Species that aren't closely related can look identical thanks to a bizarre trick of evolution. A new study reveals part of the mechanism for creating nature's unrelated twins.
Source: Evolution Keeps Repeating Itself. Scientists Are Starting to Understand Why, an article by Maddie Bender.
In the early afternoon we went to Hook of Holland. I took Alice on my bike and Esme took Adam on hers.
On the way back I took a photo of a landscape with bright green moss, close to the dunes of Hook of Holland. I had already seen this landscape on the way to the pier, but decided to take photos on the way back.
On our way back we decided to pay a short visit to garden centre "De Carlton" and have a look at the plants on display.
At the garden centre I took a photo of a hairy mock orange, Philadelphus incanus, according to the PlantSnap app on my iPhone 5.
mypy is a static type checker. This means it does not run during the code execution, so it’s mostly useful during development, much like tests. It relies on manual annotations in the code called type hints, which identify the types of arguments, return types, internal variables and member variables.
Source: Python Type Hints, an article by Guilherme Kunigami.
While talking about thinking about tests and testing in software engineering recently, I’ve come to the conclusion that there are (at least) two major ideas and goals that people have when they test or talk about testing. This post aims to outline what I see as these two schools, and explore some reasons engineers coming from these different perspectives can risk talking past each other.
Two kinds of testing, an article by Nelson Elhage.
Machine learning is one of the hottest topics in computer science today. And not without a reason: it has helped us do things that couldn’t be done before like image classification, image generation and natural language processing. But all of it boils down to a really simple concept: you give the computer data and the computer then finds patterns in that data. This is called “learning” or “training”, depending on your point of view. These learnt patterns can be extrapolated to make predictions. How? That’s what we are looking at today.
Source: An introduction to machine learning through polynomial regression, an article by Rick Wierenga.
I’ve spent a good deal of my professional life arguing against using protobuffers. They’re clearly written by amateurs, unbelievably ad-hoc, mired in gotchas, tricky to compile, and solve a problem that nobody but Google really has. If these problems of protobuffers remained quarantined in serialization abstractions, my complaints would end there. But unfortunately, the bad design of protobuffers is so persuasive that these problems manage to leak their way into your code as well.
Source: Protobuffers Are Wrong, an article by Sandy Maguire.
Haskell is a programming language that a lot of people use to learn functional programming and solve programming exercises. But not as many take the leap to use Haskell for practical projects. To try to help to bridge this gap, I will walk through how to create a "practical" Haskell application. This is of course not the only way to do it and there are alternatives to every approach I am using, but this is what I find most suitable for a small easy-to-understand project.
Source: Making a small Haskell application, an article by Morten Kolstad.
I’ve taught functional programming for years now, each time experimenting with different ways of teaching core concepts. Over time, I’ve collected and converged on simple (but reasonably precise) pedagogical definitions for a range of functional concepts.
In this post, I’ll share those definitions with you, in my first ever, Glossary of Functional Programming. Enjoy!
Source: A Glossary of Functional Programming, an article by John A De Goes.