skysight-inc/HackerNews huggingface dataset:
I think adding the ability to order by different columns would be good.

I'm actually not 100% sure if I can achieve that with dynamo db and my current data structure though. It has a great free tier, but the query language and limitations are very foreign to me.

If I wanted to sort by timeTaken, I had to set it to be one of the two keys. This also has the unfortunate consequence of blocking duplicate times.

It might just be my implementation that is strange, or the dynamo is significantly less featureful than an SQL server.
> If that holds for Caltech applicants, which is reasonable to assume

Caltech is an outlier if there ever was one, so why would it be reasonable to assume Caltech’s applicant statistics reflect broad population SAT statistics?
Last time it didn't work out , so it s not always
I'm not a rust expert by any means, but I believe there's a problem with lifetimes. There are many ways to implement double-linked lists (think C++ smart pointers), but when you try to squeeze performance and use references, then it's a fight against borrow checker, which ends with the need to use unsafe rust.

smarter sources:
https://rust-unofficial.github.io/too-many-lists/
https://softsilverwind.github.io/rust/2019/01/11/Rust-Linked...
> We now have a single, unified code style among all modern projects

Yes I mentioned that, and it's kind of ridiculous how much importance people put on it.

If you can't use another library because they use tabs rather than spaces, that should be a red flag.

> a universally supported logging library

PSRs don't define a logging library. They define a logging interface, and it's based around strings rather than the literal industry standard of SYSLOG levels, because a single implementation that pre-dated the PSR, uses integers but not SYSLOG.

If you think it's a good idea to use a logging library written by someone who doesn't understand syslog, or to use an interface written by people who think it's better to make up shit to accomodate said people who don't understand syslog, be my guest.

But as someone recently said:

> I’m not going to touch that with a ten foot pole
Spring Declarative Caching
The 800 million number is straight from the World Health Organization. The one you are linking to is a third party that says that the World Food Programme organization reports 330 million in 78 countries.

Here are two things to consider:

* the world has more than 78 countries. There are people affected by hunger even in first world countries. "Hunger in the United States of America affects millions of Americans, including some who are middle class, or who are in households where all adults are in work." [1]

* While 330 million is lower than 800 million, it is still immensely bad.

[1] https://en.wikipedia.org/wiki/Hunger_in_the_United_States
Yeah, as predicted. As a film guy I told my totally hyped colleagues a few years ago that 3D films are not going to stick in the way they expected. When Bitcoin and crypto currencies started to become the next big thing I was the only person in my circles that had actually tried purchasing something with it in a real world setting, years prior. When LLMs became The Shit, I warned against overblown expectations as I had some intuition a out the limitations about it stemming from my own machine learning experiences.

And the only reason I was right all these times was because I looked at the technology and the technology did not remotely convince me.

Don't get me wrong stereoscopic Films (or 3D as they called it) are impressive in terms of technology. But the effects within movies doesn't bring much. The little distance that remains when people look onto a screen instead of being in a world is something many people need. 3D changes that distance which is not something everybody enjoys.
this article is completely bogus. there is no shortage. this was done on purpose via government policy
I think we’d need to dig into the 80% that failed and what kind of “AI project” they were. Is this really R&D? Or were you trying to insert AI into something that didn’t need it, and failed because no one is using your expensive and annoying “chat with us” popup that your VP insisted would keep your company competitive?
I recently had the opposite, where the CEO of the startup was killing almost all ideas of adding AI to their products. Perhaps AI is just polarizing. Some companies are jumping at the opportunity. But older startups like the one I was working for are being ultra-conservative with spend and are maintaining a wait-and-see attitude.
Ok yes, thanks. 4-40 decades, so 40-400 years, as you say.

Likely 40 years eventually, for the first trip, because no point sending a craft that takes 400 when waiting results in tech that takes 40.
This sounds like a conspiracy theory to me.
Any request from the government can be characterized as pressure.
> But yes, a tax on "unrealized gains" basically amounts to a property tax, not anything related to an income tax.

The main difference being that a property tax only takes into account the assessed value and ignores what you paid for it. They tax the value, not just unrealized gains.
I think part of the reason apps like Photoshop and Word got bloated is that their audience got watered down and are fairly mainstream software.

I'm sure there's a lot of old niche software that feels modern and unbloated. Nuke[1] is 31 years old and Houdini[2] is 27. Maya[3] is 26 years old. I would say Maya feels bloated--but at larger places I've worked, people have avoided the newer viewports with more features because the "legacy" viewports are so fast.

It may be hard to make it feel "modern," but I love how optimized "old" code can be if they've been able to resist rewriting it.

[1] https://en.wikipedia.org/wiki/Nuke_(software)

[2] https://en.wikipedia.org/wiki/Houdini_(software)

[3] https://en.wikipedia.org/wiki/Autodesk_Maya
>> Second, many AI projects fail because the organization lacks the necessary data to adequately train an effective AI model.

> This is interesting, and reinforces the trend towards hoarding data assets.

Companies completely misunderstand where the data is suppose to come from and attempt to hoard user data. The issue with LLMs is that the problems they are currently best suited for require data generated by the company. Manuals, decision trees, guides, tutorials, expert knowledge in general and companies aren't producing that material, because it's expensive. Also if that data existed, then maybe they wouldn't need an LLM.

Tons of LLM implementations are poor attempts to cover up issues with internal processes, lack of tooling and lack of documentation (without which the LLM can't function).

I'd say 80% has failed, so far.
> But #2 is a good example of "show, don't tell" which is arguably a better writing style.

I think both examples are almost purely "tell", where the person who went to the doctor is telling the listener discrete facts about their doctor. The difference is that the second retelling is awkward, unrealistic, likely a lie, and just generally not how humans describe certain things in English.

In contrast, "showing" the doctor's traits might involve retelling a longer conversation between patient and doctor which indirectly demonstrates how the doctor responds to words or events in a careful way, or--if it were a movie--the camera panning over the doctor's Certificate Of Carefulness on the office wall, etc.
Nailed it, More often it is the water than the other ingredient even with the coffee
Common issue when new tech comes out. The people who know the tech, but not their companies business focus on the tech. Many of them will get promotions, and make their way up in the company. The company will lose likely millions, and the guy will leave to damage another company. If the company is lucky, another person who takes the time to understand the companies customers will come in... throw away the stuff their predecessor built, and solve some real problems. If the company is unlucky, they will double down on the over complicated solutions, and lose to a startup that ignored the sexy stuff and focused on the customer problem.
I work at a financial services company that is quite behind their peers tech-wise and watching the internal politics of AI has been fascinating.

Management seems to see this as their opportunity to catch up on the cheap.

Rather than having modern tech systems and properly staffed engineering department, let's just uh.. have non-technical people do AI hackathons! Also instead of automating excel jockey jobs with server side data pipelines, what if we.. you guessed it.. gave the excel jockeys AI!

I can imagine this playing out in a lot of industries where the underdogs think its a shortcut and yet..
> This is an impossible task and you know it. Asking your opponents to enumerate every dead comment on a thread with hundreds of comments is not approaching the issue in good faith.

No, it's not impossible. I count 15 dead now, not "hundreds" (when I said that originally, it was about 5).

Let's make it easy: why does bigbacaloa's go, and all the others stay?
This is where we always assumed the industry was going. Expensive GPUs are great for training, but inference is getting so optimized that it will run on smaller and/or cheaper processors (per token).
Has everyone forgotten when we had an oil well leaking .8 miles underwater and we couldn’t do shit to stop it?
Shorts I think are only useful if there is an imminent and concrete devaluation event such as defaulting on credit etc. A general predicted market downturn is hard to tie to stock behavior, especially within a concrete time frame.
> > Fourth, organizations might not have adequate infrastructure to manage their data and deploy completed AI models, which increases the likelihood of project failure.

> Sounds like lack of capital.

I actually think this is also an engineering problem, or at least a 'human capital' issue. The skillset for developing an AI model and the skillset for deploying a massive data-based product are highly different, but people who are good at the former often get press-ganged into doing the latter. This is kind of a capital problem (more money means maybe they can hire a second person to manage the operations), but I think it's also just a general lack of awareness that MLOps is really it's own thing. Especially when you're moving fast, tech-debt with these systems builds up really quickly (shockingly quickly). More money lets you hide these problems better, but IMO the solution is only going to come with time as people develop better and better best-practices for this type of project.

edit: There's a section in the full report called 'Too Few Data Engineers' that does a better job making this point. Everybody wants to make fancy AI models, nobody wants to be responsible for the 10K lines of uncommented Python and SQL you're using to build your test/train sets
that seems really tough for compilers.
However, there is the estate tax.

If you're a billionaire who does the "take out loans against your unrealized cap gains" trick, then you, you know... can't sell your stock. So then your stock passes to your kids -- who, due to the stepped up basis, yes, do not have to pay cap gains on that stock.

But there's a 40% estate tax.

Estate tax generally isn't very relevant even to the ordinarily-rich, because it has an extremely high deduction (about $27M for a married couple), but for a billionaire it's absolutely relevant.

Now, sure, if you paid both the cap gains and the estate tax you'd pay that much more taxes, but if you compare a normally-wealthy person (pays 15-20% cap gains and 0% estate tax) and a billionaire (pays 0% cap gains and 40% estate tax), it's obvious that the billionaire, eventually, pays a much higher tax rate.
You were not kicked off the platform. You could join any other thousands of rooms, boards, etc. And you were only booted if you truly were a real dipshit.
I wish there were more details on what he wanted on the manufacturing side.
No one seems to be telling who got pwned. Another article suggested one of the ISPs was a "big one".
I agree that the vast majority of comments people would like to make on political/social topics violate the HN rules. Having seen political threads on Reddit, where any genuine insight is buried under a flood of namecalling and polemics, I think that's for the best.
The sequel to the CharaChorder One
According to Wikipedia itself, the WHO seems to find it a likely theory...

>The report added weight to calls for a broader probe into the theory that the COVID-19 virus could have escaped from a laboratory.[6][7] However, a WHO report states "introduction through a laboratory incident was considered to be an extremely unlikely pathway".[3] Since then, the head of the WHO COVID-19 origins investigative team, Peter Ben Embarek, has stated that the Chinese authorities exerted pressure on the WHO report conclusions, and that he in fact considers an infection via a researcher's field samples to be a "likely" scenario.[8]*

* https://en.wikipedia.org/wiki/Timeline_of_the_COVID-19_pande...
> even the Gnome team

People like to hate on them, but their design is actually really good, innovative and the applications running on GTK are incredibly fast and stable.
I'm genuinely interested in understanding why you thought that ChatGPT would be a valid source. I'm not being facetious.

Edit: typo
It is easy to create content calendars for your social media marketing
Yep it is fast. Now what exactly is Llama 8B useful is another matter - what are some good use cases?

One scenario I can think of is rolepaying - but I would assume that the slow streaming speed was kind of a feature there.
I'm not sure I understand the prime density thing. Of the numbers up to 8258, about 12.5% are prime. Accounting for the fact that about a quarter of these primes ends in 101, i.e. cannot occur, I would expect about 10.7% = 12.5% * (3/4) / (7/8), which is fairly close to the observed 9.4%.

The 2.1% in the README seems to be the density of primes < 1000 among numbers up to 8258. That's not what was counted.
You can make whatever excuses you want about any individual case, but this discussion is about clearance rates, and it’s obviously preferable for a larger proportion of homicide cases to be cleared within a reasonable time span.
There aren't very many nuclear-weaponized countries in the world right now. Otherwise, the whole Iran Nuclear Deal issue would've been moot. Even Russia won't hand over nukes to Iran.
Blitzortung – real time lightning strikes around the world
By the time solar becomes a problem we have bigger issues. Namely the sun trying to eat the planet.
So if you had 3 markdown cells and 3 python cells, I would design the tool to pull all the content out of those cells and present it (sans all that ipynb markup, just contents, probably in markdown) to the model as the full context for every edit you want to make. So the tool would need to know how to transform a given notebook into a collection of markdown/python cells which it would present to the model to make edits. The model would need to return updated cells in the same format, and the tool would update the cells in the document (or just replace them entire with new cells from the response). I would be fine with this just blowing away all previous evaluation results.

Do you think that approach would work? Not sure if I'm misunderstanding the issue you're describing and I recognize it is likely much messier than I imagine.
> I do not know if there is a clean, ethical way to "run the social media"

... or any media. The messenger cannot not shape the message even if he tried. If he becomes a mere conduit, someone else will shape the message. People are (trained to be) emotionally-driven and thus their biases can be shaped
We're not talking about censoring spam, we're talking about real, verified people who are obviously not spamming being censored. The two are unrelated, and Zuck is admitting here that the current administration pushed them to censor things they fully knew were not spam.
>he sees the tea leaves in where things are headed in November

Things are headed strongly in the opposite direction you're implying.
I'm pretty far out the mainstream thinking on some topics and I've felt like I've been treated fairly in most interactions over the years on HN.
You prescribe forbidden lines of progress.

But profitable problems get solved when they are solvable.

And advances in different areas are increasingly non-separable.

—

In any case, space resource extraction & manufacturing will replace harmful counterparts on Earth.

And provide vast wealth, sustainable materials, energy, and other advancements that could enable serious Earth greening & rehabilitation efforts.

Better to welcome & harness increasing options, than command the tides to stop.
Broken windows theory: actively moderating is precisely what keeps shit posters away. There's no gain from doing it when their posts are removed so they give up quickly.
Ideally, I could fix the mousing clicking and add targets that you need to delete.

I'm actually curious who would win that race. Because you're right; mouse users have a huge advantage right now, but no one just clicks on code haha!
Just stop using platforms. For video, use PeerTube (or similar).

This was a concern in 2009, but now, 15 years later, people using platforms have only themselves to blame. Stop being part of the problem !
> It also provides the argument that wanting to bury reactors 1 mile deep shows how incredibly dangerous nuclear power really is.

If this were true we would have buried coal plants decades ago
>> By some estimates, more than 80 percent of AI projects fail — twice the rate of failure for information technology projects that do not involve AI.

So 40% of projects with more proven/experienced technologies fail? That's super high. Replace "AI" with any other project "type" in the root causes and sounds about right. So this feels more of a commentary on corporate "waste" in general than AI.
1. Capital gains tax is already essentially optional for the richest now with various tricks. Of course taxing people is difficult, are you saying because it’s hard let’s not bother?

2. Where will the capital go (all the best investments are in the US), if this happens lots of great businesses will be available to buy at a discount to people with smaller than $100m stock portfolios

3. Potentially true but I would still set up my business in the US and just pay the tax, if I make $100m it’s $20m for the government and I rate that as a great deal to be honest.

4. Why is a one off 20% tax going to lose founders control, this is only about companies post IPO.

5. IANAL are you?

6. If the rich continue to be able to accumulate wealth without paying taxes on it forever I think that is the road to serfdom personally. Taxation of the rich will make everyone better off. I pay over 50% tax in Europe, maybe if the rich were paying their share this could be reduced!
Bluesky's custom CSS letter-spacing is ugly
I'm not sure the second statement is true - there are plenty of sectors I can think of which could be massive winners that would make up for the failures.

Obvious examples are things like AI-assisted language translation for books if you are a publisher, implementation of cancer screening technologies if you are a healthcare provider, AI-assisted drug discovery if you are a pharma firm, AI-assisted discovery if you are a legal provider, AI trading algo's if you are a hedge fund...

All of these could result in some pretty profitable winners. It's also the sort of thing that can result in 'losers' if you call it wrong - e.g. if there is a 20% chance of success and I don't invest and my competitors do, what happens if I am wrong? You don't want to be the Kodak of your industry. As an example - if I am the worlds biggest provider of call centres, do I really want to bet big that my industry will never be automated, or do I want to start investing now to prepare for that as a possibility?
>The HN crowd is far right

???

Is anyone who isn't a card carrying DSA member "far right"?
Usually an off day is for kids or other urgent matters, such as water leak or basement backflow.

Our invisible middle aged family man/woman does not have the luxury to be "off".
I thought it was programmer and software engineer?
The numbers are pretty incredible. Will the competition be able to match them?
No, any ISA pretty much should be equally good for any type of workload. If you are doing assembly programming then it makes a difference but if you were doing something in Python or Unity it really isn’t going to matter.

This is more about being free of ARM’s patents and getting a fresh start using the lessons learned
Center-right, I'd argue, but that's true of the Democratic party. HN is very far from far-right in that bigotry and racism isn't tolerated here (nor should they be). But HN is USA in origin and USA politics are further right than most of Europe.
I'll invoke a no true Scotsman argument here.
I just filed this bug. Is it just me, or is anyone else bothered by Bluesky's oddly spaced-out typography?
Users who’ve paid for it can continue to use it indefinitely, albeit with no support after 1 year from now. The fridge analogy is wrong. MakeMusic is not going to remove the software from your computer.

> it’s not a major expense (also happens to be a fixed cost)

That’s unlikely to be true, and not anyone’s decision but MakeMusic’s. What if they don’t have the money? It’s not reasonable for me to ask you to pay me $10 on the grounds that it’s not a major expense and is a fixed cost, right?
This seems like a very strong selling point for B2B AI providers versus in-house enterprise builds of AI.

> First, industry stakeholders often misunderstand — or miscommunicate — what problem needs to be solved using AI.

The provider at least partially validates that this is a problem space that AI can improve which lowers the risk for the enterprise client.

> Second, many AI projects fail because the organization lacks the necessary data to adequately train an effective AI model.

The provider leverages it's own proprietary data and/or pre-trained models which lowers the risk for the enterprise client. They also have the cross-client knowledge to best leverage and verify client data.

> Third, in some cases, AI projects fail because the organization focuses more on using the latest and greatest technology than on solving real problems for their intended users.

Provider, especially startups, will lie about using the latest tech while doing something boring under the hood. This, amusingly, mitigates this risk.

> Fourth, organizations might not have adequate infrastructure to manage their data and deploy completed AI models, which increases the likelihood of project failure.

The provider manages this unless it's on-prem although in the latter it can provide support on deployments.

> Finally, in some cases, AI projects fail because the technology is applied to problems that are too difficult for AI to solve.

Still a risk but a VC or big tech budgets covers that so another win.
I'd argue what they REALLY need is domain experts who can write a good prompt.
Sounds like the burning of the library at Alexandria, but worse.
This observation is always highlighted by the absurdity of american politics when they describe candidates like Joe Biden as "far left" when on the european political spectrum (or even an absolute one, if such a thing exists) he'd almost certainly be on the right.