Trello is a collaboration tool that organizes your projects into boards. In one glance, Trello tells you what's being worked on, who's working on what, and where something is in a process.
Imagine a white board, filled with lists of sticky notes, with each note as a task for you and your team. Now imagine that each of those sticky notes has photos, attachments from other data sources like BitBucket or Salesforce, documents, and a place to comment and collaborate with your teammates. Now imagine that you can take that whiteboard anywhere you go on your smartphone, and can access it from any computer through the web. That's Trello!
Trello lets you work more collaboratively and get more done. Trello’s boards, lists, and cards enable you to organize and prioritize your projects in a fun, flexible and rewarding way.
Caffe is a deep learning framework made with expression, speed, and modularity in mind
Expression: models and optimizations are defined as plaintext schemas instead of code.
Speed: for research and industry alike speed is crucial for state-of-the-art models and massive data.
Modularity: new tasks and settings require flexibility and extension.
Openness: scientific and applied progress call for common code, reference models, and reproducibility.
Community: academic research, startup prototypes, and industrial applications all share strength by joint discussion and development in a BSD-2 project.
Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.
Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.
Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. We believe that Caffe is among the fastest convent implementations available.
IBM Sterling Order Management System (OMS) is a comprehensive software solution that brokers orders across many disparate systems, orchestrates and automates cross-channel selling and fulfillment processes, and provides a global view of supply and demand across the supply chain.
It is a comprehensive B2C and B2B order management and fulfillment solution that addresses the complexities of fulfilling orders across multiple channels, while cost-effectively orchestrating global product and service fulfillment across the extended enterprise.
The IBM Sterling OMS solution provides a central source of order information, management, and monitoring, and provides a single order repository to enter, modify, track, cancel and monitor the entire order life cycle in real time. Your company can provide customers information about their orders, from any channel or division, when and where they need it.
In addition, your store personnel, call/contact center staff, website and field sales team can leverage the system to place or modify orders, determine order status, check inventory availability across all locations, and manage the returns process.
Single view of supply and demand across channels
Coordinated, customized fulfilment execution to support omni-channel needs
Single source of order information for accurate and timely updates
Integrated omni-channel order fulfilment processes for seamless customer experience
GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.
GraphQL queries access not just the properties of one resource but also smoothly follow references between them. While typical REST APIs require loading from multiple URLs, GraphQL APIs get all the data your app needs in a single request. Apps using GraphQL can be quick even on slow mobile network connections.
GraphQL APIs are organized in terms of types and fields, not endpoints. Access the full capabilities of your data from a single endpoint. GraphQL uses types to ensure Apps only ask for what’s possible and provide clear and helpful errors. Apps can use types to avoid writing manual parsing code.
GraphQL creates a uniform API across your entire application without being limited by a specific storage engine. Write GraphQL APIs that leverage your existing data and code with GraphQL engines available in many languages. You provide functions for each field in the type system, and GraphQL calls them with optimal concurrency.
Apache Wicket, commonly referred to as Wicket, is a lightweight component-based web application framework for the Java programming language conceptually similar to JavaServer Faces and Tapestry. It was originally written by Jonathan Locke in April 2004. Version 1.0 was released in June 2005. It graduated into an Apache top-level project in June 2007.
Invented in 2004, Wicket is one of the few survivors of the Java serverside web framework wars of the mid 2000's. Wicket is an open source, component oriented, serverside, Java web application framework. With a history of over a decade, it is still going strong and has a solid future ahead. Learn why you should consider Wicket for your next web application.
Wicket uses plain XHTML for templating (which enforces a clear separation of presentation and business logic and allows templates to be edited with conventional WYSIWYG design tools). Each component is bound to a named element in the XHTML and becomes responsible for rendering that element in the final output. The page is simply the top-level containing component and is paired with exactly one XHTML template. Using a special tag, a group of individual components may be abstracted into a single component called a panel, which can then be reused whole in that page, other pages, or even other panels.
Each component is backed by its own model, which represents the state of the component. The framework does not have knowledge of how components interact with their models, which are treated as opaque objects automatically serialized and persisted between requests. More complex models, however, may be made detachable and provide hooks to arrange their own storage and restoration at the beginning and end of each request cycle. Wicket does not mandate any particular object-persistence or ORM layer, so applications often use some combination of Hibernate objects, EJBs or POJOs as models.
In Wicket, all server side state is automatically managed. You should never directly use an HttpSession object or similar wrapper to store state. Instead, state is associated with components. Each server-side page component holds a nested hierarchy of stateful components, where each component’s model is, in the end, a POJO (Plain Old Java Object)
Wicket is all about simplicity. There are no configuration files to learn in Wicket. Wicket is a simple class library with a consistent approach to component structure.
Apache Wicket is a simple and features rich component-based web framework, the real reusable components is the main selling point of this framework. However, due to the big different between component-based and MVC architecture, it makes Wicket hard to learn, especially for those classic MVC developers.