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You're sacrificing the speed and low level nature of Rust though. If performance is not critical why would one choose Rust in the first place? It's neat, but I

You're sacrificing the speed and low level nature of Rust though. If performance is not critical why would one choose Rust in the first place? It's neat, but I don't see any need or use case for this.

m-ou-se 7y ago
At work, we make high performant drone control systems in Rust. But for testing and debugging, we simply use Matplotlib (a Python library) to make plots of simulations, etc. So we prefer Rust for the control systems themselves for the speed and reliability, but for plotting results of simulations, we don't care much about performance and rather just use something with all the features we need.
- tingletech 7y ago
  My grandpa used to make drone control systems with operation amplifiers (in the 1980s). I have a picture of him with a big plotter on a stand in a field flying a drone.
- mrich 7y ago
  May I ask where you work?
  - krat0sprakhar 7y ago
    Seems like: https://github.com/dronesforwork https://github.com/dronesforwork
vegasdew 7y ago
90% of time is spent in 10% of code(for some value of 90/10). As a concept, this will give you freedom to use extensive Python ecosystem, flexibility to write glue code etc. in a more 'accessible' language.
laughingman2 7y ago
Then I guess you're not looking hard enough. Recently swift integration with tensorflow also introduced easy way to import python. There into swift. Data science engineers need performant language to do lot of stuff, and rust fits the ball. But it's hard to abandon python ecosystem. I and others hate the pattern in python to write anything performant in C, Cython or some other language and write bindings for python. The python as binding language paradigmn doesn't work when all the stuff you want don't exist in performant languages. TLDR; this fits the perfect use case for machine learning/data science, to use python only when necessary and hopefully migrate projects (parts of them atleast) to rust.

m-ou-se 7y ago

At work, we make high performant drone control systems in Rust. But for testing and debugging, we simply use Matplotlib (a Python library) to make plots of simulations, etc. So we prefer Rust for the control systems themselves for the speed and reliability, but for plotting results of simulations, we don't care much about performance and rather just use something with all the features we need.

tingletech 7y ago
My grandpa used to make drone control systems with operation amplifiers (in the 1980s). I have a picture of him with a big plotter on a stand in a field flying a drone.
mrich 7y ago
May I ask where you work?
- krat0sprakhar 7y ago
  Seems like: https://github.com/dronesforwork https://github.com/dronesforwork

vegasdew 7y ago

90% of time is spent in 10% of code(for some value of 90/10). As a concept, this will give you freedom to use extensive Python ecosystem, flexibility to write glue code etc. in a more 'accessible' language.

laughingman2 7y ago

Then I guess you're not looking hard enough. Recently swift integration with tensorflow also introduced easy way to import python. There into swift. Data science engineers need performant language to do lot of stuff, and rust fits the ball. But it's hard to abandon python ecosystem. I and others hate the pattern in python to write anything performant in C, Cython or some other language and write bindings for python. The python as binding language paradigmn doesn't work when all the stuff you want don't exist in performant languages. TLDR; this fits the perfect use case for machine learning/data science, to use python only when necessary and hopefully migrate projects (parts of them atleast) to rust.