Learnings from 100K Lines of Rust with AI | Cheng Huang’s corner

ending_slot | | C | Next multiple of 1000 after ending_slot for cleaner boundaries | | D | Same as ending_slot (both configs share final slot) | You can reply with the option letter (e.g., "A"), accept the recommendation by saying "yes" or "recommended", or provide your own short answer. Aggressive Performance Optimization Performance optimization is where AI really shines. After ensuring initial correctness, I spent about three weeks purely on throughput tuning — and AI became my co-pilot in performance engineering. Through iterative cycles, we boosted throughput from ~23K ops/sec to ~300K ops/sec on a single laptop. Here’s the loop I followed repeatedly: Ask AI to instrument latency metrics across all code paths. Run performance tests and output trace logs. Let AI analyze latency breakdowns (it writes Python scripts to calculate quantiles and identify bottlenecks). Ask AI to propose optimizations, implement one, re-measure, and repeat. This process surfaced insights I might have missed — for example, lock contention on async paths, redundant memory copies, and unnecessary task spawns. Rust’s safety model made it easy to push these optimizations confidently. Key gains came from minimizing allocations, applying zero-copy techniques, avoiding locks, and selectively removing async overhead. Each improvement felt like peeling another layer of latency off a high-performance engine — without fear of corrupting memory. Wish List for AI-Assisted Coding Reflecting on my journey, I keep wondering where AI could deliver even more value. Here are some items on my wish list: End-to-End User Story Execution: I still prefer to define the user stories myself. As an architect, I feel I have a better sense of what I’m building and how I’d like to build it. However, the delivery of a perfect execution is something I believe AI can handle increasingly well. Today, I still have to spend a fair amount of time steering the AI — telling it to continue when it pauses, suggesting refactoring, reviewing test coverage, and suggesting additional tests. I would prefer the AI take more autonomy to drive this end-to-end. Automated Contract Workflows: The flow of applying contracts seems largely automatable. While I’d still want to review the contracts and offer suggestions, I’d like the AI to drive the rest: generating tests based on contracts, debugging individual test cases, ensuring consistency between tests and contracts, and writing property-based tests. When a test fails, I’d like the AI to debug and fix trivial issues automatically, only notifying me when there are genuine correctness issues in the contracts or the implementation. Autonomous Performance Optimization: Performance tuning seems ripe for more automation. Much of what I’ve done is repetitive and parallelizable. Projects like AlphaEvolve (or OpenEvolve) show promise in this direction. Ideally, I would suggest potential optimization avenues, and the AI would execute the experiments completely by itself. While current tools handle small bodies of code, applying similar techniques to larger codebases with end-to-end measurement seems feasible. Appendix: Project Status The seed of the project is an elegant design markdown authored by Jay Lorch [4] from Microsoft Research. This design greatly simplifies all the components in multi-Paxos, making it easier to implement and reason about. So far, 2 out of the 3 RSL limitations have been addressed: pipelining and NVM support (Jay integrated the fully verified persistence log for NVM which was published in the PoWER Never Corrupts paper [5] at OSDI 2025). The RDMA support is still TBD. To date, the project has grown to over 130K lines of Rust code, with 1,300+ tests accounting for more than 65% of the codebase." /><br>ending_slot | | C | Next multiple of 1000 after ending_slot for cleaner boundaries | | D | Same as ending_slot (both configs share final slot) | You can reply with the option letter (e.g., "A"), accept the recommendation by saying "yes" or "recommended", or provide your own short answer. Aggressive Performance Optimization Performance optimization is where AI really shines. After ensuring initial correctness, I spent about three weeks purely on throughput tuning — and AI became my co-pilot in performance engineering. Through iterative cycles, we boosted throughput from ~23K ops/sec to ~300K ops/sec on a single laptop. Here’s the loop I followed repeatedly: Ask AI to instrument latency metrics across all code paths. Run performance tests and output trace logs. Let AI analyze latency breakdowns (it writes Python scripts to calculate quantiles and identify bottlenecks). Ask AI to propose optimizations, implement one, re-measure, and repeat. This process surfaced insights I might have missed — for example, lock contention on async paths, redundant memory copies, and unnecessary task spawns. Rust’s safety model made it easy to push these optimizations...