These gains come on top of already-optimized baselines in kernels that were considered "done" by their authors. The improvements are the direct result of CompileIQ discovering compiler configurations that the default heuristics would never select.
大多数人认为一旦开发者完成优化工作，就没有更多性能提升空间。但作者表明，即使是"完成"的优化代码仍可能通过编译器级别的调整获得显著提升（高达15%），这挑战了开发者对优化极限的认知。
NVIDIA GPU compilers apply the same default heuristics (register allocation strategies, instruction scheduling decisions, loop unrolling thresholds, etc.) to every kernel they compile. These heuristics are engineered to produce good results across a vast range of workloads. But "good across the board" and "optimal for your workload" are two very different things.
大多数人认为编译器已经提供了足够的优化，开发者只需关注算法和代码实现即可。但作者认为，即使是最先进的GPU编译器也使用通用的启发式方法，这些方法无法针对特定工作负载进行优化，导致性能损失。这挑战了开发者社区对编译器优化能力的普遍认知。
Without experience with compiler behavior, the agent couldn't have predicted which 'optimizations' the compiler would already handle.
这一观察揭示了AI代理在编译优化方面的局限性：代理无法准确预测编译器已经自动处理的优化。这表明AI代理需要更深入理解编译器行为和现代编译技术，以避免徒劳的优化尝试。这一发现对AI辅助编程系统的发展具有重要启示，强调了领域知识整合的重要性。
If you manage to make Svelte aware of what needs to be tracked, chances are that the resulting code will be more performant than if you roll your own with events or whatever. In part because it will use Svelte's runtime code that is already present in your app, in part because Svelte produces seriously optimized change tracking code, that would be hard to hand code all while keeping it human friendly. And in part because your change tracking targets will be more narrow.