Clojure → YAMLScript transpiler: using LLMs for normalization, SCI for execution (github.com)

A new toolchain has been announced that transpiles a subset of Clojure data queries into YAMLScript (YS), leveraging large language models (LLMs) for normalization and deterministic compilation for execution. This innovative approach aims to bridge the "expressivity gap" between Clojure's flexible syntax and the more rigid structures of traditional transpilers. By utilizing an LLM as a structural normalizer and a deterministic Python emitter, the tool reduces the complexity of adapting Clojure's expressive capabilities into a strict canonical form, ultimately generating high-performance, zero-dependency native binaries using GraalVM and SCI. The significance of this project lies in its ability to minimize manual coding while maximizing flexibility, allowing developers to efficiently transform complex Clojure code into fast-executing standalone binaries. The dual-pass architecture ensures that the strengths of LLMs in understanding intent are combined with the reliability of deterministic tools for code generation. The open-source toolchain not only enhances productivity for Clojure developers but also demonstrates an effective application of LLMs in programming language transposition, paving the way for more sophisticated tools in the AI/ML community.