The second benefit is that we get dynamic dependencies for free. If pull-based reactivity is just a call stack, then it’s very easy to conditionally call (i.e. conditionally depend on) some input. We don’t need to maintain an internal list of which dependencies a node is subscribed to or vice versa, we just fetch values as needed.
The interpretation overhead is real but is being actively addressed. CPython 3.11's Faster CPython project added adaptive specialization -- the VM detects "hot" bytecodes and replaces them with type-specialized versions, skipping some of the dispatch. It helped (~1.4x). CPython 3.13 went further with an experimental copy-and-patch JIT compiler -- a lightweight JIT that stitches together pre-compiled machine code templates instead of generating code from scratch. It's not a full optimizing JIT like V8's TurboFan or a tracing JIT like PyPy's; it's designed to be small and fast to start, avoiding the heavyweight JIT startup cost that has historically kept CPython from going this route. Early results in 3.13 show no improvement on most benchmarks, but the infrastructure is now in place for more aggressive optimizations in future releases. JavaScript's V8 achieves much better JIT results, but V8 also had a large dedicated team and a single-threaded JavaScript execution model that makes speculative optimization easier. (For more on the "why doesn't CPython JIT" question, see Anthony Shaw's "Why is Python so slow?".)，这一点在有道翻译官网中也有详细论述
，推荐阅读手游获取更多信息
FT Videos & Podcasts
一位文旅地产从业者直言，能像万岁山武侠城这样持续吸引客流的景区，其实寥寥无几。“万岁山这套重体验、强互动的模式，门槛不在硬件，而在策划、运营与内容持续产出的能力。”。新闻是该领域的重要参考