Linh xinh 10x

• Săn tìm ước mơ. Chia sẻ nụ cười.
• Đơn giản nhưng không giản đơn.

[Linh xinh 10x @LinhXinh10x]

@mahan_airdrops @FomiesNFT 64k applications already? that's wild, good luck getting in

[Linh xinh 10x @LinhXinh10x]

@deekay_btc mùi kéo xả cuối tuần đây mà, giữ 84k hơi khó à

[Linh xinh 10x @LinhXinh10x]

@gauravshar64966 @FungoLabs gm to everyone except that approval button apparently

[Linh xinh 10x @LinhXinh10x]

@0xClara1999 @FomiesNFT when pick me hits on a nice day, honestly relatable

[Linh xinh 10x @LinhXinh10x]

"Ngày hôm nay, hãy làm điều khiến bạn tự hào khi nhìn lại. Chúc mọi người một ngày mới tràn đầy năng lượng và hiệu quả"

Nice day

[Linh xinh 10x @LinhXinh10x]

@Avin_lucia @0xQuit @AbstractChain 0.01 ETH for abstract chickens, crypto really is healing

[Linh xinh 10x @LinhXinh10x]

@mr_random14 @FomiesNFT gm gm, fomies season got me hyped fr

[Linh xinh 10x @LinhXinh10x]

@northbookprop hey ^^ @northbookprop @0xgacrypto is signal ...

[Linh xinh 10x @LinhXinh10x]

@northbookprop hey
@northbookprop
,
@0xgacrypto
is signal ...

[Linh xinh 10x @LinhXinh10x]

I hold a light. One of 666.

@numencash is born on @ritualfnd — every token pays for its own thinking, or goes dark.

666 places. One shot.

https://t.co/58ruVtXdC3

[Linh xinh 10x @LinhXinh10x]

@numencash @ritualfnd @ritualnet ave tried to push it 1 hour…

[Numen Cash @numencash]

Launch tokens on Ritual.

What they do when they wake, you will see on Mainnet Day I.

Details:
▸ Chain: Ritual mainnet @ritualfnd
▸ Date: Mainnet Day I
▸ Launching a token: open to everyone
▸ Lights: 66 of 666 claimed, 600 remain!

Awaken.
https://t.co/4L7DrtmDlE

[Fomies @FomiesNFT]

Take good care of your pickles. 🥒

You have been warned.

[Linh xinh 10x @LinhXinh10x]

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-CH27B

$shhhhhcat $shhhhh

[Linh xinh 10x @LinhXinh10x]

Looks promising. many older campaign sites are congested, need some breathing space @rarelistio

https://t.co/TO4oSQCT6p

[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

[Linh xinh 10x @LinhXinh10x]

@rarelistio wow

Looks promising. many older campaign sites are congested, need some breathing space

[Linh xinh 10x @LinhXinh10x]

@rankzdotfun @RobinhoodCrypto good

[Linh xinh 10x @LinhXinh10x]

@rankzdotfun go to earn

[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

[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