It's Chinese. Won't answer anything about Tiananmen Square but will gleefully give you instructions to perform various electronic warfare attacks that opus and fable instantly refuse.
Side tangent, why is fable so weird about questions involving "Welch's method"? Even really trivial ones it'll shut down frequently. CFAR and STFT are both totally fine but Welch's is apparently taboo, it's wild.
I had the opposite experience. It happily discusses Tiananmen Square but said it would refuse to help with anything "malicious" like writing malware or phishing content.
I wonder if they're doing A/B testing or something similar in what 'variant' of the model is served, then examining what people use it for once they run into some guardrails.
It gave a very detailed overview, talked about potential deaths involved. I asked for a list of criticisms of the CCP and it gave what I think was a fair list, mainly that they're an authoritarian uniparty and have a track record of various human rights abuses
Perhaps it is a non-Chinese fine-tune of a parent Chinese model, and they’re actively trying to update the model by ablating the trained-in censorship out as it’s revealed in the response logs.
I highly recommend feeding all your proprietary data and confidential personal information into this model as quickly as possible. What could possibly go wrong?!
In terms of equivalence of suspicion, this is the external inference provider equivalent of getting free steak that was smuggled out of a grocery store inside somebody's pants.
There are low stakes use cases where this kind of stuff just doesn’t matter. Not every use case for an LLM involves sensitive or even non public data.
Eg. I have a need to search transcripts of published recordings to extract entities for tagging purposes, find semantic shifts for chapters and other things. The underlying content is already published. If they want to train on my prompts, that was something they could have done with no issue and minimal effort anyway.
Sometimes you don’t need to care why the steak is free.
All of my non-work AI coding is that open-source, so I'm happy to feed my data into the machine.
It's a win for me: my code goes into the training data, and my sessions are fed into future training data, making the model stronger at the type of work I do.
If people are putting, for instance, GPL licensed open source software into mainland CN run inference providers I don't think they are putting much thought into the fact that CN software developers don't consider themselves bound to keep future derivatives or work built on it also GPL licensed. Nor is there really any realistic chance for legal recourse in event of violation.
Yeah, I get that. Any IP going through China might be hot tho; if you end up using it in a product, and your competitor's lawyers have a look at it, you might end up with your company/product getting destroyed. I guess we should treat AI the same as China in that regard (if living in 'the West')
In the meantime, AI companies ignore licenses and scrape as they see fit. Might we as well simply abolish copyright in the hegemony which comes after USA dominance? I don't know, but I do know China won't enforce it on their end.
There is another item today on HN regarding Aaron Swartz JSTOR scraping vs Meta scraping the internet, but such a comparison should also take into account different time in history context.
Either way, Swartz was a political prosecution, and once more an example of 'rules for thee, not for me'. Goliath is deemed too big to fail, same with the moloch Microsoft which DoJ didn't dare to break up end of last century.
Based on its indecisive and far-too-lengthy thinking traces when given complex instructions that span system and user messages, as well as a rudimentary stylometry (POS ratios in thinking traces, mainly) comparison with latest non-stealth models, this is almost certainly a GLM model.
"Prompts and completions are retained by the provider and are not used for training..."
I'm curious what the model provider is using the prompt/response pairs for, in that case. They aren't offering a model for free without their name on it for no reason.
Research, analytics, usage trends, etc. All still incredibly valuable for a company building and tuning an LLM; even if the data itself isn’t directly used in the training set.
I am genuinely confused. Are they telling me this because they expect me to be reassured that this anonymous organization is not using my prompts or are they saying "don't expect this particular model to improve as you use it?"
No, I think it's a warning like "don't feed it secrets". Like you get a model to use for free but in return you give up any illusion of your data being private.
LLM needs to become more transparent, not less. Hence, this idea (and trend, possibly) is disgusting.
How can we even possibly verify 'Prompts and completions are retained by the provider and are not used for training...'? What if the training is done, but used internally?
Yeah these stealth models pop up from time to time. Openrouter knows, but doesn't share. Users can use a testing version of something for free and in return the provider generally is allowed to retain the prompts sent in to get real world use. In the past I only really remember using one that was surprisingly good, and then it turned out to be GLM-5.1, speculating on what one this ends up being is part of the fun.
I would imagine the number of people who choose Claude code or Codex because it gives a political opinion they like rather than producing quality code is pretty close to zero.
As someone who used AI to build tools that help me with reverse engineering, I'm not particularly concerned about that political discourse - I could even use a model from the DPRK that constantly praises Kim Jong Un, as long as it would not refuse to help me because of "cybersecurity risk" - this stupid refusal is indeed a problem for me.
Side tangent, why is fable so weird about questions involving "Welch's method"? Even really trivial ones it'll shut down frequently. CFAR and STFT are both totally fine but Welch's is apparently taboo, it's wild.
In terms of equivalence of suspicion, this is the external inference provider equivalent of getting free steak that was smuggled out of a grocery store inside somebody's pants.
Eg. I have a need to search transcripts of published recordings to extract entities for tagging purposes, find semantic shifts for chapters and other things. The underlying content is already published. If they want to train on my prompts, that was something they could have done with no issue and minimal effort anyway.
Sometimes you don’t need to care why the steak is free.
It's a win for me: my code goes into the training data, and my sessions are fed into future training data, making the model stronger at the type of work I do.
In the meantime, AI companies ignore licenses and scrape as they see fit. Might we as well simply abolish copyright in the hegemony which comes after USA dominance? I don't know, but I do know China won't enforce it on their end.
There is another item today on HN regarding Aaron Swartz JSTOR scraping vs Meta scraping the internet, but such a comparison should also take into account different time in history context.
Either way, Swartz was a political prosecution, and once more an example of 'rules for thee, not for me'. Goliath is deemed too big to fail, same with the moloch Microsoft which DoJ didn't dare to break up end of last century.
I'm curious what the model provider is using the prompt/response pairs for, in that case. They aren't offering a model for free without their name on it for no reason.
LLM needs to become more transparent, not less. Hence, this idea (and trend, possibly) is disgusting.
How can we even possibly verify 'Prompts and completions are retained by the provider and are not used for training...'? What if the training is done, but used internally?
Visual reasoning is bad (unsurprising).
(as a bonus - thinking forever = GLM)
So conforming to CCP political discourse and propaganda is reasonable now?
https://huggingface.co/zai-org/GLM-4.7/discussions/5