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Comment by postalcoder | Hacker News Reader

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.

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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.

> 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 shows the models, then say 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=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.

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.

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!

> some providers will ride on the good reputation of some local model, selling you a terrible quant instead.

Quants in popular local inference apps (Ollama, LM Studio, etc) are the worst possible quants (RTN).

Sam Altman, good reputation?

> With OpenAI, at least my gpt-5.5 is the same as your gpt-5.5.

How do we know that? The "orchestration" layer probably forwards to different levels of quantization. And it seems tempting to make some sort of load balancer with adaptive computation effort.

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.

> 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?

Or they were making a joke[0].

[0]: https://en.wikipedia.org/wiki/Joke

(…just like that)

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.

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)

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.

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?

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.

LLM writing style is trained in by data labellers, it’s not just emergent behavior from being trained on internet texts.

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.

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.

Honesty has become a big tell, especially if its brutal. I'm not sure if it's getting worse or if I'm becoming more sensitive, but if I'm being brutally honest I can barely stand using LLMs anymore with their tone and their cliches. It's a horrible nightmare fusion of socmed influencer and LinkedIn hustler that sounds freakish even if it were human, and the density of tropes that are accumulating feels ridiculous.

Didn't the foundries take action against those in the past? I don't see "delve" nearly as often anymore. Why are the models spiraling like this now?

What really grinds my gears is the constant need to guess what I'm doing and offer a million random follow-ups. I asked what the weather was like, I don't appreciate the 5 paragraphs of tokens burned on weather-appropriate activity suggestions.

The obnoxious cliche is mine, although I wouldn't call it "regular speech" since I tacked on that dense blob of LLMisms intentionally.

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