Faber Romanus.

The thesis.

Faber Romanus is a language for coding agents to write and for humans to read. The syntax is deliberately mechanical: type-first bindings, one locale pack per file, a small glyph set for tensor work, no mixed-language spellings. A model is supposed to emit that surface without guessing. A person is supposed to read the result, not sit and type · and ⊗.

Latin is the canonical lexical identity — the compiler’s interchange, not a costume and not the required writing language. English is the default public surface, and the project reports those keywords were not picked by taste: it describes a council of different models choosing each English keyword for the most stable, highest-probability hit on that semantic concept, so the locale agents meet first is the one they already want to emit.

HIR is what makes the second half work across languages. The compiler holds one analysed program. A .fab file is a rendering into a reader locale — English by default, also Latin, Thai, Simplified and Traditional Chinese, Arabic, Vietnamese, Hindi. Someone who thinks in Thai can talk to their model in Thai, read the program in Thai, write the program in Thai, and it still compiles. Keywords and primitive type names change; identifiers and structural glyphs do not. Meaning does not fork.

Agents write it. Humans read it. English is the high-probability default; the locale is theirs; the meaning is one program.

The same HIR then projects onto measured compilation targets. Application lowers currently include Rust, TypeScript, Go and Swift; the MIR lane includes LLVM IR and WebAssembly; the GPU lane names Metal and CUDA. Support is stated target by target. Bounded dual-backend training on Metal and CUDA is the current proven device claim; end-to-end device GPU inference is not shipped.

What it looks like.

main {
    const tensor<f32, [2, 3]> a ← empty
    const tensor<f32, [3, 4]> b ← empty
    const tensor<f32, [2, 4]> product ← a · b
    print product
}

Declarations are type-first (const tensor<f32, [2, 3]> a, not a: Tensor). Runtime binds use ←. Tensor work that is a nested index walk in ordinary code is a glyph or a method: · matmul, ⊗ outer product, ⊙ Hadamard, ↦ convert, .mean() / .sum() for reductions. Locales are sealed: fn and functio do not mix in one file.

Distinctive moves.

• Agents author; humans read. The surface is mechanical so a model can emit it. Glyphs are for the agent and the reader, not for a human to hunt on a keyboard.
• English keywords chosen for model probability. Latin is canonical interchange. The project states the English locale was set keyword-by-keyword by a council of different models for the most stable, highest-probability hit on each concept.
• Native-language loop through HIR. Speak to the model in Chinese or Thai, write and read the .fab in that locale, compile the same program.
• Sealed reader packs. One locale per file. English is the default writing surface; Latin is the interchange template, not a required writing language.
• Predictable tensor surface. Rank-aware operators carry shape contracts into typechecking (inner-dimension unification on ·, identical-shape on ⊙).
• Agent-facing docs shipped with the product. faberlang.dev/llms.txt, a skills catalogue, and faber explain for glyphs, keywords and diagnostic codes.
• Honest capability ladder. Reader-localised source is shipped. Bounded Metal/CUDA training is proven. Device GPU inference and multi-device execution are not current product claims. Version 1 is experimental; stability is a version 2 claim.

Maturity.

A working faber CLI ships as a tagged release archive; v1.0.0 publishes a macOS arm64 build. The language, public libraries (including Gradus, the MIT autograd and ML library), examples and the documentation site are MIT. Radix, the compiler, is closed while it is under active development; that is presented as temporary, not as a permanent fence. Public GitHub organisation faberlang holds the open surface; the Haskell project at faber-lang/faber is unrelated.

The strain under real use is the usual early-language one, plus a specific honesty gap: the tensor and inference surface is large (Gradus is a substantial Faber library) while device residency and GPU execution remain compiler and host concerns. A catalogue reader should not take the glyph surface as a shipped serving product.

Agent tooling.

https://faberlang.dev/llms.txt is the machine index; llms-full.txt is the expanded map. The site content-negotiates an agent guide at /en-US/ and publishes skills for install, language, packages, examples and corpus. faber explain answers glyphs, keywords, grammar terms and diagnostic codes. The inclusion claim is the design intent — agents as authors of a locale-rendered, mechanically predictable HIR, with an English keyword surface chosen for model probability — plus that shipped tooling, not a runtime chatbot wrapped around another language.