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.

I feel uneasy about design patterns.
On the one hand, my university class on design patterns revived my interest in programming.
On the other hand, I find most patterns in the Gang of Four book to be irrelevant to my daily work;
they solve problems that a choice of programming language or paradigm creates.

My litmus test of a good design pattern is its cross-disciplinary applicability.
I’m more likely to accept an idea that pops up in fields beyond software engineering.
And the most convincing patterns are the ones that help me in everyday life.

This article describes a universal pattern that billions of people rely on daily, but software engineers rarely discuss—the plan-execute pattern.

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 a lot of thoughts about the design of compiler intermediate representations (IRs). In this post I’m going to try and communicate some of those ideas and why I think they are important.

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…

In modern Java, the visitor pattern is no longer needed. Using sealed types and switches with pattern matching achieves the same goals with less code and less complexity.

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

Security testing starts with understanding vulnerabilities. The CVE website lists known software flaws. The OWASP Top Ten highlights common weaknesses. With this knowledge, we can improve our Go development. This article shows how to put in place robust practices. They are to: fuzz inputs, verify dependencies, and use static analysis tools (SAST).

I’ve used a lot of tools over the years, which means I’ve seen a lot of tools hit a plateau. That’s not always a problem; sometimes …

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.

Official website of the Pimalaya project.

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