zephmyst(✱,✱)

nft and airdrop
contributor @axisrobotics

[zephmyst(✱,✱) @zephmst]

@masssnpc @axisrobotics @AxisRoboticsID @0xRyzzu @0xRahvanaa @Stevennn77_ @ipnuxx1 @muuuraonx @Rafiadehanafi @HikalHank titd

[EnpiciFi (✱,✱) @masssnpc]

Mungkin beginilah keadaan kami ketika menjalankan task Multiemboidment @axisrobotics @AxisRoboticsID

kalian kesel juga gak wkkwkkw :
@0xRyzzu
@0xRahvanaa
@Stevennn77_
@ipnuxx1
@muuuraonx
@Rafiadehanafi
@HikalHank

[zephmyst(✱,✱) @zephmst]

@emperorjournal_ @KUONnft ej cooking

[zephmyst(✱,✱) @zephmst]

64,000+ trajectories. 400+ contributors. Real hardware, real data, real intelligence. @axisrobotics

[zephmyst(✱,✱) @zephmst]

@fairyrae_ @axisrobotics gaxis

[zephmyst(✱,✱) @zephmst]

This is how physical AI actually works.
Human motion → data → simulation → models → general robotic intelligence.
Not scraped from the internet. Captured from the real world, by real people.
64k+ trajectories. 400+ contributors. Open infrastructure. @axisrobotics

[zephmyst(✱,✱) @zephmst]

64,000+ trajectories. 400+ contributors. Real hardware, real data, real intelligence.
https://t.co/rxD9TFBM1U

[zephmyst(✱,✱) @zephmst]

@HikalHank @axisrobotics bola kapan

[zephmyst(✱,✱) @zephmst]

@brebross2 @axisroboticsai lfg

[Brebross @brebross2]

Physical AI needs experience.
Experience needs data.
Data needs infrastructure.

@AxisRoboticsAI

[zephmyst(✱,✱) @zephmst]

You dont need a robot to train a robot.
@axisrobotics uses simulation 60 virtual tasks where robots learn before touching real hardware.
Sim2Real is how Physical AI scales without burning millions.
https://t.co/rxD9TFBM1U

[zephmyst(✱,✱) @zephmst]

@hangrisss sudah saatnya

[Hangrisss @hangrisss]

The intersection of Physical AI and L2 infrastructure is where decentralized data networks actually become viable. Historically, the robotics training pipeline has been heavily gatekept by high capital expenditures forcing labs to buy expensive physical hardware just to collect data trajectories.

By utilizing @base low friction environment, @axisrobotics is effectively decoupling simulation from hardware constraints. Proving at SuperAI that anyone can contribute to training real robots without specialized rigs is a massive step for scaling the data flywheel.

This is how you democratize a highly centralized industry.
Huge milestone with @baseapac

[zephmyst(✱,✱) @zephmst]

@arkanusaa @axisrobotics lfg bang

[arkanz @arkanusaa]

Is it just me, or are the robots at @axisrobotics starting to develop a very specific sense of humor? My current task is to 'Put the Rolling Pin on the Dough Ball.' But howw 😂

This simulation feels like a prank. Just a robot prank, bro! Touche, brader.

[zephmyst(✱,✱) @zephmst]

@0xRahvanaa gas bang

[zephmyst(✱,✱) @zephmst]

@0xRahvanaa jp

[Rahvana.182 @0xRahvanaa]

Weekly event kali ini rame bangettt
Nanti ada stumble, kamis karaoke, sabtu smashkart, minggu ngobrol santai!

Jangan lupaa ramaikan guyss

[zephmyst(✱,✱) @zephmst]

Most robotics startups fail not because of hardware.
They fail because training data is too expensive, too scarce, and impossible to verify.
@axisrobotics fixed all three.
https://t.co/rxD9TFBM1U

[zephmyst(✱,✱) @zephmst]

Physical AI's bottleneck isn't compute. It's data.
@axisrobotics pools human trajectories, verifies them on-chain, and trains robots that ship.
64k+ trajectories · 400+ contributors · 60 sim tasks live.
https://t.co/rxD9TFBM1U