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    1. tldr; chunkwm has been completely rewritten and is now yabai From the chunkwm site: chunkwm is no longer in development because of a C99 re-write, yabai. yabai was originally supposed to be the...

      tldr; chunkwm has been completely rewritten and is now yabai

      From the chunkwm site:

      chunkwm is no longer in development because of a C99 re-write, yabai.

      yabai was originally supposed to be the first RC version of chunkwm. However due to major architectural changes, supported systems, and changes to functionality, it is being released separately. There are multiple reasons behind these changes, based on the experience I've gained through experimenting with, designing, and using both kwm and chunkwm. Some of these changes are performance related while other changes have been made to keep the user experience simple and more complete, attempts to achieve a seamless integration with the operating system (when possible), proper error reporting, and yet still keep the property of being customizable.

      For those who don't know, chunkwm was / is a tiling windows manager that is sort of like bspwm / i3 etc. I've been using chunkwm for a few months now and love it. If you're also an i3 user, the lack of a proper super key does make your key combos different, but overall its an excellent window manager. Both chunkwm and yabai use koekeishiya's Simple Hotkey Daemon (skhd).

      Anyway, I gave the new version the day and its pretty good, but still has some quirks. It seems like development is moving along quickly, so keep an eye on it.

      6 votes
    2. Genetic Algorithms

      Introduction to Genetic Algorithms Genetic algorithms can be used to solve problems that are difficult, or impossible to solve with traditional algorithms. Much like neural networks, they provide...

      Introduction to Genetic Algorithms

      Genetic algorithms can be used to solve problems that are difficult, or impossible to solve with traditional algorithms. Much like neural networks, they provide good-enough solution in short amount of time, but rarely find the best one. While they're not as popular as neural networks nor as widely used, they still have their place, as we can use them to solve complicated problems very fast, without expensive training rigs and with no knowledge of math.

      Genetic algorithms can be used for variety of tasks, for example for determining the best radio antenna shape, aerodynamic shapes of cars and planes, wind mill shapes, or various queing problems. We'll use it to print "Hello, World!".

      How does it work?

      Genetic algorithm works in three steps.

      1. Generate random solutions
      2. Test how good they are
      3. Pick the best ones, breed and mutate them, go to step 2

      It works just like evolution in nature. First, we generate randomised solutions to our problem (in this case: random strings of letters).

      Then, we test each solution and give it points, where better solutions gain more points. In our problem, we would give one point for each correct letter in the string.

      Afterwards, we pick the best solutions and breed it together (just combine the strings). It's not bad idea to mutate (or randomize) the string a bit.

      We collect the offsprings, and repeat the process until we find good enough solution.

      Generate random solutions

      First of all, we need to decide in which form we will encode our solutions. In this case, it will be simply string. If we wanted to build race cars, we would encode each solution (each car) as array of numbers, where first number would be size of the first wheel, the second number would be size of the second wheel, etc. If we wanted to build animals that try to find food, fight and survive, we would choose a decision tree (something like this).

      So let's start and make few solutions, or entities. One hundred should be enough.

      from random import randint
      
      goal = "Hello, World!"
      allowed_characters = list("qwertyuiopasdfghjklzxcvbnmQWERTYUIOPASDFGHJKLZXCVBNM ,!")
      
      def get_random_entity(n, string_length):
          entities = []
          for _ in range(0, n):
              entity = ""
              for _ in range(0, string_length):
                  entity += allowed_characters[randint(0, len(allowed_characters)-1)]
              entities.append(entity)
          return entities
      
      print(get_random_entity(100, 13))
      

      Test how good they are

      This is called a "fitness function". Fitness function determines how good a solution is, be it a car (travel distance), animal (food gathered), or a string (number of correct letters).

      The most simple function we can use right now will simply count correct letters. If we wanted, we could make something like Levenshtein distance instead.

      def get_fitness(entity):
          points = 0
          for i in range(0, len(entity)):
              if goal[i] == entity[i]:
                  points += 1
          return points
      

      Crossover and mutation

      Now it's time to select the best ones and throw away the less fortunate entities. Let's order entities by their fitness.

      Crossover is a process, when we take two entities (strings) and breed them to create new one. For example, we could just give the offspring one part from one parent and another part from second parent.

      There are many ways how to do this, and I encourage you to try multiple approaches when you will be doing something like this.

      P:  AAAABBB|BCCCC
      P:  DDDDEEE|FGGGG
      
      F1: AAAABBB|FGGGG
      

      Or we can just choose at random which letter will go from which parent, which works the best here. After we have the offsprint (F1), we should mutate it. What if we were unfortunate, and H (which we need for our Hello, World!) was not in any of the 100 entities? So we take the string and for each character of the string, there is a small chance to mutate it - change it at random.

      F1:  ADDDEBEFGCGG
      F1`: ADHDEBEFGCGG
      

      And it's done. Now kill certain part of old population. I don't know which percentage is best, but I usually kill about 90% of old population. The 90% that we killed will be replaced by new offsprings.

      There is just one more thing: which entities do we select for crossover? It isn't bad idea - and it generally works just fine - to just give better entities higher chance to breed.

      def get_offspring(first_parent, second_parent, mutation_chance):
          new_entity = ""
          for i in range(0, len(first_parent)):
              if randint(0, 100) < mutation_chance:
                  new_entity += allowed_characters[randint(0, len(allowed_characters)-1)]
              else:
                  if randint(0, 1) == 0:
                      new_entity += first_parent[i]
                  else:
                      new_entity += second_parent[i]
          return new_entity
      

      When add everything together, we get this output:

      Generation 1, best score: 2 ::: QxZPjoptHfNgX
      Generation 2, best score: 3 ::: XeNlTOQuAZjuZ
      Generation 3, best score: 4 ::: weolTSQuoZjuK
      Generation 4, best score: 5 ::: weTgnC uobNdJ
      Generation 5, best score: 6 ::: weTvny uobldb
      Generation 6, best score: 6 ::: HellSy mYbZdC
      Generation 7, best score: 7 ::: selOoXBWoAKn!
      Generation 8, best score: 8 ::: HeTloSoWYZlh!
      Generation 9, best score: 8 ::: sellpX WobKd!
      Generation 10, best score: 9 ::: welloq WobSdb
      Generation 11, best score: 9 ::: selloc WoZjd!
      Generation 12, best score: 10 ::: wellxX WoVld!
      Generation 13, best score: 10 ::: welltX World!
      Generation 14, best score: 10 ::: welltX World!
      Generation 15, best score: 10 ::: welltX World!
      Generation 16, best score: 11 ::: zellov Wobld!
      Generation 17, best score: 11 ::: Hellty World!
      Generation 18, best score: 11 ::: welloX World!
      Generation 19, best score: 11 ::: welloX World!
      Generation 20, best score: 11 ::: welloX World!
      Generation 21, best score: 12 ::: welloX World!
      Generation 22, best score: 12 ::: Helloy World!
      Generation 23, best score: 12 ::: Helloy World!
      Generation 24, best score: 12 ::: Helloy World!
      Generation 25, best score: 12 ::: Helloy World!
      Generation 26, best score: 12 ::: Helloy World!
      Generation 27, best score: 12 ::: Helloy World!
      Generation 28, best score: 12 ::: Helloy World!
      Generation 29, best score: 12 ::: Helloy World!
      Generation 30, best score: 12 ::: Helloy World!
      Generation 31, best score: 12 ::: Helloy World!
      Generation 32, best score: 12 ::: Helloy World!
      Generation 33, best score: 12 ::: Helloy World!
      Generation 34, best score: 13 ::: Helloy World!
      Generation 35, best score: 13 ::: Hello, World!
      

      As we can see, we find pretty good solution very fast, but it takes very long to find perfect solution. The complete code is here.

      Maintaining diversity

      When we solve difficult problems, it starts to be increasingly important to maintain diversity. When all your entities are basically the same (which happened in this example), it's difficult to find other solutions than those that are almost the same as the currently best one. There might be a much better solution, but we didn't find it, because all solutions that are different to currently best one are discarded. Solving this is the real challenge of genetic algorithms. One of the ideas is to boost diverse solutions in fitness function. So for every solution, we compute distance to the current best solutions and add bonus points for distance from it.

      9 votes
    3. I've been learning Laravel, and familiarizing myself with the framework by coding up a blogging website. Right now, it's minimally functional, and I'd like to add some more features to it. Since...

      I've been learning Laravel, and familiarizing myself with the framework by coding up a blogging website. Right now, it's minimally functional, and I'd like to add some more features to it. Since this is my first project with Laravel the code is a mess, and it's just about time for me to rewrite the whole thing. Before starting that, I'd like to have a better idea of what my final product should be. I don't want to recreate WordPress in Laravel, but I do want to have something I wouldn't spit at. Basically a project that would be good as a resume builder if I ever needed one.

      So far, my website allows users to...

      • register for an account, log in/out, update their email address and display name
      • create posts with a WISIWYG editor
      • upload files
      • create profiles
      • and manipulate everything through CRUD.

      What do you think the minimal features a blogging platform needs to have to be "complete" and usable as a stand-alone system?

      5 votes
    4. So I've been messing around with the webp encoder cli tool and I really wish I could quickly achieve a workflow more similar to photoshop's previews. Is there a GUI tool out there where I can...

      So I've been messing around with the webp encoder cli tool and I really wish I could quickly achieve a workflow more similar to photoshop's previews. Is there a GUI tool out there where I can specify the arguments for an CLI executable then it lets me adjust the values and run the command. Ideally it would automatically let me view the output file, and define presets would be great.

      10 votes
    5. What is the programming language you enjoy the most, or otherwise define as your favourite? Why is that particular language your favourite? Bonus: add just a little bit of code in it that you...

      What is the programming language you enjoy the most, or otherwise define as your favourite? Why is that particular language your favourite?

      Bonus: add just a little bit of code in it that you think exposes its beauty.

      37 votes
    6. This is of course the age old debate, which on other sites has been known to quickly devolve (one of the Internet ‘holy wars’ of yore). Nonetheless, I am curious. I personally use vim. Part of the...

      This is of course the age old debate, which on other sites has been known to quickly devolve (one of the Internet ‘holy wars’ of yore). Nonetheless, I am curious.

      I personally use vim. Part of the reason is surely that it is just the first one of the two that I happened to use, and thus by the time I tried emacs I was used to vim. But more broadly, I enjoy using vim because once you get over the hump of ‘weird’ commands and modes, I feel that vim most closely emulates the feelings and thought process that you have when building something with your hands. You can pick up different tools, yank pieces out and rearrange them, turn the object around in your hands to see different parts, etc etc. Once you internalize the movement and editing you really start flying around. At that point the interface seems less like a barrier between you and the text and more like a way to ‘hold’ it.

      Of course, people have different preferences.

      16 votes
    7. Web Design Work

      Hi everyone! Per admin recommendation I'm posting this in comp. I would like to switch Staining The Timbre from a blogspot domain to its own. I can handle the paperwork and whatnot associated with...

      Hi everyone! Per admin recommendation I'm posting this in comp.

      I would like to switch Staining The Timbre from a blogspot domain to its own. I can handle the paperwork and whatnot associated with the url change, but I would like to hire a web designer to spruce up the joint a bit. Right now I'm using a default theme provided by Blogspot and, while it serves its purpose, it makes the page look like it's run by a high schooler.

      It should be a relatively basic project. I don't need anything for commerce set up, or anything I think of as "crazy". Just a very basic blog layout that looks professional on both desktop and mobile (the latter is a little lacking in particular right now). Archive links, Tags, ability to comment on posts, Contact Info, and one that preferably preserves the large-picture format the site currently has; that's about it, aside from being able to create the posts themselves.

      If anyone is interested please send me a private message. This is very much still in the quoting phase, but I appreciate any assistance you all can provide me in getting an idea on cost.

      Thanks in advance!

      8 votes
    8. I was asked this question in an interview and I said the downsides would be extra disk space used to store it and more disk writes from the database to maintain the index on updates. I asked a...

      I was asked this question in an interview and I said the downsides would be extra disk space used to store it and more disk writes from the database to maintain the index on updates. I asked a friend and he said the downsides are space used in memory to store the index as well as the fact that an index could take days to build on a large dataset.

      What does everyone here think about this question?

      8 votes