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Introducing HiveVM: model once, run anywhere

· HiveVM

HiveVM is built around one idea: describe an application once as a model — its application and business logic — and run it anywhere, independent of technology and platform. The platform may change; the model stays. The HiveVM tools and projects apply the same idea at every level: a grammar becomes a parser, a folder of Markdown becomes a manual, a specification becomes a project.

This post is a short tour of the toolkit and the thinking behind it.

One source of truth

Most software drifts because the same knowledge is written down in several places — once in the code for each platform, once in the docs, once in the build. Each copy ages at its own pace, and every platform change means rewriting logic that has not changed at all. Modelling collapses those copies into a single declarative source.

Describe the intent once and let the tooling project it onto each target.

The three tools

The toolkit is intentionally small:

  • Parser Generator (Waggle) — an LL(k) generator that emits a parser and a full AST for Java, C++ and Rust from one grammar.
  • Manual Generator — turns a collection of CommonMark files into one coherent manual.
  • Agent Template (NUC) — a starting point for building software with coding agents inside a ready-to-use Dev Container, driven by a written specification and ADRs.

Each lives in its own public repository, so you can adopt one without the others.

A grammar, end to end

A grammar is just a declarative .waggle file. Here is a tiny expression language, Calc.waggle:

grammar Calc;

expr   : term (('+' | '-') term)* ;
term   : factor (('*' | '/') factor)* ;
factor : NUMBER | '(' expr ')' ;

NUMBER : [0-9]+ ('.' [0-9]+)? ;

Running Waggle produces a parser plus AST nodes for every target you ask for:

waggle generate Calc.waggle --target java,cpp

That is the whole loop — edit the model, regenerate, ship. No boilerplate to keep in sync by hand.

Why “Waggle”?

The waggle dance is the language of bees: a kind of grammar in which a forager encodes the direction and distance of a food source, and the other bees in the hive parse the dance to find it. That is what a parser generator is about, too: a grammar describes a language, and the generated parser reads it.

Where to go next

Browse the repositories on GitHub or start with Waggle, the parser generator. The next post digs into how LL(k) analysis actually decides what to generate.

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