I report a case study in AI-paired software engineering: five working ports of a vector illustration application across Rust, Swift, OCaml, Python, and browser-based platforms, built by a single developer in approximately 120 evening hours. The methodology pairs AI-assisted implementation with two safeguards -- a precise executable YAML specification serving as the single source of truth, and parallel implementations functioning as a built-in differential-testing layer. The five ports share a 23{,}000-line specification; per-port native code ranges from 0 to roughly 95{,}000 lines, reflecting the specification's escape hatch. I argue that AI-paired engineering, conditional on these two safeguards, makes feasible scope of work that conventionally requires multiple developer-years, and frame the methodology as a revival of N-version programming, a 1980s approach abandoned on cost grounds that AI changes. The paper reports concrete artifacts and honest limitations of the single-developer case study.

翻译：本文报告了一项AI配对软件工程的案例研究：由一名开发人员在大约120个晚间小时内完成的五款矢量插图应用工作端口，分别基于Rust、Swift、OCaml、Python及浏览器平台。该方法将AI辅助实现与双重保障机制配对——以精确的可执行YAML规范作为单一事实来源，并以并行实现作为内置差分测试层。五个端口共享23,000行规范；各端口的原生代码量从0到约95,000行不等，体现了规范的转义出口机制。我认为，在具备上述双重保障的条件下，AI配对工程可使传统上需要多人年工作量的开发范围变得可行，并将该方法框架化为1980年代因成本原因被废弃的N版本编程在AI改变下的复兴。本文报告了该单人开发案例的具体工件与真实局限性。