skysight-inc/HackerNews huggingface dataset:
When you send an email, there's your conscious intent behind it. So it doesn't matter what technology is in between, as long as your mind is moving it. If you didn't intend it (as in, I know you are on vacation and send you an email saying "if you agree to pay me $1000 send me back a vacation reply" then your mail system sending me a vacation reply does not constitute an intentional action, because it would send the reply to anything. It is true that I am not a lawyer, but laws often make sense, and derive from common sense. Not always, but is such a fundamental matter as contracts they usually do make sense.
4) Develop your pieces first.
5) Castle.
6) Don't move the queen prematurely.
7) Analyze all your games.
Exactly this. OP, credit where credit is due, appears to be someone who “hacks things together” copy pasting solutions blindly from the internet - with little intuition gained along the way.
VR is as dead this time around as it was in the mid-2000s and the mid-1990s and the mid-1980s, each of the times I've used it it was just as awful as before with nausea, eyestrain, headaches, neck and face fatigue, it's truly a f**ed space and it's failed over and over, this time with Apple and Facebook spending tens of billions on it. VR is a perfect reply to your question here.
I've missed the stories on this until now. Is it known (and is there an ELI5) how they were able to do it so much more efficiently?
Pay-as-You-Go Is the Best Pricing Model for Small Businesses
Pay-as-you-go has several clear benefits especially for startup pricing model. There’s a very low barrier to entry for customers, and offers them more flexibility around the products they want and when they want them.

Why it is essentially good for startup pricing model

- Smaller barrier to entry
- No commitment
- Better cost-per-use

A customer doesn’t need to commit to a prescribed plan and get charged on a monthly basis. They just pay once for a product or service and then start using it. It works well for products that are needed infrequently.

Consumers don’t have to make a commitment up front and can budget accordingly. Pay-as-you-go works for well in regions like Africa and I think that is why some international businesses struggle to gain market in certain region.
Evidence suggests Neanderthal blood protein may be part of reason 4 their demise
Why is 0xxx stripe so dark?
Why ADR v/s Shift-left is the wrong way to think about AppSec
Like I made very clear, it is great at some things and terrible at others.

YMMV. /shrugs/
Trilinear requires an image pyramid. Without downsampling to create that image pyramid, you can't even do trilinear sampling, so your argument strikes me as odd and circular. Like telling developers of APIs such as ID3D11DeviceContext.GenerateMips to simply use ID3D11DeviceContext.GenerateMips instead of developing ID3D11DeviceContext.GenerateMips. Also, I never took this article to be about 3D rendering and utilizing mip maps for trilinear interpolation. More about 2D image scaling.

Have you never downscaled and upscaled images in a non-3D-rendering context?
Thanks for telling me how wrong I am! I bet you're fun at parties.
I had my post removed a couple of weeks ago for linking to aeon desktop (immutable opensuse).
This take absolute boggles the mind. You don't want people compiling your code with different versions of tools so you have to debug thousands of potential combinations of everything. You don't want people running different versions of formatters/linters that leave conflicting diffs throughout your commit history.
But you can dissect it into concepts and see if it is something truly new to the model - if the output contains things which aren’t there in the weights, you have a nice specimen to study and, crucially, a recipe to get a bunch of matrices to output untrained things.
Jeez chill... it’s just back to where it was 4 months ago and even after the drop it is still up 100% compared to this time last year! And it’s all fake inflated money.

This unprecedented growth simply couldn’t continue forever.
Everyone can say things that sound smart. When it comes to markets the only thing that matters is if your portfolio was green or red.
IIUC they released a paper, it's partially algorithmic improvements partially good old low level optimization.
The models are open because if the enemy uses them they will be lagging behind. Seems like this tactic didn't work with the chinese.
um, not really. just because a tech does not gain popularity and withers on the vine has nothing to do with code and hardware maturity

you're rant on VR is just weird and out of place here
>You’re taking a definition that arose from animal observations and then filtered through anthropological and management academia. Colloquial, academic and industry definitions have diverged on this term; it’s probably being redefined in American English right now.

I cited two dictionaries as well.

>Chickens are the correct analogy. If you’re leading a team of people who remind you of chickens, leading with aggression works. (Not exaggerating. Some situations respond well to e.g. raising one’s voice or acting out frustration.)

Analogies were never part of the equation. You know they weren't. You were talking about animals explicitly not animals as an analogy. I feel you're trolling.
Can anyone comment on why Wenfeng shared his secret sauce? Other than publicity, there only seems to be downsides for him, as now everyone else with larger compute will just copy and improve?
>Because I'm not trying to criticize Elon Musk here,

But you (and others like-minded) are. You're latching on to a Nazi salute you've projected onto him and trying to use it as the point of criticism. You yourself just admitted you "see [a] perfect sieg heil", even.

>It seems like you agree that the motion is identical, but you are saying that it is a sieg heil but not a 'nazi salute'

He did it while shouting "My heart goes out to you!" to the crowd, so it's not a "sieg heil" either. If he's saluting anyone/thing at all, he's saluting the people and the spirit of America.

I couldn't care less if it's identical, though if you really want my honest opinion on judging that: I wouldn't think of Adolf Hitler as the first thing; then again I don't have my mind in the proverbial Progressive gutter either.

>elon musk "does not" support racism, advocate for cruelty, regulate speech, or centralize power.

Yeah, or to rephrase: In what ways is Elon Musk a Nazi? In the true, real, proper sense of the term "Nazi". Not the dirty word "Nazi" that gets thrown around like candy on disagreeables.

What I think you need to ask yourself is: Why is "Nazi!" the first thing that comes to your mind and is that really justifiable?

You're quite welcome to hate Musk if you want to, I have no problem with that and Musk can defend himself if he feels the need. I couldn't care less. But I am concerned for your mental health if you need to scream Nazi allegations to justify it.
> God I'm glad I didn't record all of this on a cell phone or use social media. It would have been impossible to have the patience and time to scale all of those walled gardens for this project.

Why? I've done data takeout from multiple social media sites. It's much less tedious than babysitting VHS->digital transfer (which I've also done).
Have you read Democracy for Realists?

TIL Most voters don't have well defined policy preferences. Nor can they correctly associate political parties with their stated policy positions.
google isn't doing anything with it, its not really a competitor to androidwear, and it probably has enough pebble nerd fans within google itself to push for it.
Not that I believe it's likely to happen, but it seems incredibly fucking dangerous for there to be an ASI race with one winner. To the extent these companies believe it's possible, what are they hoping will be the outcome for humanity?

That they get to be the trillionaires with an untouchable moat? Wouldn't this be like creating a Kwisitz Hadarach thinking you can control it, to borrow a Dune reference?
Trump says Microsoft is in talks to acquire TikTok
(Dominance-related) insecurity is being pathologically averse to being seen as weak, which leads to preferring dominance as form over dominance as function. If the meek hippie gets everything he wants from his wife, his neighbors, his peers, etc., and the physically impressive traditional man is ignored and rejected, then the hippie is more dominant (i.e., leading and getting what he wants) than the traditional man (even if he is abusing his wife the whole time she laughs at him).

The actually effective strategies are available to the insecure but shunned and rejected because they cannot be tolerated, creating a self-imposed impotence.

The word alpha, in almost every context I've observed, is used exclusively to refer to such dominance as form, especially in substitution for dominance as function. i.e., it is applied almost exclusively to people who are definitionally not dominant.

The only exception I have encountered is women-focused kink literature which, being fantasy, maintains that dominance as form is dominance as function so as to make sexual fantasies seem more real.

In short: you are describing a kink, not real life. Though I consider that you might be joking too; I really can't tell.
What a great take, I have thought this for a while.
White House Orders Pause in All Federal Grants and Loans
>Have you actually read the article? He does give a citation to the same author who initially coined the term alpha wolf.

I meant link to source. Like point me to the place where he defined it that way. Instead he just made a claim without a citation.

For example: https://news.ycombinator.com/item?id=42848939

These are citations proving my point.
My layman view is that more compute (more reasoning) will not solve harder problems. I'm using those models every day and when problem hits a certain complexity it will fail, no matter how much it "reasons"
waiting on 1500 5090
I’d bet most people don’t know what a paradox is.
Such an incompatibility, they note, would have likely led to hemolytic disease in offspring if a Homo sapiens or Denisova male were to mate with a Neanderthal female—a result that would have led to a host of health problems that would have prevented the offspring from maturing and reproducing. If such a practice became widespread, they suggest, it could have been a contributing factor to the demise of Neanderthals.

Isn’t that a huge IF?
This has me imagining it in terms of the game Cuphead. That’s also good for visualizing this I think.
I got a response to my Dropbox ticket asking why they're discontinuing their Vault…

"We discontinue Dropbox Vault primarily due to significant technical risks that could compromise the security we provide to our users.

We want to concentrate our efforts on further enhancing our existing security features."
In fairness, 'geopolitical experts' may not really exist. There are a range of people who make up interesting stories to a greater or lesser extent but all seem to be serially misinformed. Some things are too complicated to have expertise in.

Indeed, while the existence of socioeconomic experts seems more likely we don't have any way of reliably identifying them. The people who actually end up making social or economic policy seem to be winging it by picking the policy that most benefits wealthy people and/or established asset owners. It is barely possible to blink twice without stumbling over a policy disaster.
My hypothesis is that they're now censoring things that seem "lefty".
> That "if" there is doing a lot of work. I think you underestimate how many people out there are in a situation where it's the job they have or the street.

That's making the opposite case from the one you want. You take away the lower paying job when it was the only one available and now they're on the street because there is no alternative.

"Fortunately", in more cases than not the "if" was the actual. This is geometry: If there is one viable employer within 50 miles of where you live then statistically there are four within 100 miles, 9 within 150 miles and 16 within 200 miles, because area is pi r^2. So if you take away the first job there is a large chance that there is a second one with a much worse commute. But the commute is going to more than eliminate any value from getting paid slightly more, which is why they didn't take that job to begin with.
https://archive.is/2025.01.28-051659/https://www.nytimes.com...
zopfli is the wrong thing to use here.

If you want an example of where these things go badly:

the standard compression level for rpms on redhat distros is zstd level 19.

This has all the downsides of other algorithms - it's super slow - often 75-100x slower than the default level of zstd[1]. It achieves a few percent more compression for that speed. compared to even level 10, it's like 0-1% higher compression, but 20x slower.

This is bad enough - the kicker is that at this level, it's slower than xz for ~all cases, and xz is 10% smaller.
The only reason to use zstd is because you want fairly good compression, but fast.

So here, they've chosen to use it in a way that compresses really slowly, but gives you none of the benefit of compressing really slowly.

Now, unlike the npm case, there was no good reason to choose level 19 - there were no backwards compatibility constraints driving it, etc. I went through the PR history on this change, it was not particularly illuminating (it does not seem lots of thought was given to the level choice).

I mention all this because it has a real effect on the experience of building rpms - this is why it takes eons to make kernel debuginfo rpms on fedora. Or any large RPM. Almost all time is spent compressing it with zstd at level 19. On my computer this takes many minutes. If you switch it to use even xz, it will do it about 15-20x faster (single threaded. if you thread both of them, xz will win by even more, because of how slow the setting is for zstd. If you use reasonable settings for zstd, obviously, it achieves gigabytes/second in parallel mode)

Using zopfli would be like choosing level 19 zstd for npm.
While backwards compatibility is certainly painful to deal with here, zopfli is not likely better than doing nothing.
You will make certain cases just insanely slow.
You will save someone's bandwidth, but in exchange you will burn insane amounts of developer CPU.

zopfli is worse than level 19, it can often be 100x-200x slower than gzip -9.

Doesn't npm support insanely relaxed/etc scripting hooks anyway?

If so, if backwards compatibility is your main constraint, you would be "better off" double compressing (IE embed xz or whatever + a bootstrap decompressor and using the hooks to decompress it on old versions of npm). Or just shipping .tar.gz's than, when run, fetch the .xz and decompress it on older npm.

Or you know, fish in the right pond - you would almost certainly achieve much higher reductions by enforcing cleaner shipping packages (IE not including random garbage, etc) than by compressing the garbage more.

[1] on my computer, single threaded level 19 does 7meg/second, the default does 500meg/second. Level 10 does about 130meg/second.
I don't use it anymore, but I still have my og pebble, and miss how well it did what I care about.

Here's hoping we can get a color screen pebble time successor, maybe with one of the buttons upgraded to a dial + button.
It’s not wrong
On another note, Sourceforge just removes the malware flag, but did they actually check anything or just went with the provided explanation without any concrete details? If I hijacked some software and got caught, I'd act nonchalantly like this as well and hope it'll blow over without anyone noticing.
Does line go up forever?
there's this and that little desktop computer they announced earlier this month - digits

they claim it's able to run models with 200B parameters on a single node and 400B when paired with another node
Because they deliver models you cannot run locally on an iPhone, and those are almost always the models you want to run.
A machine learning researcher I had the pleasure of knowing when I was at MSR had a background in EE, in particular digital signal processing is a very useful skill in the field. He was the first person I heard mention the quantized model approach (back in 2012 I think?) and compared it to old 1-bit quantized noise reduction in CD players.

A bit of irony was that this researcher (from Europe) used to work in the same lab as me in Beijing. But these days the talent doesn’t flow so easily as it did a decade+ ago (but maybe it will again? Researchers aren’t very nationalistic and will look for the best toys to play with).
I feel that one of the most fundamental aspects of business reality is being skipped in all of these conversations, on and off HN.

Everyone building an AI data center is likely using Nvidia technology. Sure, there's a 20% that is partially using other technology. The bulk of it is Nvidia.

If your project is in the planning for the next, say, two years, you have already placed your orders with Nvidia or are going to in the next few months.

Hardware has real lead times. You don't compile yourself 100K chips. They have to be made and you have to wait in line to get yours. For example, I remember when, during the pandemic, we had to place orders for chips with 40 to 50 week lead times.

This means you have to make decisions today (or you already made them months ago) to get in line.

Changes in training or inference efficiency should not change these orders or plans at all. If someone can train faster, they will benefit from the hardware in the pipeline. If they can make inference more efficient, they will be able to service more requests at reduced transactional costs.

The orders are in the pipeline and will continue to be added to the pipeline. Nvidia isn't going to be shipping half the hardware because Wall Street, overnight, panicked. What the grocery store owner does with their stock portfolio because they panic has nothing whatsoever to do with reality.

The same is true in the other direction. Wall Street has been going nuts with quantum stocks. Companies like Rigetti have exploded from nothing to see insane gains. This does not mean the company went from, well, shipping nothing to shipping real working solutions at scale.

Today's market reaction was nothing less than sheep running scared because someone when "boo!". It has nothing whatsoever to do with business realities on the ground. Go build an AI data center without Nvidia chips (or with 10x less chips) and see how that goes when everyone is loading-up with them.
DeepSeek R1 just uses crappy PPO ("GRPO" is just using the sharpe ratio as a heuristic approximation to a value function) on top of distilled existing models, with tons of pipelining optimizations manually engineered in. I don't see this making leading edge research any less expensive, you won't get a "smarter" model - just a model that has a higher probability of giving an answer it could already give. If you want to try and do something interesting with the architecture the pipelining optimizations now slow down your iteration capability heavily.

The RL techniques present will only work in domains where you can guarantee an answer is right (multiple choice questions, math, etc.). It doesn't really present any convincing leap forward in terms of advancing the capability of LLMs, just a strategy for compute efficient distillation of what we know already works. The fact this shitty PPO proxy works at all is a testament to the fact that DeepSeek is bootstrapping its capability heavily off of the output of existing larger models which are much more expensive to train. What DeepSeek R1 proves is you can distill a ChatGPT et al. into a smaller model and hack certain benchmarks with RL.

If you could just do RL to predict the best next word in general this would have been done already - but the signal to noise ratio on exploration would be so bad you'd never get anything besides infinite monkeys at a typewriter. It's not a novel/complicated idea to anyone familiar with RL to try and improve probability of things you like, and whoever decided to do RLHF on an LLM surely thought of (and did) regular RL first - and found it didn't work very well with whatever pretrained model and rewards they had. it was like two weeks ago people were going crazy about O3 doing arc-agi by running the exact same kind of traces R1 is doing in "GRPO" at test time rather than train time. Doing this also isn't novel and also only helps on shitty toy problems where you can get a number to tell you good vs bad.

There is no mechanism to compute rewards for general purpose language tasks - and in fact I think people will come to see the gains in math/coding benchmark problems come at a real cost to other capabilities of models which are harder to quantify and impossible to generically assign rewards to at internet scale.

To explore the frontier of capability you will still need a massive amount of compute, in fact even more to do RL than you would need to do standard next token prediction - even if the LLM might have fewer paramters. You also can't afford to do all the optimizations as you try many different complex architectures.
Is it possible that you’re both using LLMs the same way you’d use SO and that’s the reason you see such similarities? The reason I ask is because it doesn’t not match my experience. It feels more like I’m able to Matrix-upload docs into my brain like Trinity learning to fly a helicopter.
>In short: you are describing a kink, not real life. Though I consider that you might be joking too; I really can't tell.

The fact that you said this means you're out of touch with reality. Let me provide evidence about how wrong you are: https://news.ycombinator.com/item?id=42848939

Literally you can go to the dictionary yourself and look up the definition to see how baseless your argument is.

I don't know why you people are just pulling this bs out of thin air. It can't be just BS is several people are coming from your angle despite extraordinary evidence to the contrary. Maybe it's just shared victimhood. Were you bullied by these types of people before?
Greater efficiency of light bulbs has led to more light bulb use, not less. More efficient training of LLMs could just as likely lead to more chip use, not less.
> it would still be missing out the actual experiences of many people for whom wages did not rise by that amount

And the experiences of many with cash savings, pensions that haven't kept up with inflation, etc.
Hypothetical question: is the chinese government capable of exploiting chatgpt to get around the query limit? For example, making queries through compromised devices or even snooping local traffic on devices? Let's face it, these models are closely alligned with China's national security so it's not a farfetched question to ask.
Has the supposed DeepSeek training budget been verified in any way?
searches are more content focused and accurate than other search engines, they're independent of classic page rank(in terms of backlink, domains among others etc.), but if you're able to find something please let me know.

some samples:

https://mviiii.searchall.site/search/?q=keywords%20in%20issu...

https://mviiii.searchall.site/search/?q=apple%20iphone

https://mviiii.searchall.site/search/?q=hacker%20news
China doesn't really pay hardware people well (even compared to local standard) either.
You never need to paste anything. You just press "Apply".

Edit: saw you said "paste it in". Same thing there. You either just include the whole file or select the code and press "Include". You can also let the editor handle the inclusion itself based on your prompt. It will then try to find the relevant files to include.
Still? Working for me.
Most of the talent that flows into China is Chinese, their biggest challenge has always been keeping talent flowing out of China rather than attracting talent in. I don’t think any of these new AI efforts include non-Chinese principals, while western efforts almost certainly include more than a few.