sicmutils VS pytudes

They seem to be in the middle of transitioning from the old repo.

There are some good resources here including some nicely formatted HTML versions of the book: https://github.com/sicmutils/sicmutils/wiki/SICM-and-FDG-Lea...

^ The Github repo contains a Clojure version of the Scheme library used by the book.

If you want to try this out, give the sicmutils Computer Algebra System a go (I’m the maintainer). Repo lives here: https://github.com/sicmutils/sicmutils

The library works in the browser as well, so interactive TeX rendering from Clojure symbolic expressions and functions is available at the quickstart page here: https://nextjournal.com/try/samritchie/sicmutils

I am the maintainer of the "sicmutils" computer algebra system in Clojure, and I think that you'll find it very nice for your project: https://github.com/sicmutils/sicmutils

https://github.com/sicmutils/sicmutils is a really interesting development in the direction(s) you stated in b. Since terms can be rendered in various ways (latex, js, etc.), they can be embedded in documents, web pages, etc. You can go from symbolic expressions to animated dynamic systems with relevant formulae.

- multiple stories about the same piece of code, but all with the ability to IMPORT the story as a library

I've been writing sicmutils[0] as a "literate library"; see the automatic differentiation implementation as an example[1].

A talk I gave yesterday at ELS demos a much more powerful host that uses Nextjournal's Clerk to power physics animations, TeX rendering etc, but all derived from a piece of Clojure source that you can pull in as a library, ignoring all of these presentation effects.

Code should perform itself, and it would be great if when people thought "LP" they imagined the full range of media through which that performance could happen.

[0] sicmutils: https://github.com/sicmutils/sicmutils

[1] autodiff namespace: https://github.com/sicmutils/sicmutils/blob/main/src/sicmuti...

[2] Talk code: https://github.com/sritchie/programming-2022

[3] Clerk: https://github.com/nextjournal/clerk

Hey, so fun to see this here! These demos feature work from https://github.com/sicmutils/sicmutils and the newly maintained-by-me Mathbox library.

I’m around and happy to answer any questions about the library, future plans, etc.

Give my SICMUtils computer algebra system a look as well, if you like Lisp / Clojure: https://github.com/sicmutils/sicmutils

Works on the web too, which is a big boost for sharing work.

It is good enough! Almost all code forms from the book live in the tests (see the FDG directory[0], for example), and there are a few nice environments like Nextjournal[1] where everything from the books works in the browser.

The Clojure port is quite fast, faster than the original for all benchmarks GJS has sent me, and more fleshed out. (That will change, as I've been pushing bugfixes and performance improvements back upstream as I go, as a meager gift to GJS for making this huge, amazing library in the first place.)

I actually wrote to GJS this morning asking for instructions on how to compile the original "scmutils", since I have the same problem. He responded saying he'll get back to me this afternoon, so I'll post here once I have details.

If you are still interested in getting the books going with MIT-Scheme, I put a decent amount of work into the exercises using the original codebase here[2], including a dockerized version of mit-scheme[3] and the scmutils package[4] that might be useful.

- [0] https://github.com/sicmutils/sicmutils/tree/main/test/sicmut...

- [1] https://nextjournal.com/try/samritchie/sicmutils/

- [2] https://github.com/sicmutils/sicm-exercises

- [3] https://hub.docker.com/r/sritchie/mit-scheme

- [4] https://hub.docker.com/r/sritchie/mechanics

Peter Norvig's work is great to learn from https://github.com/norvig/pytudes

I've been a programmer for 25 years. A realization that has crept up on me in the last 5 is that not everyone thinks that functions should be short: there are two cultures, with substantial numbers of excellent programmers belonging to both. My question is: how do we maintain harmonious, happy, and productive teams when people can disagree strongly about this issue?

The short-functions camp holds that functions should be short, tend toward the declarative, and use abstraction/implementation-hiding to increase readability (i.e. separable subsections of the function body should often be broken out into well-named helper functions). As an example, look at Peter Norvig's beautiful https://github.com/norvig/pytudes. For a long time I thought that this was how all "good programmers" thought code should be written. Personally, I spent over a decade writing in a dynamic and untyped language, and the only way that I and my colleagues could make that stuff reliable was to write code adhering to the tenets of the short-function camp.

The long-functions camp is, admittedly, alien to me, but I'll try to play devil's advocate and describe it as I think its advocates would. It holds that lots of helper functions are artificial, and actually make it _harder_ to read and understand the code. They say that they like "having lots of context", i.e. seeing all the implementation in one long procedural flow, even though the local variables fall into non-interacting subsets that don't need to be in the same scope. They hold that helper functions destroy the linear flow of the logic, and that they should typically not be created unless there are multiple call sites.

The short-function camp also claims an advantage regarding testability.

Obviously languages play a major role in this debate: e.g. as mentioned above, untyped dynamic languages encourage short functions, and languages where static compilation makes strong guarantees regarding semantics at least make the long-function position more defensible. Expression-oriented and FP-influenced languages encourage short functions. But it's not obvious, e.g. Rust could go both ways based on the criteria just mentioned.

Anyway, more qualified people could and have written at much greater length about the topic. The questions I propose for discussion include

- Is it "just a matter of taste", or is this actually a more serious matter where there is often an objective reason for discouraging the practices of one or other camp?

- How can members of the different camps get along harmoniously in the same team and the same codebase?

I have the same impression. Reading the code, he uses global variables [1], obscure variable (k, bw, fw, x) and module names ("pal.py" instead of "palindromes.py"), doesn’t respect conventions about naming in general (uppercase arguments [2], which even the GitHub syntax highlighter is confused about). This feels like code you write for yourself to play with Python and don’t plan to read later.

Some parts of the code feel like what I would expect from a junior dev who started learning the language a couple weeks ago.

[1]: https://github.com/norvig/pytudes/blob/952675ffc70f3632e70a7...

[2]: https://github.com/norvig/pytudes/blob/952675ffc70f3632e70a7...

Prolog is excellent for bikeshedding, in fact that might be its strongest axis. It starts with everything you get in a normal language such as naming things, indentation, functional purity vs side effects, where to break code into different files and builds on that with having your names try to make sense in declarative, relational, logical and imperative contexts, having your predicates (functions) usable in all modes - and then performant in all modes - having your code be deterministic, and then deterministic in all modes. Being 50 years old there are five decades of learning "idiomatic Prolog" ideas to choose from, and five decades of footguns pointing at your two feet; it has tabling, label(l)ing, SLD and SLG resolution to choose from. Built in constraint solvers are excellent at tempting you into thinking your problem will be well solved by the constraint solvers (it won't be, you idiot, why did you think that was a constraint problem?), two different kinds of arithmetic - one which works but is bad and one which mostly works on integers but clashes with the Prolog solver - and enough metaprogramming that you can build castles in the sky which are very hard to debug instead of real castles. But wait, there's more! Declarative context grammars let you add the fun of left-recursive parsing problems to all your tasks, while attributed variables allow the Prolog engine to break your code behind the scenes in new and interesting ways, plenty of special syntax not to be sneezed at (-->; [_|[]] {}\[]>>() \X^+() =.. #<==> atchoo (bless you)), a delightful deep-rooted schism between text as linked lists of character codes or text as linked lists of character atoms, and always the ISO-Standard-Sword of Damocles hanging over your head as you look at the vast array of slightly-incompatible implementations with no widely accepted CPython-like-dominant-default.

Somewhere hiding in there is a language with enough flexibility and metaprogramming to let your meat brain stretch as far as you want, enough cyborg attachments to augment you beyond plain human, enough spells and rituals to conjour tentacled seamonsters with excellent logic ability from the cold Atlantic deeps to intimidate your problem into submission.

Which you, dear programmer, can learn to wield up to the advanced level of a toddler in a machine shop in a mere couple of handfuls of long years! Expertise may take a few lifetimes longer - in the meantime have you noticed your code isn't pure, doesn't work in all modes, isn't performant in several modes, isn't using the preferred idiom style, is non-deterministic, can't be used to generate as well as test, falls into a left-recursive endless search after the first result, isn't compatible with other Prolog Systems, and your predicates are poorly named and you use the builtin database which is temptingly convenient but absolutely verboten? Plenty for you to be getting on with, back to the drawing boar...bikeshed with you.

And, cut! No, don't cut; OK, green cuts but not red cuts and I hope you aren't colourblind. Next up, coroutines, freeze, PEngines, and the second 90%.

Visit https://www.metalevel.at/prolog and marvel as a master deftly disecting problems, in the same way you marvel at Peter Norvig's Pytudes https://github.com/norvig/pytudes , and sob as the wonders turn to clay in your ordinary hands. Luckily it has a squeaky little brute force searcher, dutifully headbutting every wall as it explores all the corners of your problem on its eventual way to an answer, which you can always rely on. And with that it's almost like any other high level mostly-interpreted dynamic programming / scripting language.