skysight-inc/HackerNews huggingface dataset:
Yeah so MoE doesn't really come into play for production serving -- once you are batching your requests you hit every expert at a large enough batch size so you have to think about running the models as a whole.

There are two ways we can run it:

- 8xH200 GPU == 8x141GB == 1128 GB VRAM

- 16xH100 GPU == 8x80GB == 1280 GB VRAM

Within a single node (up to 8 GPUs) you don't see any meaningful hit from GPU-to-GPU communication.

More than that (e.g. 16xH100) requires multi-node inference which very few places have solved at a production-ready level, but it's massive because there are way more H100s out there than H200s.
Most european countries would like to disagree
I reject the notion that legal justification is necessary. Your LLM prefers "he" for doctors and "her" for nurses, and therefore you can be prosecuted by the state? That's ridiculous.

All such Harrison Bergeron-style speech-policing is antithetical to a free society.
> force China to send a few cruise missiles to TSMC before that happen?

Why on earth would they do that? China considers Taiwan to be rightfully theirs and would consider TMSC to be a valuable asset.
https://openai.com/global-affairs/introducing-chatgpt-gov/

And don't forget the billions in partnerships...
Can such a pathetic loophole really get past a judge?
Data is code, code is data.
What about AMD's MI300X? I see reports all over the web that it runs LLMs at a similar speed as an H100.
I bought it at launch and I'm satisfied. They fucked it up, then they fixed their fuckup for free. Sure, we can all complain and whine that they released a bad game, and they did, but they apologized and then worked hard to fix the problem. I think humans need to have more understanding for things like this, especially or something as objectively not-important as a video game. If this were the THERAC-25 I would obviously have a much different opinion, but it's entertainment so there's no sense in holding a grudge against them.
Repost of https://news.ycombinator.com/item?id=42862262 during actual hours when people are awake.
> They are doing (demos of new employees - demos of retiring employees), not just demos of new employees.

Source: I made it up.
> why don't they make iodized flaky salt?

They'll figure out a way eventually, but it is definitely harder.

It is always easy to grow something uniform as a pure crystal, without fault lines on them causing crumbling.

As a kid who spent a lot of time with chemistry, it used to fascinate me that you can crystallize out a clean salt crystal out of a mix of potassium permanganate and salt, the salt grain will grow pretty much pure salt on it without a hint of purple (also burned my nose skin off collecting chlorine from the exercise, talk to your local chemistry teacher and find out why).

I never succeeded in making a colored salt transparent crystal.
I used to use Sublime Text around 8-10 years ago, but once VS Code became a thing, I switched to that, then to Zed last year. Inspired by this post, I decided to give ST another try. Here's how it went:

1. Installed ST via brew, all good so far. Let's open my hobby project written in Go.

2. Syntax highlighting works by default, great! But uh-oh, there's no autocomplete or any LSP. Alright, let's install one.

3. Hmm I need to install package control first - right I'm starting to remember now. I'm thinking it's strange how they are still two separate entities.

4. Ok, PC installed. Let's install a... ok Package Control crashed...

5. Copying the error from the debug console points me to a two-year-old forum post where the accepted solution is to either remove OpenSSL (?!) or install a beta PC version (outdated now) or try to uninstall Package Control and
reinstall it.

6. Ok, how do I uninstall Package Control? The documentation says they are just package files stored on my machine. It doesn't tell me where those files are, and I can't find a menu point to open the folder containing them...

7. Open Zed.
> I remember a lot of praise for duck "typing" as if it was an innovation, and claims you should just write unit tests to catch type errors.

> People kept glossing over the fact that writing typed code would be so much easier than writing untyped code with sufficient manual tests to catch type errors, because no one did the last bit. Things just broke runtime instead.

That was the dumbest thing ever. Exchanging automation for lots of manual work was supposed to be innovation?

So many tradeoffs so sometimes someone could write a clever, unmaintainable bit of brainfuck in the production app.
> that's the "source code"

Then it’s never distributable and any definition of open source requiring it to be is DOA. It’s interesting, as an argument against copyright. But that academic.
Federal Reserve keeps rates unchanged
Fine tuning an llm on the output of another llm is exactly how deepseek made its progress. The way they got around the problem you describe is by doing this in a domain that can be relatively easily checked for correctness, so suggested training data for fine tuning could be automatically filtered out if it was wrong.
> Can you play games on your washing machine?

No, but it can spy on you, just like your Windows computer. /s
In that sense (having software for the most common of activities), all mainstream OSes (and some non-mainstream ones) have a “healthy native app ecosystem”. This is pointless copium.

What matters is stuff for which there are no workable alternatives elsewhere.

I can try and write a condescending automotive metaphor, if you like those, but I’d prefer not to.
Especially now that Taiwan has removed restrictions on producing TSMC's most advanced nodes on foreign soil.

https://www.techpowerup.com/331104/tsmc-granted-government-p...
A flexible, adaptive classification system for dynamic text classification
This is all a bit hyperbolic. The price is still over twice what it was a year ago, and the same price as in October, three months ago.
> It is, of course not going to produce a “child” model that more accurately predicts the underlying true distribution that the “parent” model was trying to. That is, it will not add anything new.

Unfiltered? Sure. With human curation of the generated data it certainly can. (Even automated curation can do this, though its more obvious that human curation can.)

I mean, I can randomly developed fact claims about addition, and if I curate which ones go into a training set, train a model that reflects addition of integers much more accurately than the random process which generated the pre-curation input data.

Without curation, as I already said, the best you get is a distillation of the source model, which is highly improbable to be more accurate.
The TSMC Arizona plant is already operational they started production of TSMC 4nm process node this month, and will be ramping up volume over the next few months.

https://www.reuters.com/technology/tsmc-begins-producing-4-n...
I'm not against the idea of cutting the government's budget, but this administration is trying to perform surgery with a sledgehammer.

Besides, solving the real high level problems are going to be a disaster.

There are two ways for them to cut the budget. 1: Cut programs - 2: Increase efficiency

1. Reduce programs. The problem for them is that Red states are overwhelming subsidized by Blue states. Just cutting programs across the board is going to hurt their constituents the most. https://apnews.com/article/north-america-business-local-taxe...

2. Reduce costs. The reason government projects are so expensive is because of their insane review and oversight requirements. And the reason they have these is because of their constant exposure to lawsuits from citizens and NGOs. Cutting costs would involve eliminating review processes and recusing planners - you would effectively need to increase the power of the federal government to make it more efficient.

I expect the administration to come back with a smarter list and a more targeted set of grants to cancel. But this whole endeavor is going to be a massive lark for them.
At same time, this has real effects, so I wouldn't be so dismissive of it. I actually know a parent of a friend who had the whole "deep brain, computer in chest, app to adjust it" set up installed somewhat recently (not sure if exactly the same as article), and it's a game changer for her. I mean the difference is huge, and quite striking that it can be activated/deactivated at will and the effect so dramatic.
Please consider getting in touch with a problem gambling hotline. This isn't Internet snark--going into debt to play the stock market game is a serious red flag.
I assure you I'm no troll. I think difference in our perspectives is that I form the basis of my "moral" at the individual vs. the collective.

"Moral" for me means that individuals are empowered to own their private property and should only need to sacrifice it to society for public goods, where I take the economic definition of a private good: nonrivalous and nonexcludable.

Forcing all persons to pay taxes to cover healthcare for only a subset of the population is, to me, akin to forcing all your friends to give to the charity you like because you like that charity and want it to be able to do more, where that more is a level of spending above what you can or are willing to provide.

Economically, this creates deadweight loss: people's individual preferences are violated because they are forced to spend money for something of which they receive no benefit, or at least the direct/indirect utility occupies a lower utility than the opportunity cost of those specific taxes.

I'm not saying that such a policy won't result in undue death. But since I use the individual as the basis for morality, I consider it more moral to have some death than it is to steal from others to prevent it.
Nvidia is the only one that does this on the sort of scale these AI companies need. They do boxes packed full of AI accelerators with custom high speed bus connectors and high end interconnectivity to make super computers full of GPUs very easy to purchase combined with the software to make it all work. No one else has that sort of integration to the very large that is necessary for the sort of training the top firms are doing.
I love ruby for relatively small scripts, but I wouldn’t want to attempt anything very large with it. I find that once my scripts start to get too long I find the need to start adding manual type checking to keep from tripping over myself.
Those companies can't produce money to "pay off debts", increasing the defecit. So I'd say there's a fighting chance there.

I'm no economist nor businessman though, so I won't pretend I can answer that with confidence.
YC Directory for Every Fund
I am not in this space, question: are there "bad actors" that are known to feed AI models with poisonous information?
So, this has nothing to do with "SmolLM" - a set of models (with data, training recipes, etc) released by HuggingFace? https://huggingface.co/blog/smollm
Polypropylene seems to be a fairly biologically safe plastic. It makes a great base layer. And we serve hot foods in it.
SMR is discouraged for DVR use, many vendors (e.g. Ubiquiti) specifically ask for CMR drives.
Why would bias unlearning cause performance loss? If bias is something wrong shouldn't removing it result in better performance? Is it truely bias unlearning or just training the model to be biased towards equality and against stereotyping?
This situation has been and maybe always will be common. Those who view themselves as integrated manufacturers of *products* often cannot equally package, deliver, and sell their component *services*. No matter how good those services are at delivering the end product—and sometimes they are completely world-class or genuinely unique—they're almost always highly designed, refined, and evolved over years to map to the specific end products and their one business. They are NOT designed, refined, or evolved to be reusable, retargetable capabilities for all interested parties.

By any rights, the major systems manufacturers of the 2000s—Cisco, Dell, HP, IBM, Sun Microsystems—should have had an enormous leg up in becoming the planetary scale, "red shift" cloud computing providers of today. Let's kick major operating environment vendors of that era in for good measure—Microsoft, Red Hat, VMware. Maybe even Oracle and SAP. They had the technical capabilities, the economies of scale, the vendor ecosystems, the customer relationships, the financial might. "Permission to win." Yet, only a few have made it. Microsoft has made the transition with Azure, but it's been a long hard pull. Maybe a few participation trophies for IBM and Oracle. But as a group, where are they, cloud-wise? Not too impressive that AWS and Google and others that had no classic "required assets" or "permission to win" have wiped them off the playing field or kicked them deep into the "others" bin.

You can call that classic innovator's dilemma, and there's a lot to that. It's insanely, unfathomably hard to disrupt your own current successful business for new delivery models and aspirations. But there's something else as well: services and products are different beasts. They take different mindsets, expectations, business models, and metrics. All the best to Intel in becoming that new thing, a service provider. But it not just a side gig or slight extension. It's a radically different thing, and it's no wonder the highly successful product Intel of 2011 wasn't also a good service provider.
Explanation: Iodine is a molecular element, it consists out of only one type of atom. Atoms being synthesized implies some nuclear reaction or decay going on, both would cause radiation.
Apple was after nothing

Apple was after “ios and downloadable code for vms/jits that we cannot control is a recipe for disaster” really. And it was.

Partly due to ios “security” based on appstore checks rather than technical ways (private ios apis, calls allowed only under specifically claimed features, and just bugs). Partly due to the fact that running something locally is prone to local vulnerabilities naturally (the same category as “never run curl | bash”).
On the LSP stuff, yes it did originate in VS code. I just find the experience in Sublime to be better. How ironic is that?

I didn't consider the conflict between how I said "try the OG" but then say "VS code is the OG". It is a good point.

I show an example of the block level key binding. So yeah, I needed it and used it. I only showed one example but I have a few more that are my own I just didnt write about them.

Around python vs. js for plugins, have you tried to make a VS code plugin? You need a package.json, npm, and vsce installed globally. Which language is being used is usually the least of my problems. For Sublime, you need a single .py file! Someone shared this 9 line plugin they made: https://gist.github.com/ckunte/31500c17452b0fd8c55bc9460bd9c... - I don't tthink plugin development could be more simple

I bet an LLM could spit out single file plugins very easily. VS code plugins are clearly more work to create and deploy even after taking into account my critiques of Package Control. At the end of the day you can just toss your plugin in a folder or push it to github and reference it with a URL.

I didn't say the docs were "terrible". I just said they were disjointed. They are complete and fully document the APIs. I just wish they were more like the PHP docs or the ones for VS code which are docs plus guides.

All your other points are fine critiques. I'll chalk the other complaints up as a matter of opinion
If your data is too sensitive for AWS, you're in a different realm that most enterprise users.
Ok, deride away. You may find it useful to know that "Cali" is a frequently used shortening of "California". Good luck with the crusade.
Honestly, they should do that, it would force their competitors to actually improve their own, and if they don't well, there you go, really great funding your way.
Came here to mention this too. Seem almost so obvious that I'm surprised this isn't the dominant angle.
> This is probably a massive overreaction.

You may be rights but here is the other side. The AI/software/hardware/data center/energy trade got very crowed by levered money(Hedge funds).

1) That trade has had a huge run up lead by NVidia with alot of funds hedging their gains till the new year for tax reasons. They were al looking for a reason to sell and now could continue to lighten even more.

2) Nvidia is a large portion of the market right now. If you are a hedge fund that is measured against a US index then you probably have bitten the bullet and bought some NVidia exposure so you don't underperform if it continues its growth and the AI story fulfills its promise.

Now that NVidia is falling and you can make a story that it may under perform the market t his year you may see even more fast money dumping it as they no longer need to worry as much about it skewing market returns as much now.

3) if its true that you now need atleast an order of magnitude less power and chips to train a model then Nvidia was due to have a multiple decrease. This knocks onto the data center and power trades that also will have a multiple decrease on their pricing.

You may be right that this is an undeserved sell off, but given the recent growth it also seems like you can make the case that the whole trade got ahead of themselves and we were due a pull back to more properly value these companies earnings prospects.

One thing that is always useful to remember about the markets is that when you are priced for perfection like NVidia was, its signs that your long term growth may be slowing will affect your share price more than short term signs that your are doing what was expected of you.
> So if you release the feature branch every 3 months after a few years you will have a dozen maintaining branches to backport fixes to, and in which to carefully test that the fix actually works. The problem is linearly worse if you release the feature branch more often.

This has been solved by LTS releases for some time. You have a newest release branch that gets feature updates immediately and an LTS branch which has a full release e.g. once every two or three years, at which point it catches up on features to the then-current newest release branch. The newest release branch doesn't have long-term support and you're expected to take the latest feature update, the LTS branch gets only security updates, the user can choose between the two or three LTS releases currently in the support window or the newest release branch.
Tapping into the natural aromatic potential of microbial lignin valorization
How Renaissance artists were trained
Great comment, even though I'm concerned whether my Pixel is a ticking bomb now. The update has been downloaded to my device, but before installing it, I decided to check Reddit and found out the battery issues. Since then, I'm dismissing the update prompt, praying to not miss-click.

The article got it wrong - even before the update has been published, Google already sent e-mail to registered users with a note that the upcoming update may reduce battery life and offered either battery replacement or money: https://support.google.com/pixelphone/answer/15701861 But the results people post are way beyond anyone expected.
There’s no evidence.

We can talk about hypotheticals all we want, but who wants to do that?
I looked at it when it hit HN a while ago. Looks nice, but not exactly what I want because

> Joins and source tables are automatically resolved in Trilogy. You won't ever explicitly specify one in your query; you're declaring what you want, not how to get it. The responsibility of how to get it is delegated to the semantic model.

I don't fully understand how the semantic model is created (is this documented anywhere?), but I don't think I would enjoy a hard separation between the query layer and the semantic layer regardless.

I would prefer a continuum of "how much indirection do I really want" to be available to the user. My own exploration of this topic is https://docs.racket-lang.org/plisqin/index.html and you can compare section 2 (Using define-schema) to section 7.2 (Plisqin Desugared) if you want to know what I mean about a continuum.

Unrelated, the article we are commenting on has inspired me such that I think I have an answer to the big type system questions that eluded me when I decided to put Plisqin on the shelf. Maybe time to pick it up again... but probably not.
App development company near you
Oh, that's frustrating. I think it'd be great to have some more concrete guides for modern Sublime. Maybe I'll write one about getting TypeScript and everything working starting from scratch
Yes, that is my exact argument.
That is an incorrect assumption. In fact, I bought it for full price at launch.
Yeah, from Wikipedia:

> An essential nutrient is a nutrient required for normal physiological function that cannot be synthesized in the body (...) The nutrients considered essential for humans comprise nine amino acids, two fatty acids, thirteen vitamins, fifteen minerals and choline.

While some of these can be synthesized by other animals, this is not the case of minerals like iodine. I believe only organic compounds can be synthesized by animals.
Bit of a bloated post. Seems to restate the general point at least once per paragraph (of which there are far too many), including some vaguely related tid-bit.
No grudge, but as I understand it the game loop is still too "make your own fun." Has that part changed yet?
* exempting shrapnel
Oooh, or an Obsidian competitor that doesn't take 10 minutes to boot.

Some notetaking app would be amazing
The existence of R1-zero is evidence against any sort of theft of OpenAI's internal COT data. The model sometimes outputs illegible text that's useful only to R1. You can't do distillation without a shared vocabulary. The only way R1 could exist is if they trained it with RL.
> it's always true that training on the output of another model will result in a worse model.

Not convincing.

You can imagine model doing some primitive thinking and coming to conclusion. Then you can train another model on summaries. If everything goes well it will be coming to conclusions quicker. That's at least. Or it may be able solve more complex problems with the same amount of 'thinking'. It will be self-propelled evolution.

Another option is to use one model to produce 'thinking' part from known outputs. Then train another to reproduce thinking to get the right output, unknown to it initially. Using humans to create such dataset would be slow and very expensive.

PS: if it was impossible humans would be still living on the trees.
I think it would cast doubt on the narrative "you could have trained o1 with much less compute, and r1 is proof of that", if it turned out that in order to train r1 in the first place, you had to have access to bunch of outputs from o1. In other words, you had to do the really expensive o1 training in the first place.

(with the caveat that all we have right now are accusations that DeepSeek made use of OpenAI data - it might just as well turn out that DeepSeek really did work independently, and you really could have gotten o1-like performance with much less compute)
I discount this because OpenAI is pumping the whole internet for money, and Zuckerberg torrented LibGen for its AI. We cannot blame the Chinese anymore. They went through the crappy "Made in China" phase in the 80s/90s, but they mastered the art of improving stuff instead of mere cloning, and it makes the big companies angry which is a nice bonus.

IMHO the whole world is becoming crazy for a lot of reasons, and pissing off billionaires makes me laugh.