skysight-inc/HackerNews huggingface dataset:
Well you have to keep in mind that Nvidia has a 3 trillion dollar valuation. That kind of heavy valuation comes with heavy expectations about future growth. Some of those assumptions about future Nvidia growth are their ability to maintain their heavy growth rates, for very far into the future.

Training is a huge component of Nvidia's projected growth. Inference is actually much more competitive, but training is almost exclusively Nvidia's domain. If Deepseek's claims are true, that would represent a 10x reduction in cost for training for similar models (6 million for r1 vs 60 million for something like o1).

It is absolutely not the case in ML that "there is nothing bad about more resources". There is something very bad - cost. And another bad thing - depreciation. And finally, another bad thing - the fact that new chips and approaches are coming out all the time, so if you are on older hardware you might be missing out. Training complex models for cheaper will allow companies to potentially re-allocate away from hardware into software (ie, hiring more engineering to build more models, instead of less engineers and more hardware to build less models).

Finally, there is a giant elephant in the room that it is very unclear if throwing more resources at LLM training will net better results. There are diminishing returns in terms of return on investment in training, especially with LLM-style use cases. It is actually very non-obvious right now how pouring more compute specifically at training will result in better LLMs.
Yea, and then came the dotcom bust.
Man, I used to use couchsurfing.org to get free places to crash in grad school.
Jevons was talking about coal as an input to commercial processes, for which there were other alternatives that competed on price (e.g. manual/animal labour). Whatever the process, it generated a return, it had utility, and it had scale.

I argue it doesn't apply to generative AI because its outputs are mostly no good, have no utility, or are good but only in limited commercial contexts.

In the first case, a machine that produces garbage faster and cheaper doesn't mean demand for the garbage will increase. And in the second case, there aren't enough buyers for high-quality computer-generated pictures of toilets to meaningfully boost demand for Nvidia's products.
What is stopping huawei or other Chinese vendors to make chips on deepseek specification and 1/10th NVIDIA cost and mass market it?
Avante

https://github.com/yetone/avante.nvim
Is this like how middle management reportedly wouldn't be caught dead dressing like the janitor, but super rich people supposedly dress like the janitor in order to not look like middle management?
I don't think "Ops" roles generally require a PhD.
That doesn't make any sense. A lack of investment is not an investment against by any means, unless this VC invested in the concept of less spam or more workers.
> I say DeepSeek should increase Nvidia’s demand due to Jevon’s Paradox.

If their claims were true, DeepSeek would increase the demand for GPU. It's so obvious that I don't know why we even need a name to describe this scenario (I guess Jeven's Paradox just sounds cool).

The only issue is that whether it would make a competitor to Nvidia viable. My bet is no, but the market seems to have betted yes.
The timing with the 500b deal is just perfect
So cool! So inspiring that you're bringing it back!
You can also use Garmin watches for transit in Japan, if it's a Japanese model.
https://news.ycombinator.com/item?id=42844619

We're talking about the definition in a more formal context. Of course I don't go around saying hey "that dude is an alpha" or "i'm an alpha."
In the past, I've often seen that hardware is usually more advanced than the software that runs on it. Meaning that it's usually possible to have the software run more efficiently. New hardware makes it possible to substitute raw hardware speed for efficient code and make the application run faster without having to be super concerned about efficiency.

China is going to have to get the most out of old hardware by being better coders. Their big advantage is that they can hire the best AI people at a fraction of the cost in the US. So their goal will be just to keep up but they will do it at a fraction of the cost of what the US companies will spend. In turn, they will be able to sell whatever they produce way cheaper.

It's the same problem we have seen over and over. It gets invented in the US but gets produced much cheaper in China. We've seen this same issue for the last 30+ years. How do you win a race like that?
Everyone thinks their bet is the next Ford, Wright bros, Netscape, etc.
It is way faster, especially when you're learning a new framework and don't know all the patterns by heart even if you have a good idea how they work. If you have 2000+ hours using a specific framework then it's probably not much use except for boilerplate code but for me when I have less than 300-400 hours with each individual component of my stack... it's a gamechanger. I can probably output code at 80-90% the speed of a developer that has 10x more experience than me.
It could have been great for IBM if they had done things differently
relatedly: https://restate.dev/blog/every-system-is-a-log-avoiding-coor...

https://news.ycombinator.com/item?id=42813049
Nvidia is at a premium because it's so dominant. But the sentiment now is that China can build cheap, quality AI too and presumably they can make top dog or second dog GPUs too.

US's anti-China policies further forces China to develop their own GPUs, breaking Nvidia's dominance within 10 years or something.
Here’s a take I haven’t seen yet:

If training and inference just got 40x more efficient, but OpenAI and co. still have the same compute resources, once they’ve baked in all the DeepSeek improvements, we’re about to find out very quickly whether 40x the compute delivers 40x the performance / output quality, or if output quality has ceased to be compute-bound.
VC's raise money from investors. Investors want to put their money with good VCs. If OpenAI is huge, and you missed it, that seems bad. If OpenAI flops and you strategically held off - that seems good.
And the opportunity for a shit ton of lawsuits.

Source code like this doesn’t just have no value for Google. It has negative value. It’s a massive liability.

That’s why big cos simply bury source code instead of releasing it in the open.
Or Elizabeth Holmes....
How so? There are so many tangible applications already: reduced customers service costs, legal research, analysis of medical records or imaging, self-driving Waymos, and so on. Just the things I listed will have profound impacts on cost savings, productivity, and quality of life.
Next, how do I degoogle my Pebble?
Rust does permit multiple incompatible versions of the same library in the same environment. The types/objects from one version are distinct from the types/objects of the other, it's a type error to try mix them.

But you can use two versions of the same library in your project; I've done it by giving one of them a different name.
Anthropic doesn’t train on Nvidia as far as I know. Google doesn’t either.

I don’t see how DeepSeek changes things.

By the way, DeepSeek trains on Nvidia.
Because their biggest buyer(s) just ran into a buzzsaw. NVDA's stratospheric valuation is based on a few select customer's unrestrained ability to purchase the latest products. That unrestrained spending ability was fueled by the "AI" arms race. If those companies see their ability to be profitable with "AI" as diminished then their ability to continue spending with NVDA is probably going to be diminished as well. Anything considered bad for NVDA's top spenders is going to be viewed as bad for NVDA as well.
I owned the Pebble OG, the Pebble Steel and the Pebble Time Steel and despite all my attempts, I couldn't fill the void the Pebble left. I tried the Amazfit Bip, The BangleJS 2 (that one got pretty close IMO) and now rocking a Casio watch that does bluetooth but still work on a CR2032 (GBD-200), and more than a year on the same battery, which is quite a feat when you think about it.

The software UX of the Pebble was on point, and the animations surprisingly smooth for such a device. I'm still convinced that after all there years, the Timeline UI is unrivaled.

I'm eager to see what you'll come out with :)
I built an internet search engine with MongoDB
Yeah, the ones I've had are fine on concrete and brick sidewalks, but bad for floors.

If you're walking to stores or something, you'll want ones that are easy to remove and put back on over the shoes. When indoors away from home, I carry them in a clear plastic bag in one of the water bottle pockets in my backpack. (That they look messy with water and de-icer and such means that they don't look your something you're shoplifting.)
Because most people's trips are the commute and they haven't been given more time and money to go and road trip more. That isn't analogous to computing though. People do the same things broadly they've always had with computing, but we've figured out how to create a system where your computer today running microsoft word is 100x as powerful as your computer in 1995 also running microsoft word and you feel the need to upgrade your hardware every couple of years so you can continue running microsoft word. It is the perfect model for exponentially dumping raw compute power into the void to perpetuate value creation. It will not stop in our lifetimes I expect. In 25 years our computers will be 100x more powerful still and we will still have MS word.
Yeah, and the Internet just never recovered!
"Popular" and "good" have no relation to each other. They correlate fairly well, but that's all.

Blending those dependencies already causes somewhat frequent problems for library owners / for users of libraries that do this. Encouraging it is not what I would consider beneficial.
Demis Hassabis: The Path to AGI, Deceptive AIs, Building a Virtual Cell
NVDA can't really "churn out more CUDAs" because CUDA is a software platform/framework, not a physical product.
Right but the term here is used to categorize human/animal hierarchies/behavior in an objective context. No one is here projecting their alphaness onto others. Clearly.
I worked on the storage layer for one of them many years ago. The jankiest shit I had ever encountered. Just a couple of unpatched Sun servers and default untuned Oracle instances with no indexes and no IT people and just a few sales guys showing us where the computer room was. Shocked to learn six months later they were acquired by a global bank for over $1B.

They are called “dark pools of capital”, platforms where if you and I are customers, we can trade equities without touching the public stock exchanges.

There is apparently an enormous market here in the US for people who want to trade securities without the visibility of doing so on the NYSE and NASDAQ.
I don't think LLMs lead to AGI. It's a local maxima.
Foundations don't change much with "the speed of development"
> No one ever explains how it's possible for China to simply give "massive state subsidies" and take over the entire global economy from a starting point of Haitian-level GDP per capita 25 years ago

The biggest purchaser of technology and goods and services is the US Government. It spends over $760 billion annually on products and services.

But if any other country does the same it would classify as "massive state subsidies".

I would take it a step further and say that the biggest employer in US is the US Federal Government.
It's lose-lose for their valuation regardless. This scenario might if anything be worse for them. Now they have massive sunk capital investments that the second mover might be able to avoid. If the open source models get small enough and high enough quality, the rationale for runing them in the cloud in the first place start to evaporate.

How does OpenAI get paid for a use case that can easily be run locally on an iPhone?
It's probably a recurrence of the same issue.

If your domain links to content that AVs flag as malware, it gets blocked on FB. Distrowatch is likely uniquely susceptible to this because they're constantly linking to novel, 3rd-party tarballs (via the "Latest Packages" column).

In this case, it was the Privoxy 4.0.0 release from the 18th. You can see it linked in this Jan 19 snapshot of the site: https://web.archive.org/web/20250119125004/https://distrowat...
Prices per line of code have always been falling. Before AI, it was JS frameworks. But it led to an exponential increase in the number of software engineers.

You still see a lot of frustration in working with AI today, and eventually people will just start hiring for this the way they're hiring React Native and Cordova developers.
I doubt one experience, positive, or negative counts for them all.

Learning how to build or create with a new kind of word processor is a skill unto itself.
We are a 15 year old small company with 250 full time employees. I completely bootstrapped the company and never took a dime of debt or any investor money. We do web design and online marketing work. I tried building some products, and though they were game changers internally, they never took off externally. Now Coalition Technologies is services provider with a good reputation for standing behind our work. Though I do hate telling people my email address on the phone... C o a l I t I o n T E c h n o l o g I e s dot com. Why couldn't I have named it Pin or something haha.
The shock for the industry was the claimed training costs and used hardware.
Except all living things are inherently violent and knowing when to apply what amount of violence is part of being socially intelligent.
You can also use Claude, Mistral, Llama and others on Google Vertex, similar to Bedrock.
That seems to be the key question.
unless OAI has all the optimization tricks already, which they probably do
Show HN: A simple dark mode Markdown live preview
Weirdly, there aren't MD live previews that support Dark Mode. We made using our main product our own LLM-based editor in 2hrs and deployed it.

The wordplay struck us hilariously and we just decided to ship it!
Features:
1. Live preview
2. Code Sytax highlighting
3. Also, supports light mode
4. Saves data in locally
What did they do to "disrupt" the others?
EU taxes exorbitantly and does not reinvest in people. Instead wastes money on expanding bureaucracy and making the Government fatter. Passes asinine laws that stifle companies from innovating. If a company is wasting more time trying to be compliant with crazy regulations and avoiding ridiculous fines, it won't have time to focus on innovation.
I wrote a tested prototype MMO in the last few weekends with AI as my power tools.

You're holding it wrong.
The key idea of Latent MHA is that "regular" multi-headed attention needs you to keep a bunch of giant key-value (KV) matrices around in memory to do inference. The "Latent" part just means that DeepSeek takes the `n` KV matrices in a given n-headed attention block and replaces them with a lower-rank approximation (think of this as compressing the matrices), so that they take up less VRAM in a GPU at the cost of a little extra compute and a little lost accuracy. So not caching, strictly speaking, but weight compression to trade compute off for better memory usage, which is good because the KV matrices are one of the more expensive part of this transformer architecture. MoE addresses the other expensive part (the fully-connected layers) by making it so only a subset of the fully-connected layers are active at any given forward pass.
People are announcing the death of foundational models too early. Don’t people realize that the big AI players will take all of the proprietary things they’ve been building up behind closed doors and simply layer onto them all the winning techniques everyone else is publishing (like what DeepSeek has used)? DeepSeek itself is taking ideas that have proven out in various other papers and stacking them up to produce their gains (which they’ve been transparent about in their papers).

I also still don’t believe their cost figures, and think they’re leaving out the capital to acquire their secret GPU stash and the cost of pre training their base model (DeepSeek-V3-base). I also suspect their training corpus, which they’ve only vaguely described, would reveal the savings came from working off other foundational models’ work without counting those costs in their figure.

For now, I treat the cost claim as simply a calculated strategy for China to not look like they’re behind in the most important race, to prevent investors from continuing to boost US technology by causing them to doubt the ROI, and to take value out of the US stock market as they did today.
What to Know About DeepSeek and How It Is Upending A.I
Is the model architecture actually that different from anything else? Or are you just saying that you can get away with smaller models now?
This is more or less how I use LLMs right now. They’re fantastic at the plumbing, so that I can focus on the important part - the business and domain logic.
Being cynical is almost a default of the internet and I think it squanders a lot of hope. It's better not to be cynical
This sounds like an extremely convoluted way of demonstrating a very basic fact, that the authors of the study probably knew beforehand. And that's when a player's position has many weaknesses (or a high 'fragility score') they're probably going to be losing soon. And determining how quickly many pieces can attack a given point versus defend it is, more or less, exactly how you determine if that point is weak. Publish or perish reigns supreme.

A lot of science feels like it's becoming much closer to art where you splash a canvas with a few splotches, and then spend an hour describing how it's an insightful representation of the isolation of the modern world and the struggle against depersonalization. At least there you get to then sell it for a few hundred thousand dollars, rather than just live to renew your contract.
https://planetbanatt.net/articles/mla.html this is a great overview of how MLA works.
What you’re talking about really isn’t that iPhone is or was a fad. What’s really happened is innovation has essentially halted and it has become a commodity like all smartphones.
We’ve just learned that it’s possible to do AI on less compute (deepseek). if OpenAI doesn’t scale and that’s the problem then I’d argue that in the long run, if you believe in their ability to do research, then the news this week is a very bullish sign.

IMO the equivalent of moores law for AI (both on software and hardware development) is baked into the price, which doesn’t make the valuation all too crazy.
Sort of, it means there's less of a chance for massive market domination and a monopoly.
I just got some, and it's been a big shift. I can be almost entirely fearless power-walking on well-traveled, un-shoveled paths. The only places that have given me trouble are where I encounter (a) glassy-smooth ice that's (b) in the process of melting. I still slip a little bit there. And clean pavement, I guess, that's pretty uncomfy at least with my cheapo set of cleats (though not because of slipping).
You might save a little bit by putting similar very small files next to each other in the same tarball, but in general I would not expect significant improvements. Gzip can only compress repetitions that are within 32KB of each other.
i wish there was something like this for wasm. There are wasm builds available but its not easy to extend them with your own functions.
I can't get over garmins predatory business model. The way they bin their products by activity is terrible if you have multiple hobbies. All of these devices are just a gps reciever, screen, battery, yet they sell it to you a dozen different ways for $500+ a pop usually because why unify the software and make it easy for the consumer?
Is beeper dead?
List of double meaning words as food for thought: https://word-lists.com/word-lists/100-english-words-with-mul...
At least OpenAI has a useful product
I’m not saying it doesn’t have value, but it’s not worth my time to spend 20 minutes to prompt engineer a tool to write me a plagiarized document I could write myself in 20 minutes? Why would I invest my time into using a tool that undermines my own value? What’s the value prop for me?
If it happens, I call dibs on the name "AI Nuclear Winter".
Your inability to find value propositions and use cases is your issue.
I'm tired of misinformation, clickbait, fear mongering, and media manipulation
A somewhat gruesome related bit of American Southwest folklore: https://en.wikipedia.org/wiki/Red_Ghost_(folklore)
https://archive.vn/GEDVY
> There's no redefinition here. When we refer to animals in every context, alphas are leaders.

Yes, there is a redefinition. The context of Alpha Wolf was based on the notion of a dominance hierarchy, which does occur when unrelated wolves are put together in a confined space. In the wild though, they function more like a family, with no acts of dominance. The breeding pair still lead the pack, but not through dominance.

https://web.archive.org/web/20051214072331/http://www.npwrc....
Even in 2020, before GPT came they had $100 millions worth of GPU[1]. Now I am willing to bed it is above a billion dollar. We will never know as they likely have illegal GPUs due to export restrictions.

[1]: https://en.wikipedia.org/wiki/High-Flyer
I think the big picture that many people are missing here is the motivation that all these AI/tech companies have for buying up so many GPU's in the first place: achieving AGI/ASI.

And while some still try to portray a dedication/duty to AI Alignment, I think most have either secretly or more publicly moved away from it in the race to become the first to achieve it.

And I think, given that inference time compute is so much cheaper than pre-training, the first to achieve AGI might have enough compute on hand from having been to first to build it that they would not need to purchase many more GPU's from Nvidia. So at some point, Nvidia's revenues are going to decline precipitously.

So the question is: how far away are we from AGI? Seems like most experts estimate 3-10 years. Did that timeline just shrink by 50x (or at least by some multiple) from these new optimizations from DeepSeek?

I think Nvidia's revenue is going to continue to grow at its current pace (or faster) until AGI is achieved. Just sell your positions right before that happens.

(Not an expert. Not even an amateur)
> If i asked an LLM explicitly to list the entire Bee Movie script it would probably do it, which means it was trained on it, but that's through a direct and clear request to copy the original verbatim.

Huh? The "request" part doesn't matter. What you describe is exactly like if someone ships me a hard drive with a file containing "the entire Bee Movie script" that they were not authorized to copy: it's copyright infringement before and after I request the disk to read out the blocks with the file.