When you look at the language ecosystem today, you have many camps: verbose and fast (Java, Go, C++), slow and confined (by GILs) (Python, Ruby). Then there are the languages who are fast, concise, but incredibly complex (Haskell, Scala). And Lisp sits somewhere between all of them. Standard ML is a different beast. I like to think of it as the C of strongly-typed, garbage-collected languages. But unlike C, it has been (almost) solely an academic language with little emphasis on use by "average" developers.
I first began exploring (not Standard ML, but) OCaml in college after hearing a talk by Jane Street, and I loved it. I was driven to study web servers and as a result, wrote OWebl . OWebl was a great experience that taught me a lot about the web, OCaml, and the OCaml ecosystem/community. Ultimately, I decided OCaml wasn’t the right place for me, so I continued on my journey to the frontier.
Standard ML is the direct descendant of ML: forefather of OCaml, Haskell, Scala, and many others. As the name indicates, the languages is defined by a standard – last revised in 1997. Standard ML has spawned many implementations with various intents targeting various backends. It is due to the standard, however, that Standard ML remains one of the simplest languages in the ML family. This makes it an incredible language for teaching and learning. Standard ML has (perhaps) many limitations, but these limitations also serve as a learning tool. Studying Standard ML has personally been a key guide in discovering the reasoning and design behind more complex languages in the ML family.
In addition to the simplicity of the language, the garbage collector and compile-time type-checking; one of the oldest, most stable, and continually developed implementations has first-class support for POSIX-style threading: Poly/ML . Furthermore, Poly/ML compiles quickly and produces performant binaries. These combinations are what sold me on Poly/ML (and, by extension, Standard ML).
However, you still have to deal with a 20 year-old standard library with mostly terrible documentation and little tooling. This is why Ponyo exists. My goal behind Ponyo is not just to provide a fast, high-level library and toolkit to simplify the process of developing Standard ML applications, but to improve existing documentation and tutorials and contribute many more.
Ponyo is in very early stages. But I’d like to keep you updated on my work – for your sake and mine. It has finally gotten to the point where this actually something to share, and that is exciting. Today, Ponyo has an HTTP/1.1 server and client. (This site is running on it.) It has a file library, a string library, a cli library, and a Standard ML parser capable of parsing basic signatures. Ponyo has a build tool to simplify the process of compiling Poly/ML-based Standard ML programs, and the documentation generator turning Standard ML signatures into HTML pages is my top priority.
If you’d like to get involved in Ponyo, please check out the roadmap and todo in the Github repo . If you’d like additional information to get started in Standard ML, check out the /r/sml wiki . I’ll try to post on my progress each week. So until then, happy compiling!