Quick summary: It's a programming language whose programs (and whose values) are unlabeled trees. An unlabeled tree is a tree-shaped data structure whose node

Quick summary: It's a programming language whose programs (and whose values) are unlabeled trees. An unlabeled tree is a tree-shaped data structure whose nodes hold no data. The children of a node are ordered, though. The "Tree Calculus" defines a set of rules by which you can "evaluate" an unlabeled tree to get another unlabeled tree. If you apply these rules repeatedly, either you'll get into an infinite loop, or you'll get a tree that the rules say doesn't change anymore. The rules are designed so that the rules don't effect binary trees, so if you evaluate a binary tree you'll get the same tree back out and the computation is "done". These rules are written as a small-step semantics (a standard way to write down evaluation rules in PL) in the "specifications" page. They claim that: - The evaluation rules for trees are Turing Complete, meaning that you can express any computation, e.g. any JS program, using the Tree Calculus. More precisely, the claim is that there's a straightforward way to convert any (say) JS Program into a tree, and any tree into a JS value, and now you can use tree evaluation to run a JS program by doing (i) convert the JS program into a tree, (ii) evaluate the tree to get another tree, and finally (iii) convert the tree into a JS value, which is the correct output of the JS program. To prove this you wouldn't actually use JS as the language, you'd use something simpler that we already know is Turing complete like the lambda calculus, but it's the same idea. Though glancing at the page they might have actually done this for JS. - The evaluation is asymptotically optimal, meaning that for any programming language P (like JS), there's a conversion f(prog) from programs in P to Tree Calculus trees and constants a and b such that running_time(f(prog)) <= a+b*running_time(prog). That is, you can run programs in any language using the Tree Calculus with ~constant overhead. This is true for all the programming languages you love, e.g. you could write a JS program that takes a Java program, compiles it to bytecode, and run that bytecode, and unless you did this reasonably badly the running time won't be worse than a factor of 1,000,000. - A whole bunch of other stuff too. It's all plausible at first glance (i.e., I don't think they're making any of it up), but not obviously consequential. What's it good for: Some PL people might think it's cool. Conceivably useful to simplify some theory of computation proofs. If you find this sort of thing interesting, though, I'd recommend learning the lambda calculus instead. The lambda calculus is simpler, more well known, and more useful (it's a model of functions, instead of some made up rules about trees).