Open Source Go Operating System Kernels for Mac

Go Operating System Kernels for Mac

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Browse free open source Go Operating System Kernels for Mac and projects below. Use the toggles on the left to filter open source Go Operating System Kernels for Mac by OS, license, language, programming language, and project status.

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  • 1
    SulinOS

    SulinOS

    Indipendent distro uses inary package system. Sulin is roolling donkey

    Sulin is rolling donkey Gitlab: https://gitlab.com/sulinos Github: https://github.com/sulinos Telegram: https://t.me/sulinos powered by donkey team
    Downloads: 36 This Week
    Last Update:
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  • 2
    syzkaller

    syzkaller

    syzkaller is an unsupervised coverage-guided kernel fuzzer

    syzkaller is Google’s coverage-guided, feedback-driven kernel fuzzer designed to uncover reliability and security bugs in operating system kernels at scale. It automatically generates, mutates, and minimizes system call programs, then drives them through a specialized executor (syz-executor) to exercise deep kernel paths. The system integrates tightly with sanitizers such as KASAN, KMSAN, KCSAN, and UBSAN to surface memory safety, concurrency, and undefined behavior issues with actionable reports. A distributed architecture coordinates many fuzzing VMs, collects crash signatures, deduplicates them, and bisects to the first bad commit when possible. syzkaller maintains per-kernel “syz” descriptions so it understands arguments, flags, and resources of thousands of syscalls and ioctls across Linux and other kernels. It also ships sophisticated reproducers and minimization routines so developers get small, deterministic test cases they can run locally to fix bugs quickly.
    Downloads: 1 This Week
    Last Update:
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  • 3
    elastic-jupyter-operator

    elastic-jupyter-operator

    Cloud-native way to provide elastic Jupyter Notebooks on Kubernetes

    Jupyter is a free, open-source, interactive web tool known as a computational notebook, which researchers can use to combine software code, computational output, explanatory text, and multimedia resources in a single document. For data scientists and machine learning engineers, Jupyter has emerged as a de facto standard. At the same time, there has been growing criticism that the way notebooks are being used leads to low resource utilization. GPU and other hardware resources will be bound to the specified notebooks even if the data scientists do not need them currently. This project proposes some Kubernetes CRDs to solve these problems.
    Downloads: 0 This Week
    Last Update:
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