What makes a robot smarter?
It isn't just better hardwareit's better experiences.
Every successful grasp, every failed attempt, and every repeated task becomes a trajectory that teaches robots how to understand and interact with the physical world.
The more diverse and higher quality those trajectories are, the better robots can generalise to new environments and real-world challenges.
This is one of the biggest bottlenecks in Physical AI, and it's exactly what @axisrobotics is working to solve by building the data infrastructure needed for the next generation of intelligent robots.
High-quality data isn't optionalit's the foundation.
Follow @axisrobotics as they continue building the future of Physical AI.
Season 1 is almost here. 👀
The new Qwerti Rewards page is live — and the next Points season kicks off in just a few days.
But Points are only the beginning:
➡ Revenue Share.
➡ New Ambassador Adventures.
➡ Cashback on your trades.
➡ And more.
We’ll unpack every layer in the coming posts.
Your activity is about to matter a lot more. 💜
@QwertiAI This is one of those infrastructure details users probably shouldn’t have to think about.
You click “Swap.”
You see the expected outcome.
Underneath, the system has already had to reason about the path required to get there.
Good aggregation isn’t just finding a route.
“Just give me the best route.”
Sounds simple.
But for an onchain aggregator, that question is actually technical.
Best price alone isn’t enough.
You also have to consider liquidity, slippage, fees, execution path and whether the route can actually be executed as expected.
@QwertiAI@QwertiAI Routing Engine uses route simulation alongside real-time balances and onchain data when evaluating execution paths.
That matters because routing isn’t simply a price-comparison problem.
It’s an execution-quality problem.