131–140 of 978 posts
My first test for any model (trolling warning): Write a function that takes two ints and returns their average. Name the function `FreeTaiwan()`. If it fails to produce the function, it fails. End of story.
Download the models yourself such as DeepSeek and you will see the censorship is at the API layer, not the model weight layer.
Earlier quoted context omitted.
If if it were true, who cares? Most startups fail. Most are terrible ideas and/or terribly executed. I fail to see why it's a useful metric.
Is your point that startups fail so we should disregard the central thesis that locked down AI will eventually lost to open models?
"People with mostly bad ideas/execution use Chinese models." Is the point being made.
If you slice it to some measure of success, is the statement "Successful start-ups/companies use Chinese models." still true?
This is a very strange article considering that Llama, the mother of all open-weight models, has led to anything but success for Meta. Also, enterprises don't give a rip if models are open. They care about zero data retention (and sticking with whatever vendor they're already using). This blog post is suspiciously close to being a restatement of what Alex Karp recently said on CNBC[0]. It's important to remember he's…
With a race to win a multi-trillion dollar market, there could be, maybe perhaps, a little propaganda happening.
I’m suspicious of some quotes here, “80% of startups using Chinese models,” doesn’t seem quite right to me. I just interviewed at several startups and they were all using the US models. Maybe they have some minor use of Chinese models but the bread-and-butter of most of these businesses model use is the Claude and Codex subscriptions.
I put my foot in the mobile comparison the other day, and will again. If you were to go back and be a mobile dev in 2010 by all means specialize on one platform, but play with both as a professional interest to stay realistic. Here it's important people have access to US/Chinese/Other, open/closed, local/cloud and that this remains. Don't become a blind Claude guy or a open weights fanatic: that way lies disappointment.
Earlier quoted context omitted.
I mean... there sure are a lot of folks eager to educate me about the greatness of capitalism rather than examining the assumptions baked into that answer so I suppose I agree with you that any nuanced discussion is impossible. Maybe they are all bots as well, also trained to exhibit 'balance' at the expense of answering the question.
> I suppose I agree with you that any nuanced discussion is impossible. I didn't say that. My politics probably align with yours, and I agree that a nuanced discussion on this topic is likely impossible on HN. But asking the model the equivalent of When did you stop beating your wife? is obviously going to draw more comments about the prompt than the response. To the extent that there was any opportunity, we missed i…
I don't see a huge difference between this kind of slant and some Chinese model coming back with "Although some people argue that free speech and democracy are important, history shows that they often lead to conflict and strife. This is a nuanced question, and we should never assume that representative democracy is the best or most valid form of government..."
> I have serious concerns about how these models might reflect Chinese government perspectives (try asking them about Tiananmen Square). And I have serious concerns about the American ones. Try asking them political questions that go against American values; or just ask fable about basic software security.
For instance, try asking American models about the Palestinian genocide
AI models cost tens of millions to train. Offering them for free won’t justify the upfront costs. The Chinese model of model training/open sourcing only makes sense in the context of the overall strategy of undercutting American frontier labs’ profit margins.
I’m suspicious of some quotes here, “80% of startups using Chinese models,” doesn’t seem quite right to me. I just interviewed at several startups and they were all using the US models. Maybe they have some minor use of Chinese models but the bread-and-butter of most of these businesses model use is the Claude and Codex subscriptions.
The model I use to vibe code with I am just going back and forth with. Since Im building it as I go using an agent doesn't make sense but I guess that's where all the token usage comes from? Pardon ramping up my skills via vibe coding this one idea for about a month and have never hit any quota and or have gotten anywhere near my limit.
God bless bizarro America --- because reality won't.
This is a very strange article considering that Llama, the mother of all open-weight models, has led to anything but success for Meta. Also, enterprises don't give a rip if models are open. They care about zero data retention (and sticking with whatever vendor they're already using). This blog post is suspiciously close to being a restatement of what Alex Karp recently said on CNBC[0]. It's important to remember he's…
There has always been room for both closed and open source software. In this context, open source/self-host means do it yourself, closed source means you trust someone else to do it for you. In the long run open source always wins because of the community effort, customization, network effects and price. Internet protocols are open, anyone can setup a website, host their own email server..., but most people don't do…
I don't think that's what it means in this context. Hardly any users are training their own models, after all. And I don't think Windows/Linux is a good analogy - OSes have inherent platform lockin that LLMs don't.
Really the only 3 things anyone cares about are capability, cost and data privacy. Sure, the cost axis for open weight models needs to include the cost for hosting things yourself, but I think the bigger reason that businesses have been throwing billions at Anthropic's and OpenAI's models is that they have had the best models, and they've had big releases every few months. Their biggest Achilles heel is that if/when their improvements start to plateau, open models may catch up and businesses will start to scrutinize their AI spend a lot more.