P06 · QUIC / NETWORK DATA PLANE
A long-running QUIC and network data-plane engineering effort, using architecture refactoring, repeatable benchmarks, regression analysis and observability to converge on production-grade correctness and performance.
The point is not how much code AI wrote, but how a complex production task stays verifiable, reversible and convergent under human-AI collaboration.
Cloudflare quiche and Microsoft MsQuic are engineering reference points. The comparison focuses on API level, I/O ownership, event-loop/async models, performance tooling and production design—not a leaderboard built from incomparable benchmarks.
Cloudflare quiche exposes relatively low-level QUIC/HTTP3 APIs, leaving I/O, sockets and timer/event-loop organization to the application. It is a useful reference for Rust QUIC state machines and application integration boundaries.
MsQuic is a cross-platform QUIC implementation with strong emphasis on asynchronous I/O, throughput/latency, RSS, UDP coalescing and system-level performance diagnostics—another mature reference for production transport engineering.
The focus is continuous refactoring from real regressions, performance bottlenecks and state-boundary failures, with AI-assisted development constrained by benchmarks and architecture review.
Diagnose problems through call flow, ownership, state authority and wake/readiness semantics rather than tuning constants first.
Use repeatable benchmarks to evaluate throughput, concurrency, tail latency, CPU and memory with evidence.
Use AI to widen the hypothesis space and accelerate code review and analysis; final decisions remain constrained by tests, benchmarks and invariants.
Allow falsification, rollback and hard cuts. Failed versions remain useful evidence; code changes are not treated as performance wins until measured.