The OCaml Planet

Equal parts science and art, programming language design is very much an unsolved problem. This week, Ron speaks with Leo White, from Jane Street's Tools & Compilers team, about cutting-edge language features, future work happening on OCaml, and Jane Street's relationship with the broader open-source community. The conversation covers everything from the paradox of language popularity, to advanced type system features like modular implicits and dependent types. Listen in, no programming languages PhD required! You can find the transcript for this podcast episode along with links to things we discussed at signalsandthreads.com.

Equal parts science and art, programming language design is very much an unsolved problem. This week, Ron speaks with Leo White, from Jane Street's Tools & Compilers team, about cutting-edge language features, future work happening on OCaml, and Jane Street's relationship with the broader open-source community. The conversation covers everything from the paradox of language popularity, to advanced type system features like modular implicits and dependent types. Listen in, no programming languages PhD required! You can find the transcript for this podcast episode along with links to things we discussed at signalsandthreads.com.

A conversation with Laurent Mazare about how your choice of programming language interacts with the kind of work you do, and in particular about the tradeoffs between Python and OCaml when doing machine learning and data analysis. Ron and Laurent discuss the tradeoffs between working in a text editor and a Jupyter Notebook, the importance of visualization and interactivity, how tools and practices vary between language ecosystems, and how language features like borrow-checking in Rust and ref-counting in Swift and Python can make machine learning easier. You can find the transcript for this podcast episode along with links to things we discussed at signalsandthreads.com.

A conversation with Laurent Mazare about how your choice of programming language interacts with the kind of work you do, and in particular about the tradeoffs between Python and OCaml when doing machine learning and data analysis. Ron and Laurent discuss the tradeoffs between working in a text editor and a Jupyter Notebook, the importance of visualization and interactivity, how tools and practices vary between language ecosystems, and how language features like borrow-checking in Rust and ref-counting in Swift and Python can make machine learning easier. You can find the transcript for this podcast episode along with links to things we discussed at signalsandthreads.com.

Memory issues can be hard to track down. A function that onlyallocates a few small objects can cause a space leak if it’s calledoften enough and those object...