bloriner/terse

An AI-native code language: a machine-designed prefix HIR for AI coding agents.
AI agents write human-designed languages — verbose, infix, and syntax-error-prone. Terse is a canonical prefix intermediate representation designed for machines: fixed-arity ops, one spelling per construct, structured errors built for repair loops, and a surface syntax measured to be token-cheaper than Python.
## Why
Every mainstream language was designed for human eyes: infix operators with precedence tables, multiple spellings for the same construct (if vs ternary vs statement), significant whitespace, and error messages written for people. When an AI coding agent emits code, each of those is a failure mode — a misplaced paren or a precedence guess costs a repair iteration.
Terse removes the decisions a model shouldn't have to make:
Prefix, always. (add a (mul b c)) — the op comes first, so the structure of every form is known after one token. No precedence tables, no associativity, exactly one way to parenthesize anything.
Canonical. One spelling per construct, enforced by the parser. if is always prefix; true/false/null are always lowercase; bare symbols are always variable references. Canonicality shrinks the output distribution a model must learn and makes diffs, caches, and dedup exact.
Dense. The v0.2 syntax was shaped by cl100kbase measurements: juxtaposed parens, head-position calls, n-ary if, and fusion ops for hot patterns. Reference solutions measure 0.60–1.00× Python tokens across 8 bench tasks (see bench/).
Fixed arities, checked at parse time. (add 1) fails with E103 arity-mismatch before anything runs.
Structured errors for repair loops. Every failure is a TerseError with a stable code (E100–E300), phase, op, source position, expected/got, and a concrete hint. Feed err.todict() back to the model and the fix is usually one shot.
Constrained-decoding grammar. terse/grammar.gbnf is a GBNF grammar for the full language — under constrained decoding, syntax errors become impossible, and the only remaining failures are semantic ones the error protocol already covers.
## Quickstart
python
import terse
# Parse: surface syntax -> AST tuples
ast = terse.parse("(let x 40 (add x 2))") # ("let", "x", 40, ("add", ("var", "x"), 2))
# Run: (value, printedoutput)
value, output = terse.run(ast) # (42, "")
# Transpile: AST -> readable Python, verified by exec
src = terse.topython(ast)
ns = {}
exec(compile(src, " ", "exec"), ns)
ns["terseresult"] # 42
CLI:
bash
python -m terse run examples/fib.terse # run; prints program output, "=> " to stderr
python -m terse run prog.terse --gas 1000000 # with a custom gas budget
python -m terse transpile prog.terse # print the generated Python
python -m terse check prog.terse # parse only; prints OK or error JSON (exit 0/1)
Errors are machine-readable JSON on stderr:
bash
$ python -m terse check broken.terse
{"code": "E103", "phase": "parse", "op": "add", "pos": {"line": 1, "col": 1, "index": 1}, ...}
A taste of the language:
lisp
; recursive fibonacci via def
(def fib(n)(if(lt n 2)n(add(fib(dec n))(fib(dec(dec n))))))
(print(fib 10)) ; => 55