reanimate VS Essentials-of-Compilation

Reanimate sounds almost ideal, with its support for LaTeX. But unfortunately, it is all rendered in batch, not providing for any interactivity.

Is this the discussion you're referring to? https://github.com/reanimate/reanimate/discussions/210

It's actually pretty interesting to read. The author makes a not totally unreasonable argument as for why it uses unsafePerformIO.

Now what I'm really curious about is why the very first example on the site I clicked into the source code for, a simple 59-line example, is using unsafePerformIO. That actually worries me more because it suggests that as a user I might have to use unsafePerformIO. https://github.com/reanimate/reanimate/blob/d4d3898831edb4aa...

Not really dashboard library, but reanimate is a good library for this kind of stuff.

Some other "applications" (if you're not interested in compilers) might be writing shell scripts: https://hackage.haskell.org/package/turtle Or animating stuff: https://github.com/reanimate/reanimate and https://hackage.haskell.org/package/gloss

That aside, it seems that svg-tree doesn’t support filter elements, so I recommend reanimate-svg. You can join the Discord server for Reanimate and ask for help. Good luck.

See also reanimate, a very similar Haskell library: https://reanimate.github.io/

This is very niche, but something I've wanted to do for a while is to generate some cool physics example on the surface of a sphere with https://hackage.haskell.org/package/hamilton, and display it with https://reanimate.github.io/ (using https://hackage.haskell.org/package/linear for the projection)

Hi u/Target_Organic, I wich you a warm welcome! Haskell is often very satisfying to work with, it has a sense of beauty in it. Regarding your questions: 1. I never had big problems about performance. However, I personally place more emphasis about correctness, simplicity and readability of my programs. Performance tuning comes after. 2. For graphic libraries, I know diagrams, Reanimate and Haskell-chart. Since you seems interested by mathematical approach to graphics, I think you will find happiness there. 3. I'm not sure about the AI field. Other, more practical languages such as Python seems to have taken the lead. What is sure for me, that Machine Learning/NN would be nicely describe in Haskell with solid foundations.

if you like compilers you should this book out https://github.com/IUCompilerCourse/Essentials-of-Compilation.

Essentials of compilation by Dr. Siek. There’s a GitHub repo. Just navigate to the releases and you will find a pdf https://github.com/IUCompilerCourse/Essentials-of-Compilation/releases/tag/python-MIT-press. This book is really good and it’s practical. There’s a lot of code and it guides you along the way. So it’s a great book to self study. To supplement this you can buy Engineering a Compiler by cooper. This is more comprehensive but there’s no code in this book, only pseudo code. Start with essentials of compilation my friend. It will teach you everything you need.

You can try this book if you want something that came out this year https://github.com/IUCompilerCourse/Essentials-of-Compilatio.... Go to the releases to either get the racket version or python version. But I mean cmu uses the dragon book second edition for a graduate level compiler optimization class.

This paper is my favorite introduction to compilers, it's short and hands-on: http://scheme2006.cs.uchicago.edu/11-ghuloum.pdf

There is a book-length expansion of this paper that goes into more detail: https://github.com/IUCompilerCourse/Essentials-of-Compilatio...

Introduction to Computing"

https://dcic-world.org/

# Programming Language Theory:

"Programming Languages: Application and Interpretation"

https://www.plai.org/

# Compilation:

"Essentials of Compilation: An Incremental Approach in Python"

https://github.com/IUCompilerCourse/Essentials-of-Compilatio...

# Database Systems:

"CMU: Intro to Database Systems"

https://15445.courses.cs.cmu.edu/

"CMU: Advanced Database Systems"

https://15721.courses.cs.cmu.edu/

# Calculus I/II & Real Analysis

"A Course in Calculus and Real Analysis"

https://link.springer.com/book/10.1007/978-3-030-01400-1

"A Course in Multivariable Calculus and Analysis"

https://link.springer.com/book/10.1007/978-1-4419-1621-1

# Linear Algebra & ML:

* A Series of books by prof. Joe Suzuki without using any external library for the implementations *

"Statistical Learning with Math and Python"

https://link.springer.com/book/10.1007/978-981-15-7877-9

"Sparse Estimation with Math and Python"

https://link.springer.com/book/10.1007/978-981-16-1438-5

"Kernel Methods for Machine Learning with Math and Python"

https://link.springer.com/book/10.1007/978-981-19-0401-1

# Discrete Mathematics:

"CMU 21-228 Discrete Mathematics (prof. Poh-Shen Loh"

https://www.math.cmu.edu/~ploh/2021-228.shtml

# Cryptography:

"Serious Cryptography: A Practical Introduction to Modern Encryption"

https://nostarch.com/seriouscrypto

# Problem Solving:

"Math 235: Mathematical Problem Solving"

https://www.cip.ifi.lmu.de/~grinberg/t/20f/

This compiler book explains monadic normal form which it’s anf but not 100 percent because of the difference in how let expressions are represented. https://github.com/IUCompilerCourse/Essentials-of-Compilation

I learned to write compilers by reading "Essentials of Compilation." You can find a free pdf in the book's repo https://github.com/IUCompilerCourse/Essentials-of-Compilation/releases/tag/python-MIT-press. The book is published my MIT Press although right now the racket version is out. the python version is coming out soon. the link that I just shared is for the python version. This is a great book . I recommend it

One of the best (free/open source) books for learning how to write a compiler is Essentials of Compilation. It comes in two flavors: Racket and Python. I'm less familiar with the Python version, but it might be what you're looking for.

Not sure about specifics, but maybe https://github.com/IUCompilerCourse/Essentials-of-Compilation is worth a look?

There are pdfs in the releases section: Python - https://github.com/IUCompilerCourse/Essentials-of-Compilation/releases/download/python-MIT-press/book.pdf