https://www.youtube.com/watch?v=5vfcTjosGLg&list=PL_lsbAsL_o2BUUxo6coMBFwQE31U4Eb2q&index=12

PyTorchConference2025_GithubRepos.json

@@ -133,7 +133,7 @@
     "homepage_link": "https://ollama.com"
   },
   {
-    "repo_name": "triton",
+    "repo_name": "Triton",
     "repo_link": "https://github.com/triton-lang/triton",
     "category": "parallel computing dsl",
     "github_about_section": "Development repository for the Triton language and compiler",
@@ -250,11 +250,11 @@
     "homepage_link": "https://www.kubeflow.org/docs/components/trainer/"
   },
   {
-    "repo_name": "cutlass",
+    "repo_name": "CUTLASS",
     "repo_link": "https://github.com/NVIDIA/cutlass",
     "category": "parallel computing",
     "github_about_section": "CUDA Templates and Python DSLs for High-Performance Linear Algebra",
-    "homepage_link": "https://docs.nvidia.com/cutlass/index.html"
+    "homepage_link": "https://docs.nvidia.com/cutlass/latest/"
   },
   {
     "repo_name": "aiter",
@@ -453,7 +453,7 @@
     "repo_link": "https://github.com/NVIDIA/nccl",
     "category": "distributed computing",
     "github_about_section": "Optimized primitives for collective multi-GPU communication",
-    "homepage_link": "https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html"
+    "homepage_link": "https://developer.nvidia.com/nccl"
   },
   {
     "repo_name": "SkyRL",
@@ -476,7 +476,7 @@
     "github_about_section": "PyTorch compiler that accelerates training and inference. Get built-in optimizations for performance, memory, parallelism, and easily write your own."
   },
   {
-    "repo_name": "helion",
+    "repo_name": "Helion",
     "repo_link": "https://github.com/pytorch/helion",
     "category": "parallel computing dsl",
     "github_about_section": "A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate.",
@@ -695,11 +695,11 @@
     "homepage_link": "https://scalingintelligence.stanford.edu/blogs/kernelbench"
   },
   {
-    "repo_name": "nvshmem",
+    "repo_name": "NVSHMEM",
     "repo_link": "https://github.com/NVIDIA/nvshmem",
     "category": "distributed computing",
-    "github_about_section": "NVIDIA NVSHMEM is a parallel programming interface for NVIDIA GPUs based on OpenSHMEM. NVSHMEM can significantly reduce multi-process communication and coordination overheads by allowing programmers to perform one-sided communication from within CUDA kernels and on CUDA streams.",
-    "homepage_link": "https://docs.nvidia.com/nvshmem/api/index.html"
+    "github_about_section": "GPU-initiated communication for scalable NVIDIA GPU clusters.",
+    "homepage_link": "https://developer.nvidia.com/nvshmem"
   },
   {
     "repo_name": "flashinfer-bench",