Sandmann retweeted
Elon says connecting Grok Bot to your X DMs is the best way to use it
and it shipped in the same update that made it 3x FASTER
the part crypto people should care about:
> Robinhood, Coinbase and eToro connectors just went live
> it reads your X Chat, so deal DMs and collab offers get sorted for you
> tag @bot under any post to dig into a project before you ape
> email aliases: one routine per inbox, launches in one, invoices in another
My read: speed gets the headline, but the connectors are why I'm keeping it on all day
🚨 Grok Bot just got 3x faster, and that's the least useful part of the update
what actually changed:
> computer tasks finish 3x sooner on average, other replies in less than half the time
> tag @bot under any post on X and it gets to work
> it reads your X Chat now, connect X and let it work your DMs
> email plus aliases, so a routine can fire only on mail sent to one address
> new connectors: Robinhood, Coinbase, Trello, PostHog, beehiiv, eToro
> voice calls remember past conversations better
> notifications only go to the device you're using
try these first:
> give newsletters their own plus alias and one routine that reads only that
> connect X and ask it to sort your DM requests
> drop it under a long thread and ask for the 3 points that matter
Sandmann retweeted
Sourccey functionality improving every day 😊
No training, no sim2real gaps, just a prompt and a dream.
The problem in robotics today is every task is a complex combination of locomotion, manipulation, vision, planning, and a bunch of other things that fall apart once you diverge from training data or characteristics from sim. This is why you see so many demos with perfect lighting conditions and always the same, or precisely placed objects, specifically chosen for the task and demo. You never see these things deployed in the real world despite seeing demos that look convincing, because everything falls flat in the real world with real unseen conditions.
Every single deployment is a unique edgecase.
Here, it's just a multimodal LLM (DSV4.1F) doing a task it was never deliberately trained for, using cameras it's never seen, servos/motors it didn't learn on, in an environment it's never dealt with and a form factor never tested.
It is currently slow, this video is 10x speed, but I am very confident it can be sped up as is, but also this is basically the slowest this level of intelligence will ever be again.
The more I work with general purpose multimodal LLMs, the more I am realizing that ACT, VLAs, and all this other stuff is probably just a sideshow and a distraction, and it's just going to largely get eaten up by general purpose models that just happen to also do robotics.
I don't think it'll always be LLMs, but the fact that LLMs can do some of the most challenging robotics tasks tells me it's kind of over for a large class of robotics training.
This does not include RL, however, for low level things, like gait, reaching, and other capabilities or "skills" for hardware. RL is still a king here and you really do want these models to be super fast and super small. RL is very good here and safe I'd say.
Decision models like Clef, Jev...etc remain mostly useless imho for robotics. I look forward to someone proving out a useful recipe that works these things into robotics, but I don't currently see it.
Sandmann retweeted
No training, no sim2real gaps, just a prompt and a dream.
The problem in robotics today is every task is a complex combination of locomotion, manipulation, vision, planning, and a bunch of other things that fall apart once you diverge from training data or characteristics from sim. This is why you see so many demos with perfect lighting conditions and always the same, or precisely placed objects, specifically chosen for the task and demo. You never see these things deployed in the real world despite seeing demos that look convincing, because everything falls flat in the real world with real unseen conditions.
Every single deployment is a unique edgecase.
Here, it's just a multimodal LLM (DSV4.1F) doing a task it was never deliberately trained for, using cameras it's never seen, servos/motors it didn't learn on, in an environment it's never dealt with and a form factor never tested.
It is currently slow, this video is 10x speed, but I am very confident it can be sped up as is, but also this is basically the slowest this level of intelligence will ever be again.
The more I work with general purpose multimodal LLMs, the more I am realizing that ACT, VLAs, and all this other stuff is probably just a sideshow and a distraction, and it's just going to largely get eaten up by general purpose models that just happen to also do robotics.
I don't think it'll always be LLMs, but the fact that LLMs can do some of the most challenging robotics tasks tells me it's kind of over for a large class of robotics training.
This does not include RL, however, for low level things, like gait, reaching, and other capabilities or "skills" for hardware. RL is still a king here and you really do want these models to be super fast and super small. RL is very good here and safe I'd say.
Decision models like Clef, Jev...etc remain mostly useless imho for robotics. I look forward to someone proving out a useful recipe that works these things into robotics, but I don't currently see it.
Sandmann retweeted
Nvidia acquired Huggings Face which got announced by Jensen Huang’ Huggings built $MICRODUCK ‘Microduck went 45mill MCap ATH, Microduck built by Julien ‘Huggings face co- founder, today he announced he got a new friend
Apparently Sourccey been in development for over a year now’ here is more proof confirming it name $SOURCCEY
Ca: 0xdc3861b1b2ca83421b4632e292485bdb571cac34
TG: t.me/sourccey_rh
vulcanrobotics.ai
Sandmann retweeted
@solana isn't just a chain. it's the rails that made this whole thing possible.
fast. cheap. built for things that actually move.
$140,000 to @MakeAWish in week one says it all.
Sandmann retweeted
I was doing some research on the current fee schedule by market cap. The break down is a little confusing on @Pumpfun website, but here is a readable one.
$0 – $63k → 0.30%
$63k – $220k → 0.95%
$220k – $516k → 0.85–0.90%
$516k – $1.47M → 0.75–0.80%
$1.47M – $4.4M → 0.50–0.70%
$4.4M – $11M → 0.20–0.50%
$11M – $14.7M → 0.075–0.175%
$14.7M → 0.05%
I think that for @donatedotgg Charity Coins, @a1lon9 should consider unlocking 95% fees indefinitely for $Wish otherwise there is way less incentive to send a coin to 15m+
