Interestingly LLMs are extremely bad at chess position _images_. I have to imagine if you give it positions in text it'd be pretty great but when I was learning chess and pasting images of positions in for analysis I couldn't believe how wrong it was. I actually thought it was looking at the board in reverse but even when pointing out problems it seemed completely incapable of understanding what it was missing (of course... it doesn't really "understand" anything).
LLMs truly are marvels with text but anything spatial seems to really mess it up, somehow.
I'm coming back to Django after a decade of experience with it post-0.96 and having moved to Next.js a few years ago. Going from 1,700 dependencies to 65 total with Django + Wagtail + HTMX.
If you build a house from scratch but you didn't mill the lumber, did you build it from scratch?
If you make a pizza from scratch but you used canned sauce was it from scratch? What if you used store bought dough? What if you made the sauce and the dough but you didn't grow the tomato?
Agree with pretty much everything you wrote here, I guess with the addendum that LLMs can be a part of the learning experience you're describing. It's as easy as telling the LLM "don't write a single line of code nor command, I want to do everything, your goal is to help me understand what we're doing here."
There are always going to be people who just want the end result. The only difference now is that LLM tools allow them to get much closer to the end result than they previously were able to. And on the other side, there are always going to be people who want to _understand_ what's happening, and LLMs can help accelerate that. I use LLMs as a personalized guide to learning new things.
This is pretty much my point. I use LLMs to code _and_ to learn. I read everything that comes out. Half of it is wrong or incomplete. The other half saved me a bunch of time and taught me things.
I feel I've upskilled in so many directions (not just "ability to prompt LLMs") since going all in on LLM coding. So many tools, techniques, systems, and new areas of research I'd never have had the time to fully learn in the past.
I have a hard time believing any tenured developer is not actually learning things when using LLMs to build. They make interesting choices that are repeatable (new CLIs I didn't even know existed, writing scripts to churn through tricky data, using specific languages for specific tasks like Go for concurrently working through large numerous tasks, etc.)
Anyone not learning things via LLM coding right now either doesn't care at all about the underlying code/systems, or they had no foundational knowledge or interest in programming to begin with (which is also a valid way to use these tools, but they don't work very well without guidance for too long [yet]).
Ah, thanks! Looks like that was long enough ago that my submission didn't auto-redirect me to the existing conversation (which was what I was trying to do, haha).
Yeah I think I have the original, and reading this again seems like these new ones are more "touch sensitive". It's a neat idea if they can nail the haptics.
I don't know. I would argue that driving experience and reliability are still very much going to be things in the electric car market. I'm an EV9 owner and we have issues w/ the suspension making it feel sloppy over some bumps. There's going to be a ton of nuance in terms of how all of these different electric vehicles drive, ride, and are experienced. And those are all going to come down to the vehicle manufacturers themselves, not just the technology partner for screens.
Yep. I certainly wanted an R1S, but ended up in an EV9 due to CarPlay plus huge lease incentives. No regrets, and will probably get another after this lease is up.
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