io-sim
post-rfc
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io-sim | post-rfc | |
---|---|---|
2 | 27 | |
25 | 2,185 | |
- | - | |
8.9 | 0.0 | |
7 days ago | 5 months ago | |
Haskell | ||
Apache License 2.0 | Creative Commons Attribution 4.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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io-sim
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What can I do in Haskell? UwU
It is implemented using our own io-sim library: https://github.com/input-output-hk/io-sim - which I hope to put on Hackage in future.
post-rfc
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Haskell in Production: Standard Chartered
That's what it's best for, but personally I use it for everything. If I ever get into low-level code I'll probably use Rust though.
You can confirm that parsers/tokenizers is ranked "best in class" here though:
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Recommendations for well informed, up-to-date guide to Haskell backend engineering
Note that this is ported from here: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md which comes with more exposition.
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I want to learn Haskell, but...
State of the Haskell Ecosystem
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Why are haskell applications so obscure?
According to State of the Haskell ecosystem, Haskell is THE language of choice for implementing compilers, and THE language of choice for writing parsers. Thus, it is not surprising to see more Haskell projects from those particular categories than from other categories.
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base case
This is great for understanding what libraries to use in the Haskell ecosystem: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md
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Haskell for beginners
In particular, I got comfortable reading hackage documentation to understand quickly how to use libraries (aeson, megaparsec, mtl, pipes, etc), got comfortable with the ecosystem (this helped: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md), got comfortable with the main language idioms and features (https://smunix.github.io/dev.stephendiehl.com/hask/tutorial.pdf) and got comfortable with simple things that for some reason had confused me before (case, \case, let).
- What can I do in Haskell? UwU
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Haskell for Artificial Intelligence?
With that being said, Python is without a doubt the best option, and I'd also be very interested to read the articles you found that say that Python is not a good choice because it's been the industry standard for a long time now. Data science and machine learning are one of the areas where the Haskell ecosystem is not as strong as other languages, but libraries and tools do exist. There's a great list of Haskell resources by domain here, and as you can see, there are Haskell bindings to tensorflow and pytorch, along with other libraries that support common data science programming.
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Haskell - Important Libraries
State of the Haskell Ecosystem lists libraries for many of its domains.
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What are the current challenges in Numerical Programming for Haskell?
Recently saw this repo that shows the state of Haskell in a lot of programming applications. It classifies it as Immature for Numerical Programming; I would like to know what are the challenges in it.
What are some alternatives?
ihp - 🔥 The fastest way to build type safe web apps. IHP is a new batteries-included web framework optimized for longterm productivity and programmer happiness
envy - :angry: Environmentally friendly environment variables
hackage-server - Hackage-Server: A Haskell Package Repository
rlua - High level Lua bindings to Rust
awesome-haskell - A collection of awesome Haskell links, frameworks, libraries and software. Inspired by awesome projects line.
hoogle - Haskell API search engine
superrecord - Haskell: Supercharged anonymous records
miso - :ramen: A tasty Haskell front-end framework
re-web - Experimental web framework for ReasonML & OCaml
idris-blink - A simple Idris program to blink the LED on an Arduino
cxx - Safe interop between Rust and C++
eff - 🚧 a work in progress effect system for Haskell 🚧