I feel like they're also ignoring the increase in actual real world use costs due to reasoning. Just looking at token costs doesn't capture the whole picture.
It's actually baffling that Github struggles to load a 1000 comments. It can't even load a single one it seems like. It just straight up silently fails. How is this a thing in 2026?
I don't think people's main concern is just the use of AI. The main concern seems to be that a PR of this scale was merged probably without a proper review process.
I think Studio Ghibli has made some of the most beautiful films I've ever seen and I make it a habit to rewatch many of them every year. I think Sam and the others overusing the Ghibli artstyle is absolutely a petty dig at Miyazaki saying that AI animation is "an insult to life itself" (although he said this well before AI art took off).
While the whole "Claude Code is just like a game engine" tweet was silly, this comment seems too derisive. I highly doubt engineers at Anthropic are lacking in talent.
I have to agree. It's off-putting to me too. I'm impressed by the performance of their models on this take-home but I'm not impressed at their (perhaps unintentional) derision of human programmers.
Absolutely. I honestly wouldn't ever recommend doing this. Imo, you should just spent a little extra time cleaning your display over taking dubious preventative measures.
It's not silly when you consider what longnow stands for. They look at "now" on a 20,000 year scale so the extra zero is just emphasizing that 01931 is still the "long now".
There's honestly not much you can do to prevent the "oily cubes" problem, especially if you keep the laptop docked often. You'll just have to clean the screen more often.
I've seen some people place a keyboard-sized microfibre cloth in between the keyboard and display but I'm not sure how well that actually works in practice. It might cause other issues.
There are "ungodly geniuses" within mathematics but no one is saying every mathematician is an "ungodly genius". The quality of results you get from an LLM can vary greatly depending on the environment you place it in and the context you provide it. This isn't to say it's your fault Claude Code can't fix whatever issue you're having.
Not disagreeing with you, but I don't think Tao is blowing this out of proportion either. I think it's a pretty reasonable way of saying, "Hey, AI is now capable of something it wasn't able to do before".
I think at this point it's not easy to accurately detect whether or not something is AI written. A real person can definitely write like this. In fact, that's probably where the LLMs got their writing style from.
Refactoring does always cost something and I doubt LLMs will ever change that. The more interesting question is whether the cost to refactor or "rewrite" the software will ever become negligible. Until it isn't, it's short-sighted to write code in the manner you're describing. If software does become that cheap, then you can't meaningfully maintain a business on selling software anyway.
I also like to think that Einstein would be smart enough to explain things from a common point of understanding if you did drop him 2000 years in the past (assuming he also possesses the scientific knowledge humanity accrued in that 2000 year gap). So, your analogy doesn't really make a lot of sense here. I also doubt he'd be able to prove his theories with the technology of the past but that's a different matter.
If we did have AGI models, they would be able to solve our hardest problems (assuming a generous definition of AGI) even if we didn't immediately understand exactly how they got there. We already have a lot of complex systems that most people don't fully understand but can certainly verify the quality of. The whole "too smart for people to understand that they're too smart" is just a tired trope.
Reading is usually more passive than coding. I'm often never sleepy if I'm actively coding something late at night but reading a book (no matter how engaging) or watching a tv show can very easily make me sleepy. That said, everyone's brains work very differently.
How is a compiler and an LLM equivalent abstractions? I'm also seriously doubtful of the 10x claim any time someone brings it up when AI is being discussed. I'm sure they can be 10x for some problems but they can also be -10x. They're not as consistently predictable (and good) like compilers are.
The "learn to master it or become obsolete" sentiment also doesn't make a lot of sense to me. Isn't the whole point of AI as a technology that people shouldn't need to spend years mastering a craft to do something well? It's literally trying to automate intelligence.
I've tried to make AI work but a lot of times the overall productivity gains I do get are so negligible that I wouldn't say it's been transformative for me. I think the fact that so many of us here on HN have such different experiences with AI goes to show that it is indeed not as transformative as we think it is (for the field at least). I'm not trying to invalidate your experience.