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Python

Python is a dynamically typed programming language.
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Brett Cannon snarky.ca

What exactly is Python?

Brett Cannon, writing for his personal blog:

It’s no secret that I want a Python implementation for WebAssembly. It would not only get Python into the browser, but with the fact that both iOS and Android support running JavaScript as part of an app it would also get Python on to mobile. That all excites me.

But when thinking about the daunting task of creating a new implementation of Python, my brain also began asking the question of what exactly is Python?

What follows from this point in Brett’s post is a stream of consciousness writing style of question and answer, back and forth, iteration over all the points of what makes Python be Python in an attempt to consider what it might take to “compile Python down to WebAssembly.”

Python github.com

A research framework for reinforcement learning

Acme is a library of reinforcement learning (RL) agents and agent building blocks. Acme strives to expose simple, efficient, and readable agents, that serve both as reference implementations of popular algorithms and as strong baselines, while still providing enough flexibility to do novel research. The design of Acme also attempts to provide multiple points of entry to the RL problem at differing levels of complexity.

Lj Miranda ljvmiranda921.github.io

Generate 8-bit avatars using Conway's Game of Life

Lj Miranda:

I made a website that generates cute 8-bit avatars using Conway’s Game of Life. Simply type in your name, and it will create a unique sprite just for you! Try out the changelog, jerod santo, or adam stacoviak!

Conway’s Game of Life is something that we consider as a Cellular Automaton. It was a mathematical model created by the mathematician John Conway, who, unfortunately, passed away a few weeks ago due to the coronavirus. I highly encourage you to know more about Conway, he’s such an interesting and unique individual!

Built with Vue and Python. Source code here.

Opensource.com Icon Opensource.com

The real impact of canceling PyCon due to COVID-19

An interview with Ewa Jodlowska on how the Python Software Foundation is responding to the cancelation of in-person events.

Turns out ~63% of the PSF’s 2020 revenue was projected to come from PyCon. That’s a massive hit to take. Read the entire interview to learn what they’re doing to diversify, some silver linings that have come from this, and how you can pitch in.

(The tail end of Adam’s conversation with Duane O’Brien focused on the FOSS Responders initiative which was purpose-built to help out orgs like the PSF.)

Python github.com

A modular toolbox for accelerating meta-learning research

Meta-Blocks is a modular toolbox for research, experimentation, and reproducible benchmarking of learning-to-learn algorithms. The toolbox provides flexible APIs for working with MetaDatasets, TaskDistributions, and MetaLearners (see the figure below). The APIs make it easy to implement a variety of meta-learning algorithms, run them on well-established and emerging benchmarks, and add your own meta-learning problems to the suite and benchmark algorithms on them.

This repo is still under “heavy construction” (a.k.a. unstable) so downloader beware, but it’s worth a star/bookmark for later use.

A modular toolbox for accelerating meta-learning research

Lj Miranda ljvmiranda921.github.io

Why do we need Flask, Celery, and Redis?

Lj Miranda explains their architecture decisions with a metaphor I’ve never seen applied to software systems…

In this blogpost, I’ll explain why we need Flask, Celery, and Redis by sharing my adventures in buying McNuggets from Mcdonalds. Using these three (or technologies similar to them) is integral to web backend development so that we can scale our applications.

I love these “why we did X” style posts where folks share their real-world decision making processes and how they played out over time.

Python github.com

Diagrams as Python code

Diagrams lets you draw the cloud system architecture in Python code. It was born for prototyping a new system architecture design without any design tools. You can also describe or visualize the existing system architecture as well. Diagrams currently supports four major providers: AWS, Azure, GCP and Kubernetes.

I’ve never found a diagramming tool I’ve enjoyed using. The idea of just writing some code and letting a tool do the drawing might be just what the doctor ordered. Start with the quick start.

Diagrams as Python code

Amazon Web Services github.com

The missing cron CLI for AWS Cloudwatch and Lambda

Do you have an AWS account? Great. Do you want to run cron jobs in the cloud?

Cronyo provides A simple CLI to manage your cron jobs on AWS.

In addition, Cronyo can instantly deploy a couple of super-simple, helpful and secure lambda functions to perform HTTP GET/POST requests for you. So if you need to trigger any webhooks on schedule, an AWS account and Cronyo is all you need :)

Python github.com

Efficient, reusable components for 3D computer vision research with PyTorch

PyTorch3d is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data. For this reason, all operators in PyTorch3d:

  • Are implemented using PyTorch tensors
  • Can handle minibatches of hetereogenous data
  • Can be differentiated
  • Can utilize GPUs for acceleration

Get started with tutorials on deforming a sphere mesh into a dolphin, rendering textured meshes, camera position optimization, and more.

Victor Zhou victorzhou.com

A gentle introduction to Visual Question Answering using neural networks

Show us humans a picture of someone in uniform on a mound of dirt throwing a ball and we will quickly tell you we’re looking at baseball. But how do you make a computer come to the same conclusion?

Visual Question Answering

In this post, we’ll explore basic methods for performing VQA and build our own simple implementation in Python

Python github.com

Exploring and understanding Python through surprising snippets

Here’s a fun project attempting to explain what exactly is happening under the hood for some counter-intuitive snippets and lesser-known features in Python.

While some of the examples you see below may not be WTFs in the truest sense, but they’ll reveal some of the interesting parts of Python that you might be unaware of. I find it a nice way to learn the internals of a programming language, and I believe that you’ll find it interesting too!

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