For the first time, I've actually hidden an AI story on HN. I can't even anymore. Sorry this is not going anywhere.
Here, take my downvote.
GPT-5.2
771–780 of 1001 posts
Re: GPT-5.2
#772Earlier quoted context omitted.
I know I sound like a snob but I’ve had many moments with Gen AI tools over the years that made me wonder: I wonder what these tools are like for someone who doesn’t know how LLMs work under the hood? It’s probably completely bizarre? Apps like Cursor or ChatGPT would be incomprehensible to me as a user, I feel.
Most non tech people I talked with don't care at all about LLMs. They also are not impressed at all ("Okay, that's like google and internet").
Re: GPT-5.2
#773Earlier quoted context omitted.
> I realise this sounds obvious to many people but it clearly wasn't to those guys so maybe it's not! It's worse: Gemini (and ChatGPT, but to a lesser extent) have started suggesting random follow-up topics when they conclude that a chat in a session has exhausted a topic. Well, when I say random, I mean that they seem to be pulling it from the 'memory' of our other chats. For a naive user without preconceived notion…
For ChatGPT you can turn this memory off in settings and delete the ones it's already created.
Re: GPT-5.2
#774Earlier quoted context omitted.
> Or if the chat got too long (still below 100k however) they deleted your first messages. This was 3 months ago. I can believe that, but it also seems really silly? If your max context window is X and the chat has approached that, instead of outright deleting the first messages outright, why not have your model summarise the first quarter of tokens and place those at the beginning of the log you feed as context? Sin…
Don’t think you are missing anything. I do this with the API, and it works great. I’m not sure why they don’t do it, but I can only guess it’s because it completely breaks the context caching. If you summarize the full buffer at least you know you are down to a few thousand tokens to cache again, instead of 100k tokens to cache again.
Yes, but you only re-do this every once in a while? It's a constant factor overhead. If you essentially feed the last few thousand tokens, you have no caching at all (and you are big enough that this window of 'last few thousand tokens' doesn't get you the whole conversation)?
Re: GPT-5.2
#775"All models" section on https://platform.openai.com/docs/models is quite ridiculous.
Re: GPT-5.2
#776Re: GPT-5.2
#777> “a new knowledge cutoff of August 2025” This (and the price increase) points to a new pretrained model under-the-hood. GPT-5.1, in contrast, was allegedly using the same pretraining as GPT-4o.
I think it's more likely to be the old base model checkpoint further trained on additional data.
(Also not sure how that would work, but maybe I’ve missed a paper or two!)
Re: GPT-5.2
#778Earlier quoted context omitted.
Every Google app is a chat app, except maybe search.
Is Google Drive a chat app? Is Google Photos a drive app? I don’t know what you mean
Not sure how you can access the chat in the directory view.
Re: GPT-5.2
#779Looks like they've begun censoring posts at r/Codex and not allowing complaint threads so here is my honest take: - It is faster which is appreciated but not as fast as Opus 4.5 - I see no changes, very little noticeable improvements over 5.1 - I do not see any value in exchange for +40% in token costs All in all I can't help but feel that OpenAI is facing an existential crisis. Gemini 3 even when its used from AI St…
I’m curious about if the model has gotten more consistent throughout the full context window? It’s something that OpenAI touted in the release, and I’m curious if it will make a difference for long running tasks or big code reviews.
what I am curious about is 5.2-codex but many of us complained about 5.1-codex (it seemed to get tunnel visioned) and I have been using vanilla 5.1
its just getting very tiring to deal with 5 different permutations of 3 completely separate models but perhaps this is the intent and will keep you on a chase.
Re: GPT-5.2
#780In my experience, the best models are already nearly as good as you can be for a large fraction of what I personally use them for, which is basically as a more efficient search engine. The thing that would now make the biggest difference isn't "more intelligence", whatever that might mean, but better grounding. It's still a big issue that the models will make up plausible sounding but wrong or misleading explanations…
Isn't that what no LLM can provide: being free of hallucinations?
Gemini (the app) has a "mitigation" feature where it tries to to Google searches to support its statements. That doesn't currently work properly in my experience.
It also seems to be doing something where it adds references to statements (With a separate model? With a second pass over the output? Not sure how that works.). That works well where it adds them, but it often doesn't do it.