Automerge is a local-first sync engine for multiplayer apps that works offline, prevents conflicts, and runs fast.

I started writing this post because, for whatever reason, I keep forgetting what the difference is between a ring and a group, which is funny to me because I never forget the difference between a semiring and a semigroup – although other people do, because it’s quite easy to forget! So, I wanted a fast reference to the kinds of algebraic structures that I am most often dealing with in one way or another, usually because I’m writing Haskell (which has some reliance on terminology and structure from abstract algebra and category theory) or I’m trying to read a book about category theory and they keep talking about “groups.” Wikipedia, of course, defines all these structures, and that’s fine, but what I need in those times is more of a refresher than an in-depth explanation.

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.

There are a lot of guides about how to use async Rust from a "user's
perspective", but I think it's also worth understanding how it
works, what those async blocks actually mean.

Take control of your honks and join the federation.
An ActivityPub server with minimal setup and support costs.
Spend more time using the software and less time operating it.

Every so often I come across a paper, blog post, or (occasionally) video that completely changes how I think about a topic in programming languages and compilers. For some of these posts, I can’t even remember how I thought about the idea before reading it—it was that impactful.

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…

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

Most functional languages rely on some garbage collection for automatic memory management. They usually eschew reference counting in favor of a tracing garbage collector, which has less bookkeeping overhead at runtime. On the other hand, having an exact reference count of each value can enable optimizations, such as destructive updates. We explore these optimization opportunities in the context of an eager, purely functional programming language. We propose a new mechanism for efficiently reclaiming memory used by nonshared values, reducing stress on the global memory allocator. We describe an approach for minimizing the number of reference counts updates using borrowed references and a heuristic for automatically inferring borrow annotations. We implemented all these techniques in a new compiler for an eager and purely functional programming language with support for multi-threading. Our preliminary experimental results demonstrate our approach is competitive and often outperforms state-of-the-art compilers.

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.

Using systemd to avoid having to write some risky code

From Erlang, to Elixir and now, GLEAM!?

Go programming language is known to be easy to use. Thanks to its well-thought syntax, features and tooling, Go allows writing easy-to-read…

Hard disagree on this one, but still interesting.