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Even if you leave your child to do things on their own, the smartphone safety net is with them. When we were on our own, the nearest payphone might have been several blocks away. So you needed to think for yourself, you could not check on Facebook for the latest blither from someone else. Our children do not have much exposure to "unspecified risk"; not danger, but the risk of making a poor choice. Like bringing home the wrong dish soap, or laundry detergent because you forgot to write it down and had to make a choice or come home without it. Then as a reminder, you had to walk back and get the right one. After a couple of times, you learned to be certain what you were shopping for. It is this lack of "unspecified risk" that prevents our kids from learning how to think on their feet and make "good choices" on their own! By trial and error. We survived, why won't they. But try to take their technology away, and you would think they were losing an eye!

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Self reliance is liberating.

I love this analogy that occurs in nature.

When a butterfly is about to emerge from its cocoon, it undergoes a fight for life with the cocoon that protected them while developing. The fight strengthens their wings. If one were to cut open the cocoon to "help" the butterfly it will emerge but not be able to fly.

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The authors are conflating different contemporary social phenomena in a way that weakens their argument. People of color talking about the microacressions they experience in our society has nothing to do with resilience or being left to handle enough conflict as children. Allowing children freedom has nothing to do with college students being permitted to wear offensive costumes, as Erika Christakis misleadingly frames it. The cause of giving children more autonomy does not need to get conflated with racism and white fragility.

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statement just introduced, Python knows the usual control flow statements known from other languages, with some twists.

Perhaps the most well-known statement type is the if statement. For example:

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The for statement in Python differs a bit from what you may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), Python’s for statement iterates over the items of any sequence (a list or a string), in the order that they appear in the sequence. For example (no pun intended):

>>>

If you need to modify the sequence you are iterating over while inside the loop (for example to duplicate selected items), it is recommended that you first make a copy. Iterating over a sequence does not implicitly make a copy. The slice notation makes this especially convenient:

>>>

With for w in words: , the example would attempt to create an infinite list, inserting defenestrate over and over again.

If you do need to iterate over a sequence of numbers, the built-in function range() comes in handy. It generates arithmetic progressions:

>>>

The given end point is never part of the generated sequence; range(10) generates 10 values, the legal indices for items of a sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the ‘step’):

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developers, but I believe it can be used with other frameworks as well. It’s just a handbook for writing scalable and maintainable single-page-applications. It’s important to note, that this is not the only way, but it’s a way that works for me in quite a few situations.

A lot of developers are having trouble writing large-scale, maintainable single-page-applications. This results in early technical debt, a lot of frustration when fixing bugs, an eternal struggle when writing unit tests and creating reusable chunks of code…

One of the biggest challenges might be: extending existing logic and writing new features based on a tricky foundation.

Single-page-applications are rather new, and especially older frameworks gave you 100 different ways to design an application. There wasn’t any structure, encapsulation and everything was tightly coupled to each other.

Most of the time in the beginning of a project the development process went blazingly fast. But after a few developers, features, refactors, the code started to become less maintainable. It started to look like spaghetti. Frameworks have matured a lot, but it’s also important that the architecture of the software you write matures along with the process.

To explain the architecture where this article is all about, here’s a reminder of how a web-application in 2016 might look like. These are concepts that your applications should rely on these days. It doesn’t matter whether using React , Angular 2 or something else. These principles exist to make sure that your web architecture becomes maintainable.

SPA-technologies like React and Angular2 make us use components. A component is a piece of HTML and JavaScript combined. We don’t want to use standalone views or standalone controllers anymore. They can grow exponentially and are related to each other in a way that is hard to maintain.

So basically the philosophy here is: everything should be a component , even your pages and your application. An application could look like this:

everything should be a component

Some very simple but very important tips in designing components:

If you are new to designing components, this article might be of help: components demystified

Note: Smart components are also called structural components, containers or stateful components

Before, we used to update application state in a rather inefficient manner:

Did you ever tried to make sibling components communicate with each other? Sometimes it seems pragmatic to do so, but please don’t.

This is bad design! It’s almost impossible to see in which direction the data flows. I’s also very hard to maintain this code, fix bugs in it or develop features. What we do want, is a unidirectional dataflow like or Redux .

Although you can’t import models at the module-level where AppConfig classes are defined, you can import them in ready() , using either an import statement or get_model() .

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, you can refer to the sender by its string label instead of using the model class itself.

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Note

In the usual initialization process, the method is only called once by Django. But in some corner cases, particularly in tests which are fiddling with installed applications, might be called more than once. In that case, either write idempotent methods, or put a flag on your classes to prevent re-running code which should be executed exactly one time.

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Python packages without an file are known as “namespace packages” and may be spread across multiple directories at different locations on (see PEP 420 ).

Django applications require a single base filesystem path where Django (depending on configuration) will search for templates, static assets, etc. Thus, namespace packages may only be Django applications if one of the following is true:

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The application registry provides the following public API. Methods that aren’t listed below are considered private and may change without notice.

Boolean attribute that is set to True after the registry is fully populated and all AppConfig.ready() methods are called.

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if no such application exists.

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