You can write your Webpack config in Typescript, and it’ll save you a huge amount of pain. Webpack’s docs would lead you to believe that using Typescript requires a hacky customized set up, but in…
On its third major release, Webpack introduced a new feature: scope hoisting. Many developers are already exposing data showing great positive impacts on the initial execution time of their bundles…
RequireJS is a JavaScript file and module loader. It is optimized for in-browser use, but it can be used in other JavaScript environments, like Rhino and Node. Using a modular script loader like RequireJS will improve the speed and quality of your code.
webpack is a module bundler. It packs CommonJs/AMD modules i. e. for the browser. Allows to split your codebase into multiple bundles, which can be loaded on demand.
JavaScript modules are now supported in all major browsers! This article explains how to use JS modules, how to deploy them responsibly, and how the Chrome team is working to make modules even better in the future.
Learn about Python Modules, Standard Library Modules, How Python Search for a Module, Creating, Importing, Installing and Reloading Modules, Using Aliases
Enable ES modules in Node today with a new opt-in, spec-compliant, ECMAScript (ES) module loader that enables a smooth transition between Node and ES module formats with near built-in performance!
A few months ago I wrote an article describing the various differences that exist between Node.js CommonJS modules and the new ES6 Module system; and described a number of challenges inherent with…
Starting with version 8.5.0, Node.js supports ES modules natively, behind a command line option. Most of the credit for this new functionality goes to Bradley Farias. This blog post explains the details.
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Getting comments on your articles and such is great — you know people are reading your stuff, helpful visitors will expand, refine and correct your posts. It’s all good. Except when it isn’t. When it isn’t good, you get tons of spam submissions and you feel like you’re spending all your time sifting through them.
This document formally specifies the semantics of local modules and packages - dynamically typed modules that are first-class values - as an extension to the functional programming language Standard ML. The language thus defined is a substantial subset of a larger extension of Standard ML, a language known as Alice ML. Packages are the central feature of Alice ML that enables support for typed open programming.
The need for flexible forms of serialisation arises under many circumstances, e.g. for doing high-level inter-process communication or to achieve persistence. Many languages, including variants of ML, thus offer pickling as a system service, but usually in a both unsafe and inexpressive manner, so that its use is discouraged. In contrast, safe generic pickling plays a central role in the design and implementation of Alice ML: components are defined as pickles, and modules can be exchanged between processes using pickling. For that purpose, pickling has to be higher-order and typed (HOT), i.e. embrace code mobility and involve runtime type checks for safety. We show how HOT pickling can be realised with a modular architecture consisting of multiple abstraction layers for separating concerns, and how both language and implementation benefit from a design consistently based on pickling.
Despite its powerful module system, ML has not yet evolved for the modern world of dynamic and open modular programming, to which more primitive languages have adapted better so far. We present the design and semantics of a simple yet expressive first-class component system for ML. It provides dynamic linking in a type-safe and type-flexible manner, and allows selective execution in sandboxes. The system is defined solely by reduction to higher-order modules plus an extension with simple module-level dynamics, which we call packages. To represent components outside processes we employ generic pickling. We give a module calculus formalising the semantics of packages and pickling.
ML modules provide hierarchical namespace management, as well as fine-grained control over the propagation of type information, but they do not allow modules to be broken up into separately compilable, mutually recursive components. Mixin modules facilitate recursive linking of separately compiled components, but they are not hierarchically composable and typically do not support type abstraction. We synthesize the complementary advantages of these two mechanisms in MixML. A MixML module is like an ML structure with some components specified but not defined unifing the ML structure and signature languages into one. MixML seamlessly integrates hierarchical composition, translucent MLstyle data abstraction, and mixin-style recursive linking.Tthe design of MixML is minimalist emphasizing how all the interesting features of the ML module system can be understood simply as stylized uses of a small set of orthogonal underlying constructs, with mixin composition playing a central role.
My thesis clarified the relationships between existing dialects of ML and studied extending ML with support for recursive modules. Solve a critical problem involving the interaction of recursion and data abstraction known as the double vision problem. In other work with Bob Harper and Manuel Chakravarty, I showed how to extend ML with support for overloading and ad hoc polymorphism through Haskell-style type classes. This work exposes connections between ML modules and Haskell type classes, resulting in a unifying account of the two features Andreas and I have developed a novel module system design that addresses one of the key remaining problems with recursive modules, namely separate compilation. This design seamlessly integrates elements of both traditional ML module systems and Bracha-style mixin modules, resulting in a minimalist account of the ML module system that unifies features. A prototype is available
The ML Basis system extends Standard ML to support programming-in-the-very-large, namespace management at the module level, separate delivery of library sources, and more. While Standard ML modules are a sophisticated language for programming-in-the-large, it is difficult, if not impossible, to accomplish a number of routine namespace management operations when a program draws upon multiple libraries provided by different vendors. The ML Basis system is a simple, yet powerful, approach that builds upon the programmer's intuitive notion of the top-level environment (a basis). Here are some of the key features of the ML Basis system: 1. Explicit file order 2. Implicit dependencies 3.Scoping and renaming: The ML Basis system provides mechanisms for limiting the scope of (i.e, hiding) and renaming identifiers. 4.No naming convention for finding the file that defines a module. To import a module, its defining file must appear in some ML Basis file.
"The GeoRSS Module allows users to add geographic point data to outgoing RSS feeds. It supports two formats - Simple GeoRSS and GML - and can be administered at admin/settings/georss."
"It's a module that allows you to work with mindmaps in Drupal that are compatible with Freemind. ... But it is much more than a mindmap tool: the module integrates with Drupal services ... it can render any view into mindmap branches ... you can add individual users, nodes or comments as a freemind branch ... if you use a view with an argument it will try to fill that argument using the data from the mindmap node for which you are trying to add that branch" * a freemind node that was created from a Drupal object has a hyperlink to that Drupal node
Impala allows you to divide a large Spring-based application into a hierarchy of modules. These modules can be dynamically added, updated or removed.
Because Impala-based applications are genuinely modular, they are much easier to maintain than vanilla Spring applications.
Impala radically boosts productivity of Spring application development. This is enabled by the dynamic module loading capability, the seamless integration with Eclipse, and the efficient mechanisms for running Spring integration tests, both individually and within suites. When writing applications you only rarely need to restart your JVM, allowing your application changes to be reflected almost instantly. No long restart waits required!
Impala also features a build system, based on ANT, and dependency management capabilities, which you can optionally use.
For up to date news on development of Impala, see the project blog.
Impala is developed under the Apache Licence, Version 2.
Impala 1.0M5 introduces a number of API and configuration improvements, making the framework easier to configure and extend, and usable in a wider range of environments. Following 1.0M5, only minor changes in internal APIs are now expected prior to the 1.0 final release.
The 1.0M5 release makes it much easier to configure Impala-based applications by supporting a property-based configuration. While Impala is still very heavily based on the Spring framework, 1.0M5 now also makes it possible to plug in other runtime frameworks into Impala's dynamic module loading mechanism.
The full list of issues for milestone 1.0M5 is here: http://code.google.com/p/impala/issues/list?q=label:Milestone-Release1.0M5&can=1.
Note that there are a number of package name and configuration changes in this release. If you are upgrading from an earlier release, you will probably wish to check the backward incompatible changes for 1.0M5 and an example migration sequence for this release.
If you're interested in getting involved in the Impala project, please take a look at this page: http://code.google.com/p/impala/wiki/GetInvolved.
One thing I really love with the Python programming language is its incredible extensibility. Here’s a list of 50 awesome modules for Python, covering almost all needs: Databases, GUIs, Images, Sound, OS interaction, Web, and more.
One thing I really love with the Python programming language is its incredible extensibility. Here’s a list of 50 awesome modules for Python, covering almost all needs: Databases, GUIs, Images, Sound, OS interaction, Web, and more.
Preliminary reading
Arjun Appadurai (Ed). 1986. The social life of things: commodities in cultural perspective. Cambridge University Press. Cambridge. Esp. chapters 1 and 2.
Alfred Gell. 1998. Art and Agency. Clarendon Press. Oxford.
Alfred Gell. 1999. The art of anthropology: essays and diagrams. Athlone. London. Esp. chapters 5 and 6.
Bruno Latour. 1993. We have never been modern. Harvard University Press. Cambridge. Mass.
Christopher Pinney and Nicholas Thomas (Ed). 2001. Beyond aesthetics: art and the technologies of enchantment. Berg. Oxford.
C. Wimmer, S. Brunthaler, P. Larsen, and M. Franz. Proceedings of the 11th Annual International Conference on Aspect-oriented Software Development, page 203--214. New York, NY, USA, ACM, (2012)