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@Jinx9306 Nghe ngọt đó, mà anh ở đâu thật thì chưa biết

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@0xGaCrypto @fomo tôi cũng y chang, tính xem 5 phút mà thành 2 tiếng luôn

[Black X @00x9999BTC]

@0xClara1999 585 points and still waiting? how long has it beenice day where though? it's been raining here all week

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@0xGaCrypto @FomiesNFT @muchl33t @itsguddev @onlydotmeme 585 points and still waiting? how long has it been

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@LinhXinh10x xinh quá trời. love u

[ShadowBone @0xGaCrypto]

RoyaleWeb3 @RoyaleWeb3 operates as a Web3 community that integrates marketing and project support such as AMAs, content creation, and social media promotion in exchange for community access to whitelists and partner allocations

If you want to try your luck, go ahead and sign up. It’s free to check out, and you might just score a reward; however, the approval process is quite slow only two "OG" spots have been added so far

🔗 Link: https://t.co/wVJmjAsTBk @RoyaleWeb3

[ShadowBone @0xGaCrypto]

GM GM GM

I’ve been thinking about where coding agents are heading, and @NucleusCodes caught my attention

Generating code is becoming the easy part

The harder problem is giving AI enough context to understand an entire codebase, reason through complex workflows, and actually move a project forward

That’s where NucleusCodes gets interesting

The bigger shift may be from AI that simply writes code to AI that can understand, build, test, debug, and iterate alongside developers

Instead of manually writing every line, developers could increasingly focus on describing what they want and guiding intelligent agents through the engineering process

If that becomes the new development workflow, infrastructure like NucleusCodes could end up being an important layer of the transition

[Black X @00x9999BTC]

I just joined the shhhhhcat whitelist — the first free silent NFT collection on BRC-20

Inspired by $shhhhh, the first BRC-20 deployed with Bitcoin Silent Payments (BIP352), verified with a BIP374 DLEQ proof. Quiet by design, verifiable on-chain

Shhhhh-GU42F

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[Black X @00x9999BTC]

so good job project @rarelistio

https://t.co/zeMCZ7PJ1N

[Rarelist @rarelistio]

Rarelist is live!

Curated projects. Creator campaigns. Launches. Priority access.

Familiar ways to earn, built around a new approach.

The first 500 to join get OG: https://t.co/hlGjkOzX7r

[Black X @00x9999BTC]

@rarelistio so good job

[Rarelist @rarelistio]

Rarelist is live!

Curated projects. Creator campaigns. Launches. Priority access.

Familiar ways to earn, built around a new approach.

The first 500 to join get OG: https://t.co/hlGjkOzX7r

[ShadowBone @0xGaCrypto]

Robots don’t learn how to handle the real world by reading a few textbook @axisrobotics

Unfortunately, they also can’t just watch a 10 minute YouTube tutorial and suddenly become professional baristas

They have to learn by doing

And that’s where robotics gets expensive

Imagine training a robotic arm to pick up a cup. You record a few dozen demonstrations, the robot finally gets the hang of it, and everyone celebrates

Then someone moves the cup two centimeters

Or changes the lighting

Or puts the cup in a slightly different position

Suddenly, your highly sophisticated robot starts acting like it has never seen a cup in its entire life

The problem isn’t just intelligence. It’s the cost of teaching machines to handle variation in the physical world

This is where @axisrobotics takes a different approach

Instead of relying entirely on expensive physical experiments, AXIS brings robot control into a browser based simulation

Pick up a cup. Open a drawer. Pour water. Repeat

Every session can capture a complete trajectory, including joint angles, object positions, actions, and camera imagery

But the interesting part happens after the demonstration

These trajectories can be filtered, smoothed, and replayed across different layouts, lighting conditions, and noise level

A few dozen demonstrations can become thousands of training samples, without needing to buy thousands of physical robot

Your hardware budget can finally stop having nightmares

Of course, synthetic data isn’t a magical replacement for the real world

A robot that performs perfectly in simulation but fails the moment it touches a real object isn’t exactly ready to take over the kitchen

The point is to expose the robot to more variations before deployment, so it has a better chance of handling them in reality

And not all synthetic data is created equal

Generating endless simulated movements is easy enough

Generating useful training data is much harder

The data needs to respect physics, follow task logic, and be validated against actual task success. Otherwise, you’re just teaching robots to produce very convincing nonsense

There’s another part of the AXIS approach that I find particularly interesting: failure correction

When a robot fails, the goal isn’t simply to record another attempt and hope for the best

The policy itself can be corrected

Those corrections can be especially valuable because they target the exact gaps in the robot’s current capabilities

A clean demonstration shows the robot what success looks like

A corrected failure helps explain what went wrong

And sometimes, knowing why you failed is more useful than watching someone else get everything right

This connects directly to the bigger idea behind AXIS: continuously generating, improving, and validating robot training data

More demonstrations create more training opportunities

More variations expose weaknesses

More failures reveal what the policy still doesn’t understand

Corrections improve the next attempt

And the process starts again

Demonstration → simulation → variation → failure → correction → better policy.

That’s a much more interesting proposition than simply throwing more data at a model

Because Physical AI doesn’t just need robots that can perform a task once

It needs robots that can perform that task reliably when the environment changes

The real world will always find a way to move the cup, change the lighting, or introduce some completely unreasonable new obstacle

The question is whether the robot can learn from it without requiring an entirely new and expensive round of physical experiments every time

That’s why I think the simulation based, iterative training loop being built by @axisrobotics is worth watching

The goal isn’t to keep robots inside a browser forever

It’s to use simulation to prepare them for the chaos outside it

And if AXIS can consistently turn realistic interactions, useful corrections, and validated trajectories into better real-world performance, it could become an important piece of the infrastructure behind the next generation of Physical AI

Because the future of robotics probably won’t be built by robots that never make mistakes

It’ll be built by systems that get better every time they do

[ShadowBone @0xGaCrypto]

What I find interesting about @axisrobotics is that the learning loop doesn't have to stop with human demonstrations

A trained robot can generate new attempts, learn from the outcomes, and turn those experiences into data for the next generation of models

The bigger piece is the combination of simulation + real-world experience

Simulation gives robots room to explore thousands of scenarios cheaply, while real-world interactions ground those skills in actual physics

Over time, this creates a compounding loop:

train → act → collect experience → improve → repeat

Instead of rebuilding robot intelligence from scratch, each cycle can become the foundation for the next

That's the direction I find most interesting about Axis: robots that don't just learn once, but keep getting better through experience

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@rankzdotfun @RobinhoodCrypto

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@rankzdotfun

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@rankzdotfun làm tốt lắm

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@FomiesNFT

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Applied for a printer on the Federal Reserve. Application #9592.

Dual mandate: maximum brrr, stable memes. https://t.co/FVG8M2rxaN

[ShadowBone @0xGaCrypto]

GM CT fan

One thing that stands out about @axisrobotics is that scaling a robotics network isn't simply about collecting more trajectories

The harder the task, the more important it becomes to route it to the right contributor

The current Challenger zone makes this visible. Anyone can inspect it, but submitting requires the Seek the Unseen badge. There are currently 478 badge holders, while difficult tasks can pay 2–3× the Axis Points of comparable regular tasks

But the more interesting part isn't the higher reward

It's how contributors are selected

Axis appears to factor in contributor history, including consistency, quality and experience across both pre-training and post-training work. In other words, reputation becomes part of the data pipeline.

That matters because difficult manipulation tasks are more likely to produce noisy or failed demonstrations when assigned to someone unfamiliar with the interface or task dynamics

Routing those tasks toward proven contributors could reduce that mismatch before verification even starts

There is a clear trade-off: tighter access means less openness for some contributors, while completely open access can create more low-quality data that has to be filtered later

So the scaling question for Axis isn't just:

How many contributors can join?

It's also:

Can the network learn which contributor is best suited for each data problem?

For Physical AI, contributor to task matching could become nearly as important as contributor count

$AXIS token soon

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@kurin_dh @arklisdotapp @arc 0x06f255c2d9a83f600fa02bface45a12bb8c2806f