I completely agree that unverified claims create a heavy burden for maintainers. My only point was about the language used: 'disparaging' to me implies a bad-faith attack or a dismissive attitude, whereas this was just an honest technical mix-up that the poster immediately corrected.
I think part of the confusion with that word comes from things like corporate non-disparagement clauses. In those contracts, lawyers write the terms so broad that "disparagement" means saying anything negative, regardless of malice or intent.
FWIW, I don't think the GP post is disparaging (at least as I read it right now.)
I think it is fair to list limitations from using a library that provides an abstraction; it can suggest why a tool isn't right for a person's use cases.
But it also sounds like this API handles those pretty well.
I think a reason for this is suppose the next year you run into some difficulties so it requires 14.2M. Now you have to fight to request an extra 0.2M added to your budget that you wouldn't have to worry about if you had 15M.
'Everyone over 30 knows this' is a prior assumption (it is not necessarily correct; and nothing is said about shame).
The comic strip is saying if above is true, then people still have to learn at some point so on average it would be around 10k people per day.
I think the math is this:
For people born in a given year: 4000000/365/30 = 365 people per day
but you have 30 sets of those people (those born this year, those born last year, those born two years ago, etc.) So 365 * 30 = 10950. 10k is easier to say for viral purposes.
Reminds me of ZeptoLab (company that made Cut The Rope)'s Pudding Monsters but it adds a lot more variety. (And it combines them together within one square instead of multiple squares and they don't fall off the edge of the screen.)
If you do have AI write for you, I think it is useful to indicate what is going on by having a rule set up for it. That can at least prepare the reader for reading AI text.
I personally find it okay / convenient to have AI respond to PR review comments with respect to something being addressed or why it was not. That text is often pretty mechanical.
I've frequently seen tasks that it thinks will take weeks being done in under an hour. And it will often recommend doing X instead of Y because X requires so much extra work. Basically I just remind it that it is an LLM.
If it worries something is error prone, I ask it to write tools to verify it.
I've done this manually by building a big feature branch and asking an LLM to extract out functionality for a portion of it.
For the former, it would seem to split based on frontend/backend, etc. rather than what semantically makes the most sense and for the latter it would include changes I don't want and forget some I do want. But I haven't tried this a lot.
The GP said more with LLMs than people - not no interactions at all with people and not preferring machines to people. I don't think it is that hard to spend more time talking with LLMs than people if you work in tech and I don't think that takes away from one's life meaningfulness.
One thing that is useful to remember is that if you ask AI for help on using some app, it will likely refer to the mobile UI instead of the web UI. I find it annoying that sometimes there are features that are only available in the mobile UI.
I think part of the confusion with that word comes from things like corporate non-disparagement clauses. In those contracts, lawyers write the terms so broad that "disparagement" means saying anything negative, regardless of malice or intent.