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The X220 ThinkPad is the Best Laptop in the World 2023-09-26 The X220 ThinkPad is the greatest laptop ever made and you're wrong if you think otherwise. No laptop hardware has since surpassed the nearly perfect build of the X220. New devices continue to get thinner and more fragile. Useful ports are constantly discarded for the sake of "design". Functionality is no longer important to manufacturers. Repairability is purposefully removed to prevent users from truly "owing" their hardware. It's a mess out there. But thank goodness I still have my older, second-hand X220. Specs Before I get into the details explaining why this laptop is the very best of its kind, let's first take a look at my machine's basic specifications: CPU: Intel i7-2640M (4) @ 3.500GHz GPU: Intel 2nd Generation Core Processor Memory: 16GB DDR3 OS: Arch Linux / OpenBSD Resolution: 1366x768 With that out of the way, I will break down my thoughts on the X220 into five major sections: Build quality, available ports, the keyboard, battery life, and repairability. Build Quality The X220 (like most of Lenovo's older X/T models) is built like a tank. Although sourced from mostly plastic, the device is still better equipped to handle drops and mishandling compared to that of more fragile devices (such as the MacBook Air or Framework). This is made further impressive since the X220 is actually composed of many smaller interconnected pieces (more on this later). A good litmus test I perform on most laptops is the "corner test". You grab the base corner of a laptop in its open state. The goal is to see if the device displays any noticeable give or flex. In the X220's case: it feels rock solid. The base remains stiff and bobbing the device causes no movement on the opened screen. I'm aware that holding a laptop in this position is certainly not a normal use case, but knowing it is built well enough to do so speaks volumes of its construction. The X220 is also not a lightweight laptop. This might be viewed as a negative for most users, but I actually prefer it. I often become too cautious and end up "babying" thinner laptops out of fear of breakage. A minor drop from even the smallest height will severely damage these lighter devices. I have no such worries with my X220. As for the laptop's screen and resolution: your mileage may vary. I have zero issues with the default display or the smaller aspect ratio. I wrote about how I stopped using an external monitor, so I might be a little biased. Overall, this laptop is a device you can snatch up off your desk, whip into your travel bag and be on your way. The rugged design and bulkier weight help put my mind at ease - which is something I can't say for newer laptop builds. Ports Ports. Ports Everywhere. I don't think I need to explain how valuable it is to have functional ports on a laptop. Needing to carrying around a bunch of dongles for ports that should already be on the device just seems silly. The X220 comes equipped with: 3 USB ports (one of those being USB3 on the i7 model) DisplayPort VGA Ethernet SD Card Reader 3.5mm Jack Ultrabay (SATA) Wi-Fi hardware kill-switch Incredibly versatile and ready for anything I throw at it! Keyboard The classic ThinkPad keyboards are simply that: classic. I don't think anyone could argue against these keyboards being the golden standard for laptops. It's commendable how Lenovo managed to package so much functionality into such a small amount of real estate. Most modern laptops lack helpful keys such as Print Screen, Home, End, and Screen Lock. They're also an absolute joy to type on. The fact that so many people go out of their way to mod X230 ThinkPad models with X220 keyboards should tell you something... Why Lenovo moved away from these keyboards will always baffle me. (I know why they did it - I just think it's stupid). Did I mention these classic keyboards come with the extremely useful Trackpoint as well? Battery Life Author's Note: This section is very subjective. The age, quality, and size of the X220's battery can have a massive impact on benchmarks. I should also mention that I run very lightweight operating systems and use DWM as opposed to a heavier desktop environment. Just something to keep in mind. The battery life on my own X220 is fantastic. I have a brand-new 9-cell that lasts for roughly 5-6 hours of daily work. Obviously these numbers don't come close to the incredible battery life of Apple's M1/M2 chip devices, but it's still quite competitive against other "newer" laptops on the market. Although, even if the uptime was lower than 5-6 hours, you have the ability to carry extra batteries with you. The beauty of swapping out your laptop's battery without needing to open up the device itself is fantastic. Others might whine about the annoyance of carrying an extra battery in their travel bag, but doing so is completely optional. A core part of what makes the X220 so wonderful is user control and choice. The X220's battery is another great example of that. Repairability The ability to completely disassemble and replace almost everything on the X220 has to be one of its biggest advantages over newer laptops. No glue to rip apart. No special proprietary tools required. Just some screws and plastic snaps. If someone as monkey-brained as me can completely strip down this laptop and put it back together again without issue, then the hardware designers have done something right! Best of all, Lenovo provides a very detailed hardware maintenance manual to help guide you through the entire process. My disassembled X220 when I was reapplying the CPU thermal paste. Bonus Round: Price I didn't list this in my initial section "breakdown" but it's something to consider. I purchased my X220 off eBay for $175 Canadian. While this machine came with a HDD instead of an SSD and only 8GB of total memory, that was still an incredible deal. I simply swapped out the hard-drive with an SSD I had on hand, along with upgrading the DDR3 memory to its max of 16. Even if you needed to buy those components separately you would be hard-pressed to find such a good deal for a decent machine. Not to mention you would be helping to prevent more e-waste! What More Can I Say? Obviously the title and tone of this article is all in good fun. Try not to take things so seriously! But, I still personally believe the X220 is one of, if not the best laptop in the world.
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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I've been running the Framework Desktop for a few months here in Copenhagen now. It's an incredible machine. It's completely quiet, even under heavy, stress-all-cores load. It's tiny too, at just 4.5L of volume, especially compared to my old beautiful but bulky North tower running the 7950X — yet it's faster! And finally, it's simply funky, quirky, and fun! In some ways, the Framework Desktop is a curious machine. Desktop PCs are already very user-repairable! So why is Framework even bringing their talents to this domain? In the laptop realm, they're basically alone with that concept, but in the desktop space, it's rather crowded already. Yet it somehow still makes sense. Partly because Framework has gone with the AMD Ryzen AI Max 395+, which is technically a laptop CPU. You can find it in the ASUS ROG Flow Z13 and the HP ZBook Ultra. Which means it'll fit in a tiny footprint, and Framework apparently just wanted to see what they could do in that form factor. They clearly had fun with it. Look at mine: There are 21 little tiles on the front that you can get in a bunch of different colors or with logos from Framework. Or you can 3D print your own! It's a welcome change in aesthetic from the brushed aluminum or gamer-focused RGBs approach that most of the competition is taking. But let's cut to the benchmarks. That's really why you'd buy a machine like the Framework Desktop. There are significantly cheaper mini PCs available from Beelink and others, but so far, Framework has the only AMD 395+ unit on sale that's completely silent (the GMKTec very much is not, nor is the Z3 Flow). And for me, that's just a dealbreaker. I can't listen to roaring fans anymore. Here's the key benchmark for me: That's the only type of multi-core workload I really sit around waiting on these days, and the Framework Desktop absolutely crushes it. It's almost twice as fast as the Beelink SER8 and still a solid third faster than the Beelink SER9 too. Of course, it's also a lot more expensive, but you're clearly getting some multi-core bang for your buck here! It's even a more dramatic difference to the Macs. It's a solid 40% faster than the M4 Max and 50% faster than the M4 Pro! Now some will say "that's just because Docker is faster on Linux," and they're not entirely wrong. Docker runs natively on Linux, so for this test, where the MySQL/Redis/ElasticSearch data stores run in Docker while Ruby and the app code runs natively, that's part of the answer. Last I checked, it was about 25% of the difference. But so what? Docker is an integral part of the workflow for tons of developers. We use it to be able to run different versions of MySQL, Redis, and ElasticSearch for different applications on the same machine at the same time. You can't really do that without Docker. So this is what Real World benchmarks reveal. It's not just about having a Docker advantage, though. The AMD 395+ is also incredibly potent in RAW CPU performance. Those 16 Zen5 cores are running at 5.1GHz, and in Geekbench 6 multicore, this is how they stack up: Basically matching the M4 Max! And a good chunk faster than the M4 Pro (as well as other AMDs and Intel's 14900K!). No wonder that it's crazy quick with a full-core stress test like running 30,000 assertions for our HEY test suite. To be fair, the M4s are faster in single-core performance. Apple holds the crown there. It's about 20%. And you'll see that in benchmarks like Speedometer, which mostly measures JavaScript single-core performance. The Framework Desktop puts out 670 vs 744 on the M4 Pro on Speedometer 2.1. On SP 3.1, it's an even bigger difference with 35 vs 50. But I've found that all these computers feel fast enough in single-core performance these days. I can't actually feel the difference browsing on a machine that does 670 vs 744 on SP2.1. Hell, I can barely feel the difference between the SER8, which does 506, and the M4 Pro! The only time I actually feel like I'm waiting on anything is in multi-core workloads like the HEY test suite, and here the AMD 395+ is very near the fastest you can get for a consumer desktop machine today at any price. It gets even better when you bring price into the equation, though. The Framework Desktop with 64GB RAM + 2TB NVMe is $1,876. To get a Mac Studio with similar specs — M4 Max, 64GB RAM, 2TB NVMe — you'll literally spend nearly twice as much at $3,299! If you go for 128GB RAM, you'll spend $2,276 on the Framework, but $4,099 on the Mac. And it'll still be way slower for development work using Docker! The Framework Desktop is simply a great deal. Speaking of 64GB vs 128GB, I've been running the 64GB version, and I almost never get anywhere close to the limits. I think the highest I've seen in regular use is about 20GB of RAM in action. Linux is really efficient. Especially when you're using a window manager like Hyprland, as we do in Omarchy. The only reason you really want to go for the full 128GB RAM is to run local LLM models. The AMD 395+ uses unified memory, like Apple, so nearly all of it is addressable to be used by the GPU. That means you can run monster models, like the new 120b gpt-oss from OpenAI. Framework has a video showing them pushing out 40 tokens/second doing just that. That seems about in range of the numbers I've seen from the M4 Max, which also seem in the 40-50 token/second range, but I'll defer to folks who benchmark local LLMs for the exact details on that. I tried running the new gpt-oss-20b on my 64GB machine, though, and I wasn't exactly blown away by the accuracy. In fact, I'd say it was pretty bad. I mean, exceptionally cool that it's doable, but very far off the frontier models we have access to as SaaS. So personally, this isn't yet something I actually use all that much in day-to-day development. I want the best models running at full speed, and right now that means SaaS. So if you just want the best, small computer that runs Linux superbly well out of the box, you should buy the Framework Desktop. It's completely quiet, fantastically fast, and super fun to look at. But I think it's also fair to mention that you can get something like a Beelink SER9 for half the price! Yes, it's also only 2/3 the performance in multi-core, but it's just as fast in single-core. Most developers could totally get away with the SER9, and barely notice what they were missing. But there are just as many people for whom the extra $1,000 is worth the price to run the test suite 40 seconds quicker! You know who you are. Oh, before I close, I also need to mention that this thing is a gaming powerhouse. It basically punches about as hard as an RTX 4060! With an iGPU! That's kinda crazy. Totally new territory on the PC side for integrated graphics. ETA Prime has a video showing the same chip in the GMK Tech running premier games at 1440p High Settings at great frame rates. You can run most games under Linux these days too (thanks Valve and Steam Deck!), but if you need to dual boot with Windows, the dual NVMe slots in the Framework Desktop come very handy. Framework did good with this one. AMD really blew it out of the water with the 395+. We're spoiled to have such incredible hardware available for Linux at such appealing discounts over similar stuff from Cupertino. What a great time to love open source software and tinker-friendly hardware!
I was listening to a podcast interview with the Jackson Browne (American singer/songwriter, political activist, and inductee into the Rock and Roll Hall of Fame) and the interviewer asks him how he approaches writing songs with social commentaries and critiques — something along the lines of: “How do you get from the New York Times headline on a social subject to the emotional heart of a song that matters to each individual?” Browne discusses how if you’re too subtle, people won’t know what you’re talking about. And if you’re too direct, you run the risk of making people feel like they’re being scolded. Here’s what he says about his songwriting: I want this to sound like you and I were drinking in a bar and we’re just talking about what’s going on in the world. Not as if you’re at some elevated place and lecturing people about something they should know about but don’t but [you think] they should care. You have to get to people where [they are, where] they do care and where they do know. I think that’s a great insight for anyone looking to have a connecting, effective voice. I know for me, it’s really easily to slide into a lecturing voice — you “should” do this and you “shouldn’t” do that. But I like Browne’s framing of trying to have an informal, conversational tone that meets people where they are. Like you’re discussing an issue in the bar, rather than listening to a sermon. Chris Coyier is the canonical example of this that comes to mind. I still think of this post from CSS Tricks where Chris talks about how to have submit buttons that go to different URLs: When you submit that form, it’s going to go to the URL /submit. Say you need another submit button that submits to a different URL. It doesn’t matter why. There is always a reason for things. The web is a big place and all that. He doesn’t conjure up some universally-applicable, justified rationale for why he’s sharing this method. Nor is there any pontificating on why this is “good” or “bad”. Instead, like most of Chris’ stuff, I read it as a humble acknowledgement of the practicalities at hand — “Hey, the world is a big place. People have to do crafty things to make their stuff work. And if you’re in that situation, here’s something that might help what ails ya.” I want to work on developing that kind of a voice because I love reading voices like that. Email · Mastodon · Bluesky
Previously, I wrote some sketchy ideas for what I call a p-fast trie, which is basically a wide fan-out variant of an x-fast trie. It allows you to find the longest matching prefix or nearest predecessor or successor of a query string in a set of names in O(log k) time, where k is the key length. My initial sketch was more complicated and greedy for space than necessary, so here’s a simplified revision. (“p” now stands for prefix.) layout A p-fast trie stores a lexicographically ordered set of names. A name is a sequence of characters from some small-ish character set. For example, DNS names can be represented as a set of about 50 letters, digits, punctuation and escape characters, usually one per byte of name. Names that are arbitrary bit strings can be split into chunks of 6 bits to make a set of 64 characters. Every unique prefix of every name is added to a hash table. An entry in the hash table contains: A shared reference to the closest name lexicographically greater than or equal to the prefix. Multiple hash table entries will refer to the same name. A reference to a name might instead be a reference to a leaf object containing the name. The length of the prefix. To save space, each prefix is not stored separately, but implied by the combination of the closest name and prefix length. A bitmap with one bit per possible character, corresponding to the next character after this prefix. For every other prefix that matches this prefix and is one character longer than this prefix, a bit is set in the bitmap corresponding to the last character of the longer prefix. search The basic algorithm is a longest-prefix match. Look up the query string in the hash table. If there’s a match, great, done. Otherwise proceed by binary chop on the length of the query string. If the prefix isn’t in the hash table, reduce the prefix length and search again. (If the empty prefix isn’t in the hash table then there are no names to find.) If the prefix is in the hash table, check the next character of the query string in the bitmap. If its bit is set, increase the prefix length and search again. Otherwise, this prefix is the answer. predecessor Instead of putting leaf objects in a linked list, we can use a more complicated search algorithm to find names lexicographically closest to the query string. It’s tricky because a longest-prefix match can land in the wrong branch of the implicit trie. Here’s an outline of a predecessor search; successor requires more thought. During the binary chop, when we find a prefix in the hash table, compare the complete query string against the complete name that the hash table entry refers to (the closest name greater than or equal to the common prefix). If the name is greater than the query string we’re in the wrong branch of the trie, so reduce the length of the prefix and search again. Otherwise search the set bits in the bitmap for one corresponding to the greatest character less than the query string’s next character; if there is one remember it and the prefix length. This will be the top of the sub-trie containing the predecessor, unless we find a longer match. If the next character’s bit is set in the bitmap, continue searching with a longer prefix, else stop. When the binary chop has finished, we need to walk down the predecessor sub-trie to find its greatest leaf. This must be done one character at a time – there’s no shortcut. thoughts In my previous note I wondered how the number of search steps in a p-fast trie compares to a qp-trie. I have some old numbers measuring the average depth of binary, 4-bit, 5-bit, 6-bit and 4-bit, 5-bit, dns qp-trie variants. A DNS-trie varies between 7 and 15 deep on average, depending on the data set. The number of steps for a search matches the depth for exact-match lookups, and is up to twice the depth for predecessor searches. A p-fast trie is at most 9 hash table probes for DNS names, and unlikely to be more than 7. I didn’t record the average length of names in my benchmark data sets, but I guess they would be 8–32 characters, meaning 3–5 probes. Which is far fewer than a qp-trie, though I suspect a hash table probe takes more time than chasing a qp-trie pointer. (But this kind of guesstimate is notoriously likely to be wrong!) However, a predecessor search might need 30 probes to walk down the p-fast trie, which I think suggests a linked list of leaf objects is a better option.
New Logic for Programmers Release! v0.11 is now available! This is over 20% longer than v0.10, with a new chapter on code proofs, three chapter overhauls, and more! Full release notes here. Software books I wish I could read I'm writing Logic for Programmers because it's a book I wanted to have ten years ago. I had to learn everything in it the hard way, which is why I'm ensuring that everybody else can learn it the easy way. Books occupy a sort of weird niche in software. We're great at sharing information via blogs and git repos and entire websites. These have many benefits over books: they're free, they're easily accessible, they can be updated quickly, they can even be interactive. But no blog post has influenced me as profoundly as Data and Reality or Making Software. There is no blog or talk about debugging as good as the Debugging book. It might not be anything deeper than "people spend more time per word on writing books than blog posts". I dunno. So here are some other books I wish I could read. I don't think any of them exist yet but it's a big world out there. Also while they're probably best as books, a website or a series of blog posts would be ok too. Everything about Configurations The whole topic of how we configure software, whether by CLI flags, environmental vars, or JSON/YAML/XML/Dhall files. What causes the configuration complexity clock? How do we distinguish between basic, advanced, and developer-only configuration options? When should we disallow configuration? How do we test all possible configurations for correctness? Why do so many widespread outages trace back to misconfiguration, and how do we prevent them? I also want the same for plugin systems. Manifests, permissions, common APIs and architectures, etc. Configuration management is more universal, though, since everybody either uses software with configuration or has made software with configuration. The Big Book of Complicated Data Schemas I guess this would kind of be like Schema.org, except with a lot more on the "why" and not the what. Why is important for the Volcano model to have a "smokingAllowed" field?1 I'd see this less as "here's your guide to putting Volcanos in your database" and more "here's recurring motifs in modeling interesting domains", to help a person see sources of complexity in their own domain. Does something crop up if the references can form a cycle? If a relationship needs to be strictly temporary, or a reference can change type? Bonus: path dependence in data models, where an additional requirement leads to a vastly different ideal data model that a company couldn't do because they made the old model. (This has got to exist, right? Business modeling is a big enough domain that this must exist. Maybe The Essence of Software touches on this? Man I feel bad I haven't read that yet.) Computer Science for Software Engineers Yes, I checked, this book does not exist (though maybe this is the same thing). I don't have any formal software education; everything I know was either self-taught or learned on the job. But it's way easier to learn software engineering that way than computer science. And I bet there's a lot of other engineers in the same boat. This book wouldn't have to be comprehensive or instructive: just enough about each topic to understand why it's an area of study and appreciate how research in it eventually finds its way into practice. MISU Patterns MISU, or "Make Illegal States Unrepresentable", is the idea of designing system invariants in the structure of your data. For example, if a Contact needs at least one of email or phone to be non-null, make it a sum type over EmailContact, PhoneContact, EmailPhoneContact (from this post). MISU is great. Most MISU in the wild look very different than that, though, because the concept of MISU is so broad there's lots of different ways to achieve it. And that means there are "patterns": smart constructors, product types, properly using sets, newtypes to some degree, etc. Some of them are specific to typed FP, while others can be used in even untyped languages. Someone oughta make a pattern book. My one request would be to not give them cutesy names. Do something like the Aarne–Thompson–Uther Index, where items are given names like "Recognition by manner of throwing cakes of different weights into faces of old uncles". Names can come later. The Tools of '25 Not something I'd read, but something to recommend to junior engineers. Starting out it's easy to think the only bit that matters is the language or framework and not realize the enormous amount of surrounding tooling you'll have to learn. This book would cover the basics of tools that enough developers will probably use at some point: git, VSCode, very basic Unix and bash, curl. Maybe the general concepts of tools that appear in every ecosystem, like package managers, build tools, task runners. That might be easier if we specialize this to one particular domain, like webdev or data science. Ideally the book would only have to be updated every five years or so. No LLM stuff because I don't expect the tooling will be stable through 2026, to say nothing of 2030. A History of Obsolete Optimizations Probably better as a really long blog series. Each chapter would be broken up into two parts: A deep dive into a brilliant, elegant, insightful historical optimization designed to work within the constraints of that era's computing technology What we started doing instead, once we had more compute/network/storage available. c.f. A Spellchecker Used to Be a Major Feat of Software Engineering. Bonus topics would be brilliance obsoleted by standardization (like what people did before git and json were universal), optimizations we do today that may not stand the test of time, and optimizations from the past that did. Sphinx Internals I need this. I've spent so much goddamn time digging around in Sphinx and docutils source code I'm gonna throw up. Systems Distributed Talk Today! Online premier's at noon central / 5 PM UTC, here! I'll be hanging out to answer questions and be awkward. You ever watch a recording of your own talk? It's real uncomfortable! In this case because it's a field on one of Volcano's supertypes. I guess schemas gotta follow LSP too ↩