Showing 4 open source projects for "code editoral software"

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  • Teradata VantageCloud Enterprise is a data analytics platform for performing advanced analytics on AWS, Azure, and Google Cloud. Icon
    Teradata VantageCloud Enterprise is a data analytics platform for performing advanced analytics on AWS, Azure, and Google Cloud.

    Power faster innovation with Teradata VantageCloud

    VantageCloud is the complete cloud analytics and data platform, delivering harmonized data and Trusted AI for all. Built for performance, flexibility, and openness, VantageCloud enables organizations to unify diverse data sources, run complex analytics, and deploy AI models—all within a single, scalable platform.
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  • The CI/CD Platform built for Mobile DevOps Icon
    The CI/CD Platform built for Mobile DevOps

    For mobile app developers interested in a powerful CI/CD platform for mobile app development and mobile DevOps

    Save time, money, and developer frustration with fast, flexible, and scalable mobile CI/CD that just works. Whether you swear by native or would rather go cross-platform, we have you covered. From Swift to Objective-C, Java to Kotlin, as well as Xamarin, Cordova, Ionic, React Native, and Flutter: Whatever you choose, we will automatically configure your initial workflows and have you building in minutes.
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  • 1
    Ameba

    Ameba

    A static code analysis tool for Crystal

    Code-style linter for Crystal. A single-celled animal that catches food and moves about by extending fingerlike projections of protoplasm. Ameba is a static code analysis tool for the Crystal language. It enforces a consistent Crystal code style, and also catches code smells and wrong code constructions. Ameba allows you to dig deeper into an issue, by showing you details about the issue and the reasoning behind it being reported. Starting from 0.31.0 Crystal supports parallelism. It allows...
    Downloads: 2 This Week
    Last Update:
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  • 2
    Kemal

    Kemal

    Fast, effective, simple web framework

    Lightning fast, super simple web framework. Support all REST verbs. By default Kemal gzips most files, skipping only very small files, or those which don’t benefit from zipping. Websocket support. Request/Response context, easy parameter handling. Middleware support. Built-in JSON support. Built-in static file serving. Built-in view templating via ECR. Documentation. Before filters are evaluated before each request within the same context as the routes. They can modify the request and...
    Downloads: 2 This Week
    Last Update:
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  • 3
    crystalline

    crystalline

    A Language Server Protocol implementation for Crystal

    Crystalline is an implementation of the Language Server Protocol written in and for the Crystal Language. It aims to provide limited language features (like go-to, autocompletion, syntax and semantic checking) and ease development with any compatible code editor. Crystalline is not as extensive in terms of features as other Language Servers but still provides very convenient tools. Code Diagnostics. Syntax and semantic checks on save. Limited Autocompletion. List (depending on the target)...
    Downloads: 0 This Week
    Last Update:
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  • 4
    amber

    amber

    A Crystal web framework that makes building applications fast

    Amber makes building web applications fast, simple, and enjoyable. Get started with blazing-fast performance. Amber provides the bare metal performance of the Crystal Language. It provides productivity and happiness through a powerful CLI and familiar design patterns seen in Kemal, Rails, Phoenix and other popular frameworks. Use code generation and scaffolding features to rapidly build prototypes. Amber is licensed under the MIT license, which makes it perfect for use in commercial...
    Downloads: 21 This Week
    Last Update:
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  • Ango Hub | All-in-one data labeling platform Icon
    Ango Hub | All-in-one data labeling platform

    For AI teams and Computer Vision team in organizations of all size

    AI-Assisted features of the Ango Hub will automate your AI data workflows to improve data labeling efficiency and model RLHF, all while allowing domain experts to focus on providing high-quality data.
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