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Jennifer Programming Language

Editor support and AI-assisted coding

Two things make writing Jennifer outside this repo comfortable: syntax highlighting in your editor, and a drop-in language reference so an AI coding assistant can write correct Jennifer for you. Both ship in the repository.

Editor syntax highlighting

Highlighting definitions live in editors/. Jennifer's lexical rules are regular enough that highlighting is genuinely accurate - $x is always a variable, UPPER_CASE a constant, NS.name a namespaced call, # and /* */ comments.

Per-editor install steps are in editors/README.md.

One caveat: GitHub's Linguist assigns the .j extension to Objective-J, so GitHub's web UI will not highlight Jennifer source as Jennifer. That is a GitHub-side limitation; local editors and self-hosted sites are unaffected.

Jennifer as a shell filter

jennifer run - reads a program from stdin, so Jennifer slots into a pipe like any other filter. A handy one is a json-pretty that reformats JSON flowing through it. Save the program to a file (say ~/.local/share/jennifer/json-pretty.j):

jennifer
use json;
use io;

def src as string init "";
while (not io.eof()) {
    $src = $src + io.readLine() + "\n";
}
io.printf("%s\n", json.encodePretty(json.decode($src)));

then alias it:

sh
alias json-pretty='jennifer run ~/.local/share/jennifer/json-pretty.j'

echo '{"b":2,"a":1}' | json-pretty
curl -s https://api.example.com/thing | json-pretty

Swap json for any other decode / re-encode pair to get, for example, a pretty-xml. A no-file variant that pipes the program itself through jennifer run - is in the CLI reference.

Run profiles (--env)

jennifer run --env=<profile> app.j selects a run profile for a program - prod, dev, staging, or whatever names you use. It is pure sugar: the flag sets the JENNIFER_ENV environment variable before the program runs, so

sh
jennifer run --env=prod app.j

is identical to

sh
JENNIFER_ENV=prod jennifer run app.j

The program reads the profile from that one environment variable; nothing about the language changes. The main consumer is the dotenv module, whose autoload / cascade loaders pick the .env.<profile> file to layer on top of .env - so --env=prod loads .env.prod, --env=dev loads .env.dev, and no profile loads only the base files.

The profile label is validated ([A-Za-z0-9_-], 1 to 64 characters), which is also what stops a hostile value from steering .env.<profile> at another path. The flag goes before the script file (like -I and --sysmoddir); anything after the file is passed to the program as its own arguments. An explicit --env=X overrides any JENNIFER_ENV already in the environment.

AI-assisted coding with JENNIFER.md

Jennifer is new and small, so a general-purpose AI assistant has no built-in knowledge of it and will otherwise guess (usually Python-with-dollar-signs). JENNIFER.md is a single, self-contained language reference written for exactly this: drop it into your project and point your assistant at it.

text
We're coding in Jennifer, a batteries-included interpreted language. Read
JENNIFER.md for the syntax and standard library, then let's build ...

It covers the lexical rules (the $ sigil, letters-only identifiers, UPPER_CASE constants), types, operators (including / being float division), control flow, methods, concurrency, imports, the namespaced standard library, and a checklist of the mistakes an assistant most often makes. It describes the language, not the interpreter internals, and stays in sync with this spec.

It doubles as a human quick-reference. For the exhaustive per-function detail behind it, see the library reference and cheatsheet.