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49Agents is an open-source “agentic IDE” that reimagines how developers interact with multiple AI agents, terminals, and development environments by placing everything onto a single infinite, zoomable canvas. Instead of relying on traditional tab-based workflows, it provides a spatial interface where terminals, editors, Git views, and monitoring tools coexist as movable panes, enabling users to manage complex multi-agent systems visually and intuitively. The platform is designed to work across multiple machines simultaneously, allowing agents running on different devices or servers to connect to a unified workspace without requiring SSH, which simplifies distributed development workflows. ...

...Pomodoro® and The Pomodoro Technique® are trademarks of Francesco Cirillo. Marinara is not affiliated or associated with or endorsed by Pomodoro®, The Pomodoro Technique®, or Francesco Cirillo. Configurable timer durations. Desktop & tab notifications. Audio notifications with over 20 sounds. Ticking timer sounds. Scheduled automatic timers. Open-source software. Currently, Marinara is configured for developers working and packaging releases on Mac OS. Support for Linux or Windows is welcome. Marinara uses the system ruby and makes tools to build releases. This will produce a packaged extension ready for uploading to the Chrome Web Store in the root directory of the project.

The all-built-in-one, automatic, ready-to-go out-of-box, easy-to-use state-of-the-art, and really awesome NASH OS! Over 25,000+ flexible features and controls and all scalable!! The most powerful solution ever built to instantly deliver new heights of online ecommerce enterprise to you.

ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. ConvNetJS is an implementation of Neural networks, together with nice browser-based demos. It currently supports common Neural Network modules (fully connected layers, non-linearities), classification (SVM/Softmax) and Regression (L2) cost functions, ability to specify and train Convolutional Networks that process images, and experimental Reinforcement Learning modules, based on Deep Q Learning. ...

Sample application showcasing usage of technologies such as: * OSGi R7 Promises for asynchronous generation of transcriptions and linguistic analyses * OSGi R7 Push Stream and JAX RS Server Sent Events for push notifications of processing status * Apache Camel 2.23.1 and RabbitMQ 3.7 for asynchronous communication between services * JPA 2.1 and Hibernate 5.2.12, along with OSGi R7 JPA and Transaction Control services, for persistence layer * OSGi R7 HTTP and JAX RS Whiteboard for registering servlets, resources and REST controllers * OSGi R7 Configurator, Configuration Admin and Metatype services for automatic configuration of components * OSGi R7 Declarative Services for dependency injection * Maven automated build of Docker images * Maven automated deployment into Kubernetes cluster * RabbitMQ message broker as a StatefulSet * CockroachDB relational database as a StatefulSet See 'Code' tab for detailed information