221–230 of 518 posts
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Not quantized?
Not quantized. Weights are the same. If we did change the model, we'd release it as a new model with a new name in the API (e.g., o3-turbo-2025-06-10). It would be very annoying to API customers if we ever silently changed models, so we never do this [1]. [1] `chatgpt-4o-latest` being an explicit exception
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Not quantized?
Not quantized. Weights are the same. If we did change the model, we'd release it as a new model with a new name in the API (e.g., o3-turbo-2025-06-10). It would be very annoying to API customers if we ever silently changed models, so we never do this [1]. [1] `chatgpt-4o-latest` being an explicit exception
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I swear every time a new model is released it's great at first but then performance gets worse over time. I figured they were fine-tuning it to get rid of bad output which also nerfed the really good output. Now I'm wondering if they were quantizing it.
I've heard lots of people say that, but no objective reproducible benchmarks confirm such a thing happening often. Could this simply be a case of novelty/excitement for a new model fading away as you learn more about its shortcomings?
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How are LLMs cached? Every prompt would be different so it's not clear how that would work. Unless you're talking about caching the model weights...
This document explains the process very well. It’s a good read: https://platform.openai.com/docs/guides/prompt-caching
My mind immediately goes to rowhammer for some reason.
At the very least this opens up the possibility of some targeted denial of service
Has anyone noticed that OpenAI has become "lazy"? When I ask questions now it will not give me a complete file or fix. Instead it tells me what I should do and I need to ask a second or third time to just do the thing I asked. I don't see this happening with for example deepseek. Is it possible they are saving on resources by having it answer that way?
First, I tried enabling o3 via OpenRouter since I have credits with them already. I was met with the following:
"OpenAI requires bringing your own API key to use o3 over the API. Set up here: https://openrouter.ai/settings/integrations"
So I decided I would buy some API credits with my OpenAI account. I ponied up $20 and started Aider with my new API key set and o3 as the model. I get the following after sending a request:
"litellm.NotFoundError: OpenAIException - Your organization must be verified to use the model `o3`. Please go to: https://platform.openai.com/settings/organization/general and click on Verify Organization. If you just verified, it can take up to 15 minutes for access to propagate."
At that point, the frustration was beginning to creep in. I returned to OpenAI and clicked on "Verify Organization". It turns out, "Verify Organization" actually means "Verify Personal Identity With Third Party" because I was given the following:
"To verify this organization, you’ll need to complete an identity check using our partner Persona."
Sigh I click "Start ID Check" and it opens a new tab for their "partner" Persona. The initial fine print says:
"By filling the checkbox below, you consent to Persona, OpenAI’s vendor, collecting, using, and utilizing its service providers to process your biometric information to verify your identity, identify fraud, and conduct quality assurance for Persona’s platform in accordance with its Privacy Policy and OpenAI’s privacy policy. Your biometric information will be stored for no more than 1 year."
OK, so now, we've gone from "I guess I'll give OpenAI a few bucks for API access" to "I need to verify my organization" to "There's no way in hell I'm agreeing to provide biometric data to a 3rd party I've never heard of that's a 'partner' of the largest AI company and Worldcoin founder. How do I get my $20 back?"
Has anyone noticed that OpenAI has become "lazy"? When I ask questions now it will not give me a complete file or fix. Instead it tells me what I should do and I need to ask a second or third time to just do the thing I asked. I don't see this happening with for example deepseek. Is it possible they are saving on resources by having it answer that way?
Yeah, our models are sometimes too lazy. It’s not intentional, and future models will be less lazy. When I worked at Netflix I sometimes heard the same speculation about intentionally bad recommendations, which people theorized would lower streaming and increase profit margins. It made even less sense there as streaming costs are usually less than a penny. In reality, it’s just hard to make perfect products! (I work…
takes tinfoil hat off
Oh, nvm, that makes sense.
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"... as you make more your costs come down" I'd say dropping the price of o3 by 80% due to "engineers optimizing inferencing" is a strong sign that they're doing exactly that.
You trust their PR statements?
how do we know it's not a quantized version of o3? what's stopping these firms from announcing the full model to perform well on the benchmarks and then gradually quantizing it (first at Q8 so no one notices, then Q6, then Q4, ...). I have a suspicion that's how they were able to get gpt-4-turbo so fast. In practice, I found it inferior to the original GPT-4 but the company probably benchmaxxed the hell out of the tu…
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The problem is your costs also scale with revenue. Ideally you want to have control costs as you scale (the first you build is expensive, but as you make more your costs come down). For OpenAI, the more people use the product, the same you spend on compute unless they can supplement it with another ways of generating revenue. I dont unfortunately think OpenAI will be able to hit sustained profitability (see Netflix f…
All costs are not equal. There is a classic pattern of dogfights for winner-take-most product categories where the long term winner does the best job of acquiring customers at the expense of things like "engineering to reduce costs". I have no idea how the AI space is going to shake out, but if I had to pick between OpenAI's mindshare in the broadest possible cohort of users vs. best/most efficient model, I'd pick th…