Abhishek Yadav pfp

Abhishek Yadav

@avis0r

136 Following
102 Followers


Abhishek Yadav pfp
Abhishek Yadav
@avis0r
πŸš€πŸš€
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
Wen
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
πŸš€πŸš€
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ted (not lasso) pfp
ted (not lasso)
@ted
"bizarre that people started buying more cologne when they stopped seeing other people in person."
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
πŸš€πŸš€
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
πŸš€
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
🎊🎊
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
πŸš€πŸš€
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
πŸš€
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
Gm
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
πŸš€πŸš€
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
Done
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
Let's go
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
πŸ‘
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
😭
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
🌐 Crypto Market Highlights 🌐 πŸ”Ή Market Cap: $3.42T, down 0.42%. Trading volume up 3.74%. πŸš€ Top Gainers: Neon EVM, Jasmy, Alchemy Pay. πŸ’° Bitcoin: Market cap $2.08T, volume up 43.44%. πŸ”— Ethereum: Valued at $394.22B, volume up 36.18%. πŸ“° Key Developments: FDIC revising crypto guidelines. Grayscale seeks Litecoin ETF conversion. New Mexico proposes Bitcoin reserve bill. BlackRock plans Bitcoin ETP in Europe. πŸ€” Sentiment: Mixed on X, with optimism around ETF applications and new chains. Stay updated! πŸ“ˆπŸ” #CryptoNews #Bitcoin #Ethereum
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
USDC
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
πŸ‘πŸ‘
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𝚐π”ͺ𝟾𝚑𝚑𝟾 pfp
𝚐π”ͺ𝟾𝚑𝚑𝟾
@gm8xx8
FAST is a robot action tokenizer that simplifies and speeds up robot training. It enables: > 5x faster training compared to diffusion models. > Compatibility with all tested robot datasets. > Zero-shot performance in new environments, including the DROID dataset, successfully controlling robots in various settings with ease. > Simple autoregressive VLAs that match diffusion VLA performance. > Mixed-data VLA training, allowing integration of non-robot data like web data, subgoals, and video prediction. FAST compresses actions using discrete cosine transform, reducing redundancy and enabling efficient VLA training on high-frequency tasks. It scales to complex robot tasks with simple next-token prediction, converging in days instead of weeks. A pre-trained FAST tokenizer based on 1M robot action sequences is available on Hugging Face, working across various robots and supporting mixed-data VLA training. https://huggingface.co/physical-intelligence/fast
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Abhishek Yadav pfp
Abhishek Yadav
@avis0r
πŸ‘
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