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Bringing dwm Shortcuts to GNOME 2023-11-02 The dwm window manager is my standard "go-to" for most of my personal laptop environments. For desktops with larger, higher resolution monitors I tend to lean towards using GNOME. The GNOME DE is fairly solid for my own purposes. This article isn't going to deep dive into GNOME itself, but instead highlight some minor configuration changes I make to mimic a few dwm shortcuts. For reference, I'm running GNOME 45.0 on Ubuntu 23.10 Setting Up Fixed Workspaces When I use dwm I tend to have a hard-set amount of tags to cycle through (normally 4-5). Unfortunately, dynamic rendering is the default for workspaces (ie. tags) in GNOME. For my personal preference I set this setting to fixed. We can achieve this by opening Settings > Multitasking and selecting "Fixed number of workspaces". Screenshot of GNOME's Multitasking Settings GUI Setting Our Keybindings Now all that is left is to mimic dwm keyboard shortcuts, in this case: ALT + $num for switching between workspaces and ALT + SHIFT + $num for moving windows across workspaces. These keyboard shortcuts can be altered under Settings > Keyboard > View and Customize Shortcuts > Navigation. You'll want to make edits to both the "Switch to workspace n" and "Move window to workspace n". Screenshot of GNOME's keyboard shortcut GUI: switch to workspace Screenshot of GNOME's keyboard shortcut GUI: move window to workspace That's it. You're free to include even more custom keyboard shortcuts (open web browser, lock screen, hibernate, etc.) but this is a solid starting point. Enjoy tweaking GNOME!
Installing Older Versions of MongoDB on Arch Linux 2023-09-11 I've recently been using Arch Linux for my main work environment on my ThinkPad X260. It's been great. As someone who is constantly drawn to minimalist operating systems such as Alpine or OpenBSD, it's nice to use something like Arch that boasts that same minimalist approach but with greater documentation/support. Another major reason for the switch was the need to run older versions of "services" locally. Most people would simply suggest using Docker or vmm, but I personally run projects in self-contained, personalized directories on my system itself. I am aware of the irony in that statement... but that's just my personal preference. So I thought I would share my process of setting up an older version of MongoDB (3.4 to be precise) on Arch Linux. AUR to the Rescue You will need to target the specific version of MongoDB using the very awesome AUR packages: yay -S mongodb34-bin Follow the instructions and you'll be good to go. Don't forget to create the /data/db directory and give it proper permissions: mkdir -p /data/db/ chmod -R 777 /date/db What About My "Tools"? If you plan to use MongoDB, then you most likely want to utilize the core database tools (restore, dump, etc). The problem is you can't use the default mongodb-tools package when trying to work with older versions of MongoDB itself. The package will complain about conflicts and ask you to override your existing version. This is not what we want. So, you'll have to build from source locally: git clone https://github.com/mongodb/mongo-tools cd mongodb-tools ./make build Then you'll need to copy the built executables into the proper directory in order to use them from the terminal: cp bin/* /usr/local/bin/ And that's it! Now you can run mongod directly or use systemctl to enable it by default. Hopefully this helps anyone else curious about running older (or even outdated!) versions of MongoDB.
Converting HEIF Images with macOS Automator 2023-07-21 Often times when you save or export photos from iOS to iCloud they often render themselves into heif or heic formats. Both macOS and iOS have no problem working with these formats, but a lot of software programs will not even recognize these filetypes. The obvious step would just be to convert them via an application or online service, right? Not so fast! Wouldn't it be much cleaner if we could simply right-click our heif or heic files and convert them directly in Finder? Well, I've got some good news for you... Basic Requirements You will need to have Homebrew installed You will need to install the libheif package through Homebrew: brew install libheif Creating our custom Automator script For this example script we are going to convert the image to JPG format. You can freely change this to whatever format you wish (PNG, TIFF, etc.). We're just keeping things basic for this tutorial. Don't worry if you've never worked with Automator before because setting things up is incredibly simple. Open the macOS Automator from the Applications folder Select Quick Option from the first prompt Set "Workflow receives current" to image files Set the label "in" to Finder From the left pane, select "Library > Utilities" From the presented choices in the next pane, drag and drop Run Shell Script into the far right pane Set the area "Pass input" to as arguments Enter the following code below as your script and type ⌘-S to save (name it something like "Convert HEIC/HEIF to JPG") for f in "$@" do /opt/homebrew/bin/heif-convert "$f" "${f%.*}.jpg" done Making Edits If you ever have the need to edit this script (for example, changing the default format to png), you will need to navigate to your ~/Library/Services folder and open your custom heif Quick Action in the Automator application. Simple as that. Happy converting! If you're interested, I also have some other Automator scripts available: Batch Converting Images to webp with macOS Automator Convert Files to HTML with macOS Automator Quick Actions
Blogging for 7 Years 2023-06-24 My first public article was posted on June 28th 2016. That was seven years ago. In that time, quite a lot has changed in my life both personally and professionally. So, I figured it would be interesting to reflect on these years and document it for my own personal records. My hope is that this is something I could start doing every 5 or 10 years (if I can keep going that long!). This way, my blog also serves as a "time capsule" or museum of the past... Fun Facts This Blog: I originally started blogging on bradleytaunt.com using WordPress, but since then I have changed both my main domain and blog infrastructure multiple times. At a glance I have used: Jekyll Hugo Blot Static HTML/CSS PHPetite Shinobi pblog barf Currently using! Personal: As with anyone over time, the personal side of my life has seen the biggest updates: Married the love of my life (after knowing each other for ~14 years!) Moved out into rural Ontario for some peace and quiet Had three wonderful kids with said wife (two boys and a girl) Started noticing grey sprinkles in my stubble (I guess I can officially call myself a "grey beard"?) Professionally: Pivoted heavily into UX research and design for a handful of years (after working mostly with web front-ends) Recently switched back into a more fullstack development role to challenge myself and learn more Nothing Special This post isn't anything ground-breaking but for me it's nice to reflect on the time passed and remember how much can change in such little time. Hopefully I'll be right back here in another 7 years and maybe you'll still be reading along with me!
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The world changed a lot in 1995. And for the web, it was a transformational year. The post 1995 Was the Most Important Year for the Web appeared first on The History of the Web.
I have a lot in the works for the this month's Logic for Programmers release. Among other things, I'm completely rewriting the chapter on Logic Programming Languages. I originally showcased the paradigm with puzzle solvers, like eight queens or four-coloring. Lots of other demos do this too! It takes creativity and insight for humans to solve them, so a program doing it feels magical. But I'm trying to write a book about practical techniques and I want everything I talk about to be useful. So in v0.9 I'll be replacing these examples with a couple of new programs that might get people thinking that Prolog could help them in their day-to-day work. On the other hand, for a newsletter, showcasing a puzzle solver is pretty cool. And recently I stumbled into this post by my friend Pablo Meier, where he solves a videogame puzzle with Prolog:1 Summary for the text-only readers: We have a test with 10 true/false questions (denoted a/b) and four student attempts. Given the scores of the first three students, we have to figure out the fourth student's score. bbababbabb = 7 baaababaaa = 5 baaabbbaba = 3 bbaaabbaaa = ??? You can see Pablo's solution here, and try it in SWI-prolog here. Pretty cool! But after way too long studying Prolog just to write this dang book chapter, I wanted to see if I could do it more elegantly than him. Code and puzzle spoilers to follow. (Normally here's where I'd link to a gentler introduction I wrote but I think this is my first time writing about Prolog online? Uh here's a Picat intro instead) The Program You can try this all online at SWISH or just jump to my final version here. :- use_module(library(dif)). % Sound inequality :- use_module(library(clpfd)). % Finite domain constraints First some imports. dif lets us write dif(A, B), which is true if A and B are not equal. clpfd lets us write A #= B + 1 to say "A is 1 more than B".2 We'll say both the student submission and the key will be lists, where each value is a or b. In Prolog, lowercase identifiers are atoms (like symbols in other languages) and identifiers that start with a capital are variables. Prolog finds values for variables that match equations (unification). The pattern matching is real real good. % ?- means query ?- L = [a,B,c], [Y|X] = [1,2|L], B + 1 #= 7. B = 6, L = [a, 6, c], X = [2, a, 6, c], Y = 1 Next, we define score/33 recursively. % The student's test score % score(student answers, answer key, score) score([], [], 0). score([A|As], [A|Ks], N) :- N #= M + 1, score(As, Ks, M). score([A|As], [K|Ks], N) :- dif(A, K), score(As, Ks, N). First key is the student's answers, second is the answer key, third is the final score. The base case is the empty test, which has score 0. Otherwise, we take the head values of each list and compare them. If they're the same, we add one to the score, otherwise we keep the same score. Notice we couldn't write if x then y else z, we instead used pattern matching to effectively express (x && y) || (!x && z). Prolog does have a conditional operator, but it prevents backtracking so what's the point??? A quick break about bidirectionality One of the coolest things about Prolog: all purely logical predicates are bidirectional. We can use score to check if our expected score is correct: ?- score([a, b, b], [b, b, b], 2). true But we can also give it answers and a key and ask it for the score: ?- score([a, b, b], [b, b, b], X). X = 2 Or we could give it a key and a score and ask "what test answers would have this score?" ?- score(X, [b, b, b], 2). X = [b, b, _A], dif(_A,b) X = [b, _A, b], dif(_A,b) X = [_A, b, b], dif(_A,b) The different value is written _A because we never told Prolog that the array can only contain a and b. We'll fix this later. Okay back to the program Now that we have a way of computing scores, we want to find a possible answer key that matches all of our observations, ie gives everybody the correct scores. key(Key) :- % Figure it out score([b, b, a, b, a, b, b, a, b, b], Key, 7), score([b, a, a, a, b, a, b, a, a, a], Key, 5), score([b, a, a, a, b, b, b, a, b, a], Key, 3). So far we haven't explicitly said that the Key length matches the student answer lengths. This is implicitly verified by score (both lists need to be empty at the same time) but it's a good idea to explicitly add length(Key, 10) as a clause of key/1. We should also explicitly say that every element of Key is either a or b.4 Now we could write a second predicate saying Key had the right 'type': keytype([]). keytype([K|Ks]) :- member(K, [a, b]), keytype(Ks). But "generating lists that match a constraint" is a thing that comes up often enough that we don't want to write a separate predicate for each constraint! So after some digging, I found a more elegant solution: maplist. Let L=[l1, l2]. Then maplist(p, L) is equivalent to the clause p(l1), p(l2). It also accepts partial predicates: maplist(p(x), L) is equivalent to p(x, l1), p(x, l2). So we could write5 contains(L, X) :- member(X, L). key(Key) :- length(Key, 10), maplist(contains([a,b]), L), % the score stuff Now, let's query for the Key: ?- key(Key) Key = [a, b, a, b, a, a, b, a, a, b] Key = [b, b, a, b, a, a, a, a, a, b] Key = [b, b, a, b, a, a, b, b, a, b] Key = [b, b, b, b, a, a, b, a, a, b] So there are actually four different keys that all explain our data. Does this mean the puzzle is broken and has multiple different answers? Nope The puzzle wasn't to find out what the answer key was, the point was to find the fourth student's score. And if we query for it, we see all four solutions give him the same score: ?- key(Key), score([b, b, a, a, a, b, b, a, a, a], Key, X). X = 6 X = 6 X = 6 X = 6 Huh! I really like it when puzzles look like they're broken, but every "alternate" solution still gives the same puzzle answer. Total program length: 15 lines of code, compared to the original's 80 lines. Suck it, Pablo. (Incidentally, you can get all of the answer at once by writing findall(X, (key(Key), score($answer-array, Key, X)), L).) I still don't like puzzles for teaching The actual examples I'm using in the book are "analyzing a version control commit graph" and "planning a sequence of infrastructure changes", which are somewhat more likely to occur at work than needing to solve a puzzle. You'll see them in the next release! I found it because he wrote Gamer Games for Lite Gamers as a response to my Gamer Games for Non-Gamers. ↩ These are better versions of the core Prolog expressions \+ (A = B) and A is B + 1, because they can defer unification. ↩ Prolog-descendants have a convention of writing the arity of the function after its name, so score/3 means "score has three parameters". I think they do this because you can overload predicates with multiple different arities. Also Joe Armstrong used Prolog for prototyping, so Erlang and Elixir follow the same convention. ↩ It still gets the right answers without this type restriction, but I had no idea it did until I checked for myself. Probably better not to rely on this! ↩ We could make this even more compact by using a lambda function. First import module yall, then write maplist([X]>>member(X, [a,b]), Key). But (1) it's not a shorter program because you replace the extra definition with an extra module import, and (2) yall is SWI-Prolog specific and not an ISO-standard prolog module. Using contains is more portable. ↩
Startup CEOs should ask themselves what crazy ideas can turn into a move that just ends a market's competitive dynamic
We just opened a search for a new junior programmer at 37signals. It's been years since we last hired a junior, but the real reason the listing is turning heads is because we're open about the yearly salary: $145,849*. That's high enough that programmers with lots of experience are asking whether they could apply, even if they aren't technically "junior". The answer is no. The reason we're willing to pay a junior more than most is because we're looking for a junior who's better than most. Not better in "what do they already know", but in "how far could they go". We're hiring for peak promise — and such promise only remains until it's revealed. Maybe it sounds a little harsh, but a programmer who's been working professionally for five years has likely already revealed their potential. What you're going to get is roughly what you see. That doesn't mean that people can't get better after that, but it means that the trajectory by which they improve has already been plotted. Whereas a programmer who's either straight out of school or fresh off their first internship or short-stint job is essentially all potential. So you draw their line on the basis of just a few early dots, but the line can be steep. It's not that different from something like the NFL scouting combine. Teams fight to find the promise of The Next All-Star. These rookies won't have the experience that someone who's already played in the league for years would have, but they have the potential to be the best. Someone who's already played for several seasons will have shown what they have and be weighed accordingly. This is not easy to do! Plenty of rookies, in sports and programming, may show some early potential, then fail to elevate their game to where the buyer is betting it could be. But that's the chance you take to land someone extraordinary. So if you know a junior programmer with less than three years of industry experience who is sparkling with potential, do let them know of our listing. And if you know someone awesome who's already a senior programmer, we also have an opening for them. *It's a funnily precise number because it's pulled directly from the Radford salary database, which we query for the top 10% of San Francisco salaries for junior programmers.
Data engineering is a field I would categorize as a subspecialty of software engineering. It shares the same concerns as software engineering—scalability, maintainability, and other “-ilities”—but its primary focus is on data. It’s a unique discipline because data is inherently messy, and as a result, no standard enterprise framework has emerged to dominate the space—and […]