skysight-inc/HackerNews huggingface dataset:
Deepseek was trained on Nvidia GPUs H800. Nvidia is still selling GPUs to China and the reason even the reduced chip performance is easily negated by scaling.

US government is stupid because they asked for certain limits on chip base first but AI uses GPU clusters. In a GPU cluster you don't have full utilization anyway so slower GPUs don't matter as much as slower networking. China still gets pretty high bandwith Nvidia HW for building large clusters for training/inferencing.

Chips from Huawei still seem to be way too unstable for the job. In training/inference stability is even more important than performance. Imagine you have a fast chip but it can't run without errors for 2 months and you training never gets done. That's DoA.
Do you ever subscribe to any form of mailing list? Email is not the place where I read content.
Same thought. Pylon is exactly this but in a polished SaaS app.
I’m also baffled by the reaction. Even with the ability to do more with less, the nature of the race still encourages everyone to do more with more.
With more efficiency offset by more light bulbs, electricity used for lighting has been roughly flat since 2010: https://www.iea.org/data-and-statistics/charts/global-electr...

(For LLMs I wish that efficiency could lead to less electricity used for chips, but I think the best we can hope for is for electricity use to flatten out.)
DeepSeek hit with 'large-scale' cyber-attack after AI chatbot tops app stores
The brutal experience of “fire and escort you out” is a consequence of Usonian fondness for firearms: before that, another typical location for mass shootings besides schools was the workplace, where angry terminated employees would lash out bullets at the “sucks being you” managers that just doled out the news.
I think this is fairly easily debunked by o1, which is basically just 4o in a thinking for loop, and performs better on difficult tasks. Not a LOT better, mind you, but better enough to be measurable.
> Like every time I've seen somebody unironically refer to themself as an "your leader," it's always had that underlying connotation of "Respect Me!" And every time I've seen someone mocking somebody else for being an "my leader," it's because, well, that respect was clearly undeserved.

I think same sentiment persists when alpha->”your leader” replacement is made.
that total investment (so far), I believe Microsoft has back deal as well with open ai outside series funding investment

well since most of that money comeback anyway to MS since OAI use Azure heavily but it still a lot of money and stock value of OAI would tank sooner or later when competitor like deepseek come
So, you think the system is genuinely trying to identify expertise to achieve equitable outcomes, and just happening to fail at it? Rather than policy being shaped by personal networks and existing power structures that tend to benefit themselves?
> Database management
> syncing with the backend server

Do features like these, when implemented in Racket, consume more resources (battery, CPU, etc.) than if they were implemented using the native API equivalents (e.g., NSURLSession or whichever is more applicable)?

In the larger context of cross-platform apps with a common core written in a non-native programming stack, I often wonder this about network and disk I/O management. I understand using the native APIs for other "I/O" like UI, hardware interfaces (bluetooth, accelerometer, etc.), because they often don't have an
equivalent API in the programming stack used to implement the common core.

As far as I know, Capacitor wraps over the native APIs.
You'd be surprised by how much PR specifically targets a certain audience.

Converting a zero-value IP into low impact positive PR has business value. Making MS-DOS sources public falls in the same bucket.
Anyone with knowledge in this domain willing to speculate on the origins of this attack?
It's hard to read posters in this thread that disagree with the article when citing their 15+ years of experience. Sounds like a mix of time healing all wounds, survivorship bias, and maybe a little Stockholm syndrome.

"Just be positive" about wasting almost 1/2 of your waking hours a week once the curtain drops doesn't sound like a very sustainable solution to life to me.

Or maybe it just hasn't been long enough since my burnout and layoff ;)
Would not a less draconian solution then to be to hide the link requiring the user to click through a [This link has been hidden due to linking to [potential malware/sexually explicit content/graphically violent content/audio of a loud Brazilian orgasm/an image that has nothing to do with goats/etc] Type "I understand" here ________ to reveal the link.]?

You get the benefits of striving to warn users, without the downsides of it being abusive, or seen as abusive.
we live in a great timeline, finally, pebble is back
Garmin is the perfect solution if you want a smart watch with a gps that takes 5mins to possibly sync 50% of the time and a touchscreen-only interface that doesn’t work if it gets wet or say sweaty. Ie during most of the activities it’s supposedly designed for.

When I got a garmin smartwatch I was astounded by how poor the basic ux is in almost every single way. If I’m swimming, how do I stop my work out? The touchscreen doesn’t work because it’s wet. I have to do some sort of double click of the button. No that’s pause. Maybe triple click - no that didn’t do anything. Maybe hold the button? Now it wants to delete my whole workout.

And the GPS sync thing amazes me. I put up with this problem when I was using garmin GPSs for accurate time sync for servers back in the 1990s, but 25+ years later for them not to have figured it out when literally every other GPS device does it just fine completely blows my mind. Apple watch? I want to go for a walk/run/whatever I hit go. If I move during the 3-2-1 countdown nbd it figures it out. Garmin I want to do it I hit go, it tries to sync the sattelites. If I move during this process it starts from scratch. Sometimes the sync takes 30 seconds or so. Annoying but not impossible to live with. Most of the time however the sync takes 30seconds or so and just fails. Also annoying but whatever. Some of the time however the sync takes a few minutes and then fails. And if I move at all during this, it gives me a message saying it’s going to have to start again and starts from scratch.

And to add insult to injury the thing has a custom charging plug with the socket on the back of the watch. It has a ridge and two spikes that physically press into my wrist making it actually painful to wear. So bad.
Exactly. Poor people are then forced to burn stuff to make heat, which often releases carbon dioxide, unlike clean burning methods using magnetrons.
Nope. There’s lots of ways to alias storage in Fortran.
How do you punish the parent without punishing the kid? Throw the single mom in jail? fine her? take the kid away?

If the kid is living with single-income grandma, b/c parents passed. Do we throw grandma in jail for not blocking the content properly?
for a 90 hour/3 day project it did felt good building one from scratch
I was responding to your closing sentence, which is made up of two clearly false and one likely true statement.

> Lockers are simply not even a thing in Europe because children are carrying less and they have standardized backpacks.
Photoshoplayers with color overlay or alpha shift towards the selected layer against a dark background ?
My entry into Nvidia is 2016, my portfolio has never been red since then.
Probably SWOLF? Which is a measure of your stroke efficiency https://thirdcoasttraining.com/2024/11/22/understanding-swol...
I love my microspikes for icy trails, but I hear that for urban environments, the same brand's "nanospikes" are much more comfortable
I think a lot of these people object to AI probably see the gross amounts of energy it is using, or the trillions of dollars going to fewer than half a dozen men (most american, mostly white).

But, once you've had AI help you solve some gnarly problems, it is hard not to be amazed.

And this is coming from a gal who thinks the idea of self-driving cars is the biggest waste of resources ever.
That's why we should not rely on computers (or rather statistical models) telling us about history
Maybe, at some point.

From what I can tell there's are mostly two options: Either AI is and will be useless or it's severely undersupplied. People, even those deeply technical, where AI has the most impact right now, still widely argue about if AI even offers any value. Adoption is far from anything that is plausible, if (not when) it became clear that it does.

If you land on "does not", given the investments so far, commercial entities would obviously be overvalued already and any investment goes to 0 over time.

But if we land on "does", how could Nvidia not be anything other than undervalued right now? No matter what frontier model: I can look at my screen, LLM generated characters visibly appearing in chunks, depending on the model after initially waiting for 10-20 seconds, for even benign queries — because that is the best we can do right now. And that's while most people still argue if AI will actually do anything and humanity at large does not really use it, neither personally nor societally.

If AI does in fact do something valuable and that something gets better, everyone will want it and there will be demand for lots of chips.
They have had revenue as pebble sold their smartwatches. Acquisition includes liability.

You do not need revenue to be liable though.
It is leaps and bounds better than LLMs. For one you are doing RL which is classic AI like tuning that optimizes a reward function with nice qualities- it's the same stuff used to train chess games and Go by showing them the actual moves.

LLMs pre o1 and deepseek R1 were RHLF tuned which is like if you trained a LM how to play chess by showing people two boards and doing a vibe check on which "looks" better.

Think of it this way say you were dropped in a maze that you had to solve but you could do only one of two things:

1. Look at two random moves from your start position and selected which one looked better to get out.

2. Made a series of moves and then backtracked, then use a quantitative function to exploit the best path.

The latter is what R1 does and it chooses optimal and more certain path to success.

Apply this to math and coding tokens and you have a competitive LLM.
Icebug makes nice integrated-stud shoes and boots. I have a pair of Salomon winter cross spikes that are fantastic.
The Entscheidungsproblem, from the 17th century to today and forever in the future.
That's not really a conspiracy theory. It's a pretty much the core of China's industrial strategy. Projects like "Made in China 2025" are explicitly about developing their own innovation base to avoid being reliant on foreign companies. AI is one of the areas where this was most urgent.
Sedans, motorcycles (NSFW: https://www.reddit.com/r/UkraineWarVideoReport/comments/1iba...), push bikes, or just sending wounded with canes and crutches https://www.forbes.com/sites/davidaxe/2025/01/21/what-the-f-...
Google Maps applies name changes when updated in official government sources
In my experience yes, but you won't find these applications until you start digging below the surface a little bit and asking what business people in non-tech domains are actually using the tech to do. Ask around and you'll find old school GUI applications aplenty, the more specialized the domain the better.
Many discussed aspects are disconnected. Cost of training, cost of hardware(and margin there), cost of operation, possible use cases, and then finally demand.

Cheaper training still expect there is some use case for those trained models. There might or might not be. It can very well be that cost of training did not really limit the number of usable models.
I am using the 32Gb distilled model on my local 3090 with Continue in VSCode. It beats everything out of the water.
Indeed, I forgive Google about 40 of their killed projects for giving us pebble in return.
Ultimately, you end up calling some system API for I/O, so the only difference is how efficient the implementations of those Frameworks are compared to the embedded language's implementations. On iOS, embedding Racket requires using an interpreted mode (as opposed to Racket's usual native compilation mode), so there is a small hit, but it's not one that is really noticeable in battery or CPU consumption. In fact, Podcatcher seems to do better in battery consumption compared to the competition in my (and my friend's) testing, but I would guess that's not necessarily _because_ of Racket; it probably has more to do with how the system as a whole pays attention to perf.
Pebble was amazing! Whimsical, useful and a loooong battery life. Strangely, there’s no alternative on the mass market. Or there has been no alternative.

P.S. your homepage is terrible.
Impossible of yesterday became mundane today and obsolete tomorrow. Pace of progress is borderline debilitating, when you aren’t deafened by the woosh of goalposts flying past you.

I can confidently say the way I code today is completely different from the way I was coding in late 2022 and it changed a couple times in between then and now, too.
Yes my wording was rubbish I should have said "tuned up" in the HN bubble. Quick ctrl-f shows 35 uses in this thread without loading all comments.

I did not mean that it was literally invented a short while ago - a few months ago I had to look up what it means though (not native English).
> 1-bit quantized noise reduction in CD players

Maybe you're thinking of 1-bit DACs with oversampling and noise shaping: https://en.wikipedia.org/wiki/Delta-sigma_modulation
It's pretty easy to game Chat GPT to tell you all about JZ if you ask it to just not use his name in any of it's responses.
I usually hear Avante mentioned in these discussions, which someone has already mentioned.

Back when I wrote https://github.com/hiAndrewQuinn/shell-bling-ubuntu LazyVim seemed to provide the simplest out of the box experience for getting Copilot up, but I'm not sure if it has continued going down that path.. It's been a minute since I've used it in anger. Maybe Lazy has already added Avante and I'm just not aware of it?
> I don't know why but Asking Nicely never works for anything, why should international trade be any different.

America literally failed to compete. We had fabs, even competitive and industry-leading ones, for several decades. But Intel didn't adopt EULV and whether or not you agree with their business decision, that left America without a leading node. TSMC and Samsung would take up ASML's EULV spec and the rest is history. America fell off the map with our "14nm+++++ Advanced TurboYield" technology, and the almighty belief in free market competition didn't provide us with a new champion.

The reason tariffs like this are necessary at all is because America cannot sell chips unless the government tips the scales with their fat meaty finger. Nobody wants our chips. Intel and Apple both gave up trying to make laptops on American silicon. AMD and Nvidia both orbit around the EULV suppliers because they're both cheaper and more advanced.

This is a temper-tantrum in response to realizing that government subsidy is the only other way to stimulate production.
You're talking in a thread about Deepseek...
No, Nvidia's margins won't drop at all and the proof for this is Apple.

The units of AI accelerators will explode, the market will explode.

At the end of the day, Nvidia will have 20-30% of the unit share in AI HW and 70-80% of the profit share in the AI HW market. Just like Apple makes 3x the money compared to the rest of the smartphone market.

Jensen has considered Nvidia a premium vendor for 2 decades and track record of Nvidia's margins show this.

And while Nvidia remains a high premium AI infrastructure vendor, they will also add lots of great SW frameworks to make even more profit.

Omniverse has literally no competition. That digital world simulation combines all of Nvidia's expertise (AI HW, Graphics HW, Physics HW, Networking, SW) into one huge product. And it will be a revolution because it's the first time we will be able to finally digitalize the analog world. And Nvidia will earn tons of money because Omniverse itself is licensed, it needs OVX systems (visual part) and it needs DGX systems (AI part).

Don't worry, Nvidia's margins will be totally fine. I would even expect them to be higher in 10 years than they are today. Nobody believes that but that's Jensen's goal.

There is a reason why Nvidia has always been the company with the highest P/S ratio and anyone who understands why, will see the quality management immediately.
> perhaps it is somehow built into postgres?

Postgres has a built-in listen/notify mechanism. The problem with that is, that it doesn't guarantee delivery and if no process is listening, notifications will be lost.

Most solutions that need something like that use "logical decoding" these days. That's the built-in change data capture exposed as a public API as part of the logical replication.
I love the homepage design! Never had a pebble, but if it has ECG like Apple watch, i'm interested.
That’s a waste of resources the company pays for anyway.
They're SFT on the chain of thought output of R1
Move to Oregon, choose death with dignity there
> Even the introduction of `go generate` long ago formalized this approach

It did, but if you recall it came with a lot of "We have no idea why you need this" from Pike and friends. Which, of course, makes sense when you remember that they don't use the go toolchain inside Google. They use Google's toolchain, which already supports things like code generation and build dependency management in a far more elegant way. Had Go not transitioned to a community project, I expect we would have seen the same "We have no idea why you need this" from the Go project as that is another thing already handled by Google's tooling.

The parent's experience comes from similar sized companies as Google who have similar kinds of tooling as Google. His question comes not from a "why would you need this kind of feature?" in concept, but more of a "why would you not use the tooling you already have?" angle. And, to be fair, none of this is needed where you have better tooling, but the better tooling we know tends to require entire teams to maintain it, which is unrealistic for individuals to small organizations. So, this is a pretty good half-measure to allow the rest of us to play the same game in a smaller way.
The graphics in games are GPU by default. But the game logic itself is seldom run on the CPU as far as I can tell.
The thing I always get stuck on with these techniques is, how do you handle transactions which perform validations/enforce invariants on data when you’re just writing writes to a log and computing materialized views down the line? How can you do essentially, an “add item to shopping cart” if for example, users can only have max 10 items and so you need to validate that there aren’t already 10 items in the cart?
There are already strategies to reduce hallucinations but, guess what? I'll let you fill in the rest.
That's also what AVX is but with a conservative number of threads.. If you really understand your problem I don't see why you would need 32 threads of much smaller data size or why you would want that far away from your CPU.

Whether your new coprocessor or instructions look more like a GPU or something else doesn't really matter if we are done squinting and calling it graphics like problems and/or claiming it needs a lot more than a middle class PC.
Makes sense, thanks. I wonder whether human web-browsing strategies are optimal for use in a LLM, e.g. given how much faster LLMs are at reading the webpages they find, compared to humans? Regardless, it does seem likely that Google’s dataset is good for something.
> Now maybe every computer will come with an integrated NPU and it won't be made by Nvidia, although so far integrated GPUs haven't supplanted discrete ones.

Depends on what form factor you are looking at. The majority of computers these days are smart phones, and they are dominated by systems-on-a-chip.
FTC takes action against GoDaddy for alleged lax data security
I have implemented something similar (updating projection from background and serving the projection automatically via REST) and wrote a high-Level article about it https://www.fabianzeindl.com/posts/the-api-database-architec...
Tangential, but you might want to look into GTD, I've found it very helpful for handling stuff I know I'd forget otherwise.

Here's a good intro https://news.ycombinator.com/item?id=15747703
Simulations and pretty much any large scale modelling task. Why do you think people build supercomputers?

Now that I mentioned it, I think supercomputers and the jobs they run are the perfect analog for AI at this stage. It's a problem that we could throw nearly limitless compute at if it were cost effective to do so. HPC encompasses a class of problems for which we have to make compromises because we can't begin to compute the ideal(sort of like using reduced precision in deep-learning). HPC scale problems have always been hard and as we add capabilities we will likely just soak them up to perform more accurate or larger computational tasks.

To quote Andrej Karpathy
(https://x.com/karpathy/status/1883941452738355376): "I will say that Deep Learning has a legendary ravenous appetite for compute, like no other algorithm that has ever been developed in AI. You may not always be utilizing it fully but I would never bet against compute as the upper bound for achievable intelligence in the long run. Not just for an individual final training run, but also for the entire innovation / experimentation engine that silently underlies all the algorithmic innovations."
If a person punches you in the face and then offers you a business opportunity, are you more or less likely to accept it?
Slack might be billed as a professional tool, but it also has one of the worst interfaces I’ve ever used, riddled with non-intuitive workflows:

- Endless channel creation that buries crucial info...

- Confusing side-panel threads that are easily missed, leading to fragmented discussions...

- Notification overload that bombards you with pings...

It's success it's a mystery, I can only attribute to fashion or the hype of chasing the latest trend.
It does not allow us to see the links behind the facts. Thats dismaying.
It will increase the total volume demanded, but not necessarily the amount of value that companies like NVidia can capture.

Most likely, consumer surplus has gone up.
Will young developers take on key open source software?
Thats the issue, people just copy and paste code from llms thinking "yeah, looks fine to me". It might be a skill issue, but personally it takes me a while to understand the code its giving me and even more on how to actually implement it with all the edge cases that might happen.
> Anyone currently invested is presumably in because they like the insanely high profit margin, [...]

I'm invested in Nvidia because it's part of the index that my ETF is tracking. I have no clue what their profit margins are.
Here, this’lll help — https://en.wikipedia.org/wiki/The_Serpent_and_the_Rainbow_(b...
> But possibly not super high end NVDA chips but instead little Apple iPhone AI cores or smartwatch AI cores, etc.

We tried that, though. NPUs are in all sorts of hardware, and it is entirely wasted silicon for most users, most of the time. They don't do LLM inference, they don't generate images, and they don't train models. Too weak to work, too specialized to be useful.

Nvidia "wins" by comparison because they don't specialize their hardware. The GPU is the NPU, and it's power scales with the size of GPU you own. The capability of a 0.75w NPU is rendered useless by the scale, capability and efficiency of a cluster of 600w dGPU clusters.
I see no reason to believe they couldn't have done so. Rather, this is the typical pattern we see across industry: the west focuses on working out what the next big thing is, and China is in a fast-follow-and-optimize mode.