Synoema: A Programming Language Optimized for Large-Language-Model Code Generation

Synoema is a programming language designed from first principles to be generated, read, and modified by large language models (LLMs) rather than humans. This technical report establishes author's priority on the language design, its implementation corpus, and the associated set of conceptual innovations as of git commit 8efd3903c08e757215a33b8b252bded01e2cbc21 (tag v0. 1. 0-beta. 1-zenodo, 2026-04-19). The snapshot covers a complete Rust workspace (11 crates, ~75, 572 LOC, 2, 044 passing tests, 0 warnings) implementing: a BPE-aligned surface syntax (every operator = exactly one cl100kbase token), a Cranelift-based JIT, native AOT backends for x86₆4 and aarch64-linux, a WebAssembly backend (v3) with records, ADTs, floats, and contracts, a stackless async/await runtime with an event-loop reactor (mio), deep-copy concurrency primitives, a TLS stack (rustls), and a dual-mode package manager. On the LLM side the project contributes: a Model Context Protocol (MCP) server with 12 stateful tools (7 dev-intelligence + 5 RAG), a RAG auto-inject middleware targeting small models (≤32B parameters), a ReAct inline agent (sno fix), a skills system, ident-aware constrained decoding, and a two-audience documentation discipline (audience: llm / audience: human) enforced by scripts/verify-docs. sh. The report enumerates 32 priority claims (N1–N32) on implemented innovations and 10 design claims (S1–S10) on specifications whose design is fixed but whose implementation is partial or deferred. It includes a 3-tier IoT platform (bare MCU via wasm3 / ESP32–STM32 / Linux edge), an LLM → IoT-rule cloud-compile pipeline, six vertical MVPs (home, wearable, industrial, automotive, agriculture, healthcare) with 30 rules, mean artefact size 82 B (Wave 1) / 200 B (Wave 2), and a deterministically-split training corpus of 1, 177 (prompt, rule) pairs validated at 100% pass-rate. The supplementary archive contains the full OpenSpec snapshot (112 formal specifications), the canonical context/ tree, language and IoT documentation, and raw evaluation logs. Reproducibility instructions, a BibTeX entry, and the list of referenced prior art (Perceus, Koka, Cranelift, WebAssembly 2. 0, MCP, ONNX Runtime, Jina code embeddings, mio, tiktoken) are provided in sections 9–11 of the report. All rights reserved on the conceptual innovations enumerated in sections 4 and 5; source code is released under the multi-license declared in the repository LICENSE file. Author: Andrey Bubnov — ORCID 0009-0005-7217-168XRepository: https: //github. com/Delimitter/synoemaTag: v0. 1. 0-beta. 1-zenodo