That is not true. You can tell you are on the latter part of the S-Curve you are on, if the rate of change of capabilities has decreased compared to before. That is not what we are seeing right now. The rate of change is increasing, or is at best, stable.
Using your API key in third-party harnesses has always been allowed. They just don't like using the subsidized subscription plan outside of first-party harnesses. So this seems to be out of spite
even a squirrel that needs guidance from a human grandmaster, is heavily inspired by existing games, and who can use Piece Mover library is incredible. 5 years ago the squirrel was just a squirrel. then it was able to make legal moves. now it can play a whole game from start to finish, with help. that is incredible
No. You can point e.g. Opencode/Cline/Roo Code/Kilo Code at your inference endpoint. But CC has high install base and users are used to it, so it makes sense to target it.
You personally wouldn't use live captions and dubbing, so there's no point building it for the millions of people who need it as an accessibility feature?
I've seen a few of this type of thing pop up in search results ("DeepWiki" by Cognition.) I'm not a fan. It is just LLM contentslop, basically. Actual wikis written by humans are made of actual insight from developers and consumers. "We intend you use it in X way", "If you encounter Y issue, do Z." etc. Look at arch wiki. Peak wiki-style documentation, LLMs could never recreate. Well, maybe with a future iteration of the technology they can be useful. But for now, you do not gain much by essentially restating code, API interfaces, and tests in prose. They take up space from legitimate documentation and developer instruction in search results.
I think this wound up being close enough to true, it's just that it actually says less than what people assumed at the time.
It's basically the Jevons paradox for code. The price of lines of code (in human engineer-hours) has decreased a lot, so there is a bunch of code that is now economically justifiable which wouldn't have been written before. For example, I can prompt several ad-hoc benchmarking scripts in 1-2 minutes to troubleshoot an issue which might have taken 10-20 minutes each by myself, allowing me to investigate many performance angles. Not everything gets committed to source control.
Put another way, at least in my workflow and at my workplace, the volume of code has increased, and most of that increase comes from new code that would not have been written if not for AI, and a smaller portion is code that I would have written before AI but now let the AI write so I can focus on harder tasks. Of course, it's uneven penetration, AI helps more with tasks that are well-described in the training set (webapps, data science, Linux admin...) compared to e.g. issues arising from quirky internal architecture, Rust, etc.