Showing 4 open source projects for "stack"

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    shadowhook

    shadowhook

    Android inline hook library which supports thumb, arm32 and arm64

    ...ShadowHook strives to be stable and safe for production use: it automatically avoids circular-call recursion between original and proxy functions, and supports unwind/unwind-safe proxy functions (CFI/FP unwind) so that backtraces, debugging, and stack unwinding remain correct. It also offers optional intercept and logging capabilities, letting developers record hooked calls.
    Downloads: 7 This Week
    Last Update:
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  • 2
    zclaw

    zclaw

    Your personal AI assistant at all-in 888KiB

    ...It includes support for GPIO control, scheduled tasks, memory handling, and other embedded automation features that enable real-world device interaction. The architecture is optimized for efficiency, allowing the full assistant stack to run in under one megabyte of space. By targeting low-power hardware, zclaw explores the future of edge AI assistants that operate independently of large cloud systems. Overall, the project showcases how lightweight autonomous assistants can be embedded directly into IoT devices.
    Downloads: 0 This Week
    Last Update:
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  • 3
    Cactus

    Cactus

    Low-latency AI inference engine optimized for mobile devices

    Cactus is a low-latency, energy-efficient AI inference framework designed specifically for mobile devices and wearables, enabling advanced machine learning capabilities directly on-device. It provides a full-stack architecture composed of an inference engine, a computation graph system, and highly optimized hardware kernels tailored for ARM-based processors. Cactus emphasizes efficient memory usage through techniques such as zero-copy computation graphs and quantized model formats, allowing large models to run within the constraints of mobile hardware. ...
    Downloads: 0 This Week
    Last Update:
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  • 4
    MemoryLeakDetector

    MemoryLeakDetector

    Native memory leak monitoring tool

    ...Upon activation, it tracks allocations / memory maps, logs usage, and outputs reports that can be post-processed (e.g. via provided Python scripts) to list potential leak suspects — assisting in identifying leaks that traditional tools might miss. The detector supports stack unwinding, mapping analysis, and reporting, making it suitable for complex applications where native memory misuse can lead to crashes or performance degradation. It’s been used within major ByteDance apps for native-memory leak governance.
    Downloads: 0 This Week
    Last Update:
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