7 ms·

You still have to worry about misconfigured local models. Even the professionals get it wrong, which is why local model performance is uneven across providers.

deleted 2mo ago
[deleted]
jdiff 2mo ago
But in that case you have nobody but yourself to blame, and you can stabilize things yourself at any time by refraining from making any changes. You won't be surprised by a provider. Honestly? That's not just valuable—it's essential.
- LiamPowell 2mo ago
  > Honestly? That's not just valuable—it's essential. I'm curious if you wrote this or had a LLM write it. I'm genuinely curious to be clear as I don't see why anyone would bother to go through a LLM to write such a short reply. Have we reached the point where Claudeisms that are this obnoxious have become part of regular speech?
  - UqWBcuFx6NV4r 2mo ago
    Or they were making a joke[0]. [0]: https://en.wikipedia.org/wiki/Joke https://en.wikipedia.org/wiki/Joke (…just like that)
    - LiamPowell 2mo ago
      Sure, but it doesn't really fit there as a joke, it looks like it's just meant to be part of what they were trying to say.
      - subscribed 2mo ago
        I also think it's a joke, it starts with the response / argument, and then flows into tongue-in-cheek joke about the core issue of the post (LLM)
  - topynate 2mo ago
    I've noticed them trying to creep into my writing. It doesn't help that I was a heavy em-dash user ten years before GPT-3.
    - mdgld 2mo ago
      I’ve always loved em dashes…very sad they’re a hallmark of LLM slop now. That and trios in arguments. Maybe I’m part LLM?
      - embedding-shape 2mo ago
        Some of us been writing texts on the public internet for decades, and humans invented machines trained on our texts, so suddenly the text we write now sounds like robots? Only way to win is to stop caring, let the people believe you're a LLM if so be it.
        andy99 2mo ago
        LLM writing style is trained in by data labellers, it’s not just emergent behavior from being trained on internet texts.
        embedding-shape 2mo ago
        Ultimately it's a mix-match of everything, including whatever data the pre-training uses and how exactly they do the post-training. I don't think you can say there is a single factor that decides the writing style, unless you have some particular insight into some specific pipeline. Generally though, they output text that looks like the human text they ingested for training.
      - andy99 2mo ago
        This isn’t just em-dashes—it's the empty phrase that includes both whatever the contrastive construction is called and an “Honestly”. It might have been human written but the density of LLM flags is undeniable.
  - jdiff 2mo ago
    The obnoxious cliche is mine, although I wouldn't call it "regular speech" since I tacked on that dense blob of LLMisms intentionally.
miki123211 2mo ago
And to add insult to injury, some providers will ride on the good reputation of some local model, selling you a terrible quant instead. With OpenAI, at least my gpt-5.5 is the same as your gpt-5.5. You can't say that about glm for example.
- embedding-shape 2mo ago
  > And to add insult to injury, some providers will ride on the good reputation of some local model, selling you a terrible quant instead. I just started using OpenRouter for some control testing of local models and what surprises me the most isn't that there are different providers providing different quantization levels, that makes sense, but I can't seemingly find a way of seeing what provider+model+quantization is actually used?! https://openrouter.ai/models https://openrouter.ai/models shows the models, then say https://openrouter.ai/moonshotai/kimi-k2.7-code https://openrouter.ai/moonshotai/kimi-k2.7-code shows the providers but when I go to https://openrouter.ai/moonshotai/kimi-k2.7-code?endpoint=e7a9a3c1-6f00-4524-9dd4-ca1ae87050d6 https://openrouter.ai/moonshotai/kimi-k2.7-code?endpoint=e7a... for example, why on earth is it not showing the actual details about the actual weights they're serving?! Give me details! It does have a "Precision" value that is sometimes filled out, but that seems to be a guess at best, even providers with the same values there have wildly different quality responses. I like the idea about OpenRouter but holy hell does the implementation seem very far off from what it needs to be, in order to be useful.
  - jermaustin1 2mo ago
    There are properties on the API call you can pass for specific providers, so you test which providers you like the output, then add them to the list in ranked order if you want one by default, then to fall back to the other. There might be something in the response, or in a followup API call for the session, that you get better details. I think I've seen the details in the dashboard, so they do exist.
- beacon294 2mo ago
  That's not a real equivalency. They are not necessarily the same (testing in production, hello!) And most importantly you do not have a local model because openai is not open!

deleted 2mo ago

[deleted]

jdiff 2mo ago

But in that case you have nobody but yourself to blame, and you can stabilize things yourself at any time by refraining from making any changes. You won't be surprised by a provider. Honestly? That's not just valuable—it's essential.

LiamPowell 2mo ago
> Honestly? That's not just valuable—it's essential. I'm curious if you wrote this or had a LLM write it. I'm genuinely curious to be clear as I don't see why anyone would bother to go through a LLM to write such a short reply. Have we reached the point where Claudeisms that are this obnoxious have become part of regular speech?
- UqWBcuFx6NV4r 2mo ago
  Or they were making a joke[0]. [0]: https://en.wikipedia.org/wiki/Joke https://en.wikipedia.org/wiki/Joke (…just like that)
  - LiamPowell 2mo ago
    Sure, but it doesn't really fit there as a joke, it looks like it's just meant to be part of what they were trying to say.
    - subscribed 2mo ago
      I also think it's a joke, it starts with the response / argument, and then flows into tongue-in-cheek joke about the core issue of the post (LLM)
- topynate 2mo ago
  I've noticed them trying to creep into my writing. It doesn't help that I was a heavy em-dash user ten years before GPT-3.
  - mdgld 2mo ago
    I’ve always loved em dashes…very sad they’re a hallmark of LLM slop now. That and trios in arguments. Maybe I’m part LLM?
    - embedding-shape 2mo ago
      Some of us been writing texts on the public internet for decades, and humans invented machines trained on our texts, so suddenly the text we write now sounds like robots? Only way to win is to stop caring, let the people believe you're a LLM if so be it.
      - andy99 2mo ago
        LLM writing style is trained in by data labellers, it’s not just emergent behavior from being trained on internet texts.
        embedding-shape 2mo ago
        Ultimately it's a mix-match of everything, including whatever data the pre-training uses and how exactly they do the post-training. I don't think you can say there is a single factor that decides the writing style, unless you have some particular insight into some specific pipeline. Generally though, they output text that looks like the human text they ingested for training.
    - andy99 2mo ago
      This isn’t just em-dashes—it's the empty phrase that includes both whatever the contrastive construction is called and an “Honestly”. It might have been human written but the density of LLM flags is undeniable.
- jdiff 2mo ago
  The obnoxious cliche is mine, although I wouldn't call it "regular speech" since I tacked on that dense blob of LLMisms intentionally.

miki123211 2mo ago

And to add insult to injury, some providers will ride on the good reputation of some local model, selling you a terrible quant instead. With OpenAI, at least my gpt-5.5 is the same as your gpt-5.5. You can't say that about glm for example.

embedding-shape 2mo ago
> And to add insult to injury, some providers will ride on the good reputation of some local model, selling you a terrible quant instead. I just started using OpenRouter for some control testing of local models and what surprises me the most isn't that there are different providers providing different quantization levels, that makes sense, but I can't seemingly find a way of seeing what provider+model+quantization is actually used?! https://openrouter.ai/models https://openrouter.ai/models shows the models, then say https://openrouter.ai/moonshotai/kimi-k2.7-code https://openrouter.ai/moonshotai/kimi-k2.7-code shows the providers but when I go to https://openrouter.ai/moonshotai/kimi-k2.7-code?endpoint=e7a9a3c1-6f00-4524-9dd4-ca1ae87050d6 https://openrouter.ai/moonshotai/kimi-k2.7-code?endpoint=e7a... for example, why on earth is it not showing the actual details about the actual weights they're serving?! Give me details! It does have a "Precision" value that is sometimes filled out, but that seems to be a guess at best, even providers with the same values there have wildly different quality responses. I like the idea about OpenRouter but holy hell does the implementation seem very far off from what it needs to be, in order to be useful.
- jermaustin1 2mo ago
  There are properties on the API call you can pass for specific providers, so you test which providers you like the output, then add them to the list in ranked order if you want one by default, then to fall back to the other. There might be something in the response, or in a followup API call for the session, that you get better details. I think I've seen the details in the dashboard, so they do exist.
beacon294 2mo ago
That's not a real equivalency. They are not necessarily the same (testing in production, hello!) And most importantly you do not have a local model because openai is not open!