cl-community-spec VS Petalisp

A possible approach could be to load the GCL info files from here: https://github.com/fonol/cl-community-spec/tree/main/info

This is our best bet, I think. We should all contribute and make it better: https://github.com/fonol/cl-community-spec

A very crude first version can be found at https://cl-community-spec.github.io/pages/index.html. I called it community-spec, because ideally, after I have the generated HTML on an okay-ish level (which will still take some time probably), corrections to the output could be made by anyone who finds an error. The repository can be found here: https://github.com/fonol/cl-community-spec

Someone already pointed me to them on GitHub: https://github.com/fonol/cl-community-spec/issues/2 The new version that is online now is actually made from the sources already.

While not "as fast as C" (C is not the absolute pinnacle of performance), Common Lisp is incredibly fast compared to the majority of programming languages around today. There is even a huge amount of ongoing work being done to make it faster still. We are seeing many interesting projects that make better use of the hardware in your computer (e.g. https://github.com/marcoheisig/Petalisp).

i think lisp-stat library is actually being developed. however one numerical cl library that doesnt get enough mention and is being constantly developed is petalisp for HPC

https://github.com/marcoheisig/Petalisp

However, if you have a lisp library that puts those semantics to use, then you could get it to employ magicl/ext-blas and cl-bmas to speed it up. (petalisp looks relevant, but I lack the background to compare it with APL.)

> his means cl pagckages can be "done".

this is true if there is nothing functional that can be added to a package. however its very much not true for ml frameworks right now. new things are being added all the time in the field. however even in the package i linked you have the necessary ingredients for any deep learning model: cuda and back propagation. the other person mentioned convolution which i think is pretty trivial to implement but still, if you expect everything for you to be ready made then you should probably stick to tf and pytorch. if you want to explore the cutting edge and push the boundaries then i think common lisp is a good tool. as an aside it might also be interesting to note that a common lisp package (Petalisp) is being used for high performance computing by a german university

https://github.com/marcoheisig/Petalisp

One trick is that Common Lisp can generate and compile code at runtime, whereas static languages typically do not have a compiler available at runtime. This lets you make your own lazy person's JIT/staged compiler, which is useful if some part of the problem is not known at compile-time. Such an approach has been used at least for array munging, type munging and regular expression munging.