mmaul

This reminds me a lot of Pure (https://agraef.github.io/pure-lang/). What I liked about Pure is the symbolic rewrite + the Haskell-esque syntax with out the strictness + easy ffi.

I really do like how you have smoothly integrated this into Python. Though the symbolic pattern matching is well pretty amazing and make me think about things a little different now. You could probably implement something like this in Julia with it's macros and flexibility in manipulating the AST. I hate Python, but I'm forced to bow to the ecosystem.

The trend is growing here to, sadly. It's not people disagree with experts experts but that truth told by the, disagrees with a distorted perception or reality.

Yea I guess the problem is with a party that is intent on disregarding truth or facts or verifiability or reality is not going to prevail against attacks against the system (unless it is rigged in their favor). What does code matter to them.

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Common Lisp has had Jupyter integration for a while.

First cl-jupyter (https://github.com/fredokun/cl-jupyter) which is now in maintance mode and now common-lisp-jupyter (https://github.com/yitzchak/common-lisp-jupyter).

Here is a notebook I did awhile back with cl-jupyter: https://github.com/mmaul/clml.tutorials/blob/master/CLML-Win...

You can already have lisp (common) in jupyter notebooks via cl-jupyter. Here is an example https://github.com/mmaul/clml.tutorials/blob/master/CLML-Win...

If you want to use Hy in a note book you will need a jupyter kernel for it and I belive there is one called hy_kernel

Roswell, is a "killer app", as far as using Common Lisps with Continuous Integration and Coverage services (TravisCI, CircleCI, Coveralls et al) is concerned. It is not an interface to CI systems it is an Lisp Implementation manager and makes using CI services dead easy event multiple lisp implementations. Roswell also provides a handy hash bang scripting for Lisp.

Regarding the Power Point thing you might want to check out Beamer. It is a extension for publishing presentations from org mode. http://orgmode.org/worg/exporters/beamer/tutorial.html

Lush's purpose a little dfferent than Matlabs. The abstractions are a little lower level than Matlab for instance. But then again you you can compile your functions directly to machine code. There are trade offs to everything in life.

Matlab,Ocatave,R,S are great but if you need to be closer to the metal, Lush offers a very good compromise.

If you have taken Andrew Ng's Machine class the handwriting recognition system that is mentioned in the course was implemented in Lush. I think the original code is is even included in the demos distributed with Lush.

Lush is an excellent platform for machine learning. There are bindings to gnuplot ,opencv, lapack, gsl, an optimization library for gradient descent, a machine learning framework, a nerual network simulator.

It also has very nice matrix and vector manipulations features built in to the language and is very easy to bind to C code.