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Cloudinary is a cloud service that offers a solution to a web application's entire image management pipeline. Upload images to the cloud. Automatically perform smart image resizing, cropping and conversion without installing any complex software. Integrate Facebook or Twitter profile image extraction in a snap, in any dimension and style to match your website’s graphics requirements. Images are seamlessly delivered through a fast CDN, and much much more. This Java library allows to easily integrate with Cloudinary in Java applications.

Cloudinary is a cloud service that offers a solution to a web application's entire image management pipeline. Upload images to the cloud. Automatically perform smart image resizing, cropping and conversion without installing any complex software. Integrate Facebook or Twitter profile image extraction in a snap, in any dimension and style to match your website’s graphics requirements. Images are seamlessly delivered through a fast CDN, and much much more. This Java library allows to easily integrate with Cloudinary in Java applications.

Azure Video Analyzer provides a platform to build intelligent video applications that span the edge and the cloud. The platform offers the capability to capture, record, and analyze live videos along with publishing the results, video, and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open-source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline. Use the client library for Video Analyzer Edge to simplify interactions with the Microsoft Azure IoT SDKs (https://github.com/azure/azure-iot-sdks) and programmatically construct pipeline topologies and live pipelines.

Leveraging Tencent's 21 years of experience in network and audio/video technologies, Tencent Real-Time Communication (TRTC) offers multi-person audio/video calls and low-latency interactive live streaming solutions. With TRTC, you can quickly develop cost-effective, low-latency, and high-quality interactive audio/video services. Audio/Video Call Solution TRTC provides a client SDK that works across platforms and regions. This solution can be easily used on webpages to enable audio and video call capability for all applications. Low-Latency Interactive Live Streaming Solution TRTC leverages Tencent Cloud's high-quality node resources, industry-leading network and audio/video technologies to helps you build interactive live streaming services with minimal lags and a latency below 1 second, taking live streaming into the era of CDN 2.0.腾讯实时音视频（Tencent Real-Time Communication，TRTC）将腾讯21年来在网络与音视频技术上的深度积累，以多人音视频通话和低延时互动直播两大场景化方案，通过腾讯云服务向开发者开放，致力于帮助开发者快速搭建低成本、低延时、高品质的音视频互动解决方案。

Test Drop-in Framework for Selenium WebDriver 4.1.x This repository contains these major deliverables: a) the Test Drop-in Framework, b) a WebDriver implementation offering enhanced logging capabilities, and c) the @FindByJS annotation support Provides a thin abstraction layer between the test environment and the test class plus its helpers. This makes the test code independent of the test environment, thus allowing exchange of tests between organizations or even companies. It comes with an event firing WebDriver wrapper plus corresponding interface. This allows you to implement listeners to arbitrary WebDriver activities for various purposes such as logging. Offers support of JavaScript commands to find elements in a way similar to @FindBy used on fields in Page Object classes.

The Zello Channels SDK allows you to integrate Zello push-to-talk into your own application. The SDK communicates with a Zello server over a web socket connection using a JSON-based protocol, and offers a simple API to send and receive audio, images, and text over Zello channels. Supported features include: Send voice messages from the device microphone Play incoming voice messages through the device speaker Send voice messages from your own audio code, e.g. from a file Receive voice message data with your own audio code with optional pass-through to the device speaker Send and recieve text messages Send and receive images Send the device's current location, and receive location messages from other users The protocol specification is also available if you prefer to develop your own client in-house.

This project contains a JNA (Java Native Access) implementation built on top of the native Hnswlib (Hierarchical Navigable Small World Graph) which offers a fast approximate nearest neighbor search. It includes some modifications and simplifications in order to provide Hnswlib features with native like performance to applications written in Java. Differently from the original Python implementation, the multi-thread support is not included in the bindings itself but it can be easily implemented on the Java side.