Transitioned my Omega dev work to the cloud this afternoon. Local was getting cumbersome with workspaces and multiple agents. Tried Claude and @AmpCode. Stuck with Amp because it was able to help me get my workspace scripted and working well - Claude was close but I just kept hitting random snags. I’m working across 5 repos at once and need to make cross-cutting changes to several of them pretty often and Orbs seems a bit smarter and less locked down. Feels more like having an actual computer in the cloud vs. a restricted sandbox.

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TRANSIT (TRANsparent Scale-In for multi-node Training) is a runtime that makes unified virtual memory practical for large-scale LLM training, enabling models to train on up to 50% fewer GPUs without requiring modification to existing PyTorch training code. Hyungyo Kim, Ph.D. Candidate in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign will present on TRANSIT as a poster at PyTorch Conference North America with Apoorve Mohan from IBM Research. Learn how to train more with less at PyTorch Conference North America: