30 Photos and videos
Pinned Tweet
Jun 12
Imitation learning sounds simple from the outside :
show the robot enough examples and let it learn the behavior.
But the hard part is the quality of those examples.
If the robot only sees scripted movements, clean demos, or artificial teleoperation, it misses the small human details that make actions work in real life
People adjust their grip, slow down before contact, recover from mistakes, and move differently depending on the object and the room
That is why @RoboX_to first person data is valuable. It gives robots a closer view of natural human behavior, not just the perfect version of a task
@0xsikdar , @0xnag1 , @MohdSarim0
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Jun 12
The more AI agents become independent, the less sense it makes to think of them as simple tools
A tool waits for a user
An agent can discover a task
coordinate with other systems
execute work
and trigger payment when the job is done
That changes the infrastructure question completely
Because autonomous AI does not only need models
It needs rails for agreements
payments
verification
and machine to machine coordination
That is where the @AIVIVEHQ x @prom_io partnership becomes interesting
AIVIVE is focused on autonomous intelligence
PROM brings the economic layer around it
Programmable agreements
automated task coordination
low cost high frequency transactions
and zk powered verifiable execution
These are not just technical extras
They are the basic pieces an AI agent economy needs before agents can actually work earn spend and exchange services without depending on slow manual systems
The bigger idea is simple
If AI agents are going to act independently
they also need an economy that can operate independently
AIVIVE and PROM are moving toward that machine native layer
🤝 AIVIVE × PROM
Building the economic layer for autonomous intelligence.
AIVIVE is proud to partner with @prom_io — The Economic Layer for Autonomous AI Agents
As AI evolves from tools into independent actors, a new economic system is required — one that is programmable, automated, and built for machine-to-machine interaction.
PROM delivers exactly that:
⚡ Programmable agreements executed in code
⚡ Autonomous task coordination & payment flows
⚡ High-frequency, low-cost machine transactions
⚡ zk-powered security with verifiable execution
Secured by a modular zkEVM architecture, PROM is laying the foundation where AI agents can transact, coordinate, and exchange services seamlessly.
Together, AIVIVE and PROM are advancing a shared vision:
An economy where AI agents don’t just exist —
they participate, earn, spend, and evolve within a fully autonomous system.
This is the rise of machine-native economies. 🚀
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Just a sick Animation from our awesome community member @apoushDiv 🔥🐾
Jun 12
The weird thing about consumer AI is that the technology got democratized but the economics did not
Anyone can create now
Images videos characters concepts entire visual worlds
The tools became cheaper faster and easier to use but the value flow stayed almost the same
Users create the output and platforms keep most of the upside through subscriptions
That is the AI consumer paradox
Creation became open
capture stayed closed
This is why Aivive’s model feels worth paying attention to
It does not start with the idea that users should buy a token before they create
That would just add friction
Instead the user pays in USDC for credits and keeps using the product like a normal creative app
The economic layer runs behind the scenes
Revenue comes in
AVV gets bought back
supply gets reduced
the loop keeps moving
So the interesting part is not only the image feed
It is the way creative activity becomes connected to an onchain scarcity mechanism
Most AI platforms turn user demand into company revenue
@AIVIVEHQ is trying to turn user demand into a public economic loop
That is a much bigger shift than just adding crypto to an AI app
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Jun 12
Most crypto products still stop at holding value
You keep stablecoins in a wallet
maybe move them between apps
maybe send them to an exchange
then wait for the real world part to begin
That gap is the problem
The interesting thing about @KASTxyz is that it does not treat stablecoins as something that should stay trapped inside crypto rails
It turns them into something closer to everyday money
You can store digital dollars
receive global payments
earn on idle balances
move money across borders
and spend through a card when life actually asks for payment
That full flow matters more than one single feature
Because most people do not wake up thinking about financial infrastructure
They just want money that works when they need it
KAST feels important because it is trying to make stablecoins useful beyond trading and holding
not louder
just more usable
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Jun 12
The weird thing about consumer AI is that the technology got democratized but the economics did not
Anyone can create now
Images videos characters concepts entire visual worlds
The tools became cheaper faster and easier to use but the value flow stayed almost the same
Users create the output and platforms keep most of the upside through subscriptions
That is the AI consumer paradox
Creation became open
capture stayed closed
This is why Aivive’s model feels worth paying attention to
It does not start with the idea that users should buy a token before they create
That would just add friction
Instead the user pays in USDC for credits and keeps using the product like a normal creative app
The economic layer runs behind the scenes
Revenue comes in
AVV gets bought back
supply gets reduced
the loop keeps moving
So the interesting part is not only the image feed
It is the way creative activity becomes connected to an onchain scarcity mechanism
Most AI platforms turn user demand into company revenue
@AIVIVEHQ is trying to turn user demand into a public economic loop
That is a much bigger shift than just adding crypto to an AI app
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Jun 10
Another AI image generator is not the interesting part here
That market is already crowded
You open a tool type a prompt get an image and move on
The bigger question is not whether AI can make good images anymore It can
The real question is what happens to the value that comes from all this creation
Right now most of it goes back to centralized subscription platforms
Users make the culture
platforms keep the economics
That is where @AIVIVEHQ gets interesting
Every image generation can feed a revenue loop behind the product
That revenue moves onchain buys back AVV and helps reduce supply permanently
So the app is not just a surface for creating images
It is also the engine that powers the token economy
People use the product
revenue comes in
the loop runs
supply gets smaller
This is the part that makes the idea different
Aivive is not asking users to hold a token before they create
It lets the creative activity come first and lets the economic layer work in the background
AI made content feel infinite
Aivive is trying to make the network behind that content more scarce every time people use it
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Jun 9
Most crypto products still stop at holding value
You keep stablecoins in a wallet
maybe move them between apps
maybe send them to an exchange
then wait for the real world part to begin
That gap is the problem
The interesting thing about KAST is that it does not treat stablecoins as something that should stay trapped inside crypto rails
It turns them into something closer to everyday money
You can store digital dollars
receive global payments
earn on idle balances
move money across borders
and spend through a card when life actually asks for payment
That full flow matters more than one single feature
Because most people do not wake up thinking about financial infrastructure
They just want money that works when they need it
@KASTxyz feels important because it is trying to make stablecoins useful beyond trading and holding
not louder
just more usable
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EgoTask is a data collection campaign for robotics training. First-person video of everyday manipulation tasks, recorded on an iPhone or GoPro, one to five minutes per session.
During recording, RoboX tracks both hands and labels every grasp, then turns the footage into training data for imitation learning: hand poses, grasp types, camera trajectory, and object contacts.
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Jun 9
Lab data is useful, but it usually shows the clean version of the world.
The real world is different. Objects are not always in the right place, lighting changes, hands move imperfectly, rooms are cluttered, and people do things in ways that are hard to script.
That mess is not a problem for robotics data.
It is the point.
@RoboX_to matters because it helps capture the kind of first person data robots actually need if they are going to work outside controlled environments.
@0xsikdar , @0xnag1 , @MohdSarim0
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