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.
Or the other way around; the so called experts are actually tools in a propaganda machine, and people choose to rather believe their own experiences than second hand information.
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.
The point I am trying to make here is that the creation of that agreeable consent ("I didn't like the result, but I am going to accept it") is easier when the process is tangible and people know that they can understand manipulation, tampering, tracking without an academic degree in computer science and decades of experience in the field.
However no voting system is perfect and 100% consent is next to impossible to achieve. But for major, high stakes elections we have to take any tiny sliver of trust we can take, even if it is at the expense of getting results fast or cheap.
As a young nerd I would've said: "How hard can it be", as an older nerd I understand that the computer part is the easy part, getting people to be able to trust and follow the process is the hard part.
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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.
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.
Some people really do seem to get a lot done in Lush, so I'm not discounting its utility, but the language is sort of a mess. I took Yann's class and gave up in frustration after a few homeworks. I was very happy working in Matlab and relieved to to never see a 'bloop', 'eloop', or whatever-loop again.
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.
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.