With some serious repositioning, I think there is still an opportunity for Julia to displace Python tools like polars/pandas/numpy, airflow, and pytorch -- with a unified ecosystem that makes it easy to transition to GPU and lead a differentiable programming revolution. They have the brain power to do it.
The future of Python's main open source data science ecosystem, numfocus, does not seem bright. Despite performance improvements, Python will always be a glue language. Python succeeds because the language and its tools are *EASY TO USE*. It has nothing to do with computer science sophistication or academic prowess - it humbly gets the job done and responds to feedback.
In comparison to mojo/max/modular, the julia community doesn't seem to be concerned with capturing share from python or picking off its use cases. That's the real problem. There is room for more than one winner here. However, have the people that wanted to give julia a shot already done so? I hope not because there is so much richness to their community under the hood.
The future of Python's main open source data science ecosystem, numfocus, does not seem bright. Despite performance improvements, Python will always be a glue language. Python succeeds because the language and its tools are *EASY TO USE*. It has nothing to do with computer science sophistication or academic prowess - it humbly gets the job done and responds to feedback.
In comparison to mojo/max/modular, the julia community doesn't seem to be concerned with capturing share from python or picking off its use cases. That's the real problem. There is room for more than one winner here. However, have the people that wanted to give julia a shot already done so? I hope not because there is so much richness to their community under the hood.