Posted by birdculture 5 hours ago
There's some virtualized desktop server stuff too. Run a bunch of desktop sessions on a beefy computer and send a video stream to desktop players. With the right codec settings, the latency is probably ok for many games.
I'm sure manufacturers would love saving a dollar per card, and OEMs would appreciate eliminating the support calls from "I just bought a new $2500 gaming PC and no video" because they plugged the monitor into the iGPU instead of dGPU.
https://www.ebay.co.uk/itm/406939344915?_trkparms=amclksrc%3...
I also use Qwen 3.7 27b at work and I agree with the author: it is perfectly capable of the jobs I give it.
Slowly but surely, I had to remove my beloved lists, emojis (though LLMs do less of that now, maybe I can incorporate them back), and emdashes.
In any event, not all of us have a unique writing style worth preserving just like not all of us can write clear and clean code. Just saying.
I feel like writing could use a similar harness, where it attempts to minimally reword the authors sentences, perhaps just tweaking grammar, spelling, etc. In the coding example i think the human code would be near unchangeable, the LLM would pivot around it - but in the writing example i think the human writing would have to be more mutable. I imagine it would be a configurable setting.
I've not really seen a system which focuses on this human<->LLM look, but it feels interesting to me.
So the language harness makes sense to me, but corps are already cracking down on token use ( and such a harness would likely only add to the cost ). The other question is whether the people, who could benefit it would even recognize it as a problem though.
Running Alpine/Gentoo/Devuan isn't that expensive. (I'm assuming the cost is time/effort when I say this; let me know if there's another relevant metric)
FWIW, I tried Void and Devuan, but that may have been too early for me then. Naturally, now that stuff mostly works, I am debating whether I can make that attempt again;p
I’m much more willing to read typos and bad writing than LLM writing. If I want to read the LLM rewritten version, I can run an LLM over the original writing myself. I have not yet found true that anyone is better at prompting than anyone else in a way that suggests that I wouldn’t get substantially the same results myself. Thus, I don’t think providing the version that has passed through the telephone game is accomplishing something that couldn’t be done by readers later. I have spent the vast majority of my life reading the original writing styles of people and didn’t have an issue then. I’m not convinced a problem I had was solved when we started post-processing writing with an LLM.
I’m really in the “who gives a shit” camp on something like this. A lot of people probably have an LLM punch up a blog post. It is good at turning bullet points and notes into prose, fixing run-ons, etc. Maybe I’m naive but I trust that the kind of person who posts a clearly noncommercial post like this on HN gives a crap enough that they read the final draft and confirmed it isn’t inaccurate.
This pearl-clutching about the mere use of AI regardless of how responsible or appropriate the use is, seems like a professor in 1985 throwing an essay back in a student’s face as “this was obviously printed from a computer and not typewritten like a PROPER essay! I can tell just by looking at it!”
i've ran some multi vendor frankenstein setups before and sometimes it even works, so i'm curious to hear your experience with it.
- In 2017, the v100 was a ~$10,000 GPU. I believe there was a PCI-e version but this is probably so cheap because SXM2 is going to be harder to use;
- A 5090 has 1800GB/s of internal memory bandwidth (compared to 900GB/s in the 9 year old GPU). Of course a 5090 is substantially more expensive;
- A 5090 has ~21k CUDA cores vs ~5k;
- The current $10k NVidia GPU is the RTX 6000 Pro w/ 96GB of VRAM. It has slightly more CUDA cores but it otherwise pretty much just a 5090. This is unsurprising. NVidia uses VRAM for market segmentation.
Consider this: in 5-10 years, the trillions spent on AI data centers will likewise be sold for scrap most likely. That's how short the runway is for OpenAI and Anthropic to recover that investment.
Anyway, I'm kind of impressed the author managed to get this all to work. I don't think it even would've occurred to me that someone had made an SXM2 adapter, particularly because it's not even used anymore. Like props to whoever did that.
Even more interesting: it'll devalue all of SaaS and the entire US tech sector.
We might have just shot our most valuable non-AI tech products in the foot.
The resulting economic crash will affect everyone, we're (IMHO) looking towards a dotcom-bust level wipeout. And many SaaS and other companies run asset-lean (i.e. they have no server hardware because that's all cloud, no real estate because it's all either wework or conventionally rented), margin-lean (the VC business model requires that, as the basic recipe is to achieve market domination by burning cash) and cash-lean (often enough, it's less than a quarter of expenses on the bank accounts).
All that "lean-ness" looks great on an investor's quarterly release sheet: no massive amounts of wealth tied up in assets and no cash sitting around on bank accounts that could be released towards investors as dividends or, if it comes from third parties, costs the company interest... but it prevents resiliency against crises.
Counterpoint: the fiber buildout during the dotcom boost. That crashed the economy pretty hard when the bubble burst, but we are still benefitting from all the dark fiber that was arranged for and built out back in that era. A lot of today's ISPs were able to grab up that fiber after the bust for cents on the dollar.
Assume that OpenAI and Anthropic go bust, which at least one of them likely will, and possibly a fair few of the datacenters that are under construction will also collapse. Someone will be able to snatch these physical assets again for cents on the dollar and run open-weight models on them or train new ones.
The problem isn't (and no, this is not an AI tell, everything I write here got typed on a 2022 M2 MBA by hand) the assets, they will be put up for productive usage, just as with any other large bankruptcy or bubble in history. The problem is the "IOU" that is being passed from one hand to the next like a hot potato. Assuming a recovery of, maybe, 20% after the collapse, at 1.6 trillion dollars of assets under management by some kind of private investment/debt we're looking at about 1.3 trillion dollars in valuation that is going to be wiped out.
And given that a lot of the investment market is actually backed by pension funds... this is going to be a bloodbath. Not only will there be a lot of people laid off in addition to the layoffs we already saw "due to AI", but when the pension funds and thus their payouts collapse? We'll see retirees flooding the employment markets who just try to make a living, rendering the situation for everyone else even worse. Flipping burgers used to be a gig for students, these days students compete with people of all ages desperate to survive - and thus desperate to undercut others in wages.
Another problem will be the capacity buildout in the semiconductor industry. It's already heading toward an oligopoly after numerous boom-bust cycles: you only have two and a half GPU chip vendors (NV, AMD, Intel), two vendors of general-purpose CPU vendors (Intel and AMD - I exclude Apple because they do not sell their CPUs to any third party and ARM because 99% of non-Apple ARM chips do not go towards servers, desktops and laptops), three RAM manufacturers (Samsung, SKhynix, Micron) and two and a half physical chip manufacturers (TSMC, Samsung, Intel). When the AI bubble bursts, it will be one of a hell of an effort to prevent at least one actor from going bankrupt.
[1] https://prospect.org/2025/11/19/ai-bubble-bigger-than-you-th...
A lot of the current AI business is FOMO and vanity metrics. Nobody really wants to acknowledge the support tickets where the first three responses are the customer cursing because they didn't appreciate being handed off to a chatbot, or the reworks, or the compliance/policy/privacy concerns, or the internal friction and brand damage it's causing.
Right now, a lot of that is being dazzled away by how "cheap" the alternative is, since it's built on an unsustainable cost base. It's like someone opened a "restaurant" where the food was actually supplied by making a bazillion new DoorDash accounts to claim promotional credits and having them drop the food at the "kitchen". During the initial phase, the customers will forgive that the burger was cold because it was $1.79.
Once the funny money runs out and services start shuttering or pricing for actual profitability, people are going to ask about actual quality and return on investment. There will be a demand rollback.
Even if you can do it cheaper with an open-model running on fire-sale hardware, we probably don't need 500 "chatbot listens and transcribes your meeting" services that weren't that much better than dictation software running locally on a Pentium III. We probably don't need AI-powered support experiences that manage to be worse than actually keyword-searching your company's Confluence. We probably don't need to be spinning up coding agents to spend 15 minutes discombobulating and bibblewabbling and re-reading 82 billion tokens of context before making a two-line change that an actual developer with learned experience in the code would make in 15 seconds.