We got to build mini versions of the first 4 languages (imperative, lisp, ML, Smalltalk) in the PL course at tufts which is now published as a textbook [1]. There used to be a prolog part that sadly got cut.

[1]: https://www.cambridge.org/ir/universitypress/subjects/comput...

I recently revisited a language comparison project, a specific benchmark tallying the cycle decompositions in parallel of the 3,715,891,200 signed permutations on 10 letters. I kept a dozen languages as finalists, different philosophies but all choices I could imagine making for my research programming. Rather than "ur" I was looking for best modern realizations of various paradigms. And while I measured performance I also considered ease of AI help, and my willingness to review and think in the code. I worked hard to optimize each language, a form of tourism made possible by AI.

The results surprised me:

             F#  100    19.17s  ±0.04s
            C++   96    19.92s  ±0.13s
           Rust   95    20.20s  ±0.38s
         Kotlin   89    21.51s  ±0.04s
          Scala   88    21.68s  ±0.04s
  Kotlin-native   81    23.69s  ±0.11s
   Scala-native   77    24.72s  ±0.03s
            Nim   69    27.92s  ±0.04s
          Julia   63    30.54s  ±0.08s
          Swift   52    36.86s  ±0.03s
          Ocaml   47    41.10s  ±0.10s
        Haskell   40    47.94s  ±0.06s
           Chez   39    49.46s  ±0.04s
           Lean   10   198.63s  ±1.02s

https://github.com/Syzygies/Compare

One correction I'd make to the article's taxonomy: Ruby is an object oriented language not an Algol. Its inspiration is Smalltalk, and much of the standard library naming comes from that route (eg collect rather than map).

Ruby is object oriented from the ground up. Everything (and I do mean everything) is an object, and method call is conceived as passing messages to objects.

While Ruby is most often compared to Python (an Algol), they come from very different evolutionary routes, and have converged towards the same point in the ecosystem. I think of Ruby as a cuddly Alpaca compared to Python's spitting camel.

    >>> type(42)
    <class 'int'>

    >>> dir(42)
    ['__abs__', '__add__', '__and__', '__bool__', '__ceil__', '__class__', '__delattr__', '__dir__', '__divmod__', '__doc__', '__eq__', '__float__', '__floor__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getnewargs__', '__getstate__', '__gt__', '__hash__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__le__', '__lshift__', '__lt__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__', '__ror__', '__round__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__trunc__', '__xor__', 'as_integer_ratio', 'bit_count', 'bit_length', 'conjugate', 'denominator', 'from_bytes', 'imag', 'is_integer', 'numerator', 'real', 'to_bytes']

I might add another class of languages: those intended to express proofs, via the Curry-Howard correspondence. Lean is a primary example here. This could be considered a subclass of functional languages but it might be different enough to warrant a separate class. In particular, the purpose of these programs is to be checked; execution is only secondary.

there's a few more semantic families: verilog, petri nets and variants, Kahn process networks and dataflow machines, process calculi, reactive, term rewriting, constraint solvers/theorem provers (not the same with Prolog), probabilistic programming,

plus up and coming (actual production-ready) languages that don't fit perfectly in the 7 categories: unison, darklang, temporal dataflow, DBSP

It may feel like a little bit of cheating mentioning the above ones, as most are parallel to the regular von Neumann machine setup, but was meaning for a while to do an article with 'all ways we know how to compute (beyond von Neumann)'.

I wrote something similar here: https://fmjlang.co.uk/blog/GroundBreakingLanguages.html

We agree on Algol, Lisp, Forth, APL, and Prolog. For ground-breaking functional language, I have SASL (St Andrews Static Language), which (just) predates ML, and for object oriented language, I have Smalltalk (which predates Self).

I also include Fortran, COBOL, SNOBOL (string processing), and Prograph (visual dataflow), which were similarly ground-breaking in different ways.

My favorite subject when studying CompSci (TU Delft) was called "Concepts of programming languages". We learned C, Scala (for functional) and Javascript (prototypes).

It made learning Elixir years later much easier.

We also had a course that basically summed up to programming agents to play Unreal Tournament in a language called GOAL which was based on Prolog.

For years I've wanted to use Prolog but could not figure out how. I ended up making a spellcheck to allow LLM's to iterate over and fix the dismal Papiamentu they generate.

I agree with "learn different classes of languages". OCaml was a language in which finally a function was a (mathematical) function. Mathematica thought me to look at expressions themselves as inputs. PostScript (with its reverse Polish notation going beyond simple arithmetics) rewired by brain.

At the same time, I don't agree with that it does not matter if one picks "Java, C#, C++, Python, or Ruby". If your goal is to do quick sort, then well, it does not.

If you want to use language for something (not only for its sake), then it makes a day and night difference. A person who wants to do 3D games and being shown Ruby or a person wanting to do exploratory data science and deep learning and being given Java are likely to get discouraged.

Reminds me a bit of Bruce Tate’s approach in 7 languages in 7 weeks, which is where I first encountered Erlang.

I think from a historical perspective, describing COBOL and Fortran as part of the ALGOL family is a stretch, but I suppose it’s a good reminder that all history is reductive.

Previous discussion: https://news.ycombinator.com/item?id=35813496

C++ has Algol roots, but I think the C++ template metaprogramming style is an ur-language of its own. You could draw some parallels with ML maybe, but they came at it from a different direction.

I wonder if Occam is worth a mention? It doesn't feel like anything else here, and is playing with its hardware synthesis descendants on a FPGA is another "mind expanding" paradigm.

This article is full of gross mistakes. For example it claims that Caml is "Cambridge ML" which is ridiculously false. Fact check every sentence. Really sad.

I would refer to the world _cognate_[0]. 'Fundamental programming cognates' sounds cool as a uni course.

[0] https://en.wiktionary.org/wiki/cognate

I always enjoy these summaries. I took my bachelor of computer science in the early 1990s. It covered a language in most of these categories.

We didn't learn APL (Who is teaching the use of those custom keyboards to 100s of young students for one semester?)

The processing power of systems at the time made it clear which language classes were practically useful and usable for the time and which were not.

Prolog ran like a dog for even simple sets of logic.

We had the best internet access and pretty powerful desktop systems for the time.

I'm still curious why we didn't learn smalltalk. Could have been the difficulty of submitting and marking a system in a particular state rather than a file of code :)

Most old-timers here are familiar with a Prolog-variant: make. Anyone who's struggled over a complex Makefile wishes they had a more sane declarative language!

I’ve very slowly been trying to do the “99 problems” list in each of these languages groups. It’s been a fun experience seeing the differences. Though I think I would need a larger, less algorithmic, project to really see each group’s strengths. Especially for the OOP group.

One thing the article didn’t touch on was SmallTalk’s live visual environment. It’s not a normal source code / text language.

Reminds me of the six programming language memory models:

https://canonical.org/~kragen/memory-models/

It did my heart good to see Forth listed.

Plankalkül

- Algol 68 docs: https://algol68-lang.org/resources 'a68g' it's a free as in freedom compiler.

- Forth: you can use PFE,Gforth for ANS Forth requeriments. Or EForth if you reached high skills levels where the missing stuff can be just reimplemented.

EForth under Muxleq: https://github.com/howerj/muxleq I can provide a working config where a 90% of it would be valid across SF.

Starting Forth, ANS version: https://www.forth.com/starting-forth/

Thinking Forth, do this after finishing SF: https://thinking-forth.sourceforge.net/

Also, Forth Scientific Library. You can make it working with both GForth and PFE, just read the docs.

Full pack: https://www.taygeta.com/fsl/library/Library.tgz

Helping Forth code for GForth/PFE. If you put it under scilib/fs-util.fs, load it with:

    s" scilib/fsu-util.fs" included

https://www.taygeta.com/fsl/library/fsl-util.fs

- Lisp. s9fes, it will compile under any nix/Mac/BSD out there, even with MinC.

S9fes: http://www.t3x.org/s9fes/

Pick the bleeding edge version, it will compile just fine.

For Windows users: MinC, install both EXE under Windows. First, mincexe, then buildtools*exe: https://minc.commandlinerevolution.nl/english/home.html

Then get 7zip to decompress the s9fes TGZ file, cd to that directory, and run 'make'.

Run ./s9 to get the prompt, or ./s9 file.scm where file.scm it's the source code.

In order to learn Scheme, there's are two newbie recommended books before "SICP".

Pick any, CACS, SS, it doesn't matter, both will guide you before SICP, the 'big' book on Scheme:

Simply Scheme https://people.eecs.berkeley.edu/~bh/pdf/

Simply.scm file, select from ';;; simply.scm version 3.13 (8/11/98)' to '(strings-are-numbers #t)' and save it as simply.scm

https://people.eecs.berkeley.edu/~bh/ssch27/appendix-simply....

Concrete Abstractions

Book:

https://www.d.umn.edu/~tcolburn/cs1581/ConcreteAbstractions....

The SCM files needed to be (load "foo.scm") ed in the code in order to do the exercises:

https://github.com/freezoo/scheme-concabs

If you are en Emacs user, just read the Elisp intro, it will work for a different Lisp family but with similar design.

Spot the differences:

Scheme (like s9):

    (define (square x)
     (* x x))

We try:

    >(square 20) 
    400

Elisp/Common Lisp (as the web site shows):

    (defun square (x)
     (* x x))

Same there:

     >(square 20)
     400

- Ok, ML like languages:

https://www.t3x.org/mlite/index.html

If you follow the instructions on compiling s9, mlite it's similar with MinC for Windows. If you are a Unix/Linux/Mac user, you already know how to do that.

You got the whole docs in the TGZ file, and the web.

       sudo  chicken-install srfi-203
       sudo chicken-install srfi-216

      (import scheme)
      (import (srfi 203))
      (import (srfi 216))

       csi yourfile.scm 

       chicken-csi yourfile.scm

         M-x install RET geiser-chicken

 if you are lazy. You can install the SICP book with

        M-xpackage-install RET sicp

       M-x info RET

      M-x geiser 

Isn‘t FORTRAN also a ur-language? It was invented in 1957.

I would add another to the list, which is languages where every expression yields zero or more values, particularly `jq`. there are some antecedents in Icon and xquery, but these generally require explicitly opting into either production or consumption of value streams, where jq does this stream processing automatically from the ground up. (icon requires use of a suspend and needs an every clause to walk the generated values, xquery requires explicit 'for' statements over streams as many builtin operators fail on value streams)

in jq, the comma separates expressions, which independently yield values. a span of such expressions is called a 'filter', since they are always run by passing values from the prior filter into them (with the initial values sourcing from json objects on stdin, or an implicit null if you pass -n to the program).

    $ jq -nc ' def x: "a", "b", "c" ; def y: 1, 2, 3 ; x, y '
    "a"
    "b"
    "c"
    1
    2
    3

    $ jq -c '. + 10, . + 20' <<< '1 2 3'
    11
    21
    12
    22
    13
    23

brackets collect values yielded inside of them.

    $ jq -nc ' def x: "a", "b", "c" ; def y: 1, 2, 3 ; [x,y] '
    ["a","b","c",1,2,3]

if you have a complex object that includes multiple expressions yielding multiple values, construction will permute over them.

    $ jq -nc ' def x: "a", "b", "c" ; def y: 1, 2, 3 ; {"foo": x, "bar": y} '
    {"foo":"a","bar":1}
    {"foo":"a","bar":2}
    {"foo":"a","bar":3}
    {"foo":"b","bar":1}
    {"foo":"b","bar":2}
    {"foo":"b","bar":3}
    {"foo":"c","bar":1}
    {"foo":"c","bar":2}
    {"foo":"c","bar":3}

the pipe operator `|` runs the next filter with each value yielded by the prior, that value represented by the current value operator `.`.

    $ jq -nc ' 1,2,3 | 10 + . '
    11
    12
    13
    $ jq -nc ' 1,2,3 | (10 + .) * . '
    11
    24
    39

binding variables in the language is similarly done for each value their source yields

    $ jq -nc ' (1,2,3) as $A | $A + $A '
    2
    4
    6

functions in the language are neat because you can choose to accept arguments as either early bound values, or as thunks, with the former prefixed with a $.

for example, this runs `. + 100` parameters context, with `.` as the 10,20,30 passed to it:

    $ jq -nc ' def f($t): 1,2,3|$t ; 10,20,30|f(. + 100) '
    110
    110
    110
    120
    120
    120
    130
    130
    130

where this runs `. + 100` in the context of its use inside the function, instead receiving 1,2,3:

    $ jq -nc ' def f(t): 1,2,3|t ; 10,20,30|f(. + 100) '
    101
    102
    103
    101
    102
    103
    101
    102
    103

so you could define map taking a current-value array and applying an expression to each entry like so:

    $ jq -nc ' def m(todo): [.[]|todo] ; [1,2,3]|m(. * 10) '
    [10,20,30]

it's a fun little language for some quick data munging, but the semantics themselves are a decent reason to learn it.

Which category is TRAC? https://en.wikipedia.org/wiki/TRAC_(programming_language)

Another direction to explore logic languages is Datalog.

Lots of us are having fun identifying our choice for missing family :)

One I might suggest is scripting languages, defined loosely by programming tools which dispatch high-level commands to act on data pipelines: sed, AWK, the sh family, Perl, PowerShell, Python and R as honorary members. In practice I might say SQL belongs here instead of under Prolog, but in theory of course SQL is like Prolog. Bourne shell might be the best representative, even if it's not the oldest.

AWK et al share characteristics from ALGOL and APL, but I feel they are very much their own thing. PowerShell is quite unique among modern languages.

Folks might find the following useful for studying PLs;

1) Advanced Programming Language Design by Raphael Finkel - A classic (late 90s) book comparing a whole smorgasbord of languages.

2) Design Concepts in Programming Languages by Franklyn Turbak et al. - A comprehensive (and big) book on PL design.

3) Concepts, Techniques and Models of Computer Programming by Peter Van Roy et al. - Shows how to organically add different programming paradigms to a simple core language.

[dead]

(2022) and unfortunately advice to spend significant amounts of time in learning multiple languages is becoming rapidly redundant in the LLM age.