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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

Postingan terbaru

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 2.6K views · 97 likes · 0 reposts · 88 replies Open on X →
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 · 5 reposts · 115 replies Open on X →
@1nxnn__ connected stack is the only way 16 views · 1 likes · 0 reposts · 0 replies Open on X →
@1nxnn__ Keep building on top layer of privacy 30 views · 1 likes · 0 reposts · 0 replies Open on X →
@1nxnn__ Privacy becomes far more meaningful when communication, identity and payments are protected as one connected system. 30 views · 1 likes · 0 reposts · 0 replies Open on X →
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 · 3 reposts · 103 replies Open on X →
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 5.4K views · 129 likes · 0 reposts · 117 replies Open on X →
@1nxnn__ This is goat coded 15 views · 1 likes · 0 reposts · 0 replies Open on X →
@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 80 views · 13 likes · 0 reposts · 0 replies Open on X →
@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 83 views · 14 likes · 0 reposts · 1 replies Open on X →
@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 87 views · 14 likes · 0 reposts · 1 replies Open on X →
@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 111 views · 14 likes · 0 reposts · 1 replies Open on X →
@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 184 views · 14 likes · 0 reposts · 1 replies Open on X →
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. 282 views · 14 likes · 0 reposts · 1 replies Open on X →
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 8.6K views · 258 likes · 5 reposts · 237 replies Open on X →

Dibandingkan akun berukuran sama

15 postingan dari 90 hari terakhir, dibandingkan dengan rentang 10K–100K pengikut. menjangkau lebih sedikit orang, tetapi melibatkan mereka jauh lebih kuat.

Median tayangan111akun ini924median untuk 10K–100K
Jangkauan, %0.47%akun ini3.62%median untuk 10K–100K
Interaksi, %6.25%akun ini1.52%median untuk 10K–100K
MetrikAkun iniMedian untuk 10K–100KRasio
Median tayangan per postingan1119240.12×
Jangkauan (tayangan ÷ pengikut)0.47%3.62%0.13×
Tingkat interaksi6.25%1.52%4.11×

Akun lain pada rentang ini →   Bandingkan dengan akun lain →   Bagaimana tolok ukur ini disusun →

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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