China is not the birthplace of so called '996'. Long before tech scene in China, there are a lot of investment banks doing that in HK especially for junior analysts. Calling 996 a China thing is just orientlalism. Everything bad is Chinese, everything good is western.
At least the recent popularity of the 996 originated in China, and I believe most Chinese people would agree with that. Besides, even if it started in Hong Kong, saying it originated in China is still technically correct.
China is the birthplace of the term 996. Of course it's not the birthplace of people being coerced into unhealthy work hours - that's been around for thousands of years.
There is probably little to nothing specifically Chinese about workaholism as a concept, but the word is definitely Chinese(as in language). Dialect continuum for East Asian languages are contained within borders, or in other words, each of the languages expanded and dominated to the full extents of continuum and hit with stagnation at major geographical features before entering the modern era.
That's true. You would think LLM will condition its surprise completion to be more probable if it's in a joke context. I guess this only gets good when model really is good. It's similar that GPT 4.5 has better humor.
Good completely new jokes are like novel ideas: really hard even for humans. I mean fuck, we have an entire profession dedicated just to making up and telling them, and even theirs don't land half the time.
Exactly. It feels like with LLMs as soon as we achieved the at-the-time astounding breakthrough "LLMs can generate coherent stories" with GPT-2, people have constantly been like "yeah? Well it can't do <this thing that is really hard even for competent humans>.".
> We’ve trained a large-scale unsupervised language model which generates coherent paragraphs of text...
That was big news. I guess this is because it's quite hard for the most people to distinguish the enormous difficulty gulf between "generate a coherent paragraph" and "create a novel funny joke".
- It can beat grandmasters -> but it can’t play go
…etc, etc
In a way I guess it’s good that there is always some reason the current version isn’t “really” impressive, as it drives innovation.
But as someone more interested in a holistic understanding of of the world than proving any particular point, it is frustrating to see the goalposts moved without even acknowledging how much work and progress were involved in meeting the goalposts at their previous location.
> it is frustrating to see the goalposts moved without even acknowledging how much work and progress were involved in meeting the goalposts at their previous location.
Half the HN front page for the past years has been nothing but acknowledging the progress of LLMs in sundry ways. I wish we actually stopped for a second. It’s all people seem to want to talk about anymore.
I should have been more clear. Let me rephrase as: among those who dismiss the latest innovations as nothing special because there is still further to go, it would be nice to acknowledgment when goalposts are moved.
Maybe the people raving about LLM progress are the same people holding them to those high standards?
I don’t see what’s inconsistent about it. “Due to this latest amazing algorithm, the robots keep scoring goals. What do we do? Let’s move them back a bit!” Seems like a normal way of thinking to me…
I see people fawn over technical progress every day. What are they supposed to do, stop updating their expectations and never expect any more progress?
It could of course be that there are people who “never give it up for the robots”. Or maybe they do, and they did, and they have so fully embraced the brave new world that they’re talking about what’s next.
I mean, when I sit in a train I don’t spend half the ride saying “oh my god this is incredible, big thanks to whoever invented the wheel. So smooth!”
> I mean, when I sit in a train I don’t spend half the ride saying “oh my god this is incredible, big thanks to whoever invented the wheel. So smooth!”
Two thoughts:
- In that context, neither do you expect people to be invested in why the train is nothing special, it’s basically a horse cart, etc, etc
- And maybe here’s where I’m weird: I often am overcome by the miracle of thousands of tons of metal hurtling along at 50 - 200mph, reliably, smoothly enough to work or eat, many thousands of times a day, for pennies per person per mile. I mean, I’ll get sucked in to how the latches to release the emergency windows were designed and manufactured at scale despite almost none of them ever being used. But maybe that’s just me.
My point isn’t that other people shouldn’t be amazed, it’s that I see this recurring assumption they aren’t. How do you know the people holding LLMs to higher standards aren’t also the same people who herald the dawn of a new AI era?
Emphasis in the text you quoted: “saying”, not “thinking”.
My point was more that there is a subset of technical people who delight in the “they’re not perfect, because / therefore they are just glorified spellcheck” fallacy. Search this thread for “spell” or “parrot” to see examples.
So I don’t think it’s the same people, because the tone is not “they’re amazing but have farther to go”; there is a substantial group who at least claims to believe there’s no qualitative difference between Opus 4.1 and the spellcheck in Word ‘95.
Not trying to be argumentative here; I appreciate the conversation and you’ve helped me sharpen my point, which I appreciate.
Which is notable, because GPT-4.5 is one of the largest models ever trained. It's larger than today's production models powering GPT-5.
Goes to show that "bad at jokes" is not a fundamental issue of LLMs, and that there are still performance gains from increasing model scale, as expected. But not exactly the same performance gains you get from reasoning or RLVR.
This actually makes sense because in the meantime Meta is ditching the open-source (open-weights) direction.
Before the national security narrative took over, the main argument was about "safe" AI, where releasing models as open weights was considered "not safe." Now that no major US AI players release premium open-weights models, the "safety" narrative isn't needed anymore—so cooperating with the US military is feasible again.
Hopefully we'll be lucky and brave patriots will take great risk occasionally leaking those models so we taxpayers can enjoy them too (not just Northrop-Martin).
Deepseek and Alibaba just published their froniter models in open weights weeks ago. And they happen to be the leading open weights models in the world. What are you talking about?
Interesting that the output price per 1M tokens is $0.6 for non-reasoning, but $3.5 for reasoning. This seems to defy common assumption of how reasoning models work, and you tweak the <think> token probability to control how much thinking it does, but underlying it's the same model and the same inference code path.
This common feature requires the user of the API to implement the tool, in this case, the user is responsible to run the code the API outputs. The post you replied suggests that Gemini will run the code for the user behind the API call.
