This article compares compile-time reflection capabilities across C++, Zig, and C3, examining how each language handles enum-to-string conversion, struct introspection, and validation. C++ uses verbose templates, Zig relies on comptime functions and builtins, while C3 employs a macro system with explicit compile-time syntax ($-prefixed constructs) that the author finds more readable and promising for systems programming.
Apprentice is a highly configurable Common Lisp coding harness for AI agents, built around abstractions for models, tools, anchors, and loops. It supports multiple LLM providers and enables rapid customization through Lisp macros and REPL-driven development.
Scheme is optimal for AI-generated code because its homoiconic structure aligns with how models produce constrained outputs, while macros and verification mechanisms handle runtime semantics. The language enables automatic correctness proofs through round-trip testing and declarative schemas, shifting error detection from runtime to compile time and reducing the surface for model failures.