on meta, maths, opinion, rant, programming

Why Is Science "Boring"?

OK, so, now that I don't spend my days teaching anymore, my friends and family are amazed at the fact that I can't stop learning new things, starting new projects, getting up at 6am and carrying on carrying on.

The worst part? I've kept the habit of a teacher and try to enthuse people about the stuff I discover / learn about... To mixed success.

This has led me to thinking about a worrying trend I find in online and offline conversations that may or may not be linked to conspiracy theories and "fringe" opinions.

The urge to publish was definitely tickled by this toot

Folks who don't understand AI like it more but once they understand it they find it less appealing

Where are you coming from?

First off, it's quite a big topic, one that can probably fill a few PhD thesises (thesii?) for a sociology/psychology department. This are just my thoughts about it, based on anecdotal evidence and cursory research. Feel free to hit me up about it, I'm totally open (and maybe even hoping) to be proven wrong.

As someone trained in linguistics, I feel the need to make sure the terms I'm using are somewhat well defined:

  • "Science" as in the process of learning or discovery (in a greek sense closer to Μάθημα - Máthēma, something like the act of learning) is kind of alive and kicking, albeit in a weird way
  • "Science" as in the established knowledge (in a greek sense closer to Γνόσι - Gnósī, whether collectively or privately) is very much in a state of active battle, and probably the one I have the most thoughts about

I'm sure some people will be thinking I'm preaching to the choir here, but I've seen with my own eyes the decline of visibility (in a positive sense) for the experts. Let me paint the scene for you:

We are in a meeting with half a dozen experts of their field and a couple of manager/investor types. The experts are disagreeing on many things but not on the most fundamental one: the proposal will not work. After the meeting, they are branded as killjoys, a vague social post arguing that the proposal is a good idea is brandished, and the decision to move forward is taken, to predictable results.

I'm not saying it's always the case, mind you. But more often than not, I have sat in auditoriums, meetings, what have you, and looked at a group of non-experts slowly but surely convincing themselves that the people they invited to talk (and sometimes even paid to do so) were wrong.

For someone who grew up in a time when science communicators were mostly researchers talking about their active research on live television, this is quite a culture shock. That's why I thoroughly enjoy youtube channels like Computerphile, who show actual research scientist being passionate, whether or not they are what is considered to be "good communicators" (and they are quite decent communicators, too!)

But the fact is, we have clearly entered an era where science communicators are permanent fixture. And I'm not throwing shade at them at all! They do a great and needed job.

But, when you ask them, one of the very first thing most of them will say is that they aren't "real" scientists (I beg to differ), and that they are like a gateway drug: get people interested in the topic so that they will dig deeper in the "actual" science.

What it feels like is that this worthy goal is never achieved.

The 10-minutes version of what could fill hours of lectures is enough: Γνόσι has been achieved, and Μάθημα is satisfied.

The problem is that it cuts both ways: a skilled communicator promoting pseudoscience will get the apparent same result as the one talking about what actual science says.

Milo Rossi (aka Miniminuteman on YouTube) has a fascinating lecture on his own field, right there on his channel

His central thesis seems to be that because communicating instantly to millions of people has become cheap, easy, and to some extent make enough money and/or fame back to the creator to encourage them to "challenge" the status quo, and find their audiences.

And, just like him, I find our position delicate: how do we promote the experts' words while not gatekeeping - preventing genuinely curious people to go and do their own research and experiments in good faith (as opposed to the derided "do your own research" mantra used by conspiracy theorists everywhere)?

Fine, but are you an expert in expertise?

Oof, good point, well made.

Here are my bona fides, and my experience on the matter. I will let you decide if it invalidates everything I have to say about it not.

I have been teaching classes in maths, computer science, data, and (machine) learning, as well as training other people in giving classes in front of audiences, for a couple of decades. There are definitely areas where I do decent, and others where I totally suck. You'll have to ask my students if I was overall a good teacher or not.

But objectively, a large majority of the people I trained went on to have a career in the field that I was in charge of teaching them, so I think it went decent. And while there were many, many, many, complaints, these are students we're talking about, and the power dynamic at play - me grading them based on "arbitrary" goals - means a somewhat antagonistic relationship, even when friendly. The grading game is a very heavy bias in your appreciation of teachers. I know. I even tested it: classes without an evaluation at the end tended to be lauded wayyyyyyyyy more than "strict" and friendly teachers.

Before we carry on with the central thesis of this post that will probably end up being way too long, here's a couple of observations I made as the head teacher / managing teacher of the curriculum:

  • I had the chance of heading a department where the jobs the students would get at the end paid very well. (Too well? Yes probably, especially given the fact we still refuse to be an industry)
  • Students attending class had a variety of incentives to specialize in CompSci, but let's not kid ourselves: making money was part of the equation.
  • The highest paying jobs are usually the ones requiring the most expertise, or at least this used to be the case.
  • So therefore, students should be trying to acquire as much expertise as possible, as fast as possible, to land those high-paying (and/or fun, and/or motivating, ...) jobs

Right?

Except: Students had a tendency to "check out" fairly rapidly

  • "Copypasta" has been a thing forever, but when your exam takes place on a machine literally designed to make the process of finding something on the web and copying it in your production easier (and now AI that automates - badly - most of it), the incentive to "waste" an hour on something that would "grade" the same in a minute is hard to find.
  • And during class, because the slides are available, or it's recorded, or there's a good chance to find a 10 minute video on "the same topic" online, why not do something more fun, and catch up later?
  • If the passing grade is 10/20, and having more isn't rewarded, why go the extra mile?

Subsequent conversations with them yielded interesting observations, on top of those fairly obvious ones. What they told me they yearned for was "engagement" or "entertainment". and it's a whole Thing. Make learning fun, they say!

While I do agree with the sentiment (learning is fun, to me), I find myself obligated to point out that in this particular instance, it doesn't have to be fun: they were ostensibly there to get a good paying job (among other things). I mean, I'm training people to compete with me for jobs and contracts, and I have to make them feel entertained?

Regardless of the validity of that demand, I always did strive to make the learning fun, because it's fun - to me. But I always found the reversal of expectations weird: here you are, asking me for a favor, and I have to thank you by entertaining you?

I totally understand how that may sound gatekeeper-y and demeaning. And, again, I never actually subscribed to that attitude, but when you think about it for a minute, there definitely is a difference between learning something that will be used to earn a living, and learning things without ulterior motives - at the beginning at least. Because of my well-ingrained biases regarding the former, I will keep the rest of this post to the latter: learning stuff that may not net an immediate advantage.

You're gonna talk about history now, aren't you?

Yes. Giving context and a bit of epistemology is part of the scientific method. Plus it's a fascinating topic in and of itself. It will happen.

I'm not going back too far though, only to the printing press, and the Renaissance (or Early Modern Era as I learned it should probably be called, because it was only a rebirth for Western Europe, not really the whole world where they didn't have our wonderfully repressive period known colloquially as the Dark Ages).

Scientists then, like now, had feuds about their hypothesises (hypothesi?), and wrote scathing articles, pamphlets, and letters, about their colleagues who were obviously wrong. Because of the wide dissemination of ideas and a general curiosity about how the world actually worked in a more practical way than "oh well, it's the will of a deity" or "well, my grandparents did it that way so it must carry on the same", there was a crowd - yes an actual crowd - coming to hear the latest discoveries at the various academies.

It's kind of like a Youtube channel, but you had to go to an auditorium once a month or whatever, to hear, usually from the mouth of the actual scientist or one of his proxies (student, secretary of the academy, colleague) what the exciting new scientific discovery was. And if you couldn't make it, there were local clubs and academies that would debate the written reports of the lecture.

Scientists were household names, and pride of their countries. You have a Gauss? Well we have a Newton! A lot of streets, buildings, and areas, are still named after them, and you were expected to be able to quote them before entering a debating arena.

And yet, there were close to no intermediary to the content of their theories: the people read the articles or letters verbatim. Science communication was without added value. If you wanted to participate in those discussions, you had to learn the whole thing, not just the cliff notes version.

The weird part is that while this feels super elitist - you have to be a world class mathematician to contribute to a discussion of the latest math discovery - it was fairly open. As long as you had enough free time (that is elitist, for different reasons) to learn, and could back your claims up in front of renowned scientists with the proper methodology, you were part of the club. A lot of scientists up until the Industrial Revolution were not just scientists. Enormous scientific discoveries and contributions were made by people with practical knowledge that wanted to find out the why, or people with a lot of free time just engaging in science out of curiosity.

That popular interest in discoveries and science continued unabated up until very recently. Scientific magazines, and, as I said before, scientists on TV, were very popular, up until the complete takeover of the Internet as a source of knowledge.

For reference, here are the circulation numbers for the scientific magazines here in France (may I suggest diving into those stats? Apart from being paper scans and sometimes hard to decipher, there aren't any regular format or nomenclature. Makes for a fun afternoon of research!):

"Science & Technique" category, in circulation numbers / year

What's surprising to me is the resurgence super recently. But you can clearly see the peak in 1981 among a global rise in interest about science & tech magazines, then the peak / dip around the Y2K bug, and since 2014-ish a sharp decline.

Obviously, this is only the number for France, and if you take a peek at the data itself, you'll see that there is a margin for error (what exactly is included in that category is arbitrary to a point). But it matches my gut feeling, so there is that.

Of course, nowadays, the undisputed royalty of scientific communication is the Internet. Science communicators have setup many very interesting channels, as I mentioned before, and most of the good ones are quite humble despite the fact that they have a viewership that the equivalent press would have considered a dream only a couple of decades ago.

Scientific honesty starts with, and carries on with, "I may be wrong" - always. Even when your theory (in the scientific sense, not in the vernacular "hypothesis" sense) seems to tick all the boxes and match all the data, there is a small chance that it could be at worse wrong except in this particular case, and at best right but only as as part of a more general theory. Again, watch people like Milo Rossi to see how they deconstruct their own certainties and doubts publicly.

So, with that historical context in place, let's talk about the iceberg.

The iceberg as in the thing that sunk the "insubmersible" Titanic?

More as in the thing that is mostly underwater. Because we have an Iceberg Problem.

The openness of science is still a thing most scientists strive for, but in order to understand - or even contribute - to modern research, you can't really do it in your free time and as a dilettante anymore, however brilliant.

Let me give you a personal example: I tried to teach a class on native mobile development. Despite what most platform vendors (and now IA vendors) would have you believe, software development isn't easy. Sure, if you have an image of something you want to put on the web (a landing page, a presentation page, anything that you could compose in a word processor or a page layout piece of software), making "a web site" that matches the visuals is fairly simple.

And, if you're only interested in what everybody else is doing, there's a good chance that someone out there does it already and could either sell it to you or give it for free.

This blog is easy to "make": I open a web browser, and type something in the window and blammo, you can read it. Except... this is only the tip of the iceberg.

Your web browser has more than 20 millions of lines of code, it runs on an operating system that is a couple of orders of magnitude more complex than that. And on my end, the server also has millions of lines of code. All of that requires maintenance by someone you don't know, and, more likely than not, that you haven't paid.

That's something of a marvel to me that despite the poor design choices we inherited, and the average number of bugs per 10k lines of code, when you write <center>something</center>, it is somewhat in the middle of the page. If it's not, then you're in for a whoooooooooooole lot of debugging time.

Back to my native development class: it's "lower level" than the web, which, in practice, means that there is one or two less layers of millions of lines of code for my "hello world" to be displayed somewhat properly. Oh and you add some real life issues such as battery and network performance into the mix, which you don't really have to care about "on the web".

What it meant is that my first class struggled with concepts and underpinnings that they never had to think about before. So I had to add more hours to the class. Then to add some pre-requisites in the years before. Then a whole new kind of course around designing and debugging code.

Because, you see, there's a whole lot of things under the hood that work just fine for 80% of what you want to do. The rest is trying to figure out why it doesn't and decide whether it requires a workaround of some weirdness in the underlying structure, or a completely new thing that has to be built from scratch.

In practice, what it means is that you can cobble something that looks like it's working fine enough without much training, but as your work is exposed to more users and situations, the probability of it breaking goes up exponentially. And you may not have any idea why.

Anyways, enough of my own field, it holds mostly true for every single one nowadays: in order to understand the current physics, the level of maths and prior work needed to "get it" is astounding. And it's an immediate turn off for a lot of folks.

Plus, because a lot of the most graspable concepts have been known and integrated into our lives for so long, the incentive to understand the current forefront of a scientific field boils sometimes down to "do I have time?" which correlates neatly to "what purpose does it serve?" and "how does it benefit me?"

If you knew the most current theories of physics, say 300 years ago, it might give you an edge in your daily life, like using levers instead of wrecking your back. Then it was about how does it help me "professionally"? Like using pulleys in your factory instead of 20 sweaty dudes. Nowadays? Practical applications of physics research may help someday a mega corporation that will sell me something that makes my life a lot easier.

So, it's all about curiosity, except when it's your job to be at the edge of what can be done.

But people still consume a lot of "educational" content, are they not?

Time for a tangent. Curiosity has always felt like a two-headed thing to me. There's "genuine" curiosity - as in "how is it possible? I'd like to know more about how or why this thing is" - which is almost always the starting point of research both personal and institutional, and there's ἐκρυπτός (ekrīptós), hermeticism, esotericism, arcanism, which are more about what I know that you don't, and is about power.

Because, yes, knowledge is power, even when it's not science. Power over people, power over situations, power over the machine,... If you know stuff, you're less passive, less at the mercy of whatever or whoever is guiding the situation you are in. And it's quite intoxicating to feel in control, or at least "understand the plan" for most people.

Just look at the success of gossip, magic tricks and the like! And in my field it's especially prevalent. If a company/freelancer doesn't tell the potential customer that it would probably cost a lot less to do their thing using a very well maintained and fairly easy to learn piece of free software, they may be able to extract quite a lot of monies from them.

That's why I'm particularly careful with science vulgarization in particular, and "knowledge" communication in general. Again, many people have written books and made videos and all that about it, but the incentives of the person giving you information factor in how I parse said information.

Now, the issue is, it's exactly what pseudoscience aficionados say as well. "The establishment (whatever that is) is lying to you, seek your own truth."

Let's talk incentives then shall we?

On the one hand, you have governmental and/or scientific cabals, that invest billions of monies into maintaining a fiction about some lie being the truth that involves thousands or millions of people that have to be bribed, coerced, intimidated, and what have you, in order to keep the "population" (ie you, and me) in a state of... compliance? Tranquility?

On the other hand, you have that person on the internet that knows the truth that is hidden and benefits financially and in terms of fame from your viewership.

Oh and those all powerful governments who managed to keep a lid on things for so long and at great expense somehow fail to take down the videos of Joe Schmock from Nowherville.

While doubting governments (because, yes, they do lie, hide and/or mismanage the truth sometimes) is totally fair in many many areas, science isn't "governmental". A random research team in Brazil do their thing, formulate hypotheseseseesses (hypothesi?), include them in a theory, and publish it for another set of random teams in Japan and Kenya to conform or deny.

You're telling me that the French government, which is publicly fighting with many governments over a lot of issues (and has warred because of it, too) is somehow coordinating with the UK to hide the truth about the moon landings while accusing them of lying about almost everything else?

Sorry about the choice of examples, but it's currently the 6 Nations tournament, and we French and English love to hate each other during tournaments. Especially Rugby. So, there. Apologies all around.

How does that relate to science being "too boring"?

Glad you asked. So, in my mind, the appetite for "trivia" or for useful knowledge (the kind that gives you a job, or power, or fame, or monies) hasn't abated at all. But actual modern science requires effort and dedication to understand.

Going back to my own field, if I can earn $10k with a customer who wants a website by doing $30 worth of work, why wouldn't I? (I mean except for honesty, ethics, love of humanity, long term reputation effects, risk of being found out as a fraud and a swindler, and many many more reasons)

And if I can tell that customer that they will never have the time to learn all the esoteric knowledge that justifies that price tag and they can't have a decent picture of the truth in my words, I win, right? (Power, again)

The reality is, most of my knowledge is boring tedium (for most people) about bits and bytes, algorithms that were thought of and perfected over a long period of time, a lot of "yea we do this like that because it's what we've always done, so all the tools are geared that way, like it or not", a dash of general understanding of maths and physics, and lots and lots and lots and lots of lessons learned from past mistakes.

In order to know as much as me, you don't have to be particularly smart. You have to put in the hours and do the mistakes. Experience is a catalog of dead ends that I know to avoid and you don't. It's as simple as that, mostly.

And most of science is that way: it's a pyramid that is now so high that no one has the time to learn from top to bottom, except for people whose job it is. But there is power to be gained from the tip. Funding for researchers, fame for communicants (and monies too), glory for nations, and, yes, all of the above for fakers and frauds too.

Once you start establishing your bona fides by explaining the layers your current expertise relies on, you are "boring" compared to your competition - the people who may not know as much as you, but are far more entertaining in their presentation - who just doesn't care about being right, just about being right enough.

And so we have pseudoscience that promises "secret knowledge" and power over your "sheeple" condition.

And we have noise about competing theories or discoveries for research grants.

And we have hiring issues.

In all my years training people, it's a sad observation to realize that it's not necessarily the most knowledgeable who get the high-paying job. I've met and trained genuinely curious, smart and motivated people who struggled to get hired. I've met single minded researchers that were working on something that could benefit humanity as a whole get their research grant denied, or so heavily hogtied with strings attached that it made the act of searching meaningless. It's not necessarily true for all the smart and/or knowledgeable people I've met, but the percentage is high enough to make me sad.

The scientific method works so well because it's predictable, open, and slow. As opposed to surprising, arcane, and fast. And therefore, yes, it's boring.

But it's boring in the same way that doing the scales and noodling on your instrument for hours before you take the stage is boring. It's boring in the same way the characters in your novel / movie / tv show slowly work towards winning at the end.

It's boring if you consider only the process and the ongoing effects, rather than the potential for apotheosis.

You said "AI" earlier. That was just clickbait, right?

Point of origin, rather than clickbait. And one I will address now.

Back in the Before Times, I was asked to moderate a panel on the use of AI in finance. You can still see the video here.

These people have invested a lot of time, money and expertise, into trying to make products using AI, with customers who are very picky and take big risks when using their expertise in the field.

AI as it understood these days (LLMs, agents, domain-specific models,...) works of a pyramid of technologies and assumptions, some more hidden than others. What do I mean by that?

Without delving too deep in the technical side, the pipeline is as follows:

  • Gather a lot of data (assumption: this data is relevant to the rules, that data isn't)
  • From this data try to infer correlations and "rules" (assumption: there is a rule albeit an arcane one)
  • Compile these rules into a model (assumption: the modelling will capture the rules)
  • "Apply" the model to new data, and witness that if you follow those rules, the output should be X (assumption: new data will fit in the ruleset of the model)

For Large Language Models, the data is text. The system tries to infer words (or sequences of words) based on whatever words were given as input. If you build your model on questions and answers, what you get is something that will try to guess the probability on a sequence of words being the correct answer to another sequence of words that are considered to be a question.

That model will not be able to handle anything that is not a question, or that is a question but on a topic is has never seen before. Technically it's impossible.

And herein lies my problem with the current marketing of AI as a panacea: because most people do not understand the pipeline underneath, they cannot interpret the output properly: the model will always give you an answer, but with probabilities attached.

In my example, if I give my question/answer model the input "my dog barks at birds and the sky is blue", it might output something along the lines of "the sky is blue because of refraction in the atmosphere that scatters the red light" but with a probability (aka confidence) of 0.4.

If you've ever seen a matching algorithm (recommendation for buying stuff, dating sites, whatever), the underlying principle is the same... it tries to match an output with your input.

Now if you use the user-friendly version of whatever LLM you want, it won't give you that. It will output with absolute authority the answer that best matches your input, based on whatever data it was trained on. You usually do not get the nitty gritty probability numbers, and therefore how "confident" the model is it performed correctly.

On top of that, the companies that sell you access to their LLMs also have algorithms and rules that will parse the model's output and modify it, while hiding the inner workings, unless you write code to tap into the lower level - their APIs.

Their incentive is to look authoritative so that the product itself doesn't appear wonky (can you imagine a car salesman that would tell you the car may work fine a lot of times but they don't know if it will work on your driveway?) by hiding all the genuinely cool stuff underneath, and make it appear as magical and useful as possible.

BUT in order to have a somewhat reliable model, it has to be Large, because the moment the model encounters something completely outside of what it knows the probabilities of, it will output nonsense. And that precludes most of the personal experimentations that curious people may have.

The systems, principles and techniques are simple. Data in, a bit of probabilistic maths, and data (with attached probability) out. Having access to enough training data, and the computing power to find out the relationship between every possible input and every possible output is staggeringly difficult.

So you can't really check if the model was properly trained (or train it yourself), or even if the output has a high confidence, because the one thing that models can't do is say "I don't know". They will tell you instead gibberish, with a low confidence rating, which is their way of saying that they can't fulfill your desires. And that part is hidden for a large majority of the users.

And so we're back to asserting stuff with confidence, and the difficulty of distinguishing an actual expert from someone who can say stuff nice.

Not clickbait, alright?

I don't get it. Is science boring?

I don't think so. It requires a bit more effort nowadays than witnessing parlor tricks or being told in 10 minutes about a thing. But it also ranges quite far. We do things daily that were unthinkable a couple of generations ago.It's just that, because the effect is mundane, it's lost a bit of appeal. But it's still so fascinating and so cool, if you look at how such a mundane thing is done. Just last year in 2024, we accomplished things as a species that are completely and totally amazing, if you stop and think about it:

We're hopefully, finally, on the cusp of preventing HIV. A disease that infects more than a million people every year and kills two thirds of that. Every. Single. Year. And we are making progress in understanding the virus and fighting it. The cure isn't here yet, but we're getting better at preventing and fighting it.

Japan managed to soft land (ie not crash) a human sized object on the moon with a precision of a 100 meters, after a trip of 400 000 kms! That's like putting the billiard ball in the pocket from 440 km away. How's that not amazing?

Speaking of space, our species launched a spacecraft the size of car on a journey that will take 6 years, just to take a better look at a bunch of moons in orbit around Jupiter. We can do that.

We also found the oldest settlement to date in the Amazon rainforest, and we didn't even have to remove the trees to do so. Like, these cities were built and abandoned millenia ago, they are immensely difficult to access, and there was a decent chance it was just a fluke of nature to see patterns in the jungle, but with LIDAR and lots of careful analysis, those ruins are within the realm of human knowledge once again.

And I'm sure that in a field that you take an interest in, there were many advances and discoveries.

I may submit to the unknown, but never to the unknowable
- Yama, Lord of Light, Roger Zelazny - 1967

on algorithms, coding, CS, maths, meta, opinion, programming

[AoC 2022] Recap

TL;DR

I'm not as rusty as I thought I'd be. And YES that kind of challenge has a place in the coding world (see conclusion)

Just like every year, I had a blast banging my head on the Advent of Code calendar. It so happens that this year, I had a lot less brain power to focus on them due to the exam season at school and the ensuing panicky students, but it could also be because my brain isn't up to spec.

Some of my students are/were doing the challenges, so I didn't want to post anything that would help them, but now that the year is almost over, I wanted to go over the puzzles and give out impressions (and maybe hints).

Easing in

Days 1 to 7 were mostly about setting up the stage, getting into the habit of parsing the input and using the right kind of structure to store the data.

Nothing super hard, it was "just" lists, hashmaps, and trees, until day 4. Day 4 was especially funny to me, because I wrote HoledRange / Domain just for that purpose (disjointed ranges and operations on them). Except I decided to do this year's calendar in Julia, and the library I wrote is for Swift. Just for kicks, I rewrote parts of the library, and I might even publish it.

Days 5, 6 and 7 highlighted the use of stacks, strings, and trees again. Nothing too hard.

Getting harder

My next favorite is day 9. It's about a piece of rope you drag the head of, and have to figure out what the tail does. If you've ever played zig-zaging a shoelace you'll know what I mean. String physics are fun, especially in inelastic cases.

Many ways to do that, but once you realize how the tail catches up to the head when the latter is moved, multi-segmented chains are just a recursive application of the same.

I was waiting for a day 10-like puzzle, as there tends to be one every year, and I majored in compilers all those years ago. State machines, yuuuuuusssssssss.

A lot of puzzles involve path finding after that, which isn't my strong suit for some reason. But since the algorithms are already out there (it was really funny to see the spike in google searches for Dijkstra and A*), it's "just" a matter of encoding the nodes and the edges in a way that works.

Day 13 is fun, if only because it can be instantly solved in some language with eval, which will treat the input as a program. I still wrote my own comparison functions, because I like manipulating numbers, lists and inequalities.

Day 14 is "sand simulation", that is grains of sand that settle in a conical shape that keeps expanding laterally. Once you find the landing point on each ledge and the maximum width of the pile, there's a calculable result. Otherwise, running the simulation works too, there aren't that many grains. For part 2, I just counted the holes rather than the grains.

Day 15 is about union and intersections of disjointed ranges again, except in 2D. Which, with the Manhattan distance approximation, gets back to 1D fairly quickly.

Day 16 stumped quite a few people, because of the explosive nature of path searching. Combinatorics are pretty hard to wrap your head around. Personally, I went for "reachability" within the remaining time, constructed my graph, and explored. It was probably non optimal.

Day 17 make me inordinately proud. Nuff said.

Catching up

Because of the aforementioned  workload, I was late by that point, so I decided to take my time and not complete the challenge by Xmas. Puzzles were getting hard, work was time-consuming, so the pressure needed to go down.

Because of the 3D background that I had, I tackled day 18 with raytracing, which is way over-engineered, but reminded me of the good ole times. Part 2 was trickier with that method, because suddenly I had 2 kinds of "inside".

Day 19 was path finding again, the trick being how to prune paths that didn't lead in a good direction. Probably the one that used up the most memory, and therefore the one I failed the most.

Because of my relative newness to Julia, I had to go through many hoops to solve day 20. As it turns out, screw_dog over on mastodon gave me the bit I lacked to solve it simply, although way after I solved it using other means.

Day 21 goes back to my compiler roots and tree optimizations, and Julia makes the huge integer manipulation relatively easy, so, there. Pretty proud of my solution:

Part 1:   3.709 ms (31291 allocations: 1.94 MiB)
Part 2:   4.086 ms (31544 allocations: 1.95 MiB)

Which, on my relatively slow mac mini is not bad at all! Symbolic linear equation solving (degree one, okay) is a fun thing to think about. I even think that the algorithm I devised would work on trees where the unknown appears on both sides of the tree. Maybe I'll test it some day.

Day 22. Aaaaaaaah day 22. Linked lists get you all the way if and only if you know how to fold a cube from a 2D pattern. I don't so, as many of the other participants, I hardcoded the folding. A general solution just eluded me. It's on my todo list of reading for later.

Day 23 is an interesting variant of Conway's Game of Life, and I don't believe there is a way to simplify a straight up simulation, but I fully accept I could be wrong. So I used no trick, and let the thing run for 40s to get the result.

Day 24 was especially interesting for me, for all the wrong reasons. As I mentioned, graph traversal isn't my forte. But the problem was setup in a way that "worked" for me: pruning useless paths was relatively easy, so the problem space didn't explode too quickly. I guess I should use the same method on previous puzzles that I was super clumsy with.

Finally day 25 is a straight up algorithmic base conversion problem that's a lot of fun for my brain. If you remember how carry works when adding or subtracting numbers, it's not a big challenge, but thinking in base 5 can trip you up.

Conclusion

I honestly didn't believe I could hack it this year. I don't routinely do that kind of problem anymore, I have a lot of things going on at school, on top of dealing with the long tail of Covid and its effects on education. Family life was a bit busy with health issues (nothing life threatening, but still time consuming), and the precious little free time that I had was sure to be insufficient for AoC.

I'm glad I persevered, even if it took me longer than I wished it had. I'm glad I learned how to use Julia better. And I'm happy I can still hack it.

I see here and there grumblings about formal computer science. During and after AoC, I see posts, tweets, toots, etc, saying that the "l33t c0d3" is useless in practical, day-to-day, professional development. Big O notation, formal analysis, made up puzzles that take you into voluntarily difficult territories, all these things aren't a reflection of the skills that are needed nowadays to write good apps, to make good websites, and so on.

It's true. Ish.

You can write code that works without any kind of formal training. Today's computing power and memory availability makes optimization largely irrelevant unless you are working with games or embedded systems, or maybe data science. I mean, we can use 4GB of temporary memory for like 1/4 of a second to parse and apply that 100kB json file, and it has close to no impact on the perceived speed of our app, right? Right. And most of the clever algorithms are part of the standard library anyway, or easily findable.

The problem, as usual, is at scale. The proof-of-concept, prototype, or even 1.0 version, of the program may very well work just fine with the first 100 users, or 1000 or whatever the metric is for success. Once everything takes longer than it should, there are only 3 solutions:

  • rely on bigger machines, which may work for a time, but ultimately does not address the problem
  • scale things horizontally, which poses huge synchronization issues between the shards
  • reduce the technical debt, which is really hard

The first two rely on compute power being relatively cheap. And most of us know about the perils of infrastructure costs. That meme regularly makes the rounds.

It's not about whether you personally can solve some artificially hard problem using smart techniques, so that's ok if you can't do every puzzle in AoC or other coding challenges. It's not about flexing with your big brain capable of intuiting the bigO complexity of a piece of code. It's about being able to think about these problems in a way that challenges how you would normally do it. It's about expanding your intuition and your knowledge about the field you decided to work in.

It's perfectly OK for an architect to build only 1 or 2 level houses, there's no shame in it. But if that architect ever wants to build a 20+ stories building, the way to approach the problem is different.

Same deal with computer stuff. Learning is part of the experience.

on meta, rant, opinion

[Rant?] Managing Frustrations Is More Important Than Managing Expectations

Every year, I see a plethora of books and articles affirming they can teach you how to manage your expectations - most of the time lowering them - because it's a source of unhappiness, and frustration.

In the current "mindfullness" boom, coupled with a pandemic that stresses everyone, it's not really a surprise that telling people to aim for attainable goals is popular. Why should a person add stress on top of stress by picking objectives they can't actually reach?

It irks me.

Now, of course no one should pursue truly impossible goals. But being told to "manage your expectations" puts squarely on your shoulders all the blame for being stressed, while failing to address the problem with your place in society. It reeks of "know your place" or "don't dream too big".

Most of the "expectations" aren't yours 🌍

We have customers, bosses, employees, colleagues, friends, and family who have expectations of us.

My current job is to make sure students that come into my care will leave it better prepared to work in the tech industry.

My expectations:

  • the students will trust me to know what's best for them, and (reluctantly, maybe) do what I suggest they do
  • the teachers working with me will trust me that I know what I'm doing
  • everyone involved will deal with the various inconveniences with good graces (the students will do their homework and be curious, and the teachers will be fair and uplifting)
  • I will have enough time in a day's work to deal with most emergencies

Everyone else's expectations:

  • the government, school staff, students (and their families) want me to design the classes in such a way that getting a job at the end is a guarantee
  • the school wants me to make sure the curriculum is attractive enough for prospects to sign up
  • the teachers want to have a decent environment, pay, and freedom to be flexible, so that they can impart their knowledge and experience to the next generation
  • the teachers want students prepared in advance to learn what they have to teach
  • the students want actionable knowledge - none of that philosophy and what-ifs - so that they can earn a living
  • the students want to have as much free time as possible, while being paid as much as possible
  • "the market" wants to be able to put ex-students to work as fast as possible, for a pay that's as low as possible
  • governmental rules want me to enforce regulations, such as attendance and other disciplinary actions
  • the students want me to bend the rules in their favor
  • "the market", and the students, and the school, want me to make sure that the curriculum in unmarred, and that only the "deserving" get their diploma
  • and
  • so
  • many
  • more

These are all high-level expectations, the nitty gritty is way more involved. And it's obvious that respecting all these is akin to squaring the circle.

So, "managing my expectations", in the way people have been explaining it to me over the last decades, would be to forego my expectations, or at least lower them enough, in order to make sure I can actually turn them into a reality. It says nothing about bucking against everyone else's expectations. I'm supposed to try and accomplish the goals that have been set for me by my superiors, and force people I have power over to do what they are supposed to, revising their expectations lower.

That definitely looks like a self-sustaining power structure to me, reinforced by a philosophy of "don't expect too much or you'll be sad". The higher up the chain, the more you can dream, because your dreams will become orders for others. "Manage your expectations" if you are not in a position of power.

Manage your frustrations 😤

You can't absolutely control others. My students will sometimes be "unable" to hand in homework. My teachers will sometimes be overwhelmed and forget to grade some papers. My hierarchy will sometimes forget that life outside of school exists for me too. My family will sometimes take affront at the time I devote to the pupils or the teachers.

It is frustrating, because a very large majority of people feel like they are already giving 110%, and my priorities aren't theirs.

An easy way out of that frustration is, indeed, to lower my expectations, and respect the letter of whatever law, rule, or guideline, I have to enforce. Passing the buck in terms of personal responsibility (I'm just "following orders"). I can also lower my expectations by leaving aside anything that would cost me extra energy, that isn't strictly mandated by someone in power.

And therein lies, for me, the crux of the semantics problem. By saying people should take upon themselves to "manage their expectations", even if your intention is pure and you are trying to protect their mental health, you are in essence telling them not to dream too big. It's a race to the bottom.

Collaboration doesn't work like that. Sure, personal goals rarely align perfectly, and everyone has goals outside of whatever we're working together on. But whenever humans are involved, compromise is the norm, and that means that everyone has to deal with a little bit of frustration.

Let's take a practical example: teaching a class about program design. It's one of these things that can be seen as a waste of time by a lot of people. Except, of course, by people who've learned the hard way that if a program isn't designed first, the future holds a metric ton of headaches.

  • the students don't have that experience, so they have no practical reason to spend hours learning something that goes contrary to what they know: a program either works or doesn't. Who cares if it's elegant?
  • "the market" wants them to be operational, which to them usually means fast, not spending hours seemingly doing nothing about writing code.
  • the school system isn't properly equipped for evaluating "grey area" skills. Since there is no "best" way to do something, how can anyone grade something like this?

The easy way out of this pickle is to remove that course entirely. The students are happy, "the market" is satisfied enough, the school system churns along.  Lower my expectations, right? Sure, the next generation will make the same mistakes over and over again, but they will eventually learn, graduating to senior positions in the process.

And my frustration about seeing and sometimes using badly designed software, about seeing good developers burnt out by constantly running after technical debt, about people who should have no business making technical decisions forcing bad ones on people for expediency's sake, and about users having to work around the products they use, is ultimately irrelevant. Because everyone will friggin manage their expectations.

Oooooooor... I use my frustration, and use it constructively to educate people about all that. I teach them that they should definitely not lower their expectations but demand better, even if they might not get it. I try to teach them that a little bit of frustration can be the impetus towards a better world. You're unsatisfied? Do better.

The Karen Paradox 🤬

People will point out that this is exactly what the Karens of the world do: they demand better than what they get.

That is true enough. They strive for better, which is a good thing, if you ignore everything that has to do with a context. But I would argue it's because they cannot manage their frustrations. They put their want of not having any frustration whatsoever before any other consideration, especially the frustrations of others.

This is different from having unwarranted frustrations. Sometimes, a Karen forces us to see a problem we weren't aware of, even if the problem in question is dealing with Karens.

By the way, I find it hilarious that a first name is used as a label. I apologize wholeheartedly to good-natured Karens out there... It's not your fault, I know. But it's also not the first time a first name has gathered a reputation for malice. It'll sort itself out in the end, we love the "Good Karens" still.

Manage your frustrations 😓

And so we come full circle: yes, managing your frustration does involve managing your expectations. Being frustrated at not being an astronaut, or rich, or famous, or whatever we feel we deserve shouldn't be that big of a deal, and maybe in some instances, managing expectations are a good way to manage our frustration.

But it lacks ambition. Exploration, research, innovation, charity, support, everything that's good about the human species stems from looking at a situation, being frustrated by it, and trying something else.

Mind you, a lot of things that are bad about the human species, such as greed, oppression, and the like, also stems from frustration (see the Karen Paradox above), but that's humans for you. They are complicated, and as much as the say they'd like everything to fit in neat boxes with clear delineations, they'll do their damned best to blur the lines the first opportunity they get.

As a long time player of various role playing games (pen and paper too, yes, you irreverent youngins), I like to think of my frustrations as belonging to two very distinct categories: those that stack, and those that don't.

Frustrations that stack tend to amplify each other, being connected to one another, and are usually harder to "fix". Frustrations that don't stack contribute to the overall level of stress and dissatisfaction, but are independent, and the "fix" can be anything from super easy to super hard to impossible. Factor in the "what can I do about it" part, and there's something of a path towards a better tomorrow that doesn't involve quitting or blindly following directives. It's never as well delineated as that.

Let's take an example: I'm late for a class, because the subway is stuck. I know that it means my class will be shorter, and since a good portion of the students haven't spent the necessary time to understand fully the last one (or indeed hand their homework in), it will be even shorter because of all the time we'll spend rehashing the same things. That, in turn, will mean they won't be as well prepared as I hoped for the rest of their studies, or indeed their professional lives.

First frustration: I'm late. People around me behave in a way that make me cringe because they are frustrated. The whole situation is really, really, annoying. I'm not going to yell at the driver or the subway staff (even if they might be able to do a better job at handling the situation, I'm sure they have their own set of things to deal with that I'm unaware of, not being, you know, educated in the art of making trains run on time). I could tell the guy next to me that him yelling in his phone that he'll be late prevents me from doing the same. Should I? Maybe, if the call lasts for ever, or if in a stroke of bad luck he actually starts running the meeting by yelling into his phone. Will it solve the problem, or create a new one?

I want to make sure the tardiness won't impact the class too much. Should I send a message to the class asking them to start working without me? Without guidance, it could be a waste of time, either because they won't do it seriously, or they won't know how to do it properly. If I flex my power and tell them it's going to be graded to ensure compliance, is it getting me closer to my goal? Will they be better professionals, and/or will they think that sometimes, people in power dish out punishment because they are frustrated? What if they all do it diligently but fail? If I keep the grade in their record, it can be really unfair to them, and they won't trust me much after that. And if I don't, don't I show them that it was just a power flex, and they can safely ignore the next one?

This is not one frustration, but a conflation of two. One that stacks (the impact on my students) and one that doesn't (the fact the subway is late). Recognizing that some things are beyond my control and therefore that the frustration I feel towards those things have to be endured is a good thing, in my mind. Maybe next time, I'll have a bigger margin for transport, or a good book to pass the time, or noise-cancelling ear pieces. But that frustration will come and go, and I refuse to let it affect my decisions towards the rest.

Second frustration: I need to get my students more curious, more involved, more industrious about the topic so that if I am unavailable for 15 minutes, it doesn't matter that much. This has nothing to do with the subway being late.

I want them to want to research the topic. I am frustrated that they don't see the point. Is it their fault? Probably to some extent, so I need to coerce them a bit, because I know they won't have a glorious revelation with a ticker-tape parade, lights and fanfare, out of the blue. But it's also my responsibility. Why don't they trust me that this is important? Have I not given enough proofs of that? Were they unconvincing ones? Maybe it's because this class is among many many others, and everyone is vying for their attention? Maybe it's because I keep telling them it's important, but I'm not showing why often enough?

This is a frustration that stacks: it has a direct impact on many other tasks that I have to do, will have an effect on the extra time I set aside for preparing classes, preparing exams and grading them, will impact how the colleagues see my work, my relationship with other teachers and school staff.

This is a frustration that should drive me to do better. I should treasure it as an engine of change. And, sure, if I lower my expectations, it'll go away, just like the memory that I was late once because the subway was not moving. And maybe I'll be happier. Or at least less stressed.

We progress by being unhappy about our current situation and doing something about it. Not by shifting the responsibility onto someone else fully (as opposed to Karens), and definitely not by going all fatalistic and aiming only for what we're guaranteed of getting.

Frustration is a good thing (in moderation) 🤔

Look, we all know the rush of being validated, of having things go our way, of being right.

Some of us know that the pleasure of gratification is even better if we had to overcome something in order to get it. We're not talking sexual kinks here, although they are definitely a good indicator that this is a real thing.

There is a reason why so many people like brain teasers, word games, sports competitions, etc. There is a reason why the 3 acts are such a staple of stories in every culture. Without obstacles, where is the reward?

I tell the students that there are very few pleasures more satisfying than working on a bug for a few hours or days and suddenly finding a solution. You feel smart, tall, handsome and powerful, afterwards.

That satisfaction doesn't happen if the process is:
- realizing there is a problem
- looking the solution up
- applying the solution blindly
- end of the problem

I mean, a lot of life is like that, and that's ok, but I don't think it gives pleasure to people. It is frustration without a satisfactory ending. Sure, the job gets done, but machines will do that better than us, eventually.

on swift, coding, CS, opinion, oss

[Dev Diary] Vanilla Is The Best Flavor

I have a weird thing with the multiplication of command-line tools and gizmos: I forget them.

Do I want to run supercool gitlab commands? Hell yea! Do I need to install 12 utilities (or code a new one) to archive every project older than a year? I hope not...

The setup

I am a sucker for well documented fully linted code. But the thing is, all the gizmos that help me do that have to be installed in the system or in my ~/bin and I have to remember to update them, and I have to install them on my CD machine, and on every new environment I setup, and make sure they are still compatible with the toolchain, and it freaks me out, ok?

Plus,watching the students try to do it is painful.

So, given a 100% vanilla swift-capable environment, can I manage to run documentation and linting?

The idea

We have Swift Package Manager, which is now a first-class citizen in XCode, but it can't run shell script phases without some nasty hacks.

What if some targets were (wait for it) built to do the documentation and the linting?

Linting

One of the most popular linters out there is swiftlint, and it supports SPM. It can also build a library instead of an executable, which means one of my targets could just run the linting and output it in the terminal.

In the Package.swift file, all I needed to do was add the right dependency, and the right product and voila!

let package = Package(
	name: "WonderfulPackage",
    products: [
    	// ...
         .executable(name: "Lint", targets: ["Lint"])
   	],
    dependencies: [
        // Dependencies declare other packages that this package depends on.
        // .package(url: /* package url */, from: "1.0.0"),
		// ... normal dependencies
        .package(url: "https://github.com/realm/SwiftLint", from: "0.39.0")
    ],
    targets: [
    	// ... normal targets
        .target(
            name: "Lint",
            dependencies: ["SwiftLintFramework"]),
	]
)
Package.swift

Now, SPM is very strict with paths, so I had to put a file named main.swift in the Sources/<target>/ directory, in this case Sources/Lint.

Running the linter is fairly straightforward, and goes in the main.swift file:

// Lint command main
// runs SourceDocs
import Foundation
import SwiftLintFramework

let config = Configuration(path: FileManager.default.currentDirectoryPath+"/.swiftlint.yml",
                           rootPath: FileManager.default.currentDirectoryPath,
                           optional: true,
                           quiet: true,
                           enableAllRules: false,
                           cachePath: nil,
                           customRulesIdentifiers: [])

for lintable in config.lintableFiles(inPath: FileManager.default.currentDirectoryPath, forceExclude: false) {
    let linter = Linter(file: lintable, configuration: config)
    let storage = RuleStorage()
    let collected = linter.collect(into: storage)
    let violations = collected.styleViolations(using: storage)
    if !violations.isEmpty {
        print(EmojiReporter.generateReport(violations))
    }
}

print("🎉 All done!")
Sources/Lint/main.swift

Setup the .swiftlint file as usual, and run the command via swift run Lint

Sources/WonderfulPackage/main.swift
⛔️ Line 15: Variable name should be between 3 and 40 characters long: 'f'
⚠️ Line 13: Arguments can be omitted when matching enums with associated types if they are not used.
⚠️ Line 12: Line should be 120 characters or less: currently 143 characters

Documentation

Documentation is actually trickier, because most documentation tools out there aren't built in swift, or compatible with SPM. Doxygen and jazzy are great, but they don't fit my needs.

I found a project that was extremely promising called SourceDocs by Eneko Alonso, but it isn't a library, so I had to fork it and make it into one (while providing a second target to generate the executable if needed). One weird issue is that SPM doesn't like subtargets to bear the same name so I had to rename a couple of them to avoid conflict with Swift Argument Parser (long story).

I finally found myself in the same spot than with the linter. All I needed to do was create another target, and Bob's you're uncle. Well actually he was mine. I digress.

let package = Package(
	name: "WonderfulPackage",
    products: [
    	// ...
         .executable(name: "Docs", targets: ["Docs"])
   	],
    dependencies: [
        // Dependencies declare other packages that this package depends on.
        // .package(url: /* package url */, from: "1.0.0"),
		// ... normal dependencies
        .package(url: "https://github.com/krugazor/SourceDocs", from: "0.7.0")
    ],
    targets: [
    	// ... normal targets
        .target(
            name: "Docs",
            dependencies: ["sourcedocslib"])
	]
)
Package.swift

Another well-placed main file:

// Docs command main
// runs SourceDocs
import Foundation
import SourceDocs

do {
    switch try SourceDocs().runOnSPM(moduleName: "WonderfulPackage",
                                     outputDirectory: FileManager.default.currentDirectoryPath+"/Documentation") {
    case .success:
        print("Successful run of the documentation phase")
    case .failure(let failure):
        print(failure.localizedDescription)
    }
} catch {
    print(error.localizedDescription)
}
Sources/Docs/main.swift

Now, the command swift run Docs generates the markdown documentation in the Documentation directory.

Parsing main.swift (1/1)
Removing reference documentation at 'WonderfulPackage/Documentation/KituraStarter'... ✔
Generating Markdown documentation...
  Writing documentation file: WonderfulPackage/Documentation/WonderfulPackage/structs/WonderfulPackage.md ✔
  Writing documentation file: WonderfulPackage/Documentation/WonderfulPackage/README.md ✔
Done 🎉
Successful run of the documentation phase

Conclusion

✅ Vanilla swift environment
✅ No install needed
✅ Works on Linux and MacOS
✅ Integrated into SPM
⚠️ When running in XCode, the current directory is always wonky for packages

on rant, opinion, oss

DocumentDB vs MongoDB

From AWS gives open source the middle finger:

Bypassing  MongoDB’s licensing by going for API comparability, given that  AWS  knows exactly why MongoDB did that, was always going to be a   controversial move and won’t endear the company to the open-source   community.

MongoDB is hugely popular, although entirely  for the wrong reasons in my mind, and it's kind of hard to scale it up  without infrastructure expertise, which is why it makes sense for a  company to offer some kind of a turnkey solution. Going for  compatibility rather than using the original code also makes a lot of  sense when you're an infrastructure-oriented business, because your own  code tends to be more tailored to your specific resources.

But in  terms of how-it-looks, after having repeatedly been accused of leeching  off open-source, this isn't great. One of the richest services divisions  out there, offloading R&D to the OSS community, then, once the  concept proves to be a potential goldmine, undercutting the original?

The  global trend of big companies is to acknowledge the influence of  open-source in our field and give back. Some do it because they believe  in it, some because they benefit from fresh (or unpaid) eyes, some  because of "optics" (newest trendy term for "public relations"). I'm not  sure that being branded as the only OSS-hostile name in the biz' is a  wise move.