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What can be achieved with 100,000 lines of Rust and AI? Cheng Huang shares insights from building a modern multi-Paxos consensus engine that outperforms Azure's original Replicated State Library. In just six weeks, he not only implemented all features of the library but also enhanced performance from 23,000 operations per second to 300,000.
His workflow optimized AI coding agents like Claude Code and Codex, which played crucial roles in ensuring correctness through AI-driven code contracts and testing. Huang also redefined his development process with a lightweight spec-driven approach and aggressive performance optimization strategies, uncovering insights that dramatically improved throughput.
With ambitions to further enhance AI-assisted coding, he believes that automating contract workflows and optimizing performance tuning could reshape the future of software development. This article unpacks the methodology and lessons learned, offering a glimpse into the potential of AI in programming.
continue reading onzfhuang99.github.io
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