mostly-adequate-guide VS nbdev

This project might serve as inspiration: https://github.com/MostlyAdequate/mostly-adequate-guide

i like reading this book directly from github with dark mode, also the subheading don't work in the gitbook website and gitbook is abandoned, here's the github link: https://github.com/MostlyAdequate/mostly-adequate-guide/blob/master/SUMMARY.md

It’s been a while since I focused on FP, but I recall finding this useful quite often and gleaning the concepts from it relatively easily.

https://github.com/MostlyAdequate/mostly-adequate-guide

I found a lot of articles like the OP, and ultimately they left me confused about the benefits in the beginning. I found it more useful to avoid one off articles and dig into larger pieces of work where the author put in much more care.

On the book front, there are two that I am fond of which have a focus on JavaScript and FP, Professor Frisby’s Mostly Adaquate Guide, and Functional Light JavaScript. They are nice practical books that help you lean into JS’s strength as an FP language while writing real code.

So, this is going to be an uphill battle for you. I suggest you actually learn Haskell first, and then you'll be able to apply its lessons to TypeScript.

Its tricky because these are patterns that are familiar in Haskell but are not really taught in other settings.

Additionally, to really learn these, you need to experiment with them. Use them. etc. That's pretty hard to do if the learning resources are mostly in haskell and you don't really understand it.

Alternatively, this might help: https://github.com/MostlyAdequate/mostly-adequate-guide

Also alternatively, what I would do is just go slowly through the fp-ts code. Look at it a piece at a time and slowly grow your understanding.

This may also help https://www.amazon.com/Domain-Modeling-Made-Functional-Domai...

One example I've seen is ML/DL folks using jupyter notebooks to develop DL libraries in jupyter notebooks, see https://github.com/fastai/nbdev

- https://github.com/fastai/nbdev: Increase developer productivity by 10x with a new exploratory programming workflow.

9. Nbdev: Boost developer productivity with an exploratory programming workflow - https://nbdev.fast.ai/

See if you like nbdev way of working with data through python and jupyter. nbdev is an optional part that will create python packages from jupyter notebooks. Also even the simple tutorials are opinionated and will guide you to unit test your code and write CICD pipelines.

You probably don’t have time for this for the purposes of your task, but I will also throw in the recommendation of nbdev especially if you’re a Python person. I haven’t had a project to use it on yet, but I’ve gone through the docs and the walkthrough and it seems like a great framework for starting potential projects with all the infrastructure needed for if/when they eventually get big and need all the packaging and stuff

nbdev: jupyter notebooks -> python package

Take a look at https://github.com/fastai/nbdev - haven't used it but supposedly the whole if fast.ai library was written that way. It sounds like a natural direction in your scenario - allowing your to keep working in a familiar environment and still producing production ready code (will, at least in paper 😅)