Not the original commenter, but personally I noticed my quota usage didn’t feel like it was being spent at a much lower rate when using Sonnet even on a relatively low thinking budget and based on a few comments here it seems I might not be the only one. Has anyone else noticed this? Wasn’t it different in the past? I thought I would be getting to use Sonnet much much more than Opus but it did not feel that way despite being on 20x plan.
I was working with very small local models (<= 4B ones) in my app, there was a point where the response of the model seemed so good I really had to double check I wasn't mistakenly using a cloud model. The model hadn't made tool calls or anything, but its textual answer quality was high enough that it made me hesitate. A different kind of impressive vs the frontier models, but still was a bit of a wow moment when I remember expensive hosted models having a hard time writing proper English.
Billing caps are underrated! I don't understand why they aren't present everywhere. As an indie dev there are some services I'm really hesitant on trying by fear of getting an enormous bill for a mistake, this is even more true with vibe coding IMO.
I see the Claude team wanted to make it less verbose, but that's actually something that bothered me since updating to Claude 4.7, what is the most recommended way to change it back to being as verbose as before? This is probably a matter of preference but I have a harder time with compact explanations and lists of points and that was originally one of the things I preferred with Claude.
That matches what I have seen, but I think I remember reading a tweet that had mentioned those "developing in the open" (not an exact citation, just based on what I remember), which made me wonder if it meant they considered this allowed only for open source software, or if they were intending to be much more permissive, essentially considering users can use their quotas wherever they want, or maybe even completely different rules, again I feel there could be more transparency regarding all of that.
Peter, while we are on the subject of clarifying what is and isn't allowed I have a question: has OpenAI clearly communicated about precisely where one is supposed to be able to use their Codex quota? For instance, as far as I understand, it is allowed to use it with OpenClaw, but does it extend to any other coding harness? Say I have an app (potentially a paid one) and want my users to use their Codex quota in it, is it permitted to do? As you can probably imagine that would unlock a lot of uses cases given smaller actors can't subsidize as much token costs, but unfortunately, and maybe expectedly due to the nature of subscriptions, I have not been able to find any answer regarding this.
I had a conversation right during the launch so not fully sure if it was Opus 4.7 but I also noticed the same behavior of asking questions that did not seem particularly useful to me, tho I still prefer that to not asking enough.
I'm not saying this should be every single domain. This isn't about products or management, instead I would frame it like this: I notice that multiple cases where we are worried about the impact of AI are basically just about the replacement of certain activities that some humans already aren't doing in today's society. If we are worried we will be less good at doing job X once we don't do job X anymore, why are we not worried about people who never did job X in the first place? If we are worried about people not doing jobs anymore, why are not worried for the human development of people wealthy enough not to work anymore for the rest of their days? I would not assume someone who won the lottery is going to have their life become uninteresting or see some cognitive decline. It could probably happen, but you can also see a path where the person just chooses to do the activities they always wanted to do, where they keep learning and exploring without the burden of usual life constraints. People already play chess when machines have beaten us for decades, just because they enjoy it.
Regarding education I think AI is a huge revolution waiting to happen. Usual courses have become boring? Have future super powerful AI generate per student highly personalized programs, create bespoke video games where succeeding can only happen once the student has validated all the notions you wanted them to validate etc.
Say someone uses AI, treating it as if it was a developer (probably not recommended today due to the risk of errors), and working and speaking with it as if they were some kind of product manager or senior engineer who only makes architectural decisions etc. I wonder what kind of difference would it really make? Sure the person might not be as good anymore as a developer, but how is this different from being a usual product manager or whatever the day AI truly is good enough for a developer role? I'm not saying I know what the answer to this question is, but this is something I genuinely wonder, and I think the same kind of questioning can apply to broader domains.
Very interesting! On what platforms can this run? If it can run on iOS, how would you handle attempts to access to the file system or networking, is this already wired in somehow? If not is it easy to add custom handlers to handle these actions?
It is definitely not foolproof but IMHO, to some extent, it is easier to describe what you expect to see than to implement it so I don't find it unreasonable to think it might provide some advantages in terms of correctness.
It feel a bit like this to me. That's not to say LLMs should not have detected this, but I still feel like this fits the "vibes" the question gives, and some LLMs fall into that trap. Is it actually what's happening in the neural nets? Maybe not! But I always find it interesting or at least entertaining to approach those questions that way nonetheless; especially given the pattern matching nature of LLMs.
The thing is that there is some overlap between trick questions and questions where the human is genuinely making a mistake themselves and where it would make sense for the model to step back and at least ask for clarification.