That felt like a very natural solution to me. Of course, worst case scenario, they just blindly generate and memorize the proofs without thinking. But I think the follow ups will catch them in that case. Moreover, I choose to believe that >90% of my students are willing to at least put in the work of scrutinizing those proofs which is honestly not bad exercise in its own right.

I would love to do oral exams directly, the logistics with the number of students I have just make that hard. I know we're trialing it with the less populated special topics courses.

However, I really dislike the 'magic' in the math mode syntax, and I think dropping backslashes (more generally, a delineator) for commands was a mistake. Those aren't blockers though, and I think the org is largely making good decisions. I'm really looking forward to the day I can write research in it!

I think all that's remaining is time in the community and stability. Once journals begin accepting it, I know I'll definitely try to submit in it.

EDIT: On a side note, is anyone informed about the state of VRR + fractional scaling + general gaming on GNOME? Has it gotten better?

I know you didn't want questions, but maybe you can save me some trouble?

Assuming you're talking about quarto, may I ask what you didn't like about it? I've been converting some of my course materials to it and have been enjoying it immensely.

Waiting, in anticipation and horror, for the price of the frame.

Moreover, truth be told, I don't really see myself doing any less math and requiring less from my skills. At least from the moment I've begun incorporating LLMs into my research workflow to now, the demand I've had from my own skills has only grown. At least in an era prior to Lean formalization.

Many of my colleagues and I have been experimenting with LLMs in our research process. I've had pretty great success, though fairly rarely do they solve my entire research question outright like this. Usually, I end up with a back and forth process of refinements and questions on my end until eventually the idea comes apparent. Not unlike my traditional research refinement process, just better. Of course, I don't have access to the model they're using =) .

Nevertheless, one thing that struck me in this writeup, was the lack of attribution in the quoted final response from the model. In a field like math, where most research is posted publicly and is available, attribution of prior results is both social credit and how we find/build abstractions and concentrate attention. The human-edited paper naturally contains this. I dug through the chain-of-thought publication and did actually find (a few of) them. If people working on these LLMs are reading, it's very important to me that these are contained in the actual model output.

One more note: the comments on articles like these on HN and otherwise are usually pretty negative / downcast. There's great reason for that, what with how these companies market themselves and how proponents of the technology conduct themselves on social media. Moreover, I personally cannot feel anything other than disgust seeing these models displace talented creatives whose work they're trained on (often to the detriment of quality). But, for scientists, I find that these tools address the problem of the exploding complexity barrier in the frontier. Every day, it grows harder and harder to contain a mental map of recent relevant progress by simple virtue of the amount being produced. I cannot help but be very optimistic about the ambition mathematicians of this era will be able to scale to. There still remain lots of problems in current era tools and their usage though.

However, this was not verified in Lean. This was purely plain language in and out. I think, in many ways, this is a quite exciting demonstration of exactly the opposite of the point you're making. Verification comes in when you want to offload checking proofs to computers as well. As it stands, this proof was hand-verified by a group of mathematicians in the field.

But there are absolutely pain points. In particular, especially for weaker programmers (common in academia), it's quite easy to write bad JAX. The functional programming and stateless paradigms require a little more thinking ahead. This is particularly tough when you're doing research and you're, in real time, finding out what's ahead!

From Wikipedia

Eschatology (/ˌɛskəˈtɒlədʒi/; from Ancient Greek ἔσχατος (éskhatos) 'last' and -logy) concerns expectations of the end of present age, human history, or the world itself.

I'm case anyone else is vocabulary skill checked like me

Sorry, I've never seen this perspective, why do you want the smaller ones? The small arrow keys on my MacBook are one of my least favorite parts of the keyboard.

You probably can't comment on this, but just to note it, I would be very excited to see the 16 get a similar Pro chassis.