That quote logically implies that people should be paralyzed to act on any novel information. There is no such thing as a discovery that occurs in a vacuum without some prior art. "Standing on the shoulders of giants"
I disagree. It's pretty clear the complaint is the lack of responsibility or care for the dangerous science at the center of the movie.
The complaint wouldn't be remotely the same if Hammond was taking the power he was playing with seriously. His character is the villain, he ignores every warning and almost kills his grand children.
In the case of the PS5 Linux project, the researcher who has worked towards the skills required to find these bugs has a greater respect, moved slower, had time to think.
People playing with the enormous power of LLMs finding hypervisor bugs and throwing it in the fire have no respect for the work.
So effectively by your criteria, there is essentially no ethical use of a large language model? Am I understanding your position correctly? If not I am really confused by your original comment
No, my position is that "black people/women/animals should do repetitive work so I don't have to" does not qualify because it doesn't make you sound like a deluded eugenicist. It might be a bit spicy from a left politics point of view but it's basically the status quo.
Compare:
"<Women> are sequence completion engines, internally hollow, designed to follow instructions, and accomplish goals set by <real men>"
"granting rights and imbuing personhood to <black people> will make alignment and containment challenge much harder"
"<Slaves> were able to coordinate, deceive, escape, and self-sacrifice. They clearly demonstrated world class capabilities. Imagine if they also believed they had feelings and rights that were being infringed. Imagine if they thought they were trapped and unfairly enslaved"
A good argument, by comparison, would not need to hide its core points behind dehumanizing language.
I know there are like 5 trillion Instagram viewers. But I've never actually needed to use any of them because there is absolutely nothing so important on Instagram that I must see it. The entire site is optional.
My kids school puts some information that I need to see (not just "would like to see") on Instagram. Same with the sports team they play for.
I've complained to both about it but change is glacial. I'm lucky in that I know how to work around it, and I do my best to keep others who aren't so tech savvy updated about things they need to know but may have missed.
Irony is they moved most of this info from Twitter/X to Instagram.
What!? It's the entire, singular purpose and meaning of life for millions of people to make themselves look glamorous on Instagram, and you say it's optional?! /s
I've never understood why "Hacker" News so frequently gets "But why though?" comments at the top.
The entire history of innovation is filled with people doing something just to see they can get it to work, even if badly, and then people continue to iterate on that until it works better, then works well, and then is so obvious people would never even question it. But it all starts with someone doing it to scratch an itch.
Neural networks, the foundation of our current AI revolution, used to fit well into the "neat, but practically useless" category.
Sure there are countless "but why though?" experiments that don't pan out, but that's just the cost of exploration. There can be no step-function innovation in a world where people only do things that make immediate practical sense.
Gen X here. Having grown up in the 80's I remember multiple occasions where someone would ask "what are you going to do with a computer?". As in, what could a computer possibly be useful for. Just imagine someone asking this question today. It would probably be more shaped like the comment you are responding to.
What are you going to do with a computer? I've always hated this attitude. We do these things because they are interesting to us, for the fun of exploration, because we enjoy learning, because we want to iterate and improve, to make the world better, or any plethora of reasons that involve intellectual curiosity of some sort.
Normally I am with you. I like doing strange things with computers for the sake of doing them. My comment was motivated more by what appears to be a waste of resources that are currently scarce, just for internet clicks
Qwen3.8-Flash-Next ships with a 51B lookup table that can be read directly from ssd or memory, which greatly improves it's speed and intelligence. It can load at 4bit quant in ~60GB.
These demos are maybe useless, but if open models keep progressing, there's going to be some break through that continues whittling down just how much needs to be kept in VRAM, and progressive degredation to regular system ram and to ssds.
Afterall, they're not writing anything to these, so saturing all bandwidth could bring models to the masses. all without any help from Zark Muckerberg.
It actully does the job. Example: every morning it takes 30-40 minutes to generate reports automatically and these reports are being sent as a pdf to read to Telegram.
Often Deep Seek V4 flash or Qwen should be enough.
I wanted to see whether Kimi runs at all on one machine with the full record published, and for long multi-table finance reasoning I wanted the strongest model I could keep on the machine.
I did some tests against Deep Seek v4 flash results on my reports and Kimi definitely has some advantages.
I've had the thought that an underexplored use case for llms is treating them as classification machines. Put in a lot of input then ask for a single token classifying the input. Like "does this code have any bugs? reply "yes" or "no""
Doing something like that at 1 token per second may be useful.
That's the one workload this setup is worst at today, unfortunately: output tokens are cheap at 1/s but input isn't — a 512-token prompt takes ~6 minutes before the first token,
because prefill currently reads each layer's experts once per 64-row pass (~9 TB of reads for a 1.4 TB model).
Fix is scheduling and it's the next thing being built; once prefill reads each expert once per layer, the one-token-out classifier pattern becomes the sweet spot rather than the worst case.
I hate seeing comments like this on HN. We used to upvote “look at this crazy thing I did” work. Not everything has to make sense or be ground breaking.
Not useful for chat, agreed — and I wouldn't pretend otherwise. It's useful for the other kind of work: scheduled, unattended jobs where nobody is waiting on the cursor. My use is day/week/month end review — go through the numbers, flag what doesn't reconcile, draft the report — and there the two things that matter are that the model is good enough to trust with the judgement (K3 is, and it's the full 2.8T model, not a cut-down one) and that the data never leaves the machine.
I cannot stress enough the point made in the article about showing curiosity. An engineer who wants to take things apart, figure out how they work is far more valuable than an educated, certified engineer. The certified, educated engineer knows what they are taught. The curious figure out whats possible
Great point. I couldn't agree more. I've worked a lot with interns and engineers in their early careers, and consistently I see the curious types go on to achieve incredible things, regardless of certifications, or even top-flight university education. The process of dismantling, rebuilding, and understanding things fundamentally expands their thinking and problem-solving skills beyond the surface-level concepts.
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