I noticed that some people are working on trying to close the gap of the Sim2Real, but most of the videos are on the physical stuff that are rigid. Sim2Real gap happens when the stuff are more unpredictable (i.e. cloth, rubber, etc)Thoughts on this?
I don't think there's a use case that really "works" yet at the moment and the exploration is still undergoingcurious if anyone have seen an interesting use case of humanoid robotics that "works"
"CANN 6.0 claims to support one-click migration of CUDA code, and compatibility of PyTorch framework of around 95% according to some sources."
??
I found an article that says the following:"CANN 6.0 claims to support one-click migration of CUDA code, and compatibility of PyTorch framework of around 95% according to some sources."Source: https://pstaidecrypted.substack.com/p/gpu-games-heat-up-just-as-ai-diffusion#footnote-2-162340886
Curious what would be the context length on this
I'm not sure why, I find o1 is better than o3-mini for most of the questions I'm askingAnyone seeing something similar?I can't pin point down on exactly why
Yes, it can implement features. It does 80% of the work, I still need to review and do the last 20%.
Senior-Principal guys understand architecture, design patterns, and connecting the "dots" together with business logic
It's insane to see how much I can get done on my own with AI. It's a dream for the idea guyI will only be hiring senior-principal guy who can leverage AI as part of their work
Which do you guys use, and why
I guess in the future I can add some kind of references. But when integrating with better data source (primary & secondary), those may not be "referencable" either
So I guess the real answer is I'm not sure. Any advice?
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