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Python is a dynamically typed programming language.
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Tidelift Icon Tidelift

It's the end of Python 2. Are we prepared?

In just a few short months, Python 2 will officially reach the end of its supported life. 💀 This means that anyone building applications in Python will need to have moved to Python 3 if they want to keep getting updates including, importantly, fixes for any security vulnerabilities in the core of Python or in the standard library. With over 200k Python libraries extant, I have a feeling it’ll be awhile before Python 2 is put out to pasture…

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Machine Learning github.com

The fastest way to build custom ML tools

Streamlit lets you create apps for your machine learning projects with deciptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file. No need to mess with HTTP requests, HTML, JavaScript, etc. All you need is your favorite editor and a browser. Coming soon to a Practical AI podcast near you…

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Ned Batchelder nedbatchelder.com

Why your mock doesn’t work

Mocking is a powerful technique for isolating tests from undesired interactions among components. But often people find their mock isn’t taking effect, and it’s not clear why. Hopefully this explanation will clear things up. Mocking isn’t always the best test isolation technique, but if/when you use it, you might as well use it correctly. Ned’s here to help you do just that.

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Python hypothesis.works

Hypothesis seeks to automate your test process

This interesting testing tool was pointed out to me by Ned Batchelder when he was on The Changelog. It combines human understanding of your problem domain with machine intelligence to improve the quality of your testing process while spending less time writing tests. At its core, Hypothesis is a modern implementation of property based testing, which came out of the Haskell world 20 (!) years ago. Hypothesis runs your tests against a much wider range of scenarios than a human tester could, finding edge cases in your code that you would otherwise have missed. It then turns them into simple and easy to understand failures that save you time and money compared to fixing them if they slipped through the cracks and a user had run into them instead.

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Python nicolas-hahn.com

One program written in Python, Go, and Rust

This is a subjective, primarily developer-ergonomics-based comparison of the three languages from the perspective of a Python developer, but you can skip the prose and go to the code samples, the performance comparison if you want some hard numbers, the takeaway for the tl;dr, or the Python, Go, and Rust diffimg implementations. Not only is this a good way to compare programming languages, but it’s a good way to learn a new language if you’re already familiar with one of the others.

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The Changelog The Changelog #351

Maintainer spotlight! Ned Batchelder

In this episode we’re shinning our maintainer spotlight on Ned Batchelder. Ned is one of the lucky ones out there that gets to double-dip — his day job is working on open source at edX, working on the Open edX community team. Ned is also a “single maintainer” of coverage.py - a tool for measuring code coverage of Python programs. This episode with Ned kicks off the first of many in our maintainer spotlight series where we dig deep into the life of an open source software maintainer. We’re producing this series in partnership with Tidelift. Huge thanks to Tidelift for making this series possible.

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Python anvilventures.com

Reverse engineering the Dropbox client

Dropbox’ concept is still deceptively simple. Here’s a folder. Put files in it. Now it syncs. Move to another computing device. It syncs. The folder and files are there now too! The amount of work that goes on behind the scenes of such an application is staggering though. If you’ve ever wondered how Dropbox works, or you’ve always wanted to reverse engineer some code but didn’t know how to get started, read this. We managed to successfully reverse engineer Dropbox, write decryption and injection tools for it that work with current Dropbox clients based on Python 3.6 releases and successfully reverse engineer features and enable them.

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Netflix Technology Blog Icon Netflix Technology Blog

Python at Netflix

From the Netflix Technology Blog on how they’re using Python. As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members. We use and contribute to many open-source Python packages, some of which are mentioned below. If any of this interests you, check out the jobs site or find us at PyCon. We have donated a few Netflix Originals posters to the PyLadies Auction and look forward to seeing you all there.

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Simon Willison simonwillison.net

Running Datasette on Glitch

We talked with Simon Willison about Datasette on The Changelog #296. For the uninitiated, Datasette is an awesome tool for exploring and publishing data, and now you can play with it on Glitch. The worst part of any software project is setting up a development environment. It’s by far the biggest barrier for anyone trying to get started learning to code. I’ve been a developer for more than twenty years and I still feel the pain any time I want to do something new. Glitch is the most promising attempt I’ve ever seen at tackling this problem. This evening I decided to get Datasette running on it. I’m really impressed with how well it works, and I think Glitch provides an excellent environment for experimenting with Datasette and related tools.

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Python github.com

jumpcutter automatically edits videos for you

If necessity is the mother of invention, who is its father? My vote: laziness Jumpcutter was invented when some Stanford CS students had to catch up on hours of recorded lectures prior to the exam, but didn’t want to have to watch all of the non-talking parts. What resulted was a super useful tool that can be used for vloggers, stop-motion video, and more. The whole story is told in this highly entertaining video on YouTube.

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Python github.com

GIPHY's celebrity-detecting deep learning model 🕵️‍♀️

GIPHY is proud to release our custom machine learning model that is able to discern over 2,300 celebrity faces with 98% accuracy. The model was trained to identify the most popular celebs on GIPHY, and can identify and make predictions for multiple faces across a sequence of images, like GIFs and videos. Give it a try on the demo page or download the model yourself and follow along with the examples.

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