More from Alex Meub
3D Printing has allowed me to be creative in ways I never thought possible. It has allowed me to create products that provide real value, products that didn’t exist before I designed them. On top of that, it’s satisfied my desire to ship products, even if the end-user is just me. Another great thing is how quickly 3D printing provides value. If I see a problem, I can design and print a solution that works in just a few hours. Even if I’m the only one who benefits, that’s enough. But sharing these creations takes the experience even further. When I see others use or improve on something I’ve made, it makes the process feel so much more worthwhile. It gives me the same feeling of fulfillment when I ship software products at work. Before mass-market 3D printing, creators would need to navigate the complexity and high costs of mass-production methods (like injection molding) even to get a limited run of a niche product produced. With 3D printing, they can transfer the cost of production to others. Millions of people have access to good 3D printers now (at home, work, school, libraries, maker spaces), which means almost anyone can replicate a design. Having a universal format for sharing 3D designs dramatically lowers the effort that goes into sharing them. Creators can share their design as an STL file, which describes the surface geometry of their 3D object as thousands of little triangles. This “standard currency” of the 3D printing world is often all that is required to precisely replicate a design. This dramatically lowers the effort that goes into sharing printable designs. The widespread availability of 3D printers and the universal format for sharing 3D designs has allowed 3D-printed products to not only exist but thrive in maker communities. This is the magic of 3D printing: it empowers individuals to solve their own problems by designing solutions while enabling others to reproduce those designs at minimal cost and effort.
Last summer, I was inspired by a computer that was built inside of a toaster that I saw at a local computer recycling store. The idea of a computer with the design of a home appliance was really appealing and so was the absurdity of it. It occurred to me that this would be a fun and creative way to integrate technology into my life. After thinking about it, I realized there’s also something visually appealing about how simple and utilitarian toasters are. I have major nostalgia for the famous After Dark screensaver and I think this is why. I knew now that I wanted to make my own attempt at a toaster/computer hybrid. I decided to do just that when I created the DataToaster 3000: a toaster NAS with two 3.5 inch hard disk docking stations built inside it. The hard disks can be easily swapped out (while powered off) without taking anything apart. It uses a Zimaboard x86-64 single board computer and even has a functional knob that controls the color of the power LED. I designed a fairly complex set of 3D-printed parts that attach to the base of the toaster and hold everything neatly in place. This allows it to be easily disassembled if I ever want to make any modifications and also hopefully makes the project easier to build for others. It’s a ridiculous thing but I really do love it. You can find the build guide on Instructables and the 3D models on Printables.
The Yoto Mini is one of my favorite products. The team behind it deeply understands its users and put just the right set of features into a brilliantly designed package. I have no affiliation with Yoto, I’m just a happy customer with kids who love it. If you aren’t aware, Yoto is an audio platform for kids with what they call “screen-free” audio players (even though they have little pixel LED screens on them). The players are Wi-Fi enabled and support playing audio from credit card-sized NFC tags called Yoto cards. Yoto sells audio players and also licenses audio content and offers it on its platform as well. The cards themselves do not contain any audio data, just a unique ID of the audio content that is pulled from the cloud. After content is pulled on the first play, it is saved and played locally from the player after that. Yoto also supports playing podcasts and music stations without using cards. Their marketing puts a lot of emphasis on the platform being “ad-free” which is mostly true as there are never ads on Yoto cards or official Yoto podcasts. However, some of the other podcasts do advertise their content. So, what’s so great about the Yoto Mini? This concept isn’t new as there have been many examples of audio players for kids over the years. What sets it apart is how every detail of the hardware, mobile app, and exclusive content is meticulously designed and well executed. Yoto Mini Hardware The main input methods of the Yoto Mini are two orange knobs, turning the left knob controls volume and the right knob navigates chapters or tracks. Pressing the right knob instantly plays the Yoto Daily podcast and pressing it twice plays Yoto Radio (a kid-friendly music station). These actions are both configurable in the mobile app. The NFC reader slot accepts Yoto cards and instantly starts playing where you left off after you insert one. It has a high-quality speaker that can be surprisingly loud, an on/off button, a USB-C charging port, an audio output jack, and a small pixel display that shows images related to the audio content. The Yoto Mini is also surprisingly durable. My kids have dropped it many times on hard surfaces and it still basically looks as good as new. Yoto understands that the physical audio player itself is primarily used by younger kids and the design reflects this. My 3-year-old daughter was able to figure out how to turn it on/off, start listening to books using cards, and play the Yoto Daily podcast each morning which was empowering for her. This was her first technology product that she was fully capable of using without help from an adult. I can’t think of many other products that do this better. Yoto Mobile App The Yoto team understands that parents are users of this product too, mostly for managing the device and its content. Yoto has built a very good mobile experience that is tightly integrated with the hardware and provides all the features you’d want as a parent. From the app, you can start playing any of the content from cards you own on the player or your phone (nice if your kids lose a card), you can set volume limits for both night and day time, you can set alarms, and configure the shortcut buttons. You can record audio onto a blank Yoto card (which comes with the player) if your kid wants to create their own story, link it to their favorite podcast or favorite music. The app even lets you give each track custom pixel art that is displayed on the screen. Audio Content By far the most underrated feature is a daily podcast called Yoto Daily. This ad-free podcast is run by a charming British host and it is funny, entertaining, and educational. My kids (now 4 and 7) look forward to it every morning and the fact that it’s daily free content that is integrated directly into the Yoto hardware is amazing. To me, this is the killer feature, as my kids get to enjoy it every day and it’s always fresh and interesting. Yoto licenses content from child book authors, popular kid’s shows, movies, and music (recently the Beatles) which are made available in their store. I also discovered that Yoto does not seem to lock down its content with DRM. My son traded some Yoto cards with a friend and I assumed there would be some kind of transfer or de-registration process but to my surprise, they just worked without issue. Conclusion The Yoto Mini is a delightful product. The team behind it thought through every detail and made it an absolute joy to use both as a child and parent. I’m impressed at how well the Yoto team understands their users and prioritizes simplicity and ease of use above all else.
When I first got my 3D printer, I built an enclosure to protect it from dust, maintain a consistent temperature, and minimize noise. I was surprised to find that the enclosure didn’t reduce noise that significantly. I then placed a patio paver under my printer, which made it noticeably quieter, but it was still audible from other rooms in my house. Recently, I found the most effective noise reduction solution: squash balls. These balls are designed with varying bounce levels, indicated by colored dots. The “double-yellow dot” balls have a very low bounce, making them ideal for dampening vibration, which is the primary cause of printer noise. I found an existing design for squash ball feet, printed it, and hot glued them evenly under my patio paver. My current setup includes the enclosure, patio paver, and squash balls under the paver. Now, the printer is so quiet that I actually can’t tell if it’s running, even when I’m in the same room. Occasionally, I will hear the stepper motors, but that’s rare. Most of the time I need to open the enclosure to make sure it’s still printing.
More in programming
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 ↩