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@gm8xx8

140 Following
68492 Followers


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@gm8xx8
LONG LIVE OPEN SOURCE
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@gm8xx8
we are not the same
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@gm8xx8
appreciate it. glad to have you in thereโ€”looking forward to your takes.
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@gm8xx8
I blocked alex not bc he posted in this channel but bc he pulled this.
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@gm8xx8
appreciate that v. might want to let dwr know iโ€™m also a great follow on x with under 10k.
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@gm8xx8
You ask me who to follow in AI, but donโ€™t even follow meโ€”after Iโ€™ve spent two years sharing insights and building a presence on Farcaster. Then, thereโ€™s another person who literally copied my following list, recommended others follow me, but when I tried to return the favor? Realized Iโ€™m blocked. I guess they just wanted to give the impression they are in the know. I see why so many AI folks have churned โ˜น๏ธŽ Iโ€™ll still check in and post occasionallyโ€”this isnโ€™t me quitting Farcaster or anything like that, just sharing some ๐Ÿ’ญ Attached below is one example, but Iโ€™ve received plenty of similar messagesโ€”some harsher towards farcaster, all eye-opening, all from AI folks. I also made sure they knew Iโ€™m still here, just not posting as often. My experience hasnโ€™t been all badโ€”many from Farcaster have reached out to say they only know what they do about AI because of me and have shared their gratitude. That support is why I still return.
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@gm8xx8
https://warpcast.com/gm8xx8/0xb8d6774c
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@entropybender
easily @gm8xx8
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@gm8xx8
sharing for those unfamiliar w/ lerobot https://warpcast.com/gm8xx8/0x5487dd2b
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@gm8xx8
๐Ÿ‘
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@gm8xx8
Physical-Intelligence ฯ€โ‚€ is now open source. > ฯ€โ‚€ and ฯ€โ‚€-FAST Model Weights > source code for model architecture, on-robot Inference, and fine-tuning > pre-trained fine-tuned checkpoints robotics got another boost โœ”๏ธ ๐Ÿ”—: https://github.com/Physical-Intelligence/openpi https://warpcast.com/gm8xx8/0xce446768
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@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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@gm8xx8
cc @sdv.eth
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@gm8xx8
> literally all the other content is 3 day old twitter from KOLs exactly my point. https://warpcast.com/gm8xx8/0x81c0d34a
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@gm8xx8
(๏ฝกโ€ขฬ€แด—-)โœงโŒ’โ™ก* anytime
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@gm8xx8
DeepSeek releases Janus-Pro-7B and Janus-Pro-1B ๐Ÿ‹
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@gm8xx8
|๏ฟฃ๏ฟฃ๏ฟฃ๏ฟฃ๏ฟฃ๏ฟฃ๏ฟฃ๏ฟฃ๏ฟฃ๏ฟฃ๏ฟฃ๏ฟฃ๏ฟฃl | everytime I open this app it l | feels like a step back in l l time. l |๏ผฟ๏ผฟ๏ผฟ๏ผฟ๏ผฟ๏ผฟ๏ผฟ๏ผฟ๏ผฟ๏ผฟ๏ผฟ๏ผฟ๏ผฟ| \ (โ€ขโ—กโ€ข) / \ / โ€”โ€” | | |_ |_
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@gm8xx8
Open research and open source accelerate progress by fostering collaboration, transparency, and accessibility. They enable people to build on existing work, reduce redundancy, and solve problems more efficiently. Openness has always been a key driver of innovation and growthโ€”open is the path forward!
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@gm8xx8
I stand by what I saidโ€”this is from September 15, 2023, followers were scarce + I have plenty of receipts from that time due to channel lead things and I was not getting engagement.
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@gm8xx8
Looks like theyโ€™re finally paying attention. I still remember when I posted this. It wasnโ€™t a direct response, but I shared it because someone mentioned Chinese models and how far behind they were after referencing a LinkedIn post. Zero engagementโ€”people were too busy copying and pasting everything they saw on X or elsewhere to look like they were in the know, rather than learning from those actually in the trenches.
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