Open Source Unix Shell Frameworks for Apple iPhone

DreamFactory can run on private, public, or hybrid cloud infrastructure. DreamFactory is easy to deploy with Docker, Kubernetes, or directly from GitHub using our automated installers. Eliminate the biggest bottleneck in modern IT. Launch your project in weeks rather than months. DreamFactory instantly creates a secure, standardized, reusable, fully documented, and live REST API. Connect Salesforce to SQL Server, or your ERP to AWS S3. Transform and mask data along the way. Create reports, analytics, and alerts. Empower teams to retrieve and distribute data on demand. Secure every API endpoint behind Active Directory, OAuth2, Okta, OpenID Connect, and more. Restrict capabilities using role-Based Access Controls. Easily manage API keys. Deploy behind firewalls and in air-gapped environments. Integrate legacy technology into cutting-edge applications, extending technology lifecycles and derisking legacy system replacements.

SAFS is a software test automation framework supporting data-driven (aka keyword-driven) functional test automation and associated services. The framework can run stand-alone on Windows, Linux, and Mac as well as integrate the use of IBM Rational Functional Tester, IBM Rational Robot, Smart Bear/AutomatedQA Test Complete, Apple XCode UIAutomation, Android SDK Automaton, Thoughtworks Selenium, and several other tools. Among other features, it provides advanced support of language localization testing (NLS) allowing the same tests to work for many different languages or test configurations. With various different tool configurations SAFS can be used to test Java, Web/Html, Flex, .Net, WPF, Win, iOS, and Android domains natively. And with advanced Image-Based Testing support built-in SAFS can be used to test virtually anything, anywhere.

This repository provided a pre-release of TensorFlow and TensorFlow Addons tailored for macOS 11+ with native hardware acceleration via Apple’s ML Compute, supporting both Apple Silicon (M1) and Intel Macs. It shipped ready-made Python 3.8 wheels and install scripts so developers could quickly get an accelerated stack running without building from source. As TensorFlow added a Metal PluggableDevice path, the project directed users toward using Apple’s tensorflow-metal to get GPU acceleration on Mac directly through Metal. The README and releases emphasized this was a macOS-optimized distribution, not an upstream fork of TensorFlow, and it cataloged common installation notes and issues encountered by early Apple Silicon users. In practice, the repo served as a bridge during the transition to native Mac acceleration, making modern TensorFlow viable on Apple hardware while upstream support matured.