GoKawiilIn the past few months, I’ve been stress-testing how far AI coding agents can take us when building real, production-grade distributed systems.
The result: a Rust-based multi-Paxos consensus engine that not only implements all the features of Azure’s Replicated State Library (RSL) [1] — which underpins most major Azure services — but also modernizes it for today’s hardware.
The entire project took me ~3 months, with 100K lines of Rust code written in ~4 weeks and performance optimization from 23K operations/sec to 300K ops/sec achieved in ~3 weeks.
Besides unprecedented productivity, I discovered several techniques that were instrumental. This post shares my most valuable learnings on: ensuring correctness with code contracts, applying lightweight spec-driven development, and pursuing aggressive performance optimization — plus my wish list for the future of AI-assisted coding.
Why Modernize RSL?
Azure’s RSL implements the multi-Paxos consensus protocol and forms the backbone of replication in many Azure services. However, RSL was written more than a decade ago. While robust, it hasn’t evolved to match modern hardware and workloads.
There are three key gaps motivated this project:
No pipelining: When a vote is in flight, new requests must wait, inflating latency. No NVM support: Non-volatile memory is now common in Azure datacenters and can drastically reduce commit time. Limited hardware awareness: RSL wasn’t built to leverage RDMA, which is now pervasive in Azure data centers.
Removing these limitations could unlock significantly lower latency and higher throughput — critical for modern cloud workloads and AI-driven services.
Given my interest in Rust and AI-accelerated development, I set out to build a modern RSL equivalent from scratch.
... continue reading