I think the kernel design is fantastic. I think Jupyter has a lot of legs and will be great for many, many years ahead. But I also think you can make a 10x leap in data science tools, but you need to look beyond the R/Python/Julia style languages. Observable gets to start fresh and build a language that best enables an interactive visual notebook environment, whereas the others are great languages but that kind of notebook environment came later and must be shaped to fit the languages.
It may be an awesome piece of javascript to admire and behold, but to the scientist that has decades of fortran and matlab programs he's collected over the years, it's a tall order to expect him to rewrite everything in Observable.
It would be great if people stopped using Matlab for new open source projects (in favor of e.g. Julia or Python/numpy). But nobody is expecting the majority of numerical code to be rewritten for the web anytime soon.
Still, if anyone wants to make their research results as accessible as possible, then publishing an Observable notebook is great, because it’s (a) just a hyperlink so trivial to access from any device without security concerns, need to purchase or even install and run specialized runtimes, etc., (b) very friendly for readers to inspect/modify/reuse, (c) supports rich interaction better than most alternative platforms.
I’m hoping that wasm someday soon becomes a reasonable target for optimized numerical programs written in Fortran, C, Rust, Julia, Halide, ...
> it's a tall order to expect him to rewrite everything in Observable.
I didn't say anything of the sort. That's your idea, not mine. I think Matlab and Fortran are great.
I would, however, expect new folks who don't know Fortran or Matlab to instead pick up something like Observable, if it continues to evolve in its present direction.
My point in the comment above is that, although I think Jupyter is fantastically designed, and I love the kernel abstraction, it began and is all about the notebook and graphical UI. So a thing like Observable, where they are similarly committed to the reactive interactive environment, without being committed to legacy languages (and in fact are willing to head down toward a reactive language which are far outnumbered by the languages akin to Python/R), could be very interesting down the line. I am very bullish on hybrid visual/text programming environments in data science, which have been around for a long time, but are becoming particularly more useful now that we have so many great data sources and tools to process that data.
