Aminul (아미눌) ✓
@1nxnn__ · joined 05 Mar 2022
𝗪𝗲𝗯𝟯 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗖𝗿𝗲𝗮𝘁𝗼𝗿 | 𝐋𝐞𝐚𝐫𝐧𝐞𝐫 |𝗔𝗺𝗯𝗮𝘀𝘀𝗮𝗱𝗼𝗿 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗲 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁 | 𝗗𝗠 𝗳𝗼𝗿 𝗰𝗼𝗹𝗹𝗮𝗯𝘀 & 𝘀𝗽𝗼𝗻𝘀𝗼𝗿𝘀𝗵𝗶𝗽𝘀
23 496Followers
4 379Following
62 849Posts total
27.4KViews on collected posts
Latest posts
Looking for new posts…
Good Night
BeldexCoin becomes more interesting when you stop viewing it as one privacy product.
The bigger idea is a connected privacy stack.
BChat protects communication.
BelNet supports private browsing and routing.
Private transactions protect value movement.
BNS makes
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The @BeldexCoin wallet is more than a place to send BDX.
It works like a control panel for key parts of the ecosystem.
BDX transfers.
BNS name management.
Masternode registration.
That matters because usability improves when users do not need five different tools to do basic
4.4K views · 132 likes
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@1nxnn__ Keep building on top layer of privacy
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@1nxnn__ Privacy becomes far more meaningful when communication, identity and payments are protected as one connected system.
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Beldex’s privacy thesis becomes stronger when you stop viewing it as one feature.
Users do not only need privacy in transactions.
They need it across communication, browsing, payments and identity.
Each layer can reveal a different part of someone’s digital behavior. Together,
5.5K views · 129 likes
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Privacy becomes stronger when access gets easier.
$BDX is now live on @Bridgersxyz, allowing users to swap between native $BDX, $BDX on BSC and other supported assets.
The value is practical.
Better swap routes reduce friction for users moving across networks. And for any http
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@axisrobotics 7/7
My take:
If foundation models are becoming the brain of Physical AI, data is the experience that makes the brain useful.
Axis is betting that experience can be generated by a global community, not only expensive robotics labs.
If they can scale while keeping
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@axisrobotics 6/7
Their published research gives an early signal.
Axis says its Sim Dataset V1 was used to continually pretrain π0.5, improving LIBERO Plus from 83.9 to 88.8.
That does not prove the whole thesis yet.
But it is a stronger signal than simply showing a large cont
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@axisrobotics 5/7
The scale is already worth watching.
By late August, Axis Hub showed roughly 182K users, 4.4M trajectories and 3,494 tasks.
But I would not judge Axis by the numbers alone.
The real question is whether this growing dataset can actually make robot policies bet
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@axisrobotics 4/7
That creates a simple loop:
•Human teaches.
•Model learns.
•Model fails.
•Human corrects.
•Model improves.
Repeat that across different objects, rooms, tasks and robot bodies, and you get something more valuable than a static dataset.
You get a growing pool o
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@axisrobotics 3/7
The most interesting part is what happens after the first attempt.
A policy tries the task.
If it fails, a human takes control and corrects it.
That correction becomes new training data.
So Axis is not just collecting more data.
It is collecting data from t
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2/7
That is the problem @axisrobotics is trying to solve.
Instead of building another robot, Axis is building a data engine where people can teach simulated robots through a browser.
No robot.
No GPU.
You complete a task, and your movement becomes a training trajectory.
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1/7
Everyone talks about better robots and bigger AI models.
But I think Physical AI has a more basic problem:
robots need experience.
A model can be smart, but without enough real examples of how humans pick, place, move and recover from mistakes, it still struggles in the h
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Against accounts of the same size
15 posts from the last 90 days, next to the 10K–100K follower range. reaches fewer people than peers, but engages them much harder.
Median views111this account924median for 10K–100K
Reach, %0.47%this account3.62%median for 10K–100K
Engagement, %6.25%this account1.52%median for 10K–100K
| Metric | This account | Median for 10K–100K | Ratio |
|---|---|---|---|
| Median views per post | 111 | 924 | 0.12× |
| Reach (views ÷ followers) | 0.47% | 3.62% | 0.13× |
| Engagement rate | 6.25% | 1.52% | 4.11× |
Others in this range → Compare with another account → How these benchmarks are built →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
28229 Aug
184
111
87
83
80
15
5.4K3 Sep
5.5K4 Sep
30
30
165 Sep
4.4K
2.6K6 Sep
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
5.32%29 Aug
8.15%
13.51%
17.24%
18.07%
16.25%
6.67%
4.53%3 Sep
4.28%4 Sep
3.33%
3.33%
6.25%5 Sep
5.77%
7.15%6 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes53.7%832 in total
Reposts0.8%13 in total
Replies42.9%665 in total
Quotes0.2%3 in total
Bookmarks2.4%37 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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