Showing 2 open source projects for "framework python"

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  • Network Discovery Software | JDisc Discovery Icon
    Network Discovery Software | JDisc Discovery

    JDisc Discovery supports the IT organizationss of medium-sized businesses and large-scale enterprises.

    JDisc Discovery is a comprehensive network inventory and IT asset management solution designed to help organizations gain clear, up-to-date visibility into their IT environment. It automatically scans and maps devices across the network, including servers, workstations, virtual machines, and network hardware, to create a detailed inventory of all connected assets. This includes critical information such as hardware configurations, software installations, patch levels, and relationshipots between devices.
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  • Inspections+ Mobile forms for Dynamics 365 - Resco.net Icon
    Inspections+ Mobile forms for Dynamics 365 - Resco.net

    Start collecting field data without the hassles of complicated development thanks to resco.Inspections' native integration with Dynamics 365.

    Equip your frontline teams with a robust digital solution to simplify data collection and reporting. Handle inspections and audits effortlessly, even in remote locations, and create comprehensive reports on the spot, all integrated with Dynamics 365.
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  • 1
    Archipelago

    Archipelago

    Archipelago Multi-Game Randomizer and Server

    Archipelago is an open-source multi-game randomizer framework that allows multiple players to play different games simultaneously while sharing a unified item randomization system. The software creates what is known as a “multiworld,” where items that normally appear in one game may instead appear in another player’s game. When a player finds an item belonging to someone else, the system automatically sends that item to the correct player through a networked server. This design encourages...
    Downloads: 15 This Week
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  • 2
    DreamerV3

    DreamerV3

    Mastering Diverse Domains through World Models

    DreamerV3 is an open-source implementation of a reinforcement learning algorithm that uses world models to train intelligent agents capable of learning complex behaviors across many environments. The system works by building an internal model of the environment and then using that model to simulate possible future outcomes of actions, allowing the agent to learn from imagined experiences rather than only from real interactions. This approach enables the algorithm to efficiently learn...
    Downloads: 0 This Week
    Last Update:
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