This is a little framework (just a collection of LESS/SCSS/Stylus mixins) by Tyler Tate for creating responsive grid-based CSS layouts: you declare the layout with variables in a CSS pre-processor, and then it calculates the actual pixel widths. It is what I've used for my few designs, but I might try out CSS flexbox in the future...

Here's the project's github (there are several pending PRs, and Tyler hasn't made any merges since July 2015): https://github.com/tylertate/semantic.gs

Home page of Ingemar Ragnemalm, the author of the Sprite Animation Toolkit and several games for MacOS, and a major contributor to the book "Tricks of the Mac Game Programming Gurus" (1995).

It's funny because it's true.

A more serious take: https://medium.com/front-end-hacking/how-it-feels-to-learn-javascript-in-2017-a934b801fbe

Guido recounts some of the history of adding closures to Python.

Eli Bendersky takes the derivative of a matrix.

molsson compares several JavaScript testing frameworks.

I just discovered Don Lancaster's website (of TTL Cookbook fame). This could keep me busy for a while.

He even keeps an active weblog: http://www.tinaja.com/whtnu17.shtml

A nice writeup (with illustrations) on some CSS rendering optimizations.

Brett Cannon traces the evolutionary path from Python 2's generators to Python 3.5's async/await coroutines.

I haven't used Julia for anything yet, but I like that it not only optimizes explicit for-loops, but it also fuses code using vectorized-syntax even for user-defined functions and types. Compared to Matlab/Octave, Python, and R where loops are very (very)* slow and vectorized code is only fast for certain operations and library functions, it is appealing.

Julia also has annotations that can be use to explicitly tell the compiler to turn off bounds checking and emit SIMD instructions for loops.

• The Numba package brings JIT capability to CPython (and so numpy), and probably compares most favorably to Julia than the other languages. Octave also has an experimental JIT compiler (seems like a lot of overhead going from interpreter to machine code), as do recent versions of Matlab (since R2015b). R since 2.14.0 includes the 'compiler' package, a bytecode interpreter and JIT (which compiles to bytecode, not machine code), which probably helps for long-running loops.

Another good die-exploring post from Ken Shirriff. This time he identifies a counterfeit/mis-labeled chip by examining its die photos.