CRYPTO TIGER
8.8K posts
CRYPTO TIGER
@Cryptottiger
Crypto enthusiast, contributor @axisrobotics
Web 3 Katılım Eylül 2020
1.4K Takip Edilen3K Takipçiler
I can confirm every word of this, brooo
This guy is fucking legit!
First time and I’ve already made him my gadget plug
If you’re looking for quality, call @SamuelKolawole0
That I May be True To Him.@SamuelKolawole0
Still your No. 1 Web3 gadgets plug First-time customer referred by @Viktor_ella_ needed a quality iPhone and as usual, I delivered the best eSIM Unlocked iPhone 15 128GB with Baseus 30W charger delivered to Niger State Congratulations @Darkfall_x Who's next to cop Quality💦
English
English
Daily task on @axisrobotics . Though the hub is still a bit buggy and I believe the teams are working on it for a smoother experience.
Tasks video 4📽️
Task: Separate the Sandwich from the Juice
@plpiaoliang @Rainhoole @0xsexybanana
@0xzagen @iamlogtun
Aemar (✱,✱)@Aemar00
Daily task on @axisrobotics. Though the hub is a bit buggy and I believe the teams are working on it for a smoother experience. Tasks video 3📽️ Task: Separate the Sandwich from the apple @plpiaoliang @Rainhoole @0xsexybanana @0xzagen @iamlogtun
English
Thread on @axisrobotics
Physical AI is entering a new era and Axis Robotics is building the infrastructure behind it. 🤖
The project is focused on scaling robotic intelligence through browser-based simulation and community-driven training data.
A thread 🧵
English
Axis is officially LIVE on @base. 🔵
Control robots virtually, generate training data, and help build the intelligence behind future robotics all from your browser. No hardware needed.
hub.axisrobotics.ai
English
Axis Robotics AMA Incoming 🎙️
@axisrobotics will be hosting a live AMA session featuring the Founder & CEO, offering the community a deeper look into the future of Physical AI.
Topics to be covered:
• The vision behind Axis Robotics
• Physical AI & robotics infrastructure
• Simulation and data platform insights
• Product roadmap and future direction
• Community participation and ecosystem growth
Selected questions will be answered live during the session. If you have any question, join the DC and drop your questions
🗓 Date: 15 May 2026
⏰ Time: 15:00 UTC
📍 Location: Axis Stage on Axis Discord Server
Don’t miss the conversation shaping the future of robotics.
English
THE VISION BEHIND AXIS::-
Everyone talks about AI models.
Far fewer people talk about coordination.
But coordination is ultimately what determines whether intelligence can scale beyond isolated systems.
That’s the deeper vision behind projects like AXIS Robotics
Not just robotics.
Not just AI.
But the coordination infrastructure connecting:
> humans
> simulations
> behavioral data
> learning systems
> robotic policies
> machine execution
into a unified intelligence network.
Historically, robotics development has been fragmented:
> isolated labs
> disconnected datasets
> incompatible environments
> siloed training pipelines
> non-transferable behaviors
That fragmentation slows embodied intelligence dramatically.
Physical AI cannot scale efficiently if every system learns independently from scratch.
What becomes valuable is the coordination layer capable of:
> aggregating distributed interaction data
> standardizing trajectories
> synchronizing learning loops
> orchestrating policy refinement
> enabling scalable sim-to-real transfer
In other words:
The future of robotics may depend less on individual machines…
and more on the infrastructure coordinating machine intelligence at network scale.
That’s why browser-native simulation systems are strategically important.
They transform robotics from:
!! isolated hardware experimentation
into:
!! distributed intelligence coordination.
And once intelligence generation becomes networked, the scaling dynamics begin to resemble internet infrastructure rather than traditional manufacturing.
The interesting part about Physical AI isn’t just building smarter robots.
It’s building the systems capable of coordinating intelligence itself.
@axisrobotics
NEEL VARMA...@ChanduS279
The most scalable robotics company of the next decade may not be the company manufacturing the most robots. It may be the company building the most efficient intelligence pipeline. That’s the shift many people still underestimate when analyzing Physical AI. Robotics historically scaled very slowly because intelligence acquisition was tied directly to hardware constraints: -->expensive labs -->limited environments -->operational risk -->slow iteration cycles -->physical wear -->restricted participation But simulation-first architectures fundamentally change that equation. When robotic learning moves into browser-accessible simulation environments, the scaling model becomes much closer to internet infrastructure than traditional manufacturing. Now the bottleneck shifts toward: -->trajectory collection -->behavioral diversity -->policy optimization -->environment generation -->distributed interaction loops This is where projects like AXIS Robotics become architecturally interesting. The important part isn’t “robots.” It’s the emergence of scalable machine intelligence infrastructure. Infrastructure compounds faster than hardware ever can. @axisrobotics
English
The interesting part about browser-based robotics isn’t accessibility.
It’s distributed cognition.
Most people still think of internet users as:
!! consumers
!! community members
!! participants
But in Physical AI systems, users increasingly function as:
--> distributed intelligence providers.
Every interaction inside a robotic simulation environment can become:
!! behavioral data
!! movement trajectories
!! correction feedback
!! spatial reasoning signals
!! policy training input
That creates an entirely different scaling model for robotics.
Instead of relying exclusively on internal research teams or expensive robotics labs, intelligence generation becomes network-distributed.
This matters because embodied intelligence requires dramatically more interaction complexity than language models.
Language models learned from internet text.
Physical AI must learn from:
!! environments
!! movement
!! physics
!! timing
!! adaptation
!! real-world variability
That data is exponentially harder to collect.
Projects attempting to solve scalable robotic data generation may ultimately become more important than projects focused purely on robotic hardware itself.
The infrastructure layer is where the long-term leverage exists.
@axisrobotics
English
Indonesia
🤖 The future isn’t waiting… it’s being built right now by AxisRobotics.
From intelligent automation to next-gen innovation, every move forward starts with bold technology and sharper systems.
AxisRobotics is pushing the limits of what machines can do faster.
@axisrobotics
English
English
Daily task on @axisrobotics. Though the hub is a bit buggy and I believe the teams are working on it for a smoother experience.
Tasks video 3📽️
Task: Separate the Sandwich from the apple
@plpiaoliang @Rainhoole @0xsexybanana
@0xzagen @iamlogtun
Aemar (✱,✱)@Aemar00
Completed the daily tasks on @axisrobotics today. here is one of the task Task: Put the cup beside the cube block gAxis @plpiaoliang @iamlogun @Rainhoole @0xsexybanana
English
Axis Robotics is building a future where robots and AI can work together smoothly, the same way apps and people connect through the internet today.
Their vision is not just about creating robots but creating a system that allows robots to learn, communicate, improve and coordinate with each other across the world.
When @axisrobotics says we are “building the coordination layer for robotics and AI,” they mean they want to create the foundation that helps robots understand tasks, share data and work together intelligently.
Just like Google connects information or how operating systems connect apps on a phone. Axis Robotics wants to connect robots, AI models, simulations and human input into one working ecosystem.
For example 👉🏽 imagine thousands of robots learning how to pick objects, clean homes or work in warehouses. Instead of each robot learning alone.
Axis Robotics wants every lesson, movement and experience to become shared knowledge that other robots can learn from too. That makes robots smarter, faster and more useful over time.
English
GM & gAxis 🤖
I’ll be hosting a “Fill In The Gap” game today by 5PM WAT/4PM UTC on the Axis Robotics NG Discord. discord.gg/axisrobotics
Come join us to have fun, learn together and connect with our amazing community!
Want to get onboarded to @axisrobotics? Send a DM!
@iamlogtun
English
𝐖𝐡𝐲 𝐢𝐬 𝐃𝐚𝐭𝐚 𝐭𝐡𝐞 𝐁𝐚𝐜𝐤𝐛𝐨𝐧𝐞 𝐨𝐟 𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬?
--> Currently , we are talking about how Ai Can Fast Learning Everything Now a Days .
Ex - :
(LLM ) Large Language Model has Learning Billion web pages , Books ,videos , other Ralated Real world Interaction By Information Data Learn Via Internet.
--> However, robots do not learn merely by "reading."
For them to learn, they require doing.
-> Physical interaction data is crucial for a robot:
-> How much force to apply when lifting an object
-> How to maintain balance while walking
-> How to react if an object slips or breaks
-> How to handle real-time environmental changes
--> This constitutes the biggest difference between AI models and robots.
--> Internet data teaches AI to understand language.
But physical-world data teaches robots to understand the real world.
--> Every movement a robot makes, every sensor reading, every camera frame and even its mistakes transform into learning data.
--> In robotics, data is not merely information.
It is the "experience" gained by a robot.
--> In the future, the companies that will dominate the field of robotics won't be those with just better models-
but rather those with superior real-world interaction data.
@axisrobotics @iamlogtun
English
Why is everyone talking about @axisrobotics?
1️⃣ The Tech: A 6-axis control stack for total deterministic movement.
2️⃣ The Lead: Dr. G, a veteran in industrial kinematic control.
3️⃣ The Backing: $5M seed round led by Galaxy
Here is a professional breakdown 🧵👇
English
English
Why Data Is the Backbone of Robotics 🤖
The AI boom was built on internet-scale data.
The robotics revolution will be built on something far harder to obtain:
Physical interaction data.
And this is exactly where projects like @axisrobotics are focusing their vision. 🧵 👇
English