Hacker News Reader

But compatibility with C extensions is a major selling point.

25% speedup for pure Python code is not that appealing in numerical analysis code, when you can use NumPy, or sprinkle a few typedefs into a critical function and compile with Cython for a 1000x speedup.

If C extension compatibility is broken, then many Python programs will altogether still be slower, despite the pure python speedup.

View on HN

cython community can definitely use some love -- there's a ton of low-hanging fruit (unboxing arrays of extension classes, for example) they haven't gotten to because of time / funding constraints.

Not an apples to apples comparison, but my guess is the state of the art of JIT is way ahead of what the cython compiler can detect; some of the JIT tricks will likely work in the cython static translation step.

And to be clear I love cython, it's very useful as-is -- there's a large community of people for whom C & python expertise have gone hand in hand for years, and cython is the tool they end up using to max productivity and minimize surprises.

Reply on news.ycombinator.com