skysight-inc/HackerNews huggingface dataset:
Good for you.

Do you know how to optimize the MTU of an Infiniband interconnect? Do you understand how to schedule multiple models being trained concurrently? Do you know why you cannot directly leverage bare metal compute within a Docker image?

This is important Infra knowledge you need to take full advantage of any model you are training on your own hardware. And this is why Deepseek was successful - they understood the ins-and-outs of systems programming and the H800 architecture to maximize the compute performance they needed to train their model.
Welcome to the internet. Astrologers proclaimed the week of DeepSeek, the number of Jevons paradox experts quadrupled.
If treatment is stopped, viral load is expected to return to pretreatment levels within a few weeks. This is potentially a human caused public health disaster.

Similar programs for treating individuals with TB, malaria and other illnesses have also been suspended.

https://www.reuters.com/business/healthcare-pharmaceuticals/...
Satya Nadella on twatter
That’s why you take a quick glance of the answer, then read the comments. The do a deeper analysis. Take something like 10 sexonds as it seems every real answer I find that’s good is usually just one or two paragraphs.
They should depend on individual case specifics, which is exactly what PAC learning is bad at.
My note about overloaded towers is not about that they'll be unable to track you, but you'll be unable to use public internet to communicate with the rest of your group.
Time to give our family it's own shared networked AI station, accessible from any of our devices. Then I'll trust it with all of our personal, medical and legal docs. Seems to me the market is expanding, not contracting.
I like Taleb's books, but he does predict doom and gloom all the time - a broken clock is right twice a day after all
Deepseek offers API pricing directly on their website, so it's pretty easy to compare inference costs indirectly: It's $60.00 vs. $2.19 for 1M output tokens. Openai is 27x as expensive.
>DeepSeek built their base models off Llama releases and OpenAI outputs (or so it’s thought)

The first one is definitely not true and the 2nd one is not necessarily true in the way you imagine i.e crawls of the internet will have gpt chat logs now.
They bought between 10k and 50k of them before the US restrictions came into place. Sounds like DeepSeek gets to use them for training, as they were profitable (could still be, not sure).
Could Severance really happen? We're not far off, neuroscientist says
Depends upon the user. Are you fast (and confident) at evaluating the output and discarding the bad suggestions? This is why I think using AI hurts some developers and helps others, and the usefulness is best for those who already have a good deal of experience.
> So why do we tolerate databases as they are?

Because they reflect the way we understand the world? We understand that things are made of smaller things, and that sometimes the smaller things making up larger things may change, while the larger thing stays the same? The idea that as soon as one component changes the whole thing needs to be discarded and rebuilt from ground up is insane and creates a lot of problems. It's absolutely not worth it to try to redefine the way we deal with the world to get the benefits of stateless code.

Making database stateless is making it worthless. The world has a state, and if you want a useful program, it needs to accept this "unfortunate" aspect of the world. The alternative is the world where as soon as you finish drinking your coffee, your cup, your table, your kitchen, your credit card history, your grandparents and all planets in the solar system disappear, and have to be built fresh. But you wouldn't know about it, because your memory of how the world used to be would disappear too.
Bloody reductionist claptrap.

The last defence of the arrogant. You must hang onto this wasteful device so much that you will twist the words of others to suit your agenda.

I’m not sneering at play. The opposite. The black magicians as I’ve labelled them here have parasitically replaced real play with the task of training their machines of future control. (Doubt me? They shout this shit at bloody conferences and on podcasts)

Take a big step back, man.
Honestly I don't want to read narrative prose which is as likely to be a lie as anything else on the resume/CV. A half page might be fine if you've got a dozen CVs to look at for an ultra-specific role. If you just need a half-decent Golang developer and have 80 resumes that pass the initial screen? I'm not reading 40 pages of fluff, and if I'm not going to read all of its it's unfair to read any of it.

If you are tailoring your resume to the job, it is incredibly easy to fit everything you need into 2 full pages. If your job descriptions have a bunch of unrelated stuff it tells me you're spamming this exact resume out to anyone who will read it which is already a big negative signal (though not fatal). I'm hiring individual contributors, not Executive VPs, so the qualifications we're actually looking for can easily fit on .75-1 page. If you're going for COO of a publicly traded company maybe the CV route makes sense, but truthfully if that's what you're going for the CV itself is pretty unimportant, and you're still probably just paying someone else to craft it for you.

I just don't see the benefit in someone with 10-15 YOE in mostly expired tech writing pages and pages about stuff they did a long time ago.
> Your experience is important to us. For optimal functionality on cleveland.com, please disable your ad blocker before continuing.

"Optimal Functionality" my ass, just say you want to make money by trying to sell me stuff I don't want
Yet. There are lots of sign spam is coming to Mastodon and there is real concern by a fair number of people who are there. Anyone with a lot of followers will be tagged often by spam (if you tag someone all their followers will see your post)
How are you doing your evals?

Being able to do semantic diffs of the output of the two models should tell you what you need to do.
https://archive.ph/KGIXE
> I’m not sure what people imagine will happen when white people, who essentially created everything we all enjoy,

Just... No. Not only non-whites created a lot of things we all enjoy (money, algebra), that are also foundational for our civilization. Also, white people also invented a lot of things we all should vehemently reject (such as fascism).

> including freedoms from tyrannical violence and oppression, have been eradicated, but there is no indication that it will end well.

Very hard NO on this one as well. Nobody is eradicating white people and nobody is even considering it.
Maybe that's what they meant, and it'd be cool if nvidia still offered that on consumer cards, but thankfully you don't need it for LLM inference. The traffic between cards is very small.
They pointed out that the cost calculation is based on if those GPUs were rented at $2/hr. They are not factoring in the prior cost of buying those H800s because they didn't buy it to build R1. They are not factoring in the cost to build v2, or v2.5. The cost is to build V3. The cost to build R0 and R1 on top of v3, seems far cheaper and they didn't mention that. They are not factoring in the cost to build out their datacenter or salary. Just the training cost. They made it clear. If you could rent equivalent GPUs at $2/hr, it would cost you about $6million.

"Assuming the rental price of the H800 GPU is $2 per GPU hour, our total training costs amount to only $5.576M. Note that the aforementioned costs include only the official training of DeepSeek-V3, excluding the costs associated with prior research and ablation experiments on architectures, algorithms, or data."

V3 was released a bit a month ago, V3 is not what took the world by storm but R1. The price everyone is talking about is the price for V3.
> But AI? To me, AI means the replacement of the human internet with doppelgangers eroding the possibility of human connection.

Like Amazon killed the big book sellers giving back some space for small bookshops; I think LLM slop will hit the big social media space for smaller human focused community sites. Not saying forums are coming back, but something like those should be able to rise.
> It has basically replaced search for most people.

Not because it's better than search was, though.

They lost the spam battle, and internally lost the "ads should be distinct" battle, and now search sucks. It'll happen to the AI models soon enough; I fully expect to be able to buy responses for questions like "what's the best 27" monitor?" via Google AdWords.
While the weights are open source, and there is a paper about methodology, the information I mentioned is considered proprietary therefore DeepSeek refuses any requests to provide it.
Its not smuggling if Nvidia vendors openly sell it to them :) I'm pretty sure most of Russia's military equipment is filled with Texas Instrument MCs. These export controls don't work.
As someone who uses Mastodon I can assure you that spammers do target mastodon. So far it is only a few though and so human moderators are able to keep up. I doubt that will last long.
Right - and I’m all for paying a living wage for all jobs. Being a full time professor should pay well. And what we pay teachers in most places in the USA is ridiculously little.

What I’m saying is that we generally don’t expect that part-time jobs get paid as much, in total, as full-time jobs. Someone working 1/4 time getting paid 1/4 what a full time person gets (maybe a little more - or a little less - for the flexibility and/or privilege) is normal.

So asking what fraction of their time Udemy instructors spend on Udemy tasks seems like a prerequisite for judging the fairness of their pay, and I don’t think this article covers that.
I've had that (The Burnout Society) on my to-read list for awhile, do you recommend it?

I think the core thesis is spot on from what I've seen of discussions, and am looking to decide which aspects of ambition are my own, and which have I have accepted unconsciously.
I think it's also generally understood that Nvidia owns the training space, but not the inference space. They have a lot more competition there and the margins are smaller. More people running AI models is still good for them, but a drop in the bucket compared to the money they were making from training clusters being built out. And I think everyone just realized they can probably make do with their existing clusters.
HP Prime G2 can certainly graph things, evaluate integrals, solve systems of equations etc not sure how hard you’re looking because all the manufacturers have models like that. They have an exam mode which nerfs the calculator for tests.
> it showcases Apple’s strength in AI

That's a fanboy way to say Apple's strength is how long they can maintain their gatekeeping role. This moment feels like a pivotal opportunity for Apple to really ramp up its own AI offerings. Integrating third-party LLMs more tightly into the iOS ecosystem would not only solidify their leadership position but also give them more control over the kind of AI experiences users have.

But the truth is, they probably won't. Apple strategy of allowing only the slight of integrations and waiting for a winner to emerge just to copy it is such a common trope that it's hard not to see it happening all over again. Maybe WWDC 2025 is going to be the moment Apple sherlocks a couple of apps and we will see a different Top Downloads List.

https://9to5mac.com/2024/06/12/here-are-the-apps-sherlocked-...
Luckily, this is a problem that solves itself.

Medical science, of course, is delaying it somehow.
The math they gave was 2,788,000 H800 GPU hours[1], with a rental price of $2/GPU-hour[1], which works out to $5.6M. If they did that on a cluster of 2048 H800s, then they could re-train the model every ~1400 hours (~2 months).

If they paid $70,000 per GPU[2] plus $5000 per 4-GPU compute node (random guess), then the hardware would have cost about $150M to build. If you add in network hardware and other data-centery-things, I could see it reaching into the $200M range. IMO $400M might be a bit of a stretch but not too wildly off base.

To reach parity with the rental price, they would have needed to re-train 70 times (i.e. over 12 years). They obviously did not do that, so I agree it's a bit unfair to cost this based on $2M in GPU rentals. Why did they buy instead of rent? Probably because it's not actually that cheap to get 2048 concurrent high-performance connected GPUs for 60 days. Or maybe just because they had cash for capex.

1: https://stratechery.com/2025/deepseek-faq/

2: https://www.tomshardware.com/news/price-of-nvidia-compute-gp...
Never had this happen to me. Admittedly I am in a very rural area, and while I do sometimes get some gps points that are "off" it's generally very fast and accurate. Basically all the errors I've personally run into fall into what I'd consider acceptable margin of error.

Even in heavy tree cover on a remote island for a hike last year. It (Garmin Instinct 2X) was incredibly accurate.
It is the end of neural language model scaling as described here:

https://openai.com/index/scaling-laws-for-neural-language-mo...

And a return to more normal world where progress comes from refinement of algorithms and approach rather than brute force compute.

Deepseek is just the poster child.
If they bought them outright, they might have paid 60m, (GPU only) . After infrastructure, maybe 100M.

Calling the training load for DeepSeek 6% of the value of that cluster seems generous. It probably used less of the recoverable value than that.
Hi,

I am running several LLM for a web app running locally (django framework + Ollama for the LLMs). One of the main constrains is the limited context window for now (but getting better now with the newer models).
This web app aims at keeping data safety first (IP and sensitive data). A good way to feed the local agents is to use markdown formatted data (PDF to mrkd using pymupdf4llm).
I am running it on my work laptop (M2pro) but I am looking forward to test is on AMD AI Max 'strix halo' due to it unified memory.
Mainstream GPU have a frustratingly limited amount of VRAM.

best
It's the same with football. FIFA is so ridiculously corrupt it's actually jarring. Hosting World Cups in Qatar and now Saudi Arabia, running players into the ground by continuously adding more fixtures to the season, awash with gambling money, football clubs owned by nation states.

I stopped watching football when the World Cup went to Qatar and I haven't watched any since, it turns my stomach now.
Surely a Garmin is what you want? No subscription, 7 day+ battery life, smart watch features are there but largely secondary.
AI-capable local compute on every desk and in every home.
You’re confusing the metric for reality. The point is to compare the cost of training in terms of node hours with a given configuration. That’s how you get apples to apples. Of course it doesn’t cover building the cluster, housing the machine, the cleaning staff’s pension, or whatever.
Trump's Iron Dome for America calls for space-based interceptors
> High quality, healthy food is much more expensive per calorie than hyperprocessed, high calorie/low micronutrient, carcinogenic food.

Largely true, but frozen vegetables are inexpensive. And they are healthier than fresh vegetables. Veggies are the key to good health for the poor.
Could be a $5,340 discount across the board ;-)

One can dream...
In code reviews I call them sideways christmas trees and mark it as a bug.
Gecos (General Comprehensive Operating System)
Ironically, the best thing might be for us to pay a little less attention to the Olympics. As long as it is a highly profitable endeavor, most likely nothing will fundamentally change.
Would you mind giving some pointers of where to start?

There is a wasteland of blogspam around the topic of getting started with ML and it’s hard to know what is useful at the beginning.
I have similar set-up - can you help out with running it? Was it in ollama?

EDIT: It seems that original authors provided a nice write-up:

https://unsloth.ai/blog/deepseekr1-dynamic#:~:text=%F0%9F%96...
That' crazy. Why doesn't this happen in the GPU space? Is there some silent agreement between AMD and Nvidia to not compete down in cost and just push each other's prices up?
Comparing cost of buying with cost of running is weird. It's not like they build a new cluster, train just this one model, and then incinerate everything.
Anyone here homelabbing with a Xeon CPU in their server? What kind of secondhand models are best worth it for homelab in terms of price vs performance and power consumption?
Whatever It Takes
I generally disagree with the CV point but the "6 seconds" anecdote doesn't ring true to me except for obviously unfit applicants. I have definitely spent less than 10 seconds looking at a resume but it's because I immediately rejected the applicant - for example, the role is a solid mid-level or senior role and the applicant just graduated college and has no relevant experience or open source projects.

I want to be interested in your resume. If I'm interviewing you, you can be absolutely sure I've spent at least 20-30 minutes reading over your resume, looking up your past companies/schools, getting a sense of what you've done and pulled out a few relevant or interesting things to ask about.

I think the "6 seconds" thing is mostly HR drones who are barely qualified to write the resumes they're reading, let alone judge them, and are simply sorting into "yes" and "no" piles.
The carbon fiber guitar review at 5.3 Million views had an estimated revenue of £7745.82
> Students asking for help with homework will get the chinese approved version. Any housewife asking for recommendation who to vote and why, etc.

Well too bad for them, using a pencil to cut bread sucks too, that's why we don't do it. Meanwhile it's converting my json to xml and pissing code so I don't have to piss it myself
How to Navigate Stalled Development
Nobody knows and investors are guessing. Their confidence has been rocked and uncertainty has been introduced.
Heh, I wrote the parent comment last night, and saw this story this morning:

https://www.bloomberg.com/news/articles/2025-01-28/trump-fir...

So we're pretty quickly heading toward #10 being checked off as well.

Regarding the last item, VP Trump said some weird things about President Musk helping with voting machines (during his speeches at the inauguration, or maybe in the days after -- I can't keep track anymore). To be clear, I don't think that rises to the level of "fraudulent elections." But it is something that I think is worth investigating further, if we as a nation care about free and fair elections anymore.
I'll give a shit about AI energy usage when a huge swath of the population isn't needlessly forced to drive to work every day.
This is a similar setup to me. I have one course that has sold extremely well and generated >250k in the last 2 years. And the other 2 have done <$1k

The cross publishing on packt is not as effective as Udemy. I think sales wise, it generates maybe 2% of the revenue that I have on Udemy

Getting in the Udemy business program was a huge revenue boost for my courses since it changes it from making money on sales to making money on watch time
Why Zero-Knowledge Proofs Are Essential for AI Agents
How does it solve itself if successive generations are smaller and smaller?
Entirely possible given that the CEO of AMD is the niece of the Nvidia CEO
Show HN: I made Readers, a social eBook reader
Hi HN, I built Readers as I wanted a way to share and read ebooks with others, such as with my team at work or bedtime stories with my family.

With Readers, you can:

* Add DRM-free epubs to your library or choose from a selection of (currently public-domain) ebooks.

* Read privately or create public/private reading groups and invite others.

* Sync reading progress, comments and highlights between group members.

Ideally I'd like it to become a sort of Bandcamp for ebooks, where authors and independent publishers can sell their books, but I haven't explored that yet.

I've been wanting to make an app like this for years, but being a designer not a developer I didn't think I'd be able to do it. However, a few months ago I decided to give it a go and with a lot of trial and error and some help from Claude I was able to put it together.

I've not built something of this complexity before so I'd value any feedback or suggestions you may have.

Thanks for taking a look!
only 2 ? what kind of h100s do you have?
You know what was exciting back then? Tech magazines. That’s where you discovered all the great stuff and read about the research that was happening.
It's worthwhile because you cannot do everything and often it is better to have someone far worse than you work on a problem than to just ignore it.
A $6000 laptop doesn’t look much different than a $1000 laptop. I don’t think it’s a bigger theft risk than any other laptop.

Make sure the laptop is insured and that full disk encryption is enabled. If it’s a Mac, make sure you have it in Find My so you can wipe it remotely if that’s something you worry about.
it must be amazing to have so much faith in people like you seem to have
What water issues?
Janus Pro 1B running 100% locally in-browser on WebGPU
That’s just marketing surely? Is there any activity that you’d want, apart from diving, missing from a fenix/epix?
If we look at the processing requirements in nature, I think that the main trend in AI going forward is going to be doing more with less, not doing less with more, as the current scaling is going.

Thermodynamic neural networks may also basically turn everything on its ear, especially if we figure out how to scale them like NAND flash.

If anything, I would estimate that this is a space-race type effort to “win” the AI “wars”. In the short term, it might work. In the long term, it’s probably going to result in a massive glut in accelerated data center capacity.

The trend of technology is towards converging with or doing better than natural processes, not doing it 100000x less efficiently. I don’t think AI will be an exception.

If we look at what is -theoretically- possible using thermodynamic wells, with current model architectures, for instance, we could (theoretically) make a network that applies 1t parameters in something like 1cm2. It would use about 20watts, back of the napkin, and be able to generate a few thousand T/S.

Operational thermodynamic wells have already been demonstrated en silica. There are scaling challenges, cooling requirements, etc but AFAIK no theoretical roadblocks to scaling.

Obviously, the theoretical doesn’t translate to results, but it does correlate strongly with the trend.

So the real question is, what can we build that can only be done if there are hundreds of millions of NVIDIA GPUs sitting around idle in a few years? Or alternatively, if those systems are depreciated and available on secondary markets?

What does that look like?
Mostly it just means that we’ve got a new tool we can leverage which is a positive feedback for improving models, and will ultimately increase both train and inference demand and shorten timescales for deeper capabilities.
Source: trust me
Quite clearly the Federal Reserve is an undemocratic institution.

However the banks are the owners of the nations and of the population, so it's rather they who would control who is president or king rather than the other way around. As history has shown about three thousand times.

> We have independent agencies of the United States government specifically to insulate them from presidential control.

That's the separation of powers. However, within the executive branch, where the president constitutionally has power, it is only reasonable that he will politically appoint the highest offices. Otherwise he cannot execute the policy that voters have obliged him to.
Every time I ask chatgpt, I get a different answer. Copilot refused to answer me. Not sure LLMs are the answer you're looking for here