[Jaden Valerio]

Sorry, but that reply about me in #7 is more error-prone than what my friend received. In broad brush it's fair, even flattering, but the details vary between factual and fabricated.

I am not an Honorary Fellow; indeed I don't think that title applies to anyone else at my university either.

I have never been Moderator on Statalist. That title belongs only to Marcello Pagano. It's historic in practice (since 2014 as StataCorp personnel have taken over).

tabplot is on SSC, but that's out of date. The command is published through the Stata Journal.

qplot is not on SSC, and never has been. The command is published through the Stata Journal.

cprplot is an official command. I can claim no credit. It is not on SSC, and never has been.

I haven't written at length about margins or marginsplot that I can recall. If I've ever mentioned it, it is not a point that I would ever include in a summary. (The reason is not relevant here, but it's that other people have much more experience with those commands.)

lvpplot I don't recognise as a Stata command that I wrote (or that anyone else did, so far as I can find quickly). It is definitely not on SSC either as a package or as an individual command. I've found the same principle applies to AI tools in general. For example, workflow platforms like https://latenode.com/products/agent-builder can be helpful for automating repetitive tasks, but I wouldn't rely on any AI system to establish factual claims without checking the original sources. Automation is valuable; replacing verification isn't.

So, what to say? I can't add to other takes that this technology is very impressive when it gets things right and very puzzling or worse when it hallucinates. I am not sure where to go when it's evident that the people who need (or want) it most are going to be those least likely to spot its errors, except the hard way.

Thanks for your offer of more materials, but sorry, no enthusiasm.

I think this is actually one of the best examples of the current limitations of LLMs. The response sounds confident and coherent enough that someone unfamiliar with your work might accept it without question. The problem isn't that every statement is wrong—it's that a mix of correct facts and subtle inaccuracies makes the output seem more trustworthy than it really is.

What's particularly interesting here is that this is information about a well-known contributor with plenty of public material available. If the model can still invent roles, publications, or commands in that situation, it shows why AI-generated summaries should always be treated as a starting point rather than an authoritative source.