skysight-inc/HackerNews huggingface dataset:
It's not about fault, it's about providing better than you got for your children and grandchildren. If you - and the people compensated with the same equities that fund your pension - are putting substantial downward pressure on earnings for the current generation of laborers and it's having all sorts of knock-on effects, you're failing to provide better for those coming after you.
Sure. They made a decision and stand by it as is their luxury. Yelling at the vacuum of the internet about it may score emotional points but it won't sole the core frustrations people have. The common argument is "yes we need to do it, but do it another way" to which I say, it hasn't been done another way and plenty have had time to do it. Pushing things off and procrastinating in general, combined with a President that is largely supported and on a 2nd term, with no need to pander means you get exactly what was voted for.

The left had their turn to "fix things" they didn't. The right are trying now, and maybe their methods are wrong, but they're trying. What you're seeing is a power struggle playing out, the people who've been king of the hill are being throw to the side and don't like it.
It looks really nice!
What I found challenging is the lack of namespaces for example using two different bases or two different OpenAPI specs that may override each other or be suggested in the autocomplete in the wrong situations
Show HN: B2B SaaS Landing Page Swipe File
*weak
In my opinion, climate change causing more and more overwhelming* weather events, this issue is going to get worse.

*overwhelming in the sense of responders/budgets/planners unable to mitigate the effects of worse weather.

It's partly why I'm so "doomer" about climate change. The secondary effects of these weather events are already difficult to manage. What happens when we experience mass migration of humanity, or even worse storms?

For example, Kentucky USA has an average year-to-date precipitation of 6 inches. In 2025, that has increased to 12 inches. Yes that's one year, but it only takes one major event to wreak havoc on a poorer town or state.
I've seen attempts at engineering offshoring again and it causes problems because the core part of software engineering isn't the actual coding part, it's communication, design and understanding.

If you offshore your coding, how do you know they're not using AI as well? How do you verify code quality? And what do you do when you're left cleaning up a mess? This isn't really a problem of the quality of engineers, but the inherent relationship between a company and contracted firms.
We are nowhere near being able to live outside this planet without significant struggle. It would be far easier to live in a submarine in the ocean than anywhere else in this solar system.

Our culture and foresight has brought us into such misalignment that 25% of the US population is on psychiatric drugs, you have a lot of homeless, a drug epidemic, there's a general crisis of meaning, males have given up on finding partners, women are all competing for a few men or think they're all animals and stay away from them. To be able to eat quality food costs a lot and few people have the time or energy to cook anyway because city life is so stressful to most.

We are living in a profoundly sick society and economy all over the world. WW3 is knocking on the door, one wrong move and we fuck up the only ecosystem we have in the galaxy.

I'd bet if people were given the choice between living in a small fishing village from 2000 years ago and modern lower middle class the choice would be obvious.

So to say we have mastered nature and we know better requires a lot of hubris.
> I want my UX in an app to do the damn thing I precisely intend it to do, not what it believes I intend to do

This is pretty weird epistemological phrasing. It's a bit like saying "I want to know the truth, not just what I believe to be the truth!"
That's not a mathematically meaningful statement. I'm just trying to understand what you mean in more detail.
Ask HN: event loops vs. greenthreading in modern languages
Most explanations I've seen on "async programming" bury the reason why we're doing this in the first place. It's purely to avoid making OS threads wait on I/O in a concurrent application, such as a webserver. So languages or even libraries instead have various ways to switch tasks on a single thread. If OS threads were cheap enough to just spawn one for each request, this wouldn't be a thing.

Some languages with built-in event loops have async/await syntax, like JS, C#(?), and now Python and Rust. This is convenient syntax. Java, ObjC, etc also had a lot of futures-based stuff that mangled up your code somewhat. Callbacks were also common in ObjC, which gave you crazy nesting. Either way, you're explicitly saying when it can switch tasks, aka what you expect to block.

Golang has greenthreading ("goroutines") instead, and Java recently got them back (as "virtual threads"), which is what I'm less sure about. I get that N greenthreads can run on 1 OS thread, and they're cheap enough to spawn whenever you want. This is less explicit than the async/await way, because the runtime automatically decides what is blocking (i.e. when to switch out the greenthread running on the OS thread), which I assume is based on what syscalls it uses.

So now I'm wondering, if Golang and now Java can task-switch without the user having to tell it when, is there any point of doing it explicitly like in JS or Rust? Is it faster or something, or are other runtimes just not able to tell when exactly things are blocking?
Thank you for citing that because it is really the basis of my point. It is meant to be apolitical and to demonstrate that we are not OK with this otherwise so shouldn’t be now.
so the kiva-amazon model?
The person that worked for 25 years starting at 18 for a full pension benefited me? Nah, I think they benefited far far more.
Almost no culture routinely eats meat-eating animals. It is very easy to determine, even in ancient times, that it is incredibly easy to get sick from eating meat-eating animals. This is because predators often catch and eat diseased prey, and end up having a lot of parasites and such.

Not to mention the meat of such animals tastes much worse.
My Dad's friend is a retired fire chief, my Dad told me he makes $300K a year in retirement! That is beyond insane as it ends up costing (with a retirement at say 55) about 30 or so years of payments which adds up to $9M for the reminder of his life. This is clearly not sustainable and when you add up social security and 401k it ends up with a kings ransom for a public servant.
> It is in my nature to give the benefit of the doubt.

Then you should be giving the benefit of the doubt to the people and institutions that are accused on flimsy evidence. Then you should be giving benefit of the doubt to Harris and Clinton too, to progressives, to SJWs, feminists, to centrists.
> Press release

https://openai.com/news/company-announcements/

> a consortium of 20 research institutions

https://aimagazine.com/machine-learning/google-invests-in-ai...

> awarded the STEP (Strategic Technologies for Europe Platform) seal

https://openai.com/index/strengthening-americas-ai-leadershi...

> Lots of grandiose self-congratulations

https://x.com/sama/status/1891533802779910471

> All with nothing to run, download or try of course.

https://openai.com/weights/download/
kind-a summary: 21st century c++ (still by Bjarne Stroustrup)

https://news.ycombinator.com/item?id=42946321
I have only a resume to convince them. I have job experience at major companies, with examples of what I've built when there, personal projects I've made, a github link, a great GPA from a good school.

And I know similar Junior-mid people in the same boat. We can all do Fizzbuzz, we've all built things, and somehow we're not getting interviews, but people that can't do Fizzbuzz are.

Do thousands of incompetents also machine-gun apply to, say, mechanical engineering, accounting, marketing, HR, or finance gigs? Is it just tech?

Something isn't adding up.
Pretty much this...
We have a backyard flock where the run and coop are completely enclosed. So in theory they should be more protected given that no birds or critters can get into that space to give my chickens bird flu.

That being said, I have no faith in the Trump government to do the right things required to stop the spread of this and I feel like we are pretty screwed either ways.
This hypothesis (eggs causing high triglycerides) was disproven in randomized controlled trials. The main cause is refined carbohydrates and insulin resistance.
I really like the direction of Julia and its managing of trade-offs. I hope it gains more traction.
You can’t solve that by taking away existing pensions. If that’s your preferred method of balancing budgets then expect no sympathy when your company cancels your RSUs
Hasn't it always been the case since the beginning of AI/ML trends?

Once an algorithm/technique is discovered, it becomes a free library to install.

Data and user-base are still the moat. The traffic that ChatGPT stole from Google is the valuable part.
Well to give to a concrete example.
I use it to write test cases for the CRUD applications that I sometimes have to work on. Some test cases already exist and I feed the tests and actually code including additional instructions into a model and get relatively decent output. We also use a code review bot that we feed repository relevant instructions to and get decent basic PR comments. It even caught an edge case that 3 other developers didn't consider.

I think AI can be yet another tool that takes some repetitive tasks off my hands. I still obviously check all the code it generated.
But seriously though. Compounding variance is a crazy upshot if you're lucky.
It's actually langfuse.com! Our quickstart walks you through the whole process: https://docs.confident-ai.com/confident-ai/confident-ai-intr...
> the problem you're solving, your team structure, your integration footprint

Those are factors to be accounted for in good engineering.
Truth Social blocks Ukrainian IP ranges
That process might work for your company precisely because you are not FAANG. You don't get hundreds of applicants that are dying to get in, so people don't have that strong of a motivation to do anything it takes (including lying) to get the job.
Reminder that AA is used more to discriminate against Asians that it is to help black Americans.
What specifically is going on right now in AI that's not based on "hallucinogenic LLM prompts"?
The demo video on your homepage looks great! If I may ask, what recording software did you use to create and edit the video?
Well, perhaps they could take Deepseek, feed it with all EU directives, ask on each if it's related to AI (or whatever) and spill out results.

It could be even nice idea for startup. All data are publicly available...
The biggest victims of these non-scalable process is people without a good network. As an intl PhD student, I am that person.

So now I have this weird dynamic: I get interview calls only from FAANG companies, the ones with the manpower to do your so called "cursed" scalable interviews. But the smaller companies or startups, ones who are a far better fit for my specialized kills, never call me. You need to either "know someone" or be from a big school, or there is zero chance.
Yep it's legitimate and I see no issues with it. The track record of the last admin was crazy were you saying the same things then? The left screamed trust the science in one breath over covid, then said science is fluid in the other when it came to biology. The left went to far and 15 or so years of being propped up and supported has gone by the wayside. I get it, people are upset, but at the end of the day we're here because of the "crazy" spending on "crazy" ideas.
I just finished the book “On The Move” about the coming climate driven migration and yea, I don’t see any part of the US prepared for being either the source or destination in these problems.
https://bookshop.org/p/books/unlivable-abrahm-lustgarten/199...
"The advantage they give to the country that owns them is nuclear-weapons like."

I think the idea that the United States "owns" Grok 3 would be news to Musk and the idea it "owns" ChatGPT would be news to Altman.
> for everything aside from essays, articles, papers, long emails, and some percentage of multi-paragraph site comment

That’s already the only stuff worth reading and always has been. No loss then
Computation is a sibling (or maybe child) of logic, it is not a superset of logic. I like Wadler a lot and get that the Curry-Howard isomorphism makes it tempting to view them as the same thing, but logic is field of far vaster proportions and history. I think "computer science" is properly called computability theory.

The proposal in the HN title is a bit like saying "calculus should be called mathematics".
> Where is the native HTML datagrid

Which parts of a datagrid should a browser provide? I'm familiar with AG Grid [1] and the API surface is enormous. Aligning browsers on a feature set would be challenging.

Maybe there's a core set of functionality, like Flutter's GridView or QML https://doc.qt.io/qt-6/qml-qtquick-gridview.html.

[1]: https://www.ag-grid.com/
The Handoff to Bots
There's something hilarious to me about the idea of chopping onions being a sort of benchmark for robots.
Thinking About Why YouTube Is a Monopoly
I wonder if AI is going to reduce the amount of JS UIs. AI bots can navigate simple HTML forms much easier than crazy React code with 10 layers of divs for a single input. It's either that or people create APIs for everything and document how they are related and interact with documentation.
The Reader Acquisition Funnel
There’s no other industry* that interviews experienced people the way we do. So maybe just do what everyone else does.

Everyone is so terrified of hiring someone that can’t code, but the most likely bad hires and the most damaging bad hires are bad because of things that have nothing to do with raw coding ability.

*Except the performing arts. The way we interview is pretty close to the way musicians are interviewed, but that’s also really similar to their actual job.
People can certainly decide that certain activities aren't their thing.

I don't want to push to give presentations at international events is certainly a valid decision for any of a number of reasons.

So is preferring to spend more time on coding than managing/mentoring/etc.

But it all has consequences and some branches will lead to more promotions/money/etc. than others. And you may be perfectly fine with that. But go into with eyes wide open.
That was just an Ecosia rebrand, not an official EU thing: https://betterweb.qwant.com/en/2024/11/08/ecosia-and-qwant-j...
I've found that AI has saved me time consulting Stack Overflow. It combines thorough knowledge of the documentation with a lot of practical experience gleaned from online forums

It has also saved time producing well-defined functions, for very specific tasks. But you have to know how to work with it, going through several increasingly complex iterations, until you get what you want.

Producing full applications still seems a pipedream at this stage.
> Maybe LLMs can't work on huge code bases yet

its also not just about code base size, but also about your expectation of output quality/correctness.
Screen Studio on Mac.

(OBS Elements other times)
I was reading their site, and I too have some questions about this architecture.

I'd be very interested to see what the output of their 'big model' is that feeds into the small model. I presume the small model gets a bunch of environmental input, and some input from the big model, and we know that the big model input only updates every 30 or 40 frames in terms of small model.

Like, do they just output random control tokens from big model and embed those in small model and do gradient descent to find a good control 'language'? Do they train the small model on english tokens and have the big model output those? Custom coordinates tokens? (probably). Lots of interesting possibilities here.

By the way, the dataset they describe was generated by a large (much larger presumably) vision model tasked with creating tasks from successful videos.

So the pipeline is:

* Video of robot doing something

* (o1 or some other high end model) "describe very precisely the task the robot was given"

* o1 output -> 7B model -> small model -> loss
This risks hiring candidates who can present themselves and their past projects very well but fail to actually write code and ship anything on the job. I've seen it happen.
There are some iOS apps that work as Safari extensions and enable Greasemonkey scripts. I use one called Userscripts.
We did. In fact, some of the Biden-era antitrust lawsuits are still active, AFAIK. What happened is that the capital class acted swiftly and firmly to shut down the FTC (and SEC), by brown-nosing Trump[0], so that the federal government couldn't challenge them.

The word "deep state" gets thrown around a lot here, especially by the people who thought Trump was going to stop this bullshit, but it's useful to describe what's going on: the accumulation of informal power structures that render the formal, legitimate one ineffective. Hollywood was part of the last iteration of the deep state; but they are being gradually pushed out of it, both because they are on Trump's enemies list and because tech centralizes power and control far faster than artistic industry does. In other words, Hollywood is no longer useful and is being replaced with something worse.

[0] To be clear, about half the DNC was hoping a judge would block Lina Khan's lawsuits and make her look weak enough to be replaced with a stooge.
I’ve never encountered anyone turning it off. I avoid socialising with people who think it’s rude to use capitalisation.
I would say that the working, active logicians study and invent logics, not the computer scientists, who work with the narrower field of objects known as models of computation.
The kinds of intelligence they display is really interesting.

They can't figure out obstacles very well if they can see where they want to go, but are impeded. They just pace back and forth, frustrated, instead of walking around the obstacle.

They are very social, recognize people, and can be trained in some limited ways (eg. to return to the coop with whistles, if you associate it with treats).
You would be very amazed at how many people with reasonably strong resumes can't write _any_ code. Google for fizzbuz, it's a dumb problem, but candidates often can't solve similar problems with a _take_home_ interview.
You're missing the point, there are specific reasons why these stacks have grown in complexity - even if you introduce "API for AI interface" as a requirement, you still have to balance that with performance, reliability, interfacing with other systems, and providing all of the information necessary to debug when AI gets it wrong. All of the same things that humans need apply to AI - the claim for AI isn't that it deterministically solve every problem it can comprehend.

So now we're looking at a good several decades of us even getting our human interfacing systems to amend themselves to AI will still requiring all the current complexity they already have. The end result is more complexity not less.
A company wants to hire someone to perform tasks X, Y and Z. It's difficult to cleanly evaluate someone's ability to do these tasks in a short amount of time, so they do their best to construct a task A which is easy to test, and such that most people who can do A can also do X, Y and Z.

Now someone comes along and builds a machine that can do A. It turns out that while for humans, A was a good indicator of X, Y and Z, for the machine it is not. A is easy for the machine, but X, Y and Z are still difficult.

This isn't a sign that the company was wrong to ask A, nor is it a sign that they could just hire the machine.
Imagine if I asked you how many '⊚'s are in 'Ⰹ⧏⏃'? (the answer is 3, because there is 1 ⊚ in Ⰹ and 2 ⊚s in ⏃)

Much harder question than if I asked you how many '⟕'s are in 'Ⓕ⟕⥒⟲⾵⟕⟕⢼' (the answer is 3, because there are 3 ⟕s there)

You'd need to read through like 100,000x more random internet text to infer that there is 1 ⊚ in Ⰹ and 2 ⊚s in ⏃ (when this is not something that people ever explicitly talk about), than you would need to to figure out that there are 3 ⟕s when 3 ⟕s appear, or to figure out from context clues that Ⰹ⧏⏃s are red and edible.

The former is how tokenization makes 'strawberry' look to LLMs: https://i.imgur.com/IggjwEK.png

It's a consequence of an engineering tradeoff, not a demonstration of a fundamental limitation.
> A cold code review on a codebase they never saw

What do you think happens in the first few weeks of someone joining a new team? Probably reading a lot of code they never saw...

So yeah, I think it's the opposite: explicitly testing for their ability to read code is probably kinda important.
For completeness: I also disable all autocorrect/autocomplete on mobile because it's more trouble than it's worth, but I leave auto-capitalisation on. This is a thing, they're independent settings.
Considering how crazy the general population is (and other online spaces) it's a small miracle that HN still has this (sub?)culture.

Also things are happening at a breakneck pace and, uhm, the media is tragicomically incompetent.
> If a startup can spend 20 man-hours filling a single position, why can't a big company spend 1000 man-hours filling 50 positions?

Because big companies are run by bean counters and they also don't require the same kind of talent that is useful to startups. There's less competition for hyper-specialized seniors and middle of the pack generalists.
> If you offshore your coding, how do you know they're not using AI as well? How do you verify code quality?

All of these apply to remote-only teams. We know they work. (Note: I'm commenting more on offshoring than outsourcing. Outsourcing is fraught with issues. Offshoring once was, but is increasingly becoming viable, particularly with WFH and AI.)
Sort of off-topic, but is there any generative AI for code? From my limited understanding, the code is trained on human written code, and the model adapts it to what most closely matches.

What I'm curious about is, can it find innovative ways to solve problems? Like the infamous Quake 3 inverse-sqrt hack? Can it silently convert (read: optimize) a std::string to a raw char* pointer if it doesn't have any harmful side effects? (I don't mean "can you ask it to do that for you?" , I mean can it think to do that on its own?) Can it come up with trippy shit we've never even seen before to solve existing problems? That would truly impress me.

Take a bloated electron app, analyze the UI, and output the exact same thing but in C++ or Rust. Work with LLVM and find optimizations a human could never see. I remember seeing a similar concept applied to physical structures (like a small plane fuselage or a car) where the AI "learns" to make a lighter stronger design and it comes out looking so bizarre, no right angles, lots of strange rounded connections that almost like a growth of mold. Why can't AI "learn" to improve the state of the art in CS?
Discussion (410 points, 1 day ago, 174 comments) https://news.ycombinator.com/item?id=43104071
Yeah sure. But in terms of data security, data is safer at Google than hosted by some tinfoil hat wearing schizo in his basement
It's not her fault. However, as a taxpayer, I don't want my money to be taken from me (postponing my own retirement) to fund her retirement. And that is absolutely going to happen with insolvent pension funds.
That note about EU regulations may also be dangerous. There is an increasing trend of European leaders supporting censorship of speech, on weak justifications like misinformation that are applied very aggressively. There are even videos of police showing up at people’s homes in some countries, over tweets they made. I don’t have faith that these European LLMs will be trustworthy as a result.