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.

I have been using IntelliJ Idea at work for a decade or so by now, and it’s been a reliable companion. JetBrains IDEs have a bit of a reputation for being slow, but their feature set is incredible: powerful refactoring tools, a great VCS UI (though I like magit even more!), a huge number of supported frameworks, integration with just about any testing library for any language, code coverage tools, powerful debuggers, etc.

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…

A well-equipped ASCII diagram builders freely available on the internet.
It stresses portability, simplicity, and immediateness.

Storage for private, distributed, offline-first applications. Earthstar is a specification and JavaScript library for building connected applications owned and run by their users.

Solving AoC on the DS with Rust.

I recently wrote about Async Pool, one of my favorite JavaScript / TypeScript helpers, and today I want to share an even simpler yet extremely useful utility

This blog series creates a small operating system in the Rust programming language. Each post is a small tutorial and includes all needed code.

A lightning post on logic programming in Haskell to construct a workout weekly schedule given the set of exercises, days and constraints.

Using systemd to avoid having to write some risky code

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

Sales pitches are the only place where “self-serve dashboards" work. In the real world, it's a different story.

Why "business" people don't use metabase/power-bi.

From Erlang, to Elixir and now, GLEAM!?