Learnings from 100K Lines of Rust with AI (zfhuang99.github.io)

A developer has successfully built a Rust-based multi-Paxos consensus engine, modernizing Azure’s Replicated State Library (RSL) which is critical for most Azure services. Over the course of three months, they wrote approximately 130K lines of Rust code, optimizing performance from 23K operations per second to 300K operations per second. This modernization addresses key limitations in the original RSL, such as lack of pipelining and non-volatile memory support, which could significantly reduce latency and improve throughput for modern cloud and AI-driven workloads. The project leveraged AI coding agents like Claude Code and Codex, resulting in unprecedented productivity and enhanced coding techniques. Key innovations included using AI-driven code contracts to ensure correctness, generating targeted test cases automatically, and implementing a lightweight spec-driven development approach. Additionally, performance tuning was guided by AI analysis, enabling the identification of bottlenecks and the application of optimization strategies without compromising Rust's memory safety. As the AI/ML community evolves, this project underscores the potential for AI to assist in complex coding tasks and performance optimization, while also highlighting areas for further automation within AI-assisted development processes.