A Jujutsu tutorial that requires no previous experience with Git or other version control systems.

In functional programming,
combinator libraries refer to a design style that emphasizes bottom-up program construction.
Such libraries define a few core data types
and provide constructors—functions that create initial objects—and combinators—functions that build larger objects from smaller pieces.

Combinators enable the programmer to use intuitive visual and spatial reasoning
that’s vastly more powerful than linear language processing.
As a result, solving problems with combinators feels like playing with lego pieces.

When people say Rust is a “safe language”, they often mean memory safety.
And while memory safety is a great start, it’s far from all it takes to build robust applications.
Memory safety is important but not sufficient for overall r…

Solving AoC on the DS with Rust.

Reading through the Emacs 30 NEWS file and picking
out the stuff I think is the most interesting.

Code discussions contain relevant information. Isn’t it a shame that we
keep these in the centralized GitHub/GitLab servers, far away from our
decentralized Git code? As soon as we move provider, we’ll lose all old
discussions! And how do you ever find the pull requests back from 5
years ago? Symfony has implemented a lightweight solution to this problem
years ago using a less-known feature of Git: Git Notes.

The expression problem describes how most types can easily be extended with new ways to produce the type or new ways to consume the type, but not both. When abstract syntax trees are defined as an algebraic data type, for example, they can easily be extended with new consumers, such as print or eval, but adding a new constructor requires the modification of all existing pattern matches. The expression problem is one way to elucidate the difference between functional or data-oriented programs (easily extendable by new consumers) and object-oriented programs (easily extendable by new producers). This difference between programs which are extensible by new producers or new consumers also exists for dependently typed programming, but with one core difference: Dependently-typed programming almost exclusively follows the functional programming model and not the object-oriented model, which leaves an interesting space in the programming language landscape unexplored. In this paper, we explore the field of dependently-typed object-oriented programming by deriving it from first principles using the principle of duality. That is, we do not extend an existing object-oriented formalism with dependent types in an ad-hoc fashion, but instead start from a familiar data-oriented language and derive its dual fragment by the systematic use of defunctionalization and refunctionalization. Our central contribution is a dependently typed calculus which contains two dual language fragments. We provide type- and semantics-preserving transformations between these two language fragments: defunctionalization and refunctionalization. We have implemented this language and these transformations and use this implementation to explain the various ways in which constructions in dependently typed programming can be explained as special instances of the phenomenon of duality.

In this post, I talk about pleasant but seemingly minor features in personal sites

Most programming language ecosystems provide assert functions in their testing libraries but not Go's. Go's standard testing package follows a more direct and to-the-point approach.

Aurora: An extensible, Python-based static site generator.

Official website of the Pimalaya project.

Between our ESP32 prokaryotic organisms and our 24/7 Internet-enabled megafauna servers, there exists a vast and loosely-defined ecosystem of things the B2B world likes to call computer appliances. Picture a bespoke Pi 4 packaged up neatly with some Python scripts, a little fancy plastic embossing, and maybe a well-guarded id_ed25519.pub in case you end up in hot water during the (long - very long, stable cash flow for generations long) maintenance contract, and you’re in the ballpark.

There’s plenty of information out there on how to scale Django to handle numerous requests per second, but most of it…