200k Euro at the beginning from a federal program that we did not have to pay back, for example.
by mhd | 2026-02-19 10:17:36+00:00
People doing DTP with Calamus on their Ataris stuck around for a long time after the systems weren't used for much else – MIDI tooling excepted, of course.

On the other hand, there you didn't have that many powerful packages on any system, besides Quark & the various Adobe tools du jour everything paled in comparison.

For word processing, being forced to use Word was/is usually worse than for DTP, though. But feature-wise, everything seemed to converge during the 90s, so "having" to use Word instead of e.g. WordPerfect was less and less of an issue.

With some exceptions of course, most famously GRRM and other people who got into things very early sticking with the first thing they learned (i.e. WordStar), or apparently some journalists being really into XyWrite.
by pndy | 2026-02-19 10:18:02+00:00
If you manage to install decent apps in time, you can use these as e-readers or video players. But batteries will eventually fail and all what you'll have will be a fancy fragile chopping board.

I was gifted with iPad 1, Air 1 - first won't charge for 6 years now and data I didn't synchronize are gone, second one needs a serious "warm-up" before charging and while Apple released 12.5.8 in the end of January, it won't get any new apps.
by barrkel | 2026-02-19 10:18:19+00:00
They have rate limits, but they also want to control the nozzle, and not all their users use all their allocation all the time.

In reality, heavy subscription users are subsidized by light subscription users. The rate limits aren't everything.

If agent harnesses other than Claude Code consume more tokens than average, or rather, if users of agent harnesses other than CC consume more tokens than average, well, Anthropic wouldn't be unhappy if those consumers had to pay more for their tokens.
by fouc | 2026-02-19 10:18:49+00:00
can look for macos downloader scripts in github. I noticed the readme here shares some URLs though I'm not sure if they still work https://github.com/Comp-Labs/Download-macOS

https://github.com/chris1111/Download_Install_macOS could also be another option.

I know I used one of the macos downloaders from github before, I just forget which one though.
by adrian_b | 2026-02-19 10:18:49+00:00
While implementing double-precision by double-single may be a solution in some cases, the article fails to mention the overflow/underflow problem, which is critical in scientific/technical computing (a.k.a. HPC).

With the method from the article, the exponent range remains the same as in single precision, instead of being increased to that of double precision.

There are a lot of applications for which such an exponent range would cause far too frequent overflows and underflows. This could be avoided by introducing a lot of carefully-chosen scaling factors in all formulae, but this tedious work would remove the main advantage of floating-point arithmetic, i.e. the reason why computations are not done in fixed-point.

The general solution of this problem is to emulate double-precision with 3 numbers, 2 FP32 for the significand and a third number for the exponent, either a FP number or an integer number, depending on which format is more convenient for a given GPU.

This is possible, but it lowers considerably the achievable ratio between emulated FP64 throughput and hardware FP32 throughput, but the ratio is still better than the vendor-enforced 1:64 ratio.

Nevertheless, for now any small business or individual user can achieve a much better performance per dollar for FP64 throughput by buying Intel Battlemage GPUs, which have a 1:8 FP64/FP32 throughput ratio. This is much better than you can achieve by emulating FP64 on NVIDIA or AMD GPUs.

Intel B580 is a small GPU, so it has only a FP64 throughput about equal to a Ryzen 9 9900X and smaller than a Ryzen 9 9950X. However it provides that throughput at a much lower price. Thus if you start with a PC with a 9900X/9950X, you can double or almost double the FP64 throughput for a low additional price with an Intel GPU. Multiple GPUs will proportionally multiply the throughput.

The sad part is that with the current Intel CEO and with NVIDIA being a shareholder of Intel, it is unclear whether Intel will continue to compete in the GPU market, or they will abandon it, leaving us at the mercy of NVIDIA and AMD, which both refuse to provide products with good FP64 support to small businesses and individual users.
by conductr | 2026-02-19 10:19:44+00:00
It’s a great point really. They’re both unhealthy products used by large portion of the human population. We treat one with moderation and regulate it heavily. The other we treat with utter gluttony and have not formed any social norms regarding restrictions, moderation, and things that would lessen the addiction and impacts to wellness it causes.

They’re both unhealthy products and I feel they deserve to be just that. Allow social media to be what it wants. But also approach it with moderation and regulation around access. The wellness experts shouldn’t be dictating what social media is, they should be promoting more healthy ways regarding how it’s used. It’s an uphill battle for a reason though, we like it too much.
by 9dev | 2026-02-19 10:20:24+00:00
> […] the reason the old tools were slow was not that the JS runtime is impossible to build fast tools with.

I don't have them at hand right now but there are various detailed write-ups from the maintainers of Vite, oxc, and more, that are addressing this specific argument to point out that indeed the JavaScript runtime was a hard limitation on the throughput they could achieve, making Rust a necessity to improve build speeds.
by seanhunter | 2026-02-19 10:20:40+00:00
I’m pretty sure that one of the fundamental premises of this argument is not correct, but I haven’t done mathematical statistics so I would defer to anyone who has and says I’m wrong. The author expresses this in a variety of forms throughout the article, but for instance

> Significance tests are meaningful and valid if AND ONLY IF assignment was
properly randomized.

OK I agree with the “if” but not the “only if” part of this implication. My intuition is that significance tests are still meaningful if a non-random method of assignment is chosen, but the bias introduced by the method is not in any way correlated with the observations under study.

This leads to the whole thing the author gets into about PRNGs not being valid for assignment. But a much simpler and more deterministic method of sampling is stratified sampling, which is used all over the place for various types of statistical experiments. The random entropy of a stratified sample is nowhere near enough to be fully random, yet I haven’t seen anyone claim that all experiments done using stratified sampling have invalid or meaningless p-values.

Basically stratified sampling works as follows. Say you want to study the effects of age and gender on income. One way is to just study everyone, record their age, gender and income and do your study. But that gets pretty unweildy and you may not be able to get accurate data over the whole population. What you can do instead is get a list of people, sort them by age and gender and pick a size that’s convenient. So say you have capacity to analyse a sample 1/20th the size of the total. Cool. Then you just take every 20th person on your list and that’s your sample. By the magic of stratified sampling, because your list is sorted by age and gender, the sample will more or less have the same proportions as the total population with respect to age and gender.

Not random, but used all over the place for statistical studies.
by muzani | 2026-02-19 10:20:44+00:00
It's because the risk ratio has changed for different people. If you're poor, there's much less to lose - you just become in debt, but you'd be there anyway.

America also has the megacorporations. Trillion dollar companies which can hire hundred of thousands of people and pay them what a medium-sized business owner would make. They have the economy of scale; they can pay a single engineer to increase profit by 0.01% and it would be more than a startup growing stuff by 3x.

The odds of me joining a megacorp is nearly nil due to immigration restrictions, so businesses are still more sensible. It's probably the case for most of the world.
by vessenes | 2026-02-19 10:22:03+00:00
The lede is sort of in there, but buried - or at least not talked about from an economic perspective:

Right now, women consumers put up with one-ish body type (although fit model shapes vary by brand) - manufacturers thus make up to ten sizes or so of any given garment. Google fashion industry waste if you want to learn some depressing things, but - because of fashion lead times, production methods, etc, a lot of these clothes will not get sold. So there’s production waste.

There are roughly 10 core body types according to this website. So, to make ‘properly’ shaped garments for a much larger group of women is going to take roughly 120 different garments for a single design.

This just isn’t going to work for manufacturers given current production methods. I’m working on a fun sweater company right now, and it’s a very analog process - with humans and production and yarn all in different countries - ending in a single garment for analysis that is then put on a model for photography. I cannot imagine trying to scale it to 100 different shape/size combos.

Upshot - right now: choose from the following:

1) create mild differentiation and hit a product target that blends looking good on the site/shelf/model with one that looks good on the customer; keep 90% of the market

2) lean in hard on one of the “10”-ish body types - give up the rest of the market, but have happy customers

3) Try to sell stuff that can get auto-sized properly via algorithm and delivered “on-demand”

Most big companies are big, and therefore chose 1. Some smaller companies chose 2. In the happy circumstance that they chose 2 for conventionally attractive bodies, you’ve heard of them (Chanel). Some have transitioned into this space over a longer period, like Burberry. If you’re not a target customer, they may still have fans, but you might not have heard of them, e.g. Good American.

A few companies have tried 3 — direct to consumer via brick and mortar retail — (there was an MIT company deploying Shima Seki knitting on Newbury street in boston years ago), but they inevitably seem to move to a fast deployment D2C shipping model.

I think this is likely because if you go into a boutique you do not want to pay $600 for a garment and then have to wait three hours for it to get made. Online this feels more palatable.

So, we’ll probably see some continued innovation on the robot-knitting side of the world over the next ten years. In the meantime, companies mostly do what makes economic sense. And, it’s worth noting, operating the “automated” knitting machines and designing for them is no joke, it’s hard — really hard, and the software can be abysmal. So, this is an industry that’s a long way from rapid change, at least right now.
by spinningslate | 2026-02-19 10:22:54+00:00
All true but it's a circular argument: these are unhealthy products because they're _designed_ that way. That design is directed from the top - no more so that Facebook/Instagram. Zuckerberg retains a controlling interest in Meta so he can't use the excuse of other public firms where CEOs throw up their hands and say "yeah, but we need to deliver shareholder return - it's out of my hands". Zuckerberg could choose differently. As GP notes, he hasn't - he's gone consistently hard the other way.

> It’s clear, people want to be addicted to social media

I'd say people are susceptible to addiction rather than wanting it. Suppliers of any addictive product - whether its tobacco, class A drugs, alcohol, gambling or social media - know that. Going too hard the other way into full prohibition is impractical because it starts to impinge on civil liberties: as a capable adult, why shouldn't I be able to smoke/drink/doomscroll instagram if I want?

That's why it's dificult; neither extreme liberty nor extreme prohibition is the answer. It's a grey area as GP notes. The trouble is it creates opportunities for people like Zuckerberg to exploit the middle ground and amass huge personal wealth paid for, in part, by the health detriment of those unable to self-regulate the addiction.
by metalman | 2026-02-19 10:23:16+00:00
I also have built my own offgrid sytems, but sodium is here now, and lithium is not competitive in any metric that counts in a static instalation especialy cost per kw/hr, and will very likely get insured to death as it will never be as safe.
My next system upgrade will be aimed at home power/heat/hot water, shop, and car charging.Possibly with the car charging bieng a discreet system that works at a higher voltage and is all DC.
by rwmj | 2026-02-19 10:24:54+00:00
Use of AI is based on self-reported data. From the paper:

> The respondents to the interviews are senior managers or financial directors with responsibility for investment decisions and how investments are financed – for example, the owner, chief financial officer or chief executive officer

> Firms are asked the following question: “To what extent, if at all, are big data analytics and artificial intelligence used within your business? A. Not used in the business. B. Used in parts of the business. C. Entire business
is organized around this technology.”

AI adoption is defined as the manager answering B or C.

I'm doubtful that this data is going to be very robust. Some senior tech managers are very keen to talk about AI, while at the same time knowing little about how much AI is actually being used by workers. At other companies you'll have people using free or personal ChatGPT accounts without the knowledge of management.

Also "big data" is not exactly AI.

The productivity information is robust as it's based on company accounts, albeit from 2024 so a couple of years out of date now.
by wink | 2026-02-19 10:24:59+00:00
This is kinda topical for me as I just scanned some barcodes off some CDs and my results were: 90-95% detection rate on MusicBrainz, and for the rest it ranged from "yeah, this is clearly the same thing with 10 tracks" to "oh my, there are 7 different regional versions with 10, 11, 12, 13, 13 tracks and I need to pay attention to grab the correct one so the last 3 songs are not wrong" and "this is some 5 EUR sample from an unknown label and really hard to find. Or their docs are not great, I had wished for something like "artist of track 1 = X and artist of track 2 = Y" that probably would have narrowed it down the most.
by MezzoDelCammin | 2026-02-19 10:25:01+00:00
For everyone struggling with clothes sizing and having a hacker mindset, I can't recommend enough buying a sewing machine (~100EUR on a used market, ~150 new gets you a reasonable starter one you won't outgrow any time soon) and giving clothes alterations a try.

Finding a tailor that understands you / you agree with is an option too, if time is a hard limit (though I'm not sure it's altogrther that much quicker).

In my case, I started with tailors, but kept running into small misunderstandings. Also, my taste keeps evolving.

Start small with simple stuff, ideally old / second hand cheap clothes. Shirts, T-Shirts and bodice waistlines / "darts" are almost trivial once you can follow a straight line. First one will take a while, second will be much quicker, by third / fourth it's almost a routine and you can start iterating on your own preferences. They likely "will" evolve as you keep wearing the altered clothes.

Depending on how much help you can get in the beginning, with maybe a 2-3h intro on how to use a sewing machine done by a friend who has sewing as a hobby, I'm pretty sure most people should be able to get their first alterations done within 4-5h. By second or third attempt, this time should be down to around 1h per item, including some setup (pinning - trying - ironing). At that point the DIY option is probably quicker than going to a tailor.
by keybored | 2026-02-19 10:25:22+00:00
> And about the reaction time - politics is in a way expression of the will of the masses.

Then you go on to list all the astroturfing that people are “waking up to”. You just contradict yourself. Politics writ large is astroturfing that you get gaslit into thinking is da will of da masses.

> And that depends on how they are informed. They are maybe not yet on point, but they are getting there.

But you have the uncorrupted view from God.
Ask HN: Onboarding UX for long-running agent workflows
by kevinkatzke | 2026-02-19 10:26:08+00:00
We’re currently rebuilding our onboarding experience and are thinking through the UX when project setup depends on long-running agent calls.

What’s the best user experience in that situation?

Should users be blocked from the dashboard and see a loading screen until everything is ready (with a notification once setup is complete)? Or is it better to show a more transparent, step-by-step “chain-of-thought” style progress view while the agents are running?

How did you approach onboarding UX in this case, and what has worked well for you?
by andreagrandi | 2026-02-19 10:26:11+00:00
No offense taken here :)

First, we are not talking about a cheap service here. We are talking about a monthly subscription which costs 100 USD or 200 USD per month, depending on which plan you choose.

Second, it's like selling me a pizza and pretending I only eat it while sitting at your table. I want to eat the pizza at home. I'm not getting 2-3 more pizzas, I'm still getting the same pizza others are getting.
by pjmlp | 2026-02-19 10:26:20+00:00
Microsoft has already been there before as well, they are adopting SPIR-V only to cut down their fork, and because Google already did part of the work for them.

Apple apparently got very pissed on how Khronos took care of OpenCL.

Then Sony and Nintendo couldn't care less about Khronos, people keep forgetting about game consoles when discussing the portability of Khronos APIs, or ignoring the fact that in reality they aren't really that portable due to the extension spaghetti.
by Drup | 2026-02-19 10:27:31+00:00
I'm going to be contrarian: Yes, you should learn about type systems if you want to design a programming language, and decide in full conscience what you need. At the very least, it will give you a concrete idea of what safety means for a programming language.

It doesn't mean you have to use an advanced one, but your choice choose be based on knowledge, not ignorance.

A lot of harm; including the billion dollar mistake, has been done by badly designed type systems from the Java/C/C++ family.
Show HN: Global macOS shortcut that rewrites selected text anywhere with AI
by matthewtoast | 2026-02-19 10:27:46+00:00
I built Spackle, a small macOS menu bar app that rewrites text inline in any app using your LLM of choice. Highlight text, press a keyboard shortcut, and the selected text gets replaced in place — Mail, Notes, Slack, Messages, Keynote, PowerPoint, Gmail (any text field), text editors, etc. No copy/paste or separate chat window.

You just:
- Select text
- Hit a hotkey (default ⌘⌥⌃I)
- Spackle sends the selection to your configured model

And the rewritten text replaces your selection It’s bring-your-own API key and supports OpenAI, Anthropic, OpenRouter. No local model support (yet...PRs open).

I made this because I wanted something closer to a universal "rewrite shortcut" for macOS. When I get into a flow with writing I don't always want to jump into a separate context, and this makes it easy to get quick rewrites or LLM completions in just about any text field in any app across macOS.

Would love feedback, especially on:
- UX friction
- Rewrite quality expectations (the prompt could be improved, but you know, ship early)
- Other workflows this could support

Repo: https://github.com/tsortwehttam/spackle
by rester324 | 2026-02-19 10:28:26+00:00
You would be surprised to see how much the japanese IT industry is behind the times (a decade at least IMO). There is only a very limited startup culture here (both in size and talentpool and business ideas), there is no real risk taking venture capital market here (maybe Masayoshi Son is the exception here, but again he tends to invest in the US mostly) and most software companies use very very very outdated management practices. On top of that most software development had been/has been outsourced to India, Vietnam, China, etc, so management see no value in software talent... SW engineers' social recognition here are mostly on the level of accountants. Under such circumstances japan will never have a chance to contribute to AI meaningfully (other than niche academic research)
by xserhio | 2026-02-19 10:28:56+00:00
Ah, I stand corrected! The varlock run -- approach and the standalone binary completely solve the vendor lock-in concern I mentioned.

I have to say, patching console and http in JS to auto-redact sensitive data based on the schema is genuinely brilliant. Leaking secrets in logs is a massive pain point, and handling it automatically at the tool level is a great solution.

Really impressive work on the entire toolkit. It’s definitely a much more comprehensive ecosystem than what I built. I appreciate you taking the time to explain the architecture—definitely taking some notes from this!
by jongjong | 2026-02-19 10:29:10+00:00
I'm white and got 7/18 and it said "You can't tell the difference" but I think it's because some of them I had no idea. There were a few where I was really sure and I turned out to be right.

I think it's the same with white people. There are some who look unique to their country and I can tell with high confidence but for others, I have no idea; they just look like a generic white person.

For example, I think these public figures look/looked very stereotypical for their country:

German: Otto Von Bismarck

English/Scottish: Hugh Grant, David Bowie, Winston Churchill, Maggie Smith

French: Napoleon, Jacque Chirac, Alain Delon, Gerard Depardieu, Francoise Hardy

American: Clint Eastwood, Abraham Lincoln, JFK

Swedish: Agnetha Faltskog
by rf15 | 2026-02-19 10:29:33+00:00
lots of contact with asian people here, definitely all look different, watch japanese and korean dramas on the regular so should be able to tell at least those. 6/18, funniest thing was to me though thinking "oh you're definitely japanese" and being spot on twice. The biggest shortcoming on this quiz: not telling you the correct answer, or the site giving guidance/examples of their data set.

This website is just the author's personal judgment exercise.