3 projects for "pc benchmark testing" with 2 filters applied:

  • MongoDB Atlas runs apps anywhere Icon
    MongoDB Atlas runs apps anywhere

    Deploy in 115+ regions with the modern database for every enterprise.

    MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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    Ship Agents Faster

    Transform your applications and workflows into powerful agentic systems at global scale.

    Gemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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  • 1
    pcsc-tools

    pcsc-tools

    Some tools to be used with smart cards and PC/SC

    pcsc-tools is a suite of tools used to test smart cards and PC/SC drivers. It provides utilities like pcsc_scan to monitor smart card readers and scriptor to send commands to smart cards, aiding in development and troubleshooting.
    Downloads: 24 This Week
    Last Update:
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  • 2
    ARC-AGI

    ARC-AGI

    The Abstraction and Reasoning Corpus

    ARC-AGI is a benchmark dataset and experimental framework designed to evaluate and advance artificial general intelligence by testing systems on abstract reasoning tasks that require human-like problem-solving abilities. It consists of a curated set of tasks where models must infer patterns from input-output examples and apply those rules to new unseen cases, without relying on memorization or prior training data.
    Downloads: 0 This Week
    Last Update:
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  • 3
    benchm-ml

    benchm-ml

    A benchmark of commonly used open source implementations

    This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different...
    Downloads: 0 This Week
    Last Update:
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