Any sign of the tens of thousands of Optimus robots?

There's days I no longer need to sit at my computer: Claude Remote prompts my phone when it needs direction, the hardware stays busy and I can just go out with my family instead.

That fairly obviously doesn't turn into "bugs per hour" to verify, but if you find 10 candidates in an hour and then get reproducers of them out over the next few days, they are real bugs.

Like sure, "not properly reviewing" could be a thing but if I'm looking at a hard crash on my console, that's not behavior as designed.

The biggest problem I see with how a bunch of people use these tools is they go to them as an oracle, rather then letting them be plugged into and interactive with problem.

And it's in that later context that Claude is amazing: it can run tests and setup scenarios which would take days or get stuck in some weird problem loop. And then you can just say "okay, walk me through this problem" and see it yourself right there.

Our biology is immensely adaptable, so anyone declaring you absolutely need some finely decided very specific thing in order to be "be healthy" should be treated skeptically.

With regards to protein specifically: your body doesn't trade in proteins as inputs, it trades in amino acids which cracked off the protein chains you eat. Everything else is just trying to keep the crowd of gut bacteria from making you feel uncomfortable.

The trades would benefit enormously from such a thing.

But now other people's code is just a starting point for me forking an app to do exactly what I want it to - it's inspiration because code is cheap.

Of course for larger projects we will still need solid components underlying them, but what you're really talking about is just maturity - it's very easy to produce a lot of output now but you can't replace actual history or having more then exactly 1 maintainer.

Of course with all the running around with AI programming, it would be nice if we could task sometime to making remote desktop not suck on Linux.

The concern is being raised without evidence, because the evidence points to the gap simply being frontier models have just started to be able to get a raw proof out. Why, given existing progress, should we expect them to be unable to distill insights from those proofs?

Certainly this even more likely doesn't matter at all for applications: if I can send a radio signal further because my AIs design it a certain way, that's an unambiguous result. Which is really the next step here: turn a proof into a "mechanical" application.

OAI obviously have a fiscal incentive here, but to presume a year from now we won't see improvements and more succinct work on the results coming from models?

It's the same argument which is invariably wrong yet comes up over and over again.

There's no real reason to think AIs solving lots of problems will stop further work on alternative paths - certainly a machine which never tires and can be trained on its own solutions is going to continue to improve.

There's precedent for this: just look at any overconfident post regarding what China will clearly never be able to do, despite decades of steady if frequently flawed progress.

There's no persuasive argument being presented as to why machine mathematical research should have a limit beyond hardware capabilities.

If the proofs work thennwe can put them to work doing more things.

It is not particularly important that Einstein discovered relativity, just that it was discovered (Maxwell was very close).

If you're actually doing the thing who cares how achieve it.

The problem isn't when people do things, it's when they spend all their time coming up with the game and then stop playing, which makes it just a form of procrastinating yet far too common.