Hey y'all, first time actually posting something here! Just curious what editor people use, whether its for coding, writing, or just the occasional note, whatever. I've gone through most of the well-known ones (vim, emacs, atom, vs code for starters), but only ever really messed around with vim enough to like it, and I've also been trying out gedit for the last little while and really liking it, but I'm curious to see what other people use!

Here’s a problem I had to solve at work this week that I enjoyed solving. I think it’s a good programming challenge that will test if you really grok XML.

Your input is some XML such as this:

<DOC>
<TEXT PARTNO="000">
<TAG ID="3">This</TAG> is <TAG ID="0">some *JUNK* data</TAG> .
</TEXT>
<TEXT PARTNO="001">
*FOO* Sometimes <TAG ID="1">tags in <TAG ID="0">the data</TAG> are nested</TAG> .
</TEXT>
<TEXT PARTNO="002">
In addition to <TAG ID="1">nested tags</TAG> , sometimes there is also <TAG ID="2">junk</TAG> we need to ignore .
</TEXT>
<TEXT PARTNO="003">*BAR*-1
<TAG ID="2">Junk</TAG> is marked by uppercase characters between asterisks and can also optionally be followed by a dash and then one or more digits . *JUNK*-123
</TEXT>
<TEXT PARTNO="004">
Note that <TAG ID="4">*this*</TAG> is just emphasized . It's not <TAG ID="2">junk</TAG> !
</TEXT>
</DOC>

The above XML has so-called in-line textual annotations because the XML <TAG> elements are embedded within the document text itself.

Your goal is to convert the in-line XML annotations to so-called stand-off annotations where the text is separated from the annotations and the annotations refer to the text via slicing into the text as a character array with starting and ending character offsets. While in-line annotations are more human-readable, stand-off annotations are equally machine-readable, and stand-off annotations can be modified without changing the document content itself (the text is immutable).

The challenge, then, is to convert to a stand-off JSON format that includes the plain-text of the document and the XML tag annotations grouped by their tag element IDs. In order to preserve the annotation information from the original XML, you must keep track of each <TAG>’s starting and ending character offset within the plain-text of the document. The plain-text is defined as the character data in the XML document ignoring any junk. We’ll define junk as one or more uppercase ASCII characters [A-Z]+ between two *, and optionally a trailing dash - followed by any number of digits [0-9]+.

Here is the desired JSON output for the above example to test your solution:

{
  "data": "\nThis is some data .\n\n\nSometimes tags in the data are nested .\n\n\nIn addition to nested tags , sometimes there is also junk we need to ignore .\n\nJunk is marked by uppercase characters between asterisks and can also optionally be followed by a dash and then one or more digits . \n\nNote that *this* is just emphasized . It's not junk !\n\n",
  "entities": [
    {
      "id": 0,
      "mentions": [
        {
          "start": 9,
          "end": 18,
          "id": 0,
          "text": "some data"
        },
        {
          "start": 41,
          "end": 49,
          "id": 0,
          "text": "the data"
        }
      ]
    },
    {
      "id": 1,
      "mentions": [
        {
          "start": 33,
          "end": 60,
          "id": 1,
          "text": "tags in the data are nested"
        },
        {
          "start": 80,
          "end": 91,
          "id": 1,
          "text": "nested tags"
        }
      ]
    },
    {
      "id": 2,
      "mentions": [
        {
          "start": 118,
          "end": 122,
          "id": 2,
          "text": "junk"
        },
        {
          "start": 144,
          "end": 148,
          "id": 2,
          "text": "Junk"
        },
        {
          "start": 326,
          "end": 330,
          "id": 2,
          "text": "junk"
        }
      ]
    },
    {
      "id": 3,
      "mentions": [
        {
          "start": 1,
          "end": 5,
          "id": 3,
          "text": "This"
        }
      ]
    },
    {
      "id": 4,
      "mentions": [
        {
          "start": 289,
          "end": 295,
          "id": 4,
          "text": "*this*"
        }
      ]
    }
  ]
}

Python 3 solution here.

If you need a hint, see if you can find an event-based XML parser (or if you’re feeling really motivated, write your own).

I've been thinking a lot about dark mode lately, now that macOS and Windows 10 both officially offer some implementation of it. I think dark modes make a compelling case for eye strain prevention, but the dealbreaker for me is revealed when switching between apps and one of them isn't dark. That jarring flash of bright light completely ruins whatever gentleness the dark environment provided in the first place. So despite my curiosity I've kept everything in light mode for years, tempered by f.lux to keep myself sane after sundown.

Anyway, now that there's official OS support I'm reconsidering. I think there's a growing pro-dark movement that was just waiting for that formal recognition. Today the programs I use most all offer dark modes so I'm taking an experimental plunge. My goal: 90% elimination of white flashes while in my normal workflow.

The biggest obstacle is, not surprisingly, the web. There are some beautiful dark browser themes available but that really only affects the UI elements around the page, not the page itself. I want to darken the web too. I have a few thoughts about this:

• Plugins like this one try to automate a dark mode for every site you visit. This is hit-or-miss, resulting in ugly color combinations, sometimes unreadable text. Some methods just invert the page colors, which can lead to all sort of other visual wonkiness. I haven't found a plugin like this that isn't fiddly and annoying.
• This plugin looks interesting. From what I can tell, it uses some kind of server-side heuristics to determine the optimal way to darken every page you visit. I haven't actually tried it because I'm concerned about the privacy/security implications of sending all my web activity to this unknown third party. Or what kind of performance hit that would involve. Also, they bury this information on their site, but this is a paid service with an annual subscription.
• I'm aware of Stylish and its huge library of user-maintained custom site styles. This seemed like a good approach, except that following a recent acquisition, the new owners of Stylish betrayed their users' trust in a very shady way so I'm afraid to go near it now. If there's a credible alternative with a decent style library I'd love to know about it. Especially if there's a way to automate style application so I don't have to manually activate it for every site I visit.
• Tangentially, the W3C is having an interesting conversation about adding CSS media query support for recognizing user dark-mode preferences. This could absolutely be the future of the web(!!), but I suspect it won't because it puts the responsibility on designers to basically double the amount of work they have to do. Speaking as someone in that field, I would not want to have to add this to my already-long list of design considerations.

Are there any other good web darkening methods I've overlooked? How do you deal with the white flash problem? Should I just give up and go back to black-on-white? Interested in any and all thoughts on the matter.

It's time for another programming challenge!

Given a list of coordinate pairs on a 2D plane that describe the vertices of a polygon, determine whether the polygon is concave or convex.

Since a polygon could potentially be any shape if we don't specify which vertices connect to which, we'll assume that the coordinates are given in strict order such that adjacent coordinates in the list are connected. Specifically, if we call the list V[1, n] and say that V[i] <-> V[j] means "vertex i and vertex j are connected", then for each arbitrary V[i] we have V[i-1] <-> V[i] <-> V[i+1]. Moreover, since V[1] and V[n] are at the ends of the list, V[1] <-> V[n] holds (i.e. the list "wraps around").

Finally, for simplicity we can assume that all coordinates are unique, that all polygon descriptions generate valid polygons with 3 or more non-overlapping sides, and that, yes, we're working with coordinates that exist in the set of real numbers only. Don't over-complicate it :)

For those who want an even greater challenge, extend this out to work with 3D space!

It's been nearly a week, so it's time for another programming challenge!

This time, let's create a calculator that accepts reverse Polish notation (RPN), also known as postfix notation.

For a bit of background, RPN is where you take your two operands in an expression and place the operator after them. For example, the expression 3 + 5 would be written as 3 5 +. A more complicated expression like (5 - 3) x 8 would be written as 5 3 - 8 x, or 8 5 3 - x.

All your program has to do is accept a valid RPN string and apply the operations in the correct order to produce the expected result.

Another week, another challenge!

This time, assume you're given a grid where each . represents an empty space and each # represents a "wall". We'll call any contiguous space of .s a "region". You can also think of a grid with no walls the "base" region. The walls may subdivide the base region into any number of isolated sub-regions of any shape or size.

Write a program that will, given a grid description, compute the total number of isolated regions.

For example, the following grid has 5 isolated regions:

....#....#
....#.###.
....#.#.#.
#...#..#..
.#..#...#.

Anybody here have a good setup for batch-downloading articles/news from several sites you specify, similar to youtube-dl but for general websites? I'm sure it could be scripted with not too much effort but I'm interested what polished solutions there are.

The idea would be so people with rare internet access could go to a hotspot weekly or something and sync that week's worth of content.

It looks like it's been over a week and a half since our last coding challenge, so let's get one going. This challenge is a relatively simple one, but it's complex enough that you can take a variety of different approaches to it.

As the title suggests, write a program that accepts an arbitrary number of arrays, in whatever form or manner you see fit (if you want to e.g. parse a potentially massive CSV file, then go nuts!), and returns a single array containing all of the elements of the other arrays in sorted order. That's it!

Bonus points for creative, efficient, or generalized solutions!

Let's do something a little more challenging this time.

Given an MxN grid of arbitrary size, and given a random starting place on that grid and a list of points to visit, find the shortest path such that you visit all of them. Path lengths will be computed using taxicab distances rather than strict coordinate distance calculations.

There are no restrictions on expected input this time. Output should be the total distance traveled between points.

Example

Assume that we use the character # to denote a spot on the grid, the character @ to denote your starting point, and the character * to denote a place on the grid that you're required to visit. One such grid may look something like this:

######
######
**####
#*####
#*#*##
#@####
######

In this case, let's say that the bottom-left point on the grid is point (0, 0) and we're starting on point (1, 1). One valid solution would be to move to point (3, 2), then (1, 2), then (1, 3), then (1, 4), and finally (0, 4). The shortest path available is thus 8. Note that it's not enough just to visit the next nearest point on the grid!

Preface

One of the most common questions I see from prospective programmers and computer scientists is "where should I start?". My answer to that is a pretty consistent one: learn how to solve problems effectively. But that's vague and not really all that helpful, so I figured that I should actually tackle this in a little more depth by touching on something more specific.

Specifically, I want to touch on the subject of how to think about complex problems.

The Rationale Behind Learning

Before we can better understand how to effectively solve problems, it's important to consider how it is that we learn. With any subject, the standard approach is to begin with the bare basics. For programming, that's writing a Hello, World! program in the new language you're working with. For foreign languages, you learn basic common words and sentence structure. For math, you learn your basic arithmetic operations like addition and multiplication.

From there, we add on more additional complexity and string together everything we've learned. For a foreign language, this looks like learning about new words, stringing them together in your own sentences, then learning about verb tenses and throwing them into the mix as well. With math, you take your normal number crunching and suddenly throw the concept of order of operations into the mix, then variables and how to solve for them.

As a general rule, we first get comfortable with solving a simple problem and gradually build up toward solving increasingly more difficult ones.

The Missing Piece

Odds are that we've all sat in a math class at one point, and when the teacher asked a student how to solve a problem, they received an immediate "I don't know". You may or may not have been that kid yourself. I have no intention of shaming the kids who struggled (or those who still struggle) with math. Rather, I want to point to what I believe is the fundamental cause of that mental barrier that has frustrated students for generations.

Learning is not simply a matter of adding more complexity to problems. A key part of learning, and one that I don't recall ever having emphasized during my grade school studies, is your ability to break problems down into the steps that you know how to complete and combine the different, simpler skills you've already learned to arrive at a solution. Instead, you were expected to solve many of those complex problems and learn through practice, or through pure rote memorization.

What determined whether or not you could solve those problems was then a question of whether or not you could intuit or memorize how to solve those specific problems, and brand new problems that still made use of the same skill sets but had completely different forms would throw a wrench in that. Those who could solve any of those problems--those who, I would argue, were often mistakenly referred to as "geniuses" or "talented"--were really just those who knew how to break a problem down into simpler pieces.

This isn't a failing on the students, but on the way they've been taught to think about problems.

Reducing Problems

What does it mean to "break down" a problem, though? The few times I recall a teacher ever touching on the subject, "break down the problem" and "use the skills you've already learned" were the kinds of pieces of advice passed around, completely vague and devoid of meaning for anyone who didn't already understand. How can we better grasp this important step?

There's a term in complexity theory known as "reduction". The general idea is that if you have problems A and B, where you already know how to solve B, then if you can transform problem A so that it looks like problem B, then you can use your solution for B to solve at least part of A.

In other words, finding the solution to a more complex problem is just a matter of finding a way to make it look like a problem you already know how to solve.

The advice to "break down" a problem really means to perform this process of "reduction", of transforming your more complicated problem A into your simpler, known problem B.

In Practice

We're still discussing a vague concept, but now that we have more specific language to work with, we can more easily see how it works in practice (a reduction of its own!).

Let's consider a conceptually simple problem: grabbing the k^th largest (or smallest) item from a list. How do we solve this problem? Probably the most obvious and straightforward answer is to sort the list then grab the k^th item, right?

Notice that we gave two high-level descriptions of the steps we need to solve this problem: sorting, then grabbing the appropriate item. We can therefore then state that the problem of "grab the k^th largest/smallest item from a list" can be reduced to the two problems "sort a list" and "grab the k^th item from a list".

Now, let's say we're given the problem "take this list of competitor times from the race and tell me what the top 10 race times were". What do we know about this problem? We know that we're being given a list, and we know that we need the 10 smallest items from that list. We also know that "10 smallest items" is just shorthand for "the 1st smallest item, the 2nd smallest item, ..., and the 10th smallest item". We can therefore reduce this problem to the previous one we solved by transforming it into "grab the k^th smallest item from a list" and "repeat for values 1-10 for k".

Practical Advice

In the end, my explanation may not have helped much at all in actually grasping the concept of reduction. My intent isn't necessarily to help you understand it immediately, but to provide you a framework for a way of thinking. Even if you do grasp the general concept, you may even wonder how you're supposed to recognize these kinds of reductions out in the wild in non-academic environments. The answer, perhaps annoying, is practice. Much like an appraiser can only become good at discerning details through experience, a programmer or computer scientist can only recognize these patterns through repeated exposure.

In general, if I had to narrow it down to a small list of tips for improving your problem solving skills, this would be it:

• Work on grasping the concept of reduction itself.
• Expose yourself to lots of new problems.
• Don't shy away from difficult problems. Reduce them as much as you can and solve the pieces you're able to. Try to research the pieces you're struggling with. Return to the problem later when you have more experience if you have to, but take a crack at it first.
• Don't accept "I don't know" as an answer in itself. Ask yourself why you don't how to solve a problem. Narrow down which pieces you're able to solve and which pieces you're not.
• Just solve problems. Any problems. Easy ones, hard ones, and anything in between. Solving problems is a skill, and practicing it will make you better at solving problems in general, and better at recognizing the simpler problems inside of more complicated ones.
• Don't just come up with a solution to a problem. Ensure that you understand how each piece of it works and why it works. Copy-pasting from StackOverflow can be a valid tool at your disposal, but doing so mindlessly isn't nearly as valuable as reviewing the solution, being able to determine whether or not it works before ever executing the code, and being able to discard anything unnecessary from it.

Final Thoughts

I'm not an authoritative voice on this subject. I'm not an educator. More than anything, I'm a life-long student and an enthusiast. There's seldom a day when I don't have to research something new in order to solve a problem I'm not familiar with, or remind myself the syntax for a function I've used several times in the past. I don't know anything about teaching others, but I do know plenty about learning, and if there's anything that has stood out to me over the years, it's the fact that I find it easier to learn about something or to solve a problem if I can transform the concept into something that's easier for me to grasp.

Moreover, I'm human and thus prone to mistakes. Call me out on them if you notice them. I'll take any of my mistakes as learning opportunities :)