Everyone Wants To Be a Reinforcement Learning Startup - The Information
<a href="https://news.google.com/rss/articles/CBMinwFBVV95cUxPU29zUHdMMVJHLUhVWEVWSkM4Mjlma1V2MFY5bGFXT2Zyc0FsUGdPTkpTWXR3NEQ5ZVdzQUR3ekJPTE1rY0dtNlllcXIyaWdMbHp2T2cwcjc4QTlhZHJRMlhnZlEwcWRseFNGS3BLQkhNY2Y3SmlDRlZGRGFpSXpKMGV1UjVCX3NmOE1YUFNDMDJVWXExbS1QTVZFbVpHaWs?oc=5" target="_blank">Everyone Wants To Be a Reinforcement Learning Startup</a> <font color="#6f6f6f">The Information</font>
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Cursor Launches New AI Agent Experience to Compete With Claude and OpenAI
Cursor has launched a next-generation AI agent experience for coding, positioning itself to compete more directly with major AI players like OpenAI and Anthropic's Claude. This represents a significant product evolution for the AI coding startup as it enters a more competitive phase in the developer Cursor has launched a next-generation AI agent experience for coding, positioning itself to compete more directly with major AI players like OpenAI and Anthropic's Claude. This represents a significant product evolution for the AI coding startup as it enters a more competitive phase in the developer tools market. Source: Discussion on r/CursorAI Originally published on gentic.news
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![viable/strict/1775228618: [DTensor] redistribute from/to _StridedShard through Replicate (#179059)](https://avatars.githubusercontent.com/u/134637289?s=60&v=4)
viable/strict/1775228618: [DTensor] redistribute from/to _StridedShard through Replicate (#179059)
why care about redistributing from/to _StridedShard. As I was fixing _StridedShard.full_tensor(), I found cartesian_prod goes through _view_ops.py to generate _StridedShard, becuase of decomposation to meshgrid → flatten → stack. It triggers _StridedShard-to-Shard redistribution and ended up with Runtime error This PR propose redistributing from/to _StridedShard through Replicate. It's not optimal but it ensures correctness. @zpcore might have a more efficient solution repro cartesian_prod import torch import torch.distributed as dist from torch.distributed.tensor import DTensor, Shard, Replicate, init_device_mesh import os dist.init_process_group(backend="gloo") rank = dist.get_rank() mesh = init_device_mesh("cpu", (2,)) # Reference result on full tensors a_full = torch.tensor([1, 2, 3, 4
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