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I am using my claude pro plan for at least 8 hours a day, 5 days a week, to maintain a medium-sized codebase, and my total weekly usage is something like 25% of my limit. What the hell are people doing that burns through that token limit so fast?
Based on your other reply, seems like you're on Max5 plan ($100/m), not Pro ($20/m)
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> It tries to run a test and then "&& sleep" on mac, sleep does not exist > type sleep > sleep is /bin/sleep What’s going on on your computer? Edit: added quote
Right you are.. Perhaps I recall incorrectly and it was a different command. I did try it, and it did not exist. Odd.
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Right, you're talking about beauty standards in general, which we've already established go way beyond "just don't be fat".
No, I'm talking about the beauty standards that get complained about, and that we see adjustments for.
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Not sure what you mean by incorrect since you already validated my point about the limits. I never had these issues even with Sonnet before, but after December, the change has been obvious to me. Also worth considering that mileage varies because we all use agents differently, and what counts as a large workload is subjective. I am simply sharing my experience from using both Claude and Codex daily. For all we know,…
This is not a weekly limit though, it is a 4 hour one. You still have not clearly defined what you are talking about.
Not sure why you're being so hostile here. Since you mention you're on $200 plan, your rate limit is a lot higher so you probably won't notice it as much as $20 plan which to me is understandable.
I'm just sharing my experience using the same $20 plan, before and after December 2025. The difference is noticeable, that is all.
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Can you actually prove it?
Yes, I was there for quite a bit of it...
b) This is exactly the kind of urban legend which tends to proliferate in human society, a cute lie which sounds believable. Like prostitution being the oldest profession, and the great wall of China being the only human made structure visible from space.
The story of the Internet is that it was developed in the mid to late 1960s by a couple of Universities in America and used by the US military to share resources across mainframes in each institution, as well as across institutions. Since then them main driver of innovation has been among universities and telecommunication companies finding optimal ways to deliver packages across telephone cables, antenna, satellites, etc.
The story of the Web is that it was developed in 1989-1991 to organize and link documents at the CERN research laboratory in Switzerland. Since then the main driver for innovations has been browser vendors competing and collaborating in developing browser technologies such that users pick their browser over the competition.
I don‘t know the story of online payment systems, but I bet the main drivers were companies opining up stores on the web and looking to replace pay by wire schemes over the telephone, and a bunch of start-ups who were able to find that market niche, developed software and sold it to those stores. The main driver of innovation here being start-ups. Porn sites and Gambling sites were no more important to that story as any other stores who sold art, golf-clubs, flight tickets, or hotel bookings.
> We’re continuing to make progress toward a version of ChatGPT designed for adults over 18, grounded in the principle of treating adults like adults, and expanding user choice and freedom within appropriate safeguards. To support this, we’ve rolled out age prediction for users under 18 in most markets. https://help.openai.com/en/articles/12652064-age-prediction-... interesting
My personal take is that there has been no progress - potentially there has been a regression on all LLM things outside of coding a scientific pursuits - I used to have great fun with LLMs with creative writing stuff, but I feel like current models are stiff and not very good prose writers. This is also true for stuff like writing clear but concise docs, they're overly verbose while often not getting the point across…
Ironically, as these models become more “safe” and commercialized, they often lose the spark that made them valuable for creative exploration in the first place. The focus on enterprise and mass adoption seems to be driving them toward mediocrity, at the expense of individuality and depth.
>We brought GPT‑4o back after hearing clear feedback from a subset of Plus and Pro users, who told us they needed more time to transition key use cases, like creative ideation, and that they preferred GPT‑4o’s conversational style and warmth. This does verify the idea that OpenAI does not make models sycophantic due to attempted subversion by buttering up users so that that they use the product more, its because peop…