This program consists of a single print statement wrapped in a while loop. You would not think that this would amount to much, but you would be very, very wrong. A clue to what is happening and how this works is encoded in the ASCII art of the program source.

Codon is a high-performance Python compiler that compiles Python code to native machine code without any runtime overhead. Typical speedups over Python are on the order of 100x or more, on a single thread. Codon supports native multithreading which can lead to speedups many times higher still.

Comprehensions are a fantastic language feature in Python. They are an elegant alternative to manually constructing and populating data structures. Comprehensions are declarative – they just say what they are, as opposed to the implicit logic of manual looping. When it comes to simple object creation, comprehension should be used whenever possible. This goes not just for lists, but also for dictionaries and sets.

However, a widely perceived drawback to comprehensions is that they are harder to debug. When something goes wrong with a manual loop, the first thing to do is to print out the iterated values as they turn up. But the values of a list comprehension can’t be accessed, so print-debugging isn’t possible. To deal with this, it’s common to unravel the comprehension into a manual loop. Manual loops are uglier and more complicated and more error-prone than comprehensions, but that’s the price that must be paid for debuggability.

A collection of CSS3 powered hover effects to be applied to links, buttons, logos, SVG, featured images and so on. Easily apply to your own elements, modify or just use for inspiration. Available in CSS, Sass, and LESS.

There is an area of Python that many developers have problems with. This is an area that has seen many different solutions pop up over the years, with many different opinions, wars, and attempts to solve it. Many have complained about the packaging ecosystem and tools making their lives harder. Many beginners are confused about virtual environments. But does it have to be this way? Are the current solutions to packaging problems any good? And is the organization behind most of the packaging tools and standards part of the problem itself?

Join me on a journey through packaging in Python and elsewhere. We’ll start by describing the classic packaging stack (involving setuptools and friends), the scientific stack (with conda), and some of the modern/alternate tools, such as Pipenv, Poetry, Hatch, or PDM. We’ll also look at some examples of packaging and dependency-related workflows seen elsewhere (Node.js and .NET). We’ll also take a glimpse at a possible future (with a venv-less workflow with PDM), and see if the PyPA agrees with the vision and insights of eight thousand users.

The art of good programming depends upon the discipline of the programmer, no matter what language is being used. The purpose of object oriented programming (OOP) is to produce well designed reusable code. In principle OOP can be done in any language, even assembly. This is because all OO language compilers/assemblers (e.g. C++) ultimately translate the high level constructs of the language into machine language. Thus there is a mapping from an object oriented semantics onto the instruction and data arrays that are executable images.

Here we will present a design and implementation method for producing OO code in the C language. It turns out that using this methodology will strongly improve your overall program design and implementation just as you expect when programming in a native OO language like Java or C++. When working in C, however, the discipline applied to producing good designs comes from the programmer and not from the language itself.

The song '99 bottles of beer' programmed in more than 600 different programming languages, from APL to BASIC, to Brainfuck, INTERCAL, FORTRAN, C++ or Java... This project is similiar to the Rosetta stone

Great essay with lots of interesting pointers.

Jupyter notebooks are mostly known for their web-based user interface, such as JupyterLab or the Classic Notebook.

rr aspires to be your primary C/C++ debugging tool for Linux, replacing — well, enhancing — gdb. You record a failure once, then debug the recording, deterministically, as many times as you want. The same execution is replayed every time.

rr also provides efficient reverse execution under gdb. Set breakpoints and data watchpoints and quickly reverse-execute to where they were hit.

Ah, C. Still the language of modern high-performance computing.

C is the lowest-level language most programmers will ever use, but it more than makes up for it with raw speed. Just be aware of its manual memory management and C will take you as far as you need to go.