1 project for "scrcpy 32 bit" with 2 filters applied:

  • Attack Surface Management | Criminal IP ASM Icon
    Attack Surface Management | Criminal IP ASM

    For security operations, threat-intelligence and risk teams wanting a tool to get access to auto-monitored assets exposed to attack surfaces

    Criminal IP’s Attack Surface Management (ASM) is a threat-intelligence–driven platform that continuously discovers, inventories, and monitors every internet-connected asset associated with an organization, including shadow and forgotten resources, so teams see their true external footprint from an attacker’s perspective. The solution combines automated asset discovery with OSINT techniques, AI enrichment and advanced threat intelligence to surface exposed hosts, domains, cloud services, IoT endpoints and other Internet-facing vectors, capture evidence (screenshots and metadata), and correlate findings to known exploitability and attacker tradecraft. ASM prioritizes exposures by business context and risk, highlights vulnerable components and misconfigurations, and provides real-time alerts and dashboards to speed investigation and remediation.
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  • Curtain LogTrace File Activity Monitoring Icon
    Curtain LogTrace File Activity Monitoring

    For any organizations (up to 10,000 PCs)

    Curtain LogTrace File Activity Monitoring is an enterprise file activity monitoring solution. It tracks user actions: create, copy, move, delete, rename, print, open, close, save. Includes source/destination paths and disk type. Perfect for monitoring user file activities.
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  • 1
    BitNet

    BitNet

    BitNet: Scaling 1-bit Transformers for Large Language Models

    BitNet is a machine learning research implementation that explores extremely low-precision neural network architectures designed to dramatically reduce the computational cost of large language models. The project implements the BitNet architecture described in research on scaling transformer models using extremely low-bit quantization techniques. In this approach, neural network weights are quantized to approximately one bit per parameter, allowing models to operate with far lower memory usage than traditional 16-bit or 32-bit neural networks. The architecture introduces specialized layers such as BitLinear, which replace standard linear projections in transformer networks with quantized operations. ...
    Downloads: 0 This Week
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