Here’s some Unix tools everybody should know about. They’re third-party and not especially well-known.

fish

fish - the friendly interactive shell

“Finally, a shell for the 90s” is a great tag line. For a long time we have sort of had to decide if we wanted a better programming experience or a better interactive experience with the shell. Fish has a very nice programming language, but the ooh-ahh factor is definitely the command line. Check it out.

pv

pv - pipe viewer

A utility that works like cat, except shows progress bars. This thing makes a huge improvement on the UI of your shell scripts. I highly recommend it.

parallel

parallel - xargs for multiple cores

It has a number of nice modes, but basically it does what it says on the tin: run a bunch of stuff concurrently. By default it will figure out how many cores you are and make your machine busy, but not create a bunch of contention. I use this in file conversion scripts now to good effect.

entr

entr - watch the filesystem

This is a great little utility for tying behavior to filesystem changes.

cronic

cronic - a cure for cron’s email problem

Useful for making it so you only get mail from cron when something happens you should know about.

November 8, 2017

Who is your hero? Mine is Cook Ting.

Cook Ting laid down his knife and [said], “What I care about is the Way, which goes beyond skill. When I first began cutting up oxen, all I could see was the ox itself. After three years I no longer saw the whole ox. And now—now I go at it by spirit and don’t look with my eyes. Perception and understanding have come to a stop and spirit moves where it wants. I go along with the natural makeup, strike in the big hollows, guide the knife through the big openings, and follow things as they are. So I never touch the smallest ligament or tendon, much less a main joint.”

— Chuang Tzu, ch. 3: “The Secret of Caring For Life”

Cook Ting is my hero. He doesn’t care about fame. He just wants to do the thing he does, well. And in doing it, he has become so good at it, it almost defies explanation. He’s a cook, but not a chef.

November 8, 2017

If I had to average a list of numbers, I would probably do it like this:

averagelist(List, Avg) :- 
  length(List, N), sumlist(List, Sum), 
  Avg is Sum / N.

This resembles the actual mathematical definition. Then you could just make a list of numbers and average that. @lurker is right, this is a terrible way to go, but it would work:

average(N, Avg) :- 
  findall(I, between(1, N, I), Is),
  averagelist(Is, Avg).

This is building up abstraction. But of course, this is for a class and the important thing is to not use Prolog or learn declarative programming or solve actual problems but rather to perform meaningless inductive calisthenics to prove you understand recursion. So a “better” (i.e. worse but likelier to be accepted by a clueless professor) solution is to take the procedural code:

average(list) ::= 
  sum := 0
  count := 0
  repeat with i ∈ list
    sum := sum + i
    count := count + 1
  return sum / count

and convert it into equivalent Prolog code:

average(List, Result) :- average(List, 0, 0, Result).

average([], Sum, Count, Result) :- Result is Sum / Count.
average([X|Xs], Sum, Count, Result) :- 
  Sum1 is Sum + X,
  succ(Count, Count1),
  average(Xs, Sum1, Count1, Result).

The list result of my findall/3 must be delicately hand-assembled using only tools available in the 18th century lest anyone develop a sense that Prolog can be used effectively in fewer than 40 lines of code:

iota(N, Result)        :- iota(1, N, Result).
iota(X, Y, [X|Result]) :- X < Y, succ(X,X1), iota(X1, Y, Result).
iota(X, X, [X]).

Then you could build averagelist/2 without the taint of library code (of course, you’ll have to write length/2 and sumlist/2, and probably member/2 even though it isn’t used, but just because it’s clever and useful and it sort of seems like it should be in the source file next to all this other stuff we might need), but it would look generally like this:

average(N, Avg) :-
  iota(N, List),
  averagelist(List, Avg).

Now, of course, it will be pointed out that the introduction of additional predicates that are not directly answers to the take home assignment are illegitimate and will be penalized as doing such leads to readability, maintainability, breaking problems down into manageable pieces and other things that are not directly related to the goal of the assignment (to make Prolog appear tedious yet opaque) so we could now look at this and realize that if we want to flatten these two predicates together we ought to be able to by just smushing together their state variables and doing all the work of both, like this:

average(N, Avg) :- average(1, N, 0, 0, Avg).

average(X, Y, Sum, Count, Avg) :-
    X < Y,
    Sum1 is Sum + X,
    succ(Count, Count1),
    succ(X, X1),
    average(X1, Y, Sum1, Count1, Avg).
average(X, X, Sum, Count, Avg) :-
    Sum1 is Sum + X,
    succ(Count, Count1),
    Avg is Sum1 / Count1.

Now this is starting to look like Professor of Programming Languages code! We went from basically four little readable lines to 9 or 10 repetitive lines and a lot of book-keeping and state! I think we’re on the right track now, let’s review how it works:

1. average/2 is just a call to average/5 with our state initialized (no sum, no count, starting value = 1).
2. average/5 has two cases: a base case where the count-up-to value and the current-count value are equal, and an inductive case where the current-count is less.
3. add up the blah blah blah you get the point

The key takeaways here are: 1) Prolog has a terse, high-level, readable and comprehensible standard library, which you are prohibited from using in school, and 2) any procedural loop can be made working Prolog by creating a recursive helper predicate and moving the code around.

June 9, 2017 prolog

Update 2025-05-09: There is now a page on this topic on sona pona! Read that instead, the below is very incomplete and out of date!

Natural Semantic Metalanguage is a theory that claims there is a common set of semantics underlying all natural languages. This is a descriptive theory, but we can also use it to evaluate constructed languages and perhaps use it prescriptively to help us create effective constructed languages (or at least let us restrict them consciously rather than accidentally).

I’ve taken the chart of NSM semantic primes from 2016 and written the Toki Pona equivalent of each prime in it, and crossed it off a list of Toki Pona words. The result is a mapping of NSM to Toki Pona, but it also tells me a few other things:

• There are some primes in NSM that have no direct representation in Toki Pona. This highlights areas of Toki Pona that contribute to the sense that there are things you can’t say in it.
• There are several overloaded words in Toki Pona that function as more than one NSM prime. This helps explain the “fuzziness” you feel using Toki Pona.
• The non-primes in Toki Pona’s lexicon form a useful minimal vocabulary for people interested in language construction.

Interestingly, there are only a few primes that appear to be handled grammatically in Toki Pona. Almost all the primes in NSM are realized as independent Toki Pona words. This suggests to me that Toki Pona is extremely well-constructed.

Now, onto the chart:

In the first section, we see a pretty good mapping from NSM prime to Toki Pona. “KIND” has no mapping, which makes sense, because Toki Pona is in general very bad at making distinctions between type (by design). I’ve marked wan in bold to draw attention to the fact that it represents multiple NSM primes. In fact, wan means one, unit, element, part, piece, or make one—in other words, as a noun, it means a single thing or one of many things, but as a verb it means to unify them into a wholeness, so it is a kind of auto-antonym, which is surprising in a constructed language but occurs not uncommonly in natural languages.

mute and lili are both pretty polyvalent; mute winds up covering a lot of scenarios.

As you can see, Toki Pona is fairly weak at time. tenpo pini and tenpo kama mean “finished time” and “time to-come” and that’s about all you get. There’s definitely no distinction between points and intervals. The ambiguity here is probably intentional—intended to focus your attention on the here-and-now rather than placing sentences into arbitrary points in time and space (unlike Lojban).

Toki Pona has a full set of the mental predicates, the only ambiguity is the merging of “think” and “feel”, which was definitely an intentional choice by the inventor to steer discourse in a certain direction.

Here we see lon used to place things and people as well as define them. It doesn’t seem like a perfect fit for any of these primes but it is closer than nothing. Toki Pona probably relies more on the null copula to say things like “this is a cat” (ni li soweli). I’ve underlined ali because “life” is an oblique meaning. Toki Pona doesn’t seem to have a word for “live.”

I’ve underlined tomo because the sense “a general place” is definitely secondary to the sense “a room (indoors)” so it is an oblique association with the NSM prime. Also weka means “away” in a sort of vague way that might mean far but isn’t the main sense. And once again we have pilin for something sensory. That there are no real words for near and far is probably intentional—again, Toki Pona emphasizes the here-and-now—the surprise is that there is not really a word for here at all. It’s apparently just always implied; soweli mute li lon means both “there are cats here” and “lots of cats exist” and no distinction between them is possible.

la functions as a strange bit of grammar in Toki Pona, separating an “adverb” or “context” from the rest of the sentence. Conditionals are handled this way, as are temporal constructions. I think it’s likely that this area was intended to be simple and weak but grew more complex as the community expanded. Anyway, all the primes are available here, but not all as single words.

Once again, mute and lili reappear as common adjectives. lon in the sense of “true” is probably oblique. There’s no explicit way of saying false; you would simply negate the statement somehow.

So, what does that leave? Quite a bit:

• a (ah, ha, uh, oh, ooh, aw, well)
• akesi (non-cute animal, reptile, amphibian)
• anu (or)
• awen (stay, wait, remain)
• en (and)
• esun (market, shop)
• ilo (tool, device, machine)
• jaki (dirty, gross, filthy)
• jelo (yellow)
• jo (have, contain)
• kala (fish, sea creature)
• kalama (sound, noise, voice)
• kasi (plant, leaf, herb, tree, wood)
• kepeken (use)
• kili (fruit, pulpy vegetable, mushroom)
• kiwen (hard thing, rock, stone, metal, mineral, clay)
• ko (semi-solid or squishy substance)
• kon (air, wind, smell, soul)
• kule (color, paint)
• kulupu (group, community, society, company, people)
• lape (sleep, rest)
• laso (blue, blue-green)
• lawa (head, mind)
• len (clothing, cloth, fabric)
• lete (cold)
• linja (long, very thin, floppy thing)
• lipu (flat and bendable thing)
• loje (red)
• luka (hand, arm)
• lupa (hole, orifice, window, door)
• ma (land, earth, country)
• mama (parent, mother, father)
• mani (money, material wealth, currency, dollar)
• meli (woman, female, girl, wife, girlfriend)
• mije (man, male, boy, husband, boyfriend)
• moku (food, meal, eat, drink)
• monsi (back, rear end, butt, behind)
• mu (cute animal noise)
• mun (moon)
• musi (fun, playing, game, recreation, art)
• nanpa (number)
• nasa (silly, crazy, foolish, drunk, strange, stupid, weird)
• nena (bump, nose, hill, mountain, button)
• noka (leg, foot)
• o (vocative)
• oko (eye)
• olin (love)
• ona (she, he, it, they)
• open (open, turn on)
• pakala (blunder, accident, mistake)
• palisa (long, mostly hard object)
• pan (grain, cereal)
• pana (give, put, send, place, release, emit, cause)
• pimeja (black, dark)
• pini (end, tip)
• pipi (bug, insect, spider)
• poki (container, box, bowl, cup, glass)
• seli (fire, warmth, heat)
• selo (outside, surface, skin, shell, bark, shape, peel)
• seme (what, which)
• sike (circle, wheel, sphere, ball, cycle)
• sin (new, fresh, another, more)
• sinpin (front, chest, torso, face, wall)
• sitelen (picture, image, draw, write)
• soweli (animal, especially land mammal, lovable animal)
• suno (sun, light)
• supa (horizontal surface, e.g furniture, table, chair, pillow, floor)
• suwi (candy, sweet food)
• taso (only, sole, but)
• telo (water, liquid, juice, sauce)
• unpa (sex, sexuality)
• uta (mouth)

More details on the Toki Pona vocabulary can be found at the semi-official wordlist, by consulting pu, the official Toki Pona Book or perhaps by reading a tutorial.

Ideas for further work:

• Create an NSM-complete Toki Pona by filling in the missing primes and disambiguating multi-valent words
• Create an inflected Toki Pona by converting NSM primes into bound morphemes on the remaining TP lexicon

April 27, 2017 language

I want you to close your eyes for a second and picture your biggest hero. Here’s mine.

hint: it’s buckaroo banzai

The man you see pictured here is Buckaroo Banzai. According to the highly informative documentary The Adventures of Buckaroo Banzai Across the 8th Dimension, Buckaroo is both a physicist and neurosurgeon while heading a rock band as well as running the fan club. One gets the sense these are just a few of the salient features of a fairly rich backstory.

Importantly, early in the film during what appears to be the informal job interview of Sidney, another neuroscientist, Buckaroo asks if he can dance. The implication is that he’s not interested in bringing a neurosurgeon onto his team if he can’t perform in the band.

Obviously, we can’t run our own workplaces like Buckaroo does. But, there’s a cue in here about how to live. I see programmers talking about how to be better programmers by learning, learning, learning about programming. Acquire new languages, try new frameworks, have side projects, be constantly writing code. I see other programmers talking about how to be better programmers by meditating, by working out, hiking the wilderness and getting to 4% body fat, by getting better sleep and eating healthier foods. And I think they’re both missing the point.

The way to be a better programmer is to be a better, bigger human. Indulge your interests. Call your mom. Care about yourself, your family, your friends, strangers. Buckaroo Banzai didn’t have his band to be a better physicist. His being a physicist wasn’t there to improve his being a neuroscientist or vice versa. They were simply expressions of his being. Sidney doesn’t have to dance to be a good neurosurgeon, he has to dance because he’s human and Buckaroo wants to be around humans. (Sidney can’t dance for shit, by the way.)

So quit trying to be a better programmer so you can write more code faster and better and be more powerful at it so you can be a better cog and get more head pats. Be a better programmer to help save the planet from red lectroids from the 8th dimension. That’s a much better reason than to get a job at Google or Facebook, of all fucking places. And give her your coat.

Because you’re perfect.

March 1, 2017

Here’s a philosophical question about the “White Elephant” game: is it more likely that everyone will leave with a gift they like or that at least a few people will be miserable?

If you don’t know the game, the basic rules are these: every person brings one present to the game. An order is determined for players to take turns. On each turn, you may either open a present or steal a present from someone else.

This question came up over the weekend because my wife and I had a dispute about the run-off rules she came up with. What happens to you after your present is stolen by someone else? My suggested rule was basically recursion: you get to open or steal, but you can only steal things that haven’t yet been stolen this round. (A round begins when someone who hasn’t played yet chooses to open a present or steal one.) Her suggested rule was that you open a new present, full stop.

I did some math and realized that run-off rule preference is probably biased by whether you think it’s more likely that everybody is trying to get to their preferred gift and there isn’t much competition for that gift, or whether you think it’s likely that someone brought a highly-desirable gift that everybody is after. Is everyone in your party a beautiful and unique snowflake, or is somebody Michael Scott?

Let’s say you have N people participating (let’s call them person A, B, …), and each person brings a gift (let’s call them a, b, …). Let’s model preferences as simply as possible: each person has a single gift which is their preferred gift. The possible preferences for the case of two people is very simple: person A could prefer gift a or gift b, and person B could prefer gift a or gift b. Let’s write person A’s preference next to person B’s preference, so ab represents person A preferring gift a (their own) and person B preferring gift b, also their own. There are clearly four possibilities here: aa, ab, ba, bb. In two cases, everybody can leave happy (ab, ba) but physics prevents satisfaction in both the aa and bb cases.

It should already be clear that this looks like two binary bits (a=0, b=1; 00, 01, 10, 11) so it’s starting to look like we have an obvious encoding for this problem. The 3-person scenario is very similar: 000, 001, 002, 010, 011, 012, … 222. The cases where everyone is happy are 012, 021, 102, 120, 201, 210. So we have N^N person-desire possibilities, against N! ideal outcomes. If you plot these against each other, you will see that total outcomes quickly exceed ideal outcomes. In our particular case we had 12 participants; the likelihood of the ideal outcome is 12! / 12¹², or about 0.00537%. This means someone’s going home unhappy 99.995% of the time.

You could argue that this is being melodramatic. For one thing, people probably actually have a preference list. Maybe they are getting their second choice. Most white elephants have post-game exchanges. In the game we actually played, I know with some confidence that one person left unhappy. On the other hand, I don’t know what could have been done about that; some people are just built for misery.

I’m not sure how to analytically tackle the question of whose algorithm results in more happiness. I think a stronger model may be necessary: maybe we should model preference lists and assign an integer score for happiness instead of the boolean-flavor of a single preference and a “did I get it?” boolean value. Or, maybe this is a sufficient model; we could always simulate and see. One thing that bothers me about Liz’s rule is that player one never gets a chance to make a choice, even if they are stolen from. Liz’s retort was that if they are not stolen from, it doesn’t matter which ruleset is used, and if there’s always an iPod, it’s just going to keep getting stolen, and lots of people are going to be miserable anyway, so who cares.

Another question I’d like to analyze, which I think can be done with combinatorics, is, what is the likelihood of M ≤ N people being unsatisfied? The work above is for 1 or more, what’s the exact curve for 1, 2, 3, etc. people being dissatisfied?

December 28, 2016 math