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 is a query language created by Facebook in 2012 which provides a common interface between the client and the server for data fetching and manipulations.
The client asks for various data from the GraphQL server via queries. The response format is described in the query and defined by the client instead of the server: they are called client‐specified queries.
The structure of the data is not hardcoded as in traditional REST APIs - this makes retrieving data from the server more efficient for the client.
GraphQL is a different way of structuring the contract between client and server. The servers publish a set of rules specific to an application. GraphQL then supplies the language to query your data for the correct answer, within the constraints of those rules.
Django CMS is a modern web publishing platform built with Django, the web application framework “for perfectionists with deadlines”.
django CMS offers out-of-the-box support for the common features you’d expect from a CMS, but can also be easily customized and extended by developers to create a site that is tailored to their precise needs.
Integrate Django applications painlessly; build sophisticated sites with easy-to-use tools.
Nagare is a free and open-source web framework for developing web applications in Stackless Python. This allows web applications to be developed in much the same way as desktop applications, for rapid application development.
Nagare is a components based framework: a Nagare application is a composition of interacting components each one with its own state and workflow kept on the server.
Each component can have one or several views that are composed to generate the final web page. This enables the developers to reuse or write highly reusable components easily and quickly.
Nagare is also a continuation-based web framework which enables to code a web application like a desktop application, with no need to split its control flow in a multitude of controllers and with the automatic handling of the back, fork and refresh actions from the browser.
Its component model and use of the continuation come from the famous Seaside Smalltalk framework.
Furthermore, Nagare integrates the best tools and standard from the Python world. For example:
WSGI: binds the application to several possible publishers,
lxml: generates the DOM trees and brings to Nagare the full set of XML features (XSL, XPath, Schemas …),
setuptools: installs, deploys and extends the Nagare framework and the Nagare applications too,
Delphi is both an object oriented programming language (OOP) and an Integrated Development Environment (IDE). Published by the Embarcadero company (formerly CodeGear and more formerly Borland), Delphi is an alternative to language such as Visual Basic offering development with both rapidity and good quality.
Delphi includes the RunTime Library (RTL) that provides basic functionality across all the platforms. For Windows it provides the Visual Component Library (VCL), and for cross platform development it includes FireMonkey (FMX).
Delphi includes a code editor, a visual designer, an integrated debugger, a source code control component, and support for third-party plugins. The code editor features Code Insight (code completion), Error Insight (real-time error-checking), and refactoring.
The visual forms designer has traditionally used Visual Component Library (VCL) for native Windows development, but the FireMonkey (FMX) platform was later added for cross-platform development. Database support in Delphi is very strong. A Delphi project of a million lines to compile in a few seconds – one benchmark gave 170,000 lines per second.
It provides interfaces for the programmer to build an application using the Extensible Markup Language (XML), Extensible Stylesheet Language (XSL), Simple Object Access Protocol (SOAP), and Web Services Description Language (WSDL).
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.