amemi

In my opinion, doing good mathematics is very different from writing good exposition.

I have observed that models are not able to effectively come down to a human level. They are mediocre at approximating what a first-time reader would want to know. If you will forgive the anthropomorphization, they are not very empathetic. In this book for example, the author pauses the flow of thought with countless little anecdotes, anticipating where the reader might have a question or want to know more.

Of course, a well-guided AI is capable of anything a human is capable of. But is that not tautological?

I don't think there is educational worth in an novice publishing the outputs from their chats.

I think there is some worth in having someone knowledgeable using AI to generate educational material, although it will be in my opinion, of less use than if that knowledgeable person wrote the material by hand.

For example, many professors in universities have started generating slides or even assignments using AI. A knowledgeable teacher who cares about their students outcomes can generate better material.

But that does not seem to be related to academics, as GP called it.

Perhaps it is the contrast of "freely recommending" and "always should be considered carefully"?

Or taking a larger leap: the awkwardness of saying how hard (isolating) it is to do a PhD in an unfamiliar country, then saying that you enjoyed it, but others should consider it carefully?

Personally, I think Orwell's version is a lot easier to read. I read the Ecclesiastes verse several times and can't really get it: "time and chance happens to everyone"? Okay?

While phrases like "contemporary phenomena" and "tendency to be commensurate" are a bit over-the-top, summarily the picture is conveyed much more clearly imo: Luck plays a factor, no matter how good you are.

I don't see how "nor yet favor to men of skill" is more natural or clearer, at least to the modern reader.

Not well informed on the topic- but the title made me think of the recently launched Roman Space Telescope.

The difference: LUX-ZEPLIN, which is underground, is waiting to detect a dark matter particle itself. On the other hand, NGRST seeks to observe the effects of dark matter.

thank you, I did not notice.

It is useful to know the level of how "hands-off" to specify directions for each task. With some experience one can learn when to specify the high level requirements, and when to stop and think about the problem at hand.

For example, with frontend, I can get an LLM to design half a page 80% to my satisfaction with two paragraphs worth of a prompt. "It should look like so, and have a text box here, and room for a demo there".

With ML training or backend or user-facing code, I might instead spend a paragraph thinking out my design intentions for a single function or even a single line, more for myself than the LLM. A harness generates a plan based on that paragraph, which one can then comment on and review the pseudocode it provided, ensuring it aligns with expectations.

Lastly have the LLM output some sort of documentation and "here's what I did" after each change. Your final step is to handwrite (paraphrasing what it gave) into any docs or commit messages, and ensure your commits are small enough to keep this maintainable. Paraphrasing the LLM, rather than the LLM paraphrasing you, is helpful to ensure the commit messages make sense to you three months from now.

I think it's interesting how the same books recommended for rebuilding yourself after a breakup can also be "manuals of statecraft". E.g. Marcus Aurelius' Meditations