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nbdev tries to solve this problem by giving you - automatically turning notebooks into publishable Python modules - bidirectional sync with plaintext .py for IDE usage - fixes for other "real" project needs: tests, continuous integration, documentation export, conflict resolution
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While nbdev seems focused on helping individual developers avoid the "switch" when implementing their own projects, I think layers like this could help solve a big problem with "executable books": the huge barrier for *readers* to build on embedded code to do anything real.
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The fast.ai docs get at something pretty exciting, then. Like many notebooks, it contains narrative content which explains computational material and lets readers explore. But the executable book is *also* the implementation of a published production-level library
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The narrative in those docs is a bit limited: it's more documentation than prose. "Deep Learning for Coders" is the expository text from the same authors, but it isn't made available in an executable context AFAICT. I think that could be really powerful!
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My wishlist for exec. books: – author did their "real thinking" in the authoring computational environment – reading environment invites + supports meaningful experimentation/exploration – book elements transparently and usefully reusable by author and readers in derivative works
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Depends what “serious” means — the goal is precisely to bring notebook style development to normal programming: 1. Tons of bugs filed using RunKit notebooks (no more “works on my computer”, always reproducible forever since packages frozen in time):
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This is something I’m pretty interested in: the fact that the former model is “host on service” (and don’t own the post on your machine), or host yourself (and probably lose interactivity since it’s just a “rendering”). (Cont.)
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