Showing 2 open source projects for "framework python"

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    Employees get more done with Rippling

    Streamline your business with an all-in-one platform for HR, IT, payroll, and spend management.

    Effortlessly manage the entire employee lifecycle, from hiring to benefits administration. Automate HR tasks, ensure compliance, and streamline approvals. Simplify IT with device management, software access, and compliance monitoring, all from one dashboard. Enjoy timely payroll, real-time financial visibility, and dynamic spend policies. Rippling empowers your business to save time, reduce costs, and enhance efficiency, allowing you to focus on growth. Experience the power of unified management with Rippling today.
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  • Contract Management Software | Concord Icon
    Contract Management Software | Concord

    AI-powered contract management that helps businesses track spending, negotiate smarter, and never miss deadlines.

    Concord serves small and mid-sized businesses and Fortune 500 companies. This robust, web-based platform is used by human resource, sales, procurement, and legal teams, and virtually anyone who deals with contracts.
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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: 18 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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