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- Hacker News
- Do you want Terminators? Because this is how you get Terminators.by etchalon
- It feels like the prevailing view is: if I get $100m before every one loses their job and build the terminator before it un-livings everyone else then I winby digitaltrees
- Is that a Unitree?by InkCanon
- Yep looks like a go1by aabhay
- by LoganDark
- > However, once again, we are seeing a pattern whereby first, models are helpful to humans. Then, humans are helpful to models. Finally, models are largely able to do things themselves. We have seen this in cybersecurity and now the same dynamics are starting to take shape at the intersection of AI and the physical world.
It’s good they are the one seeing those things because otherwise no one else would have. Now if only seeing things would translate into getting any actual economic value out of them… instead of losing billions. But hey, who am I to do a reality check on this shameless piece of hype.
- stop trying to make fetch happenby joshu
- Fast? Sure. Good maintainable code? Doubted. I think they skip the right metrics there. So that's just their AI promo.by nickosh
- Why does the code need to be maintainable by humans?by fassssst
- This mostly reads as a comparison between Opus 4.7 and 4.1 it would be more interesting if they reran the experiment against a team of humans with 4.7 and see how much the humans still improve the results today.by jascha_eng
- > Preliminary trials with Claude Mythos Preview showed that it would not provide an apples-to-apples comparison with other models because of how we had set up the experiment and how the model was served.
What does this mean? My guess is they couldn’t co-locate Mythos close enough to reduce latency?
(I’m assuming this experiment pre-dates the export controls)
by bob778 - Because this was a staged demo, not an experiment. Mythos performed more poorly but they don't want to admit it. The phrase "because of how we had set up the experiment" means "we didn't have experimental controls and got a bunch of bullshit noise that we cherry picked."
At least that's my guess.
by daveguy - > My guess is they couldn’t co-locate Mythos close enough to reduce latency?
I doubt network latency is the reason. Even when connecting from literally across the world network latency is lost in the noise of overall response latency of even fast models.
The overall response latency of the model very well could have been the difference, though. AFAIK Mythos is structured to do relatively slow "deep thinking".
by georgemcbay - I'm getting a bit tired of these disguised adverts.
Here's how non robotics engineers used AI to do a short robot integration task faster than other non robotics engineers without AI.
Where "better" mostly means faster, and who knows what happens on longer horizons, with actual robotics experts, robustness requirements, or tasks where the hard part is control rather than API spelunking.
by didibus - Disguised ad or not, I learned that LLMs have the emergent capability of learning to complete tasks in physical space, without being fine-tuned for it.by skeledrew
- > I'm getting a bit tired of these disguised adverts.
Its not disguised. Corporate blogs exist overtly to promote the company and its work.
Disguised promotions where notionally independent media publish promotional pieces as news concealing that they were fed to them by party whose products they promote area thing, but this is just the most overt undisguised promotion.
by dragonwriter