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    "github_about_section": "an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM",
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    "github_about_section": "Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.",
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    "github_about_section": "Building the Virtuous Cycle for AI-driven LLM Systems",
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  },
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    "repo_name": "KernelBench",
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    "github_about_section": "KernelBench: Can LLMs Write GPU Kernels? - Benchmark with Torch -> CUDA problems",
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    "repo_name": "terminal-bench",
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    "github_about_section": "A benchmark for LLMs on complicated tasks in the terminal",
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    "github_about_section": "BitBLAS is a library to support mixed-precision matrix multiplications, especially for quantized LLM deployment.",
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    "category": "Basic Linear Algebra Subprograms (BLAS)",
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    "repo_name": "hipBLASLt",
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    "category": "Basic Linear Algebra Subprograms (BLAS)",
    "github_about_section": "hipBLASLt is a library that provides general matrix-matrix operations with a flexible API and extends functionalities beyond a traditional BLAS library",
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    "github_topic_closest_fit": "matrix-multiplication"
  },
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    "repo_name": "AdaptiveCpp",
    "repo_link": "https://github.com/AdaptiveCpp/AdaptiveCpp",
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    "github_about_section": "The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.",
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  },
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    "category": "compiler",
    "github_about_section": "NumPy aware dynamic Python compiler using LLVM",
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  },
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    "repo_name": "nvcc4jupyter",
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    "category": "compiler",
    "github_about_section": "A plugin for Jupyter Notebook to run CUDA C/C++ code"
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    "repo_name": "CU2CL",
    "repo_link": "https://github.com/vtsynergy/CU2CL",
    "category": "CUDA / OpenCL",
    "github_about_section": "A prototype CUDA-to-OpenCL source-to-source translator, built on the Clang compiler framework",
    "homepage_link": "http://chrec.cs.vt.edu/cu2cl",
    "github_topic_closest_fit": "opencl"
  },
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    "repo_name": "cuda-python",
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    "category": "CUDA / OpenCL",
    "github_about_section": "CUDA Python: Performance meets Productivity",
    "homepage_link": "https://nvidia.github.io/cuda-python/"
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    "repo_name": "OpenCL-SDK",
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    "github_about_section": "OpenCL SDK",
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  },
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    "category": "CUDA / OpenCL",
    "github_about_section": "pocl - Portable Computing Language",
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    "repo_name": "SYCL-Docs",
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    "category": "CUDA / OpenCL",
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    "repo_name": "triSYCL",
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    "category": "CUDA / OpenCL",
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    "repo_name": "ZLUDA",
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    "category": "inference engine",
    "github_about_section": "LLM inference in C/C++",
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    "repo_name": "mistral-inference",
    "repo_link": "https://github.com/mistralai/mistral-inference",
    "category": "inference engine",
    "github_about_section": "Official inference library for Mistral models",
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    "github_topic_closest_fit": "llm-inference"
  },
  {
    "repo_name": "ollama",
    "repo_link": "https://github.com/ollama/ollama",
    "category": "inference engine",
    "github_about_section": "Get up and running with OpenAI gpt-oss, DeepSeek-R1, Gemma 3 and other models.",
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    "github_topic_closest_fit": "inference"
  },
  {
    "repo_name": "sglang",
    "repo_link": "https://github.com/sgl-project/sglang",
    "category": "inference engine",
    "github_about_section": "SGLang is a fast serving framework for large language models and vision language models.",
    "homepage_link": "https://docs.sglang.ai",
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  },
  {
    "repo_name": "TensorRT",
    "repo_link": "https://github.com/NVIDIA/TensorRT",
    "category": "inference engine",
    "github_about_section": "NVIDIA TensorRT is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.",
    "homepage_link": "https://developer.nvidia.com/tensorrt",
    "github_topic_closest_fit": "inference"
  },
  {
    "repo_name": "vllm",
    "repo_link": "https://github.com/vllm-project/vllm",
    "category": "inference engine",
    "github_about_section": "A high-throughput and memory-efficient inference and serving engine for LLMs",
    "homepage_link": "https://docs.vllm.ai",
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  },
  {
    "repo_name": "kernels",
    "repo_link": "https://github.com/huggingface/kernels",
    "category": "kernels",
    "github_about_section": "Load compute kernels from the Hub"
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    "repo_name": "kernels-community",
    "repo_link": "https://github.com/huggingface/kernels-community",
    "category": "kernels",
    "github_about_section": "Kernel sources for https://huggingface.co/kernels-community"
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    "repo_name": "Liger-Kernel",
    "repo_link": "https://github.com/linkedin/Liger-Kernel",
    "category": "kernels",
    "github_about_section": "Efficient Triton Kernels for LLM Training",
    "homepage_link": "https://openreview.net/pdf?id=36SjAIT42G",
    "github_topic_closest_fit": "triton"
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  {
    "repo_name": "quack",
    "repo_link": "https://github.com/Dao-AILab/quack",
    "category": "kernels",
    "github_about_section": "A Quirky Assortment of CuTe Kernels"
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    "repo_name": "reference-kernels",
    "repo_link": "https://github.com/gpu-mode/reference-kernels",
    "category": "kernels",
    "github_about_section": "Official Problem Sets / Reference Kernels for the GPU MODE Leaderboard!",
    "homepage_link": "https://gpumode.com",
    "github_topic_closest_fit": "gpu"
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    "repo_name": "pytorch",
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    "category": "machine learning framework",
    "github_about_section": "Tensors and Dynamic neural networks in Python with strong GPU acceleration",
    "homepage_link": "https://pytorch.org",
    "github_topic_closest_fit": "machine-learning"
  },
  {
    "repo_name": "tensorflow",
    "repo_link": "https://github.com/tensorflow/tensorflow",
    "category": "machine learning framework",
    "github_about_section": "An Open Source Machine Learning Framework for Everyone",
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    "github_topic_closest_fit": "machine-learning"
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    "repo_name": "torchdendrite",
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    "category": "machine learning framework",
    "github_about_section": "Dendrites for PyTorch and SNNTorch neural networks"
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    "repo_name": "onnx",
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    "category": "machine learning interoperability",
    "github_about_section": "Open standard for machine learning interoperability",
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    "repo_name": "executorch",
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    "category": "model compiler",
    "github_about_section": "On-device AI across mobile, embedded and edge for PyTorch",
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    "repo_name": "cutlass",
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    "category": "parallel computing",
    "github_about_section": "CUDA Templates and Python DSLs for High-Performance Linear Algebra",
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    "repo_name": "ThunderKittens",
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    "category": "parallel computing",
    "github_about_section": "Tile primitives for speedy kernels",
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    "repo_name": "TileIR",
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    "repo_name": "ComfyUI",
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    "github_about_section": "Composable Kernel: Performance Portable Programming Model for Machine Learning Tensor Operators",
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    "repo_name": "cuJSON",
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