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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
Running VSCode in Chromium on OpenBSD 2023-06-05 VSCode and its many variations are not available on OpenBSD. This doesn't cause issue with many OpenBSD users, but those making the jump from Linux might miss access to such a popular editor. Lucky for us, there is a hacky workaround to solve this problem. VSCode in the Browser I tried my best to build something like code-server locally and run that directly in my browser - but I failed miserably. Instead, I fell back on vscode.dev which is essentially a remote version of code-server. Getting things to work seamlessly proved a little more challenging. I found the best performance was running everything through Chromium with special parameters enabled on launch. Note: The following assumes you have already installed chromium First we need to disable unveil for Chromium. This will allow us to access our system files through vscode.dev using the "Open folder..." or "Open file..." commands without issue: chrome --disable-unveil Everything should work pretty solid right out the box now - except it doesn't. Syntax highlighting does not work without enabling WASM/WebAssembly. Your experience might be different, but I had to include the following when launching Chromium from the terminal: ENABLE_WASM=1 chrome --enable-wasm Success! We can avoid typing out these complex commands everytime we want to launch our editor by setting up an alias (in my case via .zshrc): alias vscode="ENABLE_WASM=1 chrome --enable-wasm --disable-unveil" That's it! Now I can just pop open VSCode on OpenBSD by simply running vscode in my terminal. Hopefully this can help others slowly transition over to OpenBSD - which you should do because it is amazing!
Converting My X201 ThinkPad into a Slabtop 2023-05-01 I recently wrote about physically disabling the WiFi toggle switch on my X201 which was a fun "hack" to an annoying issue I was running into. Since then, the laptop has been running flawlessly. The only other minor issue I had was the poor display quality. The screen works perfectly fine but the X201's age prevents it from being the best possible display for day-to-day tasks. Both the resolution and viewing angles are quite poor. So, what were my options to remedy this problem? Swap out and upgrade the laptop's display (not many options for this though) Connect an external display, keyboard, mouse and dock the X201 Something else? Looking into "Something Else" I found that the possible replacement screens weren't worth the cost/hassle to swap out. And setting the laptop aside, docked with external peripherals sounded fine in theory - but then I would be missing out on the X201's amazing classic keyboard... Then I thought to myself, "Why not just remove the display entirely?" So that's what I did. The X201 display (still inside the top lid) disconnected from the body of the laptop. Removing the entire top lid was easier than I initially thought it would be. The beautiful thing about these older, classic ThinkPads is the ability to completely dissemble them. I won't go into heavy details on how to take one of these machines apart, since great documentation already exists. But once you have the keyboard and hinge screws removed it is essentially as easy as lifting the lid out of the chassis. (After disconnecting the small display connectors, of course) Ditching Alpine for Debian I love Alpine Linux. It's normally my go-to distro (paired with dwm) for most of my laptop devices. Unfortunately, I find tiling window managers a slight pain to use on larger, 4K displays. I also wanted this slabtop to "just work", instead of needing to fiddle around with WiFi, xrandr, audio etc. Not to mention that Debian is rock solid stable. Defaulting to the XFCE DE was a no-brainer as well since this device is somewhat old. The X201 "slabtop" connected to my 27" 4K monitor via the ThinkPad UltraDock. Closing Thoughts This slabtop gives me those old-school "Commodore64" vibes (or more recent devices like the Raspberry Pi 400). This setup certainly isn't for everyone but for my use case it's working well. Traveling with a machine like this might prove a little more challenging - although you could simply pair it with a smaller travel monitor (I'm sure the overall carrying weight would be similar to the standard X201). If nothing else, it is good to keep this option in mind if you ever break your laptop's display and don't feel like spending the money to fix it. Just grab an external monitor you have laying around and make a slabtop!
More in programming
A clip of me singing a funny song from Gilbert and Sullivan’s Ruddigore back in 2013
In this video, we look at why fork() needs copy-on-write, how it works inside the kernel, and a memory usage problem that Instagram encountered with Python.
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An aggregation is some kind of summary of a set of data. This can be the sum, length, minimum, etc. It is quite common to want to calculate such a summary repeatedly, e.g. “the maximum noise level in dB for the past 30 seconds” for a nuisance detector. In such a case we say there is a sliding window over our data, and we want to aggregate over our window. If our aggregation is a binary operator with an inverse, like integer sums, there is a very easy solution using a double-ended queue: from collections import deque class SlidingWindowSum: def __init__(self): self.sum = 0 self.elems = deque() def push(self, x): self.sum += x self.elems.append(x) def pop(self): self.sum -= self.elems.popleft() def eval(self): return self.sum But what if our operator has no inverse? This is actually the case for most interesting summaries such as minimum, quantile, approximate unique count (for example using HyperLogLog), etc. In fact, even something as simple as a floating-point sum suffers from the fact that floating-point addition is not invertible. For example, if you ever have a NaN in your input data with the above naive algorithm your sum will forever remain NaN, even long after the bad value has left your window. Six years ago I came up with an algorithm for maintaining just the minimum/maximum in a sliding window and posted it to cs.stackexchange. I now consider this algorithm pointless, because it turns out there is a simple and efficient algorithm that solves this problem for a very wide class of aggregations. I’m writing this blog post to spread the word, because I feel it should be more widely known. Folklore I came across this algorithm while reading a far more advanced paper, Low-Latency Sliding-Window Aggregation in Worst-Case Constant Time by Tangwongsan et al. Why is this paper titled low-latency? Because it does the same as what I’m about to describe, but in O(1) time for each step. However, in it they also described a “two-stack” algorithm, which does it in amortized O(1), and is far, far simpler. Amortized O(1) means that across many operations the total amount of work per element is constant, but an individual operation can take much longer. This is almost always fine, unless you absolutely need a low upper bound on latency. Funnily enough that paper attributes this algorithm to “adamax” from a 2011 Stack Overflow post. They in turn credit a 2001 lecture note by D. Sleator for the inspiration. However, this lecture note does not describe a sliding window aggregate, it describes the classical two-stack algorithm for implementing a FIFO queue and does amortized analysis on it. Ultimately I would not be surprised to find that this algorithm was already described in an obscure paper from the 1970s, seeing how simple and brilliant it is. Two stacks Like the authors of the paper, I will generalize the two-stack algorithm to arbitrary associative aggregation functions. By abstracting the aggregation as a set of functions, empty(), unit(x), combine(x, y) and finalize(x), you can describe many possible aggregations, for example a mean: empty = lambda: (0, 0) unit = lambda x: (x, 1) combine = lambda x, y: (x[0] + y[0], x[1] + y[1]) finalize = lambda x: x[0] / x[1] if x[1] else None I’d like to note here that these functions have the following signatures: fn empty() -> Agg; fn unit(x: Value) -> Agg; fn combine(x: Agg, y: Agg) -> Agg; fn finalize(x: Agg) -> Out; I’m making a distinction here between Value, Agg and Out because while they seem superficially similar for something like an integer sum, for an approximate unique count on strings you would have (Value, Agg, Out) = (String, HyperLogLogSketch, u64), three wildly different types. Without further ado, the algorithm: class TwoStackAgg: def __init__(self): self.values = [] self.values_agg = empty() self.cum_aggs = [] def push(self, x): self.values.append(x) self.values_agg = combine(self.values_agg, unit(x)) def pop(self): if not self.cum_aggs: cum_agg = empty() while self.values: cum_agg = combine(unit(self.values.pop()), cum_agg) self.cum_aggs.append(cum_agg) self.values_agg = empty() self.cum_aggs.pop() def eval(self): return finalize( combine(self.cum_aggs[-1], self.values_agg) if self.cum_aggs else self.values_agg ) That’s it, the entire algorithm. There’s two stacks (values and cum_aggs) and one more aggregate, values_agg. At any point in time values_agg holds the aggregate of values, and cum_aggs contains the cumulative aggregates of all values in our window that aren’t in values, in reverse order. From this we can get the aggregate over our entire window in constant time by by combining the last value of cum_aggs with values_agg. The neat part is that (assuming w is our window size) every wth operation we drain all of values and maintain a running aggregate while pushing the partial cumulative aggregates onto cum_aggs. This is what makes it amortized O(1), doing O(w) internal operations every wth pop bounds the total amount of work per element to O(1), even though a singular operation might not be constant time. I think this is best visualized. Suppose we sum [1, 2, ..., 10] with a fixed-size sliding window of four elements, then the state on each eval() call would look like this (values_agg not shown as it is simply the aggregate of the values): cum_aggs values out [] [] = 0 [] [1] = 1 [] [1, 2] = 1 + 2 [] [1, 2, 3] = 1 + 2 + 3 [] [1, 2, 3, 4] = 1 + 2 + 3 + 4 [4, 3 + 4, 2 + 3 + 4] [5] = 2 + 3 + 4 + 5 [4, 3 + 4] [5, 6] = 3 + 4 + 5 + 6 [4] [5, 6, 7] = 4 + 5 + 6 + 7 [] [5, 6, 7, 8] = 5 + 6 + 7 + 8 [8, 7 + 8, 6 + 7 + 8] [9] = 6 + 7 + 8 + 9 [8, 7 + 8] [9, 10] = 7 + 8 + 9 + 10 [8] [9, 10] = 8 + 9 + 10 [] [9, 10] = 9 + 10 [10] [] = 10 [] [] = 0 In total the memory usage is O(w), where w is your maximum window size. Note that for simplicity of analysis and the example I assumed a fixed-size window w, but there is nothing about the two-stack algorithm that requires this. You can call push(x) and pop() as many times as you’d like between each eval(), growing and shrinking the window size as needed. Floating-point non-associativity Note that we required above that our aggregate combine is associative, meaning: combine(combine(x, y), z) = combine(x, combine(y, z)) Technically speaking, floating-point addition doesn’t respect this. Nevertheless, the above algorithm is still very useful because the results closely match the expected outcome, even more so if you use a compensated summation algorithm like Kahan summation. Another neat thing about the two-stack algorithm is that it doesn’t require commutativity, if you follow the above implementation precisely. The order of operands is maintained, which can matter for things like string concatenation. However, there is a second very useful property of the above algorithm. Each aggregate is strictly a combination of the elements in the window, and none outside the window. This means if your window contains a NaN or infinity (or some other outlier), that value only poisons the windows that contain it rather than the rest of your computation. But even without NaN or infinity it is useful, due to not propagating errors endlessly. E.g. if your sliding window starts with [1e20, 1], this is what would happen with a naive rolling sum: >>> 1e20 + 1 - 1e20 - 1 -1.0 Compensated summation will reduce these effects, but not making your result depend on values outside of the window will eliminate long-term error accumulation entirely.
