1. Steep learning curve without benefits: APLs like K/Q/J optimize for concision to the point of unreadability. Sure, it might be fun to decipher clever K/Q one-liners, but if you have to do it everyday for work in production, it's just exhausting. This lack of readability has limited adoption and the growth of its user community. For any technology is to have a viable community, it needs to be usable by a critical mass. K/Q in specific but APLs in general never had that, especially with more user-friendly alternatives like R/Matlab for scientific computing.

2. No open source community. There's Kona (https://github.com/kevinlawler/kona) by Kevin Lawler, but again, its community has been small because the underlying language is not design to be usable by enough people.

3. Domain specificity of array languages: These days, array languages are just too limited in its scope. Scientific computing, the bread and butter of any array programming language, has been made possible in other languages (ex: Python), making array languages not powerful nor compelling enough to use because most of its strengths can be found in other languages and its weaknesses are too crippling.

I heard that Arthur Whitney found his successors (a pair of Russian programmers from St. Petersburg, iirc), so K/Q will likely to be around beyond his retirement. That said, I think its user base will continue to shrink for above reasons.

Also, terseness may be a problem, but only when you're not used to it. Normal languages use a vertical structure, while APL languages use a horizontal structure. It is just a matter of looking for meaning in each line, instead of trying to combine lines to get the meaning of code, like we do in standard languages.

Maybe you're one of the happy few that spends more time writing code than reading it, but for an overwhelming majority of devs, the opposite is true. That's why even small readability improvements matter much more than a couple cool tricks a language may have.

Now, I don't know either K or Q, but I have a very, very, very hard time believing that they are too terse and unreadable because of that.

From my experience, the "improvements in readability" are of course good, but they only shorten the time to master the language. Once you learn the language well enough, it becomes readable. And the very few syntactic issues which are objectively hard to distinguish/are confusing the eye, etc. tend to be worked around with formatting and font settings.

Written Japanese is totally impossible to read if you don't know it - and it takes reasonably longer to learn it. However, once you know it, it's readable. A Japanese person reading a newspaper in Japanese uses as much effort as me reading a newspaper in my native language.

    [1] From Pascal and later Delphi users.
    [2] From C users.
    [3] From PERL users.
    [4] From PHP users.
    [5] From Python users.

I have not found this definition of "read" to be common in other language communities.

Edit: I recently started learning the J language. While it may not necessarily be a language you want to write your production code in, it teaches powerful concepts - it let's you write functions which can deal with radically different shapes of data with ease, via use of the operators to change the behavior of a verb to monad or dyad. It provides great insight into higher level descriptions of your domain

A comparison I can think of is that Lisp's whole code/data duality works well because the code is s-exprs, and that it would be pretty awkward to transform that if the syntax were more python-y

On the other hand, semicolons and braces have nothing to do with any Java features....

code/data duality = homoiconicity

There are many languages which do not use sexps but are homoiconic.

See "examples" at https://en.wikipedia.org/wiki/Homoiconicity

Yes, most of the examples look very limited compared to Lisp but there is Rebol / Red (http://www.red-lang.org/) which is atleast as powerful as Lisp in terms of homoiconicity (as well as in other things), if not more. Red/Rebol don't use sexps, don't look like this (((())))), are highly dynamic and homoiconic.

See "Homoiconicity in Rebol" in https://en.wikipedia.org/wiki/Homoiconicity (well, no good examples over there which shows Rebol's features properly.)

> RE the learning curve: Do you think the APL family can exist without the terse syntax?

So yes, I think that the APL family could exist without the terse syntax, but well hey, everything has its tradeoffs and all this is subjective - everyone has his own thinking - there might be someone in this world who in love with the APL syntax :D

That said, Red looks like a godsend. I was looking for something I can use without much setup and learning costs, that is small, elegant, efficient, fast, gets the job done, and is long-term maintainable. Red seems to fit the bill. I'm still evaluating it and it still looks good.

Also remember that Red is still alpha, and lots of cool, planned features are not yet implemented.

You can see the current roadmap on https://trello.com/b/FlQ6pzdB/red-tasks-overview

Considering the evolution of APL, yes.

Ken Iverson was teaching math in Harvard, when he was growing the - mathematical - notation to describe the subject formally. It was happening in late 1950's, and the computers were entering our life. At some point it became reasonable to use that evolved notation as A Programming Language - or APL, for short.

However, the idea was to make understanding easier - all along! Same idea which Alan Kay is pursuing with his works, same idea with many good technologies, when they are young - Java, JavaScript... Importance of notation - be it in math, or in formal-and-executable-math is the ideology of early APL. Now they say "it's hard to beat expressiveness of a whiteboard", but still attempt to use, say, an integral sign (and, similarly, one-letter variables), as one would do when showing the idea on a whiteboard. The history of hand-written math preferred terse notation - and that is carried on in APL as executable math.

So... yes, terseness is important, and APLers will also say that choice of symbols is important too. It's only to beginners that they feel strange - pretty soon programmer learns them, gets used to them and refuses to substitute them with more readable longer variants - even though that would make certain things better. You don't name variables on a whiteboard with long_names or camelCase - at best you use sub- and superscripts. Granted, you can have cups and power towers and other things, and they were really impractical with 1960's level technologies... yet at least you have a singular text direction for expressions, in editors or in print.

J makes certain things more logical, and switches to ASCII. May be we'll invent even better notation (to me, this - http://matt.might.net/articles/discrete-math-and-code/ - looks like a good start to think about basic building blocks), but so far, we have APL family languages as arguably closest thing to math notation. To some, it's the closest way they can imagine between making a solution in the head and explaining that to computer.

Do you think mathematics would exist or be shared and done as efficiently if it did away with the terseness of its symbols, Greek letters, etc...?

Yes, in J you can define average as:

  +/%#

  (+/%#) 2 3 4 5

Or,

  avg=: +/%#

Or,

  sum =: +/
  divided_by =: %
  tally =: #

avg=: sum divided_by tally

avg 2 3 4 5 returns 3.5

But, that defeats the purpose of being able to concisely manipulate abstractions to get work done for the sake of readability, and ONLY for those people who refuse to learn the symbols as in mathematics. Have you ever heard from an adult who has had basic mathematics say, 'I have to keep looking up that greek letter π'? It only takes me a very little while to review my concise code. I'd rather learn this sort of pattern recognition of short code when dealing with high abstractions to the spaghetti-like appearance, to me, of Java or C (I like C!). Strangely enough I find Lisp more clear to C-like langs. It may be more than just subjective preference, but order of reasoning in the syntax.

For my part, the notation was certainly part of it. It matters. I used to love maths but I started finding it impossible to read even as the concepts were still easy enough to understand. I'd write things out as programs instead to understand it without being hampered by the notation. I learned symbolic differentiation that way, for example.

But I quickly realised that this basically closed maths off to me as a viable subject, and I opted out of all optional maths courses other than boolean logic for my CS studies.

I get that for those who find mathematical notation easy to work with, it seems indispensable, but don't underestimate the amount of people for whom the notation is the barrier that basically makes mathematics inaccessible.

I've picked up quite a bit of maths since, but always by understanding the concepts through code rather than trying to parse mathematical notation.

APLs are - as Arthur Whitney likes to employ - supposed to be read symbol-by-symbol, since each one represent a whole operation. That's why APL program are so dense - you may have plenty of work done in a short line (but many APL writers prefer to keep lines short, to help with understanding). A screenful of APL can be moderately sized program, without need for scrolling... When you write J, you might be tempted to add more and more operations to the left (APL and J work from right to left), until it's hard to "explain" what the whole expression does - because it does so much. With an alternative - short, understandable expressions - you have another problem, that of naming :) - trying to give a traditional, long-worded name to an intermediate result of expression doesn't fit well with the rest of the style... So a good sense of balance is very valuable.

Another saying which may help to understand APL's mindset is that APLers spend 5 minutes to write a program, then spend next 1 hour to write the same program better and cleaner. That's what I understand as refactoring; the idea is to make the code more readable, the expression more obvious, more obviously correct, more reuseable... Properly done, the expression is a pleasure to look at and well worth the efforts to decipher it symbol-by-symbol. And another great option is documentation - lots of comments, just as in good math texts, where the idea is explained in plain English and then succinctly put to code.

This way I better see the math background behind the problem. I certainly have an easier time to change something here and there - the changes required are so small. I also see similarities between different pieces of code, if they are put as similar expressions close to each other, on the same screen. Yes, the notation is terse... but it at least has good sides.

There is nothing difficult about reading these languages to people who use them, anymore than it is difficult for a musician to read sheet music. In fact, APL languages have less ambiguous parsing and precedence rules which I find make them easier to read.

I'd argue you only think that because they've opted for a familiar syntax. E.g. the object model (or lack of one) is vastly different between these languages and they employ drastically different type systems.

Ruby is closer to Smalltalk than C in most respects other than syntax, for example, and provides most of the abilities lisp gives you, including the ability to define domain-specific languages. The major aspect you're not getting from Ruby would be homoiconicity.

Here's and article comparing Lisp and Ruby[1].

I'm not saying learning languages outside of this group isn't important, but that a lot of the reason why languages like Lisp and APL are seen as so different has more to do with syntax than semantics. Most people don't know what the semantic differences even are, because getting past the alien syntax is too much effort. When you do, the differences aren't all that huge.

[1] http://www.randomhacks.net/2005/12/03/why-ruby-is-an-accepta...

That reminds me of how McCarthy always intended to replace S-expressions with M-expressions, but it never happened because programmers found they liked the S-expressions after all.

  [1]  http://shenlanguage.org/
  [2]  https://github.com/deech/shen-elisp

[0] http://www.kerfsoftware.com/

I have yet to meet a programming model that can only exist in one specific syntax. The closest I've seen is lisp-style metaprogramming, which requires syntax that makes its nesting structure obvious (you're operating on pieces of syntax, so of course you need to know something about it); of course, you could use indentation instead of parens to show that structure if you like. APL-like operator lifting is entirely a semantic issue. It can exist just fine in s-expressions, ML-like syntax, Python-like syntax, etc.

APL-family languages typically even tie their own hands in terms of semantic flexibility because of their syntax. They have separate syntactic classes for (first-order) data, first-order functions, and second-order functions. The parser doesn't know what to do with an identifier unless it knows that identifier's definition, which makes compiling APL pretty awkward (ever seen a parser that tries to do data flow analysis?). It also limits their flexibility as functional programming languages. J, at least, by keeping a symbolic representation of all functions at run time, enables a sort of workaround where you can package up a vector of first-order functions, but this is much more awkward to work with than actual first-class functions.

But what really disappoints me about APL-style syntax is limiting its flexibility as an array-processing language. The language semantics offer this great ability to automatically "lift" any operation to work on arbitrarily high-dimensional arrays. Want the square root of every number in a 3-tensor? Go for it! Want to average every column in a matrix? Just average it! In more recent incarnations, you can even mix and match the shapes you're lifting up to, like adding a vector to every column in a matrix.

But that flexibility stops short if your function needs three inputs. Infix-only syntax has no way to write function application with more than two arguments, so you're forced to pack some of those inputs together into a single argument. Now you can't choose to lift the operation to every argument separately: you're forced to put the same array structure around multiple arguments. If you try to get around this by writing a second-order function (rather like currying), you now have one argument you can't lift to because only first-order functions get the dimension lifting.

So not only is the programming model not inextricably tied to this particular syntax, I'm not even convinced it's the best syntax for the programming model.

[1] http://www.sac-home.org/ [2] http://dhmunro.github.io/yorick-doc/

http://convergepl.org/

'type

They're not directly comparable, but kdb+ would crush InfluxDB head to head in an apple/orange comparison if used for the same thing. I actually can't think of any time series-like store that remotely comes close to the feats I have seen kdb+ with Q accomplish. Too bad the productivity and knowledge overhead is so high.

Seriously, I'm working in realtime metrics right now and I spent a week playing with kdb+ as the central store. I can tell there's a gold mine there, but I'm just not swinging a big enough pickaxe. If there was a dumb Python/Go/etc typical ops hacker frontend to that whole system I would be throwing every dollar I have to my name at it; my kingdom for burying that system under a few layers of abstraction and paying some quant Q hacker a Ferrari or two a year to hide the sausagemaking from everyone else.

(Related aside: Like building furniture from stacks of cash? Learn K/Q/kdb+ and head for NYC.)

I suspect there is a religious/mythological aspect to K/Q's reputation for speed. During the year that I was using Q I often found it to be slower than the equivalent Matlab code (which benefits from a JIT) and or even NumPy (which has many naively implemented operations).

The biggest advantage of a language like Q is that it's inherently parallel. Map-reduce is naturally built into the language, so this allows the users to think in terms of breaking problems down into sub-problems and parallelizing them via the "parallel apply" construct[1]

Also another very powerful feature is the braindead simplicity of Inter process communication between KDB+ instances [2]

[1] http://code.kx.com/wiki/Reference/peach [2] http://code.kx.com/wiki/Startingkdbplus/ipc

This makes a real difference in ad-hoc environments where you want to setup and tear down complex trading software in real-time

I hear this often about both array and functional languages but I think this kind of slogan leads to a misunderstanding of the MapReduce framework (and what's special about it).

In a functional or array language, map and reduce have the following signatures:

    map : key list -> (key -> value) -> value list

    reduce : value list -> (value, value -> value) -> value

    read : block -> (input_key, input_value) list
    map : (input_key, input_value) -> (output_key, output_value) list
    reduce : (output_key, (output_value list)) -> (output_value, output_value -> output_value) -> (output_key, output_value)

[1] http://code.kx.com/wiki/Reference/peach

[2] http://code.kx.com/wiki/Reference/peach#Peach_using_multiple...

E.g. http://kparc.com/q4/readme.txt (Trillion Row Benchmarks)

It depends what you're doing, but the vast superiority of kdb+ versus its competitors has been demonstrated:

http://kparc.com/q4/readme.txt

1) What happens when you run these benchmarks on a machine with only 16GB of RAM?

2) How does the KDB performance compare to doing the equivalent operations on a Pandas DataFrame (which, since these are simple in-memory operations, seems like the only fair comparison).

kdb+ uses ~ 1.2 MB of Resident RAM at startup. In addition, kdb+ storage model in memory and on disk have very little overhead (a few hundred bytes) per column over the raw binary data.

The above, combined with memory mapping, allow for large databases to be queried with great performance.

I query billion row futures databases on a Macbook Pro with 16 GB RAM with kdb+.

> 2) How does the KDB performance compare to doing the equivalent operations on a Pandas DataFrame (which, since these are simple in-memory operations, seems like the only fair comparison).

Could someone provide the equivalent Pandas code running against similar TAQ data for the queries in the following benchmark?

http://kparc.com/q4/readme.txt

Do you have any more specific use cases for the kind of analysis that kdb+ is used for? I'm working with timeseries sources that represent streams from internal variables from embedded systems and would like to evaluate whether kdb+ brings something to the table that we could use. (What we are doing probably falls into the category Complex Event Processing, i.e. windowed operations on the data stream with some predicates being evaluated globally.)

kdb+ does this very efficiently.

0 - https://blog.step.com/2016/04/08/an-open-source-project-for-...

That sounds like the low-end base salary for ~entry level. It probably gets topped up with ~100k bonus. There are a couple places that hire many but don't pay well.

At what I think is the high end, I see about an offer per quarter of $500-900k base with $1-2M bonus (usually guaranteed for the first year) for pure technology role. Add more front office work and the bonus potential shoots up (but it's a tough gig at the moment at least).

For context, non-kdb principal level roles I've seen on west coast top out at around $1M, mostly in RSUs, from a say a $250k base at the usual names.

I've seen offers in the $1-10M range on the east coast to build competitive technology. Amusingly, on the west coast, I've only seen the stereotypical "be my technical cofounder and get screwed on comp" offers to do so.

It's not just knowing an APL though -- far from it. For the chi/nyc it's a mix of hardcore low-level stuff, and some market knowledge. For the west coast, a mix of low level stuff (less hard core) and more distributed systems stuff.

I'm interested to know more about this, assuming you have time.

Occasionally someone pops up wanting to do a company to compete with them but they're typically offering paper of dubious value, covered in slime.

In any event, I'm not particularly interested in going to war to steal their market share. I do worry that they're going to go into a long decline to irrelevancy though -- I'd very much prefer that not to happen. I'd rather they/FD write a new generation building on the best of arthur's work plus some things more suited for new platforms and workloads. There are big opportunities there.

Maybe kOS will be open-source. Maybe it will be the big one, Arthur's chance to change the course of software development history.

http://code.jsoftware.com/wiki/JDB/Announcement

http://www.jsoftware.com/jdhelp/overview.html

https://scottlocklin.wordpress.com/2012/09/18/a-look-at-the-... comes very close to capturing the ingenuity of J language.

After going through the J primer, KDB+/Q feels like a distillation of J - it basically took the most accessible ideas of J and packaged them for mainstream production use - some of their features such as these make it easy to setup a computing harness for electronic trading in no time:

http://code.kx.com/wiki/Cookbook/LoadingFromLargeFiles

http://code.kx.com/wiki/Startingkdbplus/tick

Also, the footprint of the kdb runtime and installation is unparalleled. I've run systems 24 x 7 x 365

they tried. About a year ago they released 32 bit version for free with shockingly liberal license - anything goes, including commercial use, unlimited. About couple of months ago they pulled it back and replaced with free non-commercial use only. well, they are the owners, it is their call, but still k will remain as "one of the most impressive software achievements around", orthogonal to most of the [fucked up] IT trends of the past 15 years. It is a shame if it fades into a quantitative elitist oblivion.

Although mongoDB is a demonstration of how to build a successful business around open-source, it's very different to start out that way rather than move that way after you're already established as closed/licensed product. Kx would have to essentially kill-off their existing profit stream with the goal that they could build a larger business around FOSS kdb+. That's a risky proposition, fraught with many pitfalls.

It is a shame they backed-off of the fully-open 32-bit version. At least that had some potential to spur some user-base/ecosystem growth (at a minimum, this could have encouraged the development of novel clients/editors/REPLs/debuggers/charting/etc...), without threatening their core profit stream.

You are correct though that their model over the years has been to extract a large amount, up front, from a small number of users. They (mostly FD) have failed to make the leap to users outside finance due to what I'd call cultural reasons. They also lack strong technical leadership imnsho (they're, at heart, not an engineering company).

I'd certainly take a swing at doing an open source version for them but not clear to me that they'd know how to play it.

I do know in addition to changing the license they have made changes to the software since that time. The size of the binary has increased.

I worry more about Kx being acquired by some large company, maybe a competing database vendor, that cares little about software quality.

Ask the commenter one down from me, 'wsfull, he knows what I'm talking about!

RE: 36MM - that makes sense. q/kdb is the definition of "low volume, high margins". You only have so many IB's in Midtown, wealth management funds in Stamford, and a PIMCO here and there in Newport to shop your product to. (As opposed to say, Oracle, where there's broad appeal, residual government contracts as a result of legacy PL/SQL code from 20 year old code that's kept in production.)

[1] E-mails in the profile if you want to hear how I got a symbol table in, but I'm sure you already guessed by now.

http://archive.vector.org.uk/art10501320

The APL family gets a bad rap for being "read-only", but I think it's mostly because they've fallen out of favor and have become unpopular. People who have experience working with these languages can encode/decode incredible amounts of information to/from a few lines once they see "phrases" and "sentences" instead of "my cat chewed the 56k modem line". We don't write Integral() in calculus when we want to take an integral, so why not have similar shorthands in computation?

Here's the vocabulary of the sister language J for comparison:

http://www.jsoftware.com/help/dictionary/vocabul.htm

"The one-page thing":

http://code.jsoftware.com/wiki/Essays/Incunabulum

Naive K-Nearest Neighbors implementation:

https://github.com/wyc/snippets/blob/master/j/knn.ijs

Community: https://www.reddit.com/r/apljk/

http://code.jsoftware.com/wiki/NuVoc

It's a fun language to use to do /r/dailyprogrammer puzzles.

A specification would describe, among many, many, many other things, how big an int is and what happens for overflow, etc.

https://github.com/JohnEarnest/ok/tree/gh-pages/ike

Edit: found this paper which appears to be around the birth of array languages by the developer of APL. http://www.jsoftware.com/papers/tot.htm

If you just want to play around (no commercial use) you can download kdb+ from here: https://kx.com/software-download.php but you only get the 32-bit version for free, so keep your DB under 4 GB. With kdb+ you get K, the language, Q, which is a superset of K, and a column (time-series) oriented database engine. I recommend against using it, because if you want to do something meaningful (like make money somehow) then you need to buy a commercial license. I hear that they cost around $2k per machine (or possibly per core).

Kona is nice but it is a full version behind K (Kona is a copy of K3, and kdb+ is on K4) and it isn't quite as fast/optimized. It's great if you are set on using K but don't have the cash and don't need database integration.

Kerf (by the same guy who made Kona) has a friendlier syntax but seems to be commercially oriented similar to K (as in email us for free demo software but pay up when you want to do it for real).

J is open source, free for commercial use, has some decent tutorials written for it, and even has a free database engine (JDB requires a license for commercial use, but Jd seems to be free for everything. It probably isn't as fast as K, but it has a nice feel to it. The runtime is under 3 MB and even with a Qt IDE and a bunch of extras it's under 30 MB. You can make Qt base GUIs with J and do quite a few things.

I started learning J just last week and have been doing some problems on Project Euler to get familiar with it. I like it quite a bit. It even has built in functions for prime factorization, finding the prime number sieve, and and extended precision mode which makes solving some problems very easy.

K is like Scheme but with vectors instead of lists.

If you cannot handle the one character APL verbs, you can always sacrafice some resources and wrap them in verbosely named functions.

Look at q/q.k

PageRank can be done in a few lines of code - with clarity approaching the mathematical description.

The solutions to the Advent of Code (http://adventofcode.com/ and http://kparc.com/advent/ad.k) are readily expressed/solved in k/Q.

That said, it excels above other languages/systems at systems like real-time trading.

    On Wed, 12 Oct 2005, Fermin Reig wrote:

    > Dear Rico,
    > 
    > I have read Mark Joshi's book on C++ design patterns (as well as your
    > review) and I'd like to share my opinion of the book with you. (By the
    > way, I find the information in your website very useful.)
    > 
    > My interest in the book was to try to learn how theory is put into
    > practise. The best way to do that is by actually implementing things, so
    > I did: I reimplemented his C++ code in OCaml, a functional, OO language
    > that I like using. However, instead of a direct translation of C++
    > classes to OCaml classes, I chose a non-OO design. The result is quite
    > interesting from the point of view of code complexity and programming
    > productivity (and hence cost). Here's a summary:
    > 
    > Monte Carlo (datatypes only)
    > C++: 264 lines of code (LOC), (7 classes, 20 methods), 12 files
    > ML : 33 LOC, (3 datatypes, 1 function), 2 files
    > LOC ML/C++: 33/264 = 13%
    > Binomial trees (datatypes only)
    > C++: 264 + 138 = 402 LOC, (10 classes, 30 methods), 18 files
    > ML : 33 + 12 = 45 LOC, (3 datatypes, 3 functions), 2 files
    > LOC ML/C++: 45/402 = 11%
    > Binomial trees (datatypes + main algorithm)
    > LOC ML/C++: 165/522 = 32%
    > 
    > I'm not claiming that Mark's C++ code is bad (actually, it's quite
    > good). However, using a better tool results in obtaining a better final
    > product. (Performance of Ocaml's code is competitive with C++ as well.)
    > 
    > I have written slides with more details about this, which I have shown
    > to a few friends. If you would like to see them, I can email them to
    > you. (I have also told Mark Joshi about my evaluation.)
    > 
    > Regards,
    > Fermin

    Hi Fermin,

    Not bad. But my K implementation only uses 2 lines of code though...

    EuroOpt:{[S;K;r;v;T;n;po] / spot, strike, rate, vol, expir, steps, payoff

    / initialize constants
    p:(%2*m)*(1%D)-d:a-m:_sqrt -1+a*a:.5*(_exp dt*r+v*v)+D:_exp -r*dt:T%n;

    / apply n binomial steps
    :*n{-1_ D*(p*1!x)+x*1-p}/K po' S*(1%d)^(2*!n+1)-n
    }

    ...and here are some test cases...

    po:{0|x-y}; / arbitrary payoff function: max(0, K-S) for put

    / compare results for n = 16, 32, 64, 128, and 256
    \t EuroOpt[5;10;.06;.3;.5;;po]' _16*2^!5

    / pass in payoff functions for put, call, and digital \t 
    EuroOpt[8;10;.06;.3;.5;500;]' {0|x-y},{0|y-x},{y>x}

    / investigating convergence for a digital option with 1-300 steps...
    EuroOpt[8;10;.06;.3;.5;;{y>x}]' 1+!300

    So yes, I agree with you. Functional languages are cool and C++ is
    verbose & restrictive. Unfortunately, in the real world people don't care
    about any of this... :-)

    Cheers,
    - Rico

• Functions have a fixed arity (e.g. `+` never means flip)

• You can create user-defined infix operators