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$TRX

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Snapshot Window: 2026-10-08 03:50 UTC · ← Back to Crypto Overview

Tracked Posts
5
Total Impressions
548
Total Likes
13
Retweets & Quotes
2
Comments
9

Social Momentum Summary

Total Engagement - Comments: 9, Retweets: 2, Likes: 13, Impressions: 548

Verbatim Community Citations & Social Evidence 5 source posts analyzed

@SolanaMBS

1999 → 2026 Same collector brain. Different era. 1999: Pokémon cards. 2026: JPEGs. Plot twist: some of us still collect both

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@Jimmyyweb3

I always track whether decentralized data projects back their vision with open code and peer-reviewed research. @axisrobotics just published full proof on arXiv and Hugging Face, confirming that crowdsourced teleoperation directly empowers embodied foundation models. The

The image is a pipeline diagram for AXIS, showing a five-stage workflow for embodied AI: (1) Task Generation & Asset Normalization using LLMs with IsaacLab, MujocoCo, Isaac Sim, Hunyuan 3D, Sim2Real, RoboVerse, and LIBERO; (2) Web-Based Teleoperation for demonstration collection; (3) Offline Data Cleaning & Refinement producing refined trajectories from smoothed points, original points, original lines, end, and start; (4) Realistic Augmentation via visual, pose, noise, and dynamic variation; and (5) Policy Learning & Real-World Validation with sim-to-real transfer, measured by robustness, generalization, and success rate.

This visually supports the post by illustrating how AXIS's crowdsourced teleoperation pipeline—backed by their arXiv paper and Hugging Face release—translates distributed human demonstrations into data that trains and validates embodied foundation models.

AI visual note: The image is a pipeline diagram for AXIS, showing a five-stage workflow for embodied AI: (1) Task Generation & Asset Normalization using LLMs with IsaacLab, MujocoCo, Isaac Sim, Hunyuan 3D, Sim2Real, RoboVerse, and LIBERO; (2) Web-Based Teleoperation for demonstration collection; (3) Offline Data Cleaning & Refinement producing refined trajectories from smoothed points, original points, original lines, end, and start; (4) Realistic Augmentation via visual, pose, noise, and dynamic variation; and (5) Policy Learning & Real-World Validation with sim-to-real transfer, measured by robustness, generalization, and success rate. This visually supports the post by illustrating how AXIS's crowdsourced teleoperation pipeline—backed by their arXiv paper and Hugging Face release—translates distributed human demonstrations into data that trains and validates embodied foundation models.

@asif883500

all I need at the moment: - a badge - a 1000x meme trade :) - lifetime monetized - got my ex back again who’s making this real?

@abrarayuv_clone

One detail in the Bear rules is easy to miss: Checked the Bear transfer rule before the mint hype @PlayOnMint has $MNTD planned on Base and MintABear on Robinhood Chain. 4,444 free mints. 1B fixed supply, with 30% set for community rewards. TGE is still Q4. No public sale XP

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@NamLe98

rgb要出usdt这消息挺猛