The goal of this article is to provide a historical context of how JavaScript tools have evolved to what they are today in 2017. We’ll start from the beginning and build an example website like the dinosaurs did — no tools, just plain HTML and JavaScript. Then we’ll introduce different tools incrementally to see the problems that they solve one at a time. With this historical context, you’ll be better able to learn and adapt to the ever-changing JavaScript landscape going forward. Let’s get started!

A target image is provided as input. The algorithm tries to find the single most optimal shape that can be drawn to minimize the error between the target image and the drawn image. It repeats this process, adding one shape at a time. Around 50 to 200 shapes are needed to reach a result that is recognizable yet artistic and abstract.

In my first decade writing Makefiles, I developed the bad habit of liberally using GNU Make’s extensions. I didn’t know the line between GNU Make and the portable features guaranteed by POSIX. Usually it didn’t matter much, but it would become an annoyance when building on non-Linux systems, such as on the various BSDs. I’d have to specifically install GNU Make, then remember to invoke it (i.e. as gmake) instead of the system’s make.

I’ve since become familiar and comfortable with make’s official specification, and I’ve spend the last year writing strictly portable Makefiles. Not only has are my builds now portable across all unix-like systems, my Makefiles are cleaner and more robust. Many of the common make extensions — conditionals in particular — lead to fragile, complicated Makefiles and are best avoided anyway. It’s important to be able to trust your build system to do its job correctly.

This tutorial should be suitable for make beginners who have never written their own Makefiles before, as well as experienced developers who want to learn how to write portable Makefiles.

Hello! This is part one of a short series of posts on writing a simple raytracer in Rust. I’ve never written one of these before, so it should be a learning experience all around.

This is part one of a short series of posts on writing a simple raytracer in Rust. I’ve never written one of these before, so it should be a learning experience all around.

Every line of code has some probability of having an undetected flaw that will be seen in production. Process can affect that probability, but it cannot make it zero. Large diffs contain many lines, and therefore have a high probability of breaking when given real data and real traffic.

Welcome to fast.ai’s 7 week course, Practical Deep Learning For Coders, Part 1, taught by Jeremy Howard (Kaggle’s #1 competitor 2 years running, and founder of Enlitic). Learn how to build state of the art models without needing graduate-level math—but also without dumbing anything down. Oh and one other thing… it’s totally free!

Pharo is a pure object-oriented programming language and a powerful environment, focused on simplicity and immediate feedback (think IDE and OS rolled into one).

This article outlines the scale of that codebase and details Google's custom-built monolithic source repository and the reasons the model was chosen. Google uses a homegrown version-control system to host one large codebase visible to, and used by, most of the software developers in the company. This centralized system is the foundation of many of Google's developer workflows. Here, we provide background on the systems and workflows that make feasible managing and working productively with such a large repository. We explain Google's "trunk-based development" strategy and the support systems that structure workflow and keep Google's codebase healthy, including software for static analysis, code cleanup, and streamlined code review.

Our extensive use of Perl to build many of our internal services often comes as a surprise to many and we can understand why. Perl is a dinosaur among mainstream programming languages. It lacks the glamour that other, relatively younger languages have. There is also a common misconception in the programming world that modern software engineering practices cannot be followed with a language like Perl. In this post, we hope to debunk that myth. We want to give you a glimpse of the developer experience (DX) here at Semantics3 where we write a lot of Perl code but still manage to employ the latest engineering best-practices. We would like to highlight that we are able to do so with the help of a tool-chain written entirely in Perl.