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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...
7 months ago

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More from Alex Meub

Building the DataToaster 3000

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.

10 months ago 82 votes
Building a Removable Bike Basket for the Yepp Rack

I wanted to add more hauling capacity to my bike and was looking for something compatible with my Yepp rear rack. I also use my rack with a child seat (the Yepp Maxi) which has a mechanism that allows it to attach and detach easily without sacrificing safety. I was thinking it would be great to build a Yepp compatible rear basket that could I just as quickly attach/detach from my rack. I designed a removable Yepp-rack-compatible rear basket that consists of a milk crate, some plywood for stability and a 3D printed bracket threaded for M6 bolts which hold it all together. It can be attached and removed in seconds and is very secure. 3D Printed Mounting Bracket I modeled my mounting bracket after the one on the Yepp Maxi childseat. After a few iterations I was able to make it perfectly fit. I printed it in PETG filament so it was UV resistant and then installed threaded inserts for M6 bolts to attach it to the milk crate and my rear rack. 3D Print and Build Instructions You can find the 3D print on Printables and a full build guide on Instructables.

11 months ago 77 votes
The Yoto Mini is Perfect

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.

a year ago 71 votes
How To Quiet Down Your 3D Printer

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.

a year ago 38 votes

More in programming

Executives should be the least busy people

If your executive calendar is packed back to back, you have no room for fires, customers, or serendipities. You've traded all your availability for efficiency. That's a bad deal. Executives of old used to know this! That's what the long lunches, early escapes to the golf course, and reading the paper at work were all about. A great fictional example of this is Bert Cooper from Mad Men. He knew his value was largely in his network. He didn't have to grind every minute of every day to prove otherwise. His function was to leap into action when the critical occasion arose or decision needed to be made. But modern executives are so insecure about seeming busy 24/7 that they'll wreck their business trying to prove it. Trying to outwork everyone. Sacrificing themselves thin so they can run a squirrel-brain operation that's constantly chasing every nutty idea. Now someone is inevitably going to say "but what about Elon!!". He's actually a perfect illustration of doing this right, actually. Even if he works 100-hour weeks, he's the CEO of 3 companies, has a Diablo VI addiction, and keeps a busy tweeting schedule too. In all of that, I'd be surprised if there was more than 20-30h per company per week on average. And your boss is not Elon. Wide open calendars should not be seen as lazy, but as intentional availability. It's time we brought them back into vogue.

2 days ago 4 votes
Dispatch 012: Local-first talks, Automerge 3, and Scribbling on a Google Calendar

A secret master plan, the official launch of Automerge 3, and an update on Sketchy Calendars

2 days ago 3 votes
React Server Components with Vite and React-Router (tip)

Create a small example app and send payloads from the server to the client using RSC's

3 days ago 9 votes
2000 words about arrays and tables

I'm way too discombobulated from getting next month's release of Logic for Programmers ready, so I'm pulling a idea from the slush pile. Basically I wanted to come up with a mental model of arrays as a concept that explained APL-style multidimensional arrays and tables but also why there weren't multitables. So, arrays. In all languages they are basically the same: they map a sequence of numbers (I'll use 1..N)1 to homogeneous values (values of a single type). This is in contrast to the other two foundational types, associative arrays (which map an arbitrary type to homogeneous values) and structs (which map a fixed set of keys to heterogeneous values). Arrays appear in PLs earlier than the other two, possibly because they have the simplest implementation and the most obvious application to scientific computing. The OG FORTRAN had arrays. I'm interested in two structural extensions to arrays. The first, found in languages like nushell and frameworks like Pandas, is the table. Tables have string keys like a struct and indexes like an array. Each row is a struct, so you can get "all values in this column" or "all values for this row". They're heavily used in databases and data science. The other extension is the N-dimensional array, mostly seen in APLs like Dyalog and J. Think of this like arrays-of-arrays(-of-arrays), except all arrays at the same depth have the same length. So [[1,2,3],[4]] is not a 2D array, but [[1,2,3],[4,5,6]] is. This means that N-arrays can be queried on any axis. ]x =: i. 3 3 0 1 2 3 4 5 6 7 8 0 { x NB. first row 0 1 2 0 {"1 x NB. first column 0 3 6 So, I've had some ideas on a conceptual model of arrays that explains all of these variations and possibly predicts new variations. I wrote up my notes and did the bare minimum of editing and polishing. Somehow it ended up being 2000 words. 1-dimensional arrays A one-dimensional array is a function over 1..N for some N. To be clear this is math functions, not programming functions. Programming functions take values of a type and perform computations on them. Math functions take values of a fixed set and return values of another set. So the array [a, b, c, d] can be represented by the function (1 -> a ++ 2 -> b ++ 3 -> c ++ 4 -> d). Let's write the set of all four element character arrays as 1..4 -> char. 1..4 is the function's domain. The set of all character arrays is the empty array + the functions with domain 1..1 + the functions with domain 1..2 + ... Let's call this set Array[Char]. Our compilers can enforce that a type belongs to Array[Char], but some operations care about the more specific type, like matrix multiplication. This is either checked with the runtime type or, in exotic enough languages, with static dependent types. (This is actually how TLA+ does things: the basic collection types are functions and sets, and a function with domain 1..N is a sequence.) 2-dimensional arrays Now take the 3x4 matrix i. 3 4 0 1 2 3 4 5 6 7 8 9 10 11 There are two equally valid ways to represent the array function: A function that takes a row and a column and returns the value at that index, so it would look like f(r: 1..3, c: 1..4) -> Int. A function that takes a row and returns that column as an array, aka another function: f(r: 1..3) -> g(c: 1..4) -> Int.2 Man, (2) looks a lot like currying! In Haskell, functions can only have one parameter. If you write (+) 6 10, (+) 6 first returns a new function f y = y + 6, and then applies f 10 to get 16. So (+) has the type signature Int -> Int -> Int: it's a function that takes an Int and returns a function of type Int -> Int.3 Similarly, our 2D array can be represented as an array function that returns array functions: it has type 1..3 -> 1..4 -> Int, meaning it takes a row index and returns 1..4 -> Int, aka a single array. (This differs from conventional array-of-arrays because it forces all of the subarrays to have the same domain, aka the same length. If we wanted to permit ragged arrays, we would instead have the type 1..3 -> Array[Int].) Why is this useful? A couple of reasons. First of all, we can apply function transformations to arrays, like "combinators". For example, we can flip any function of type a -> b -> c into a function of type b -> a -> c. So given a function that takes rows and returns columns, we can produce one that takes columns and returns rows. That's just a matrix transposition! Second, we can extend this to any number of dimensions: a three-dimensional array is one with type 1..M -> 1..N -> 1..O -> V. We can still use function transformations to rearrange the array along any ordering of axes. Speaking of dimensions: What are dimensions, anyway Okay, so now imagine we have a Row × Col grid of pixels, where each pixel is a struct of type Pixel(R: int, G: int, B: int). So the array is Row -> Col -> Pixel But we can also represent the Pixel struct with a function: Pixel(R: 0, G: 0, B: 255) is the function where f(R) = 0, f(G) = 0, f(B) = 255, making it a function of type {R, G, B} -> Int. So the array is actually the function Row -> Col -> {R, G, B} -> Int And then we can rearrange the parameters of the function like this: {R, G, B} -> Row -> Col -> Int Even though the set {R, G, B} is not of form 1..N, this clearly has a real meaning: f[R] is the function mapping each coordinate to that coordinate's red value. What about Row -> {R, G, B} -> Col -> Int? That's for each row, the 3 × Col array mapping each color to that row's intensities. Really any finite set can be a "dimension". Recording the monitor over a span of time? Frame -> Row -> Col -> Color -> Int. Recording a bunch of computers over some time? Computer -> Frame -> Row …. This is pretty common in constraint satisfaction! Like if you're conference trying to assign talks to talk slots, your array might be type (Day, Time, Room) -> Talk, where Day/Time/Room are enumerations. An implementation constraint is that most programming languages only allow integer indexes, so we have to replace Rooms and Colors with numerical enumerations over the set. As long as the set is finite, this is always possible, and for struct-functions, we can always choose the indexing on the lexicographic ordering of the keys. But we lose type safety. Why tables are different One more example: Day -> Hour -> Airport(name: str, flights: int, revenue: USD). Can we turn the struct into a dimension like before? In this case, no. We were able to make Color an axis because we could turn Pixel into a Color -> Int function, and we could only do that because all of the fields of the struct had the same type. This time, the fields are different types. So we can't convert {name, flights, revenue} into an axis. 4 One thing we can do is convert it to three separate functions: airport: Day -> Hour -> Str flights: Day -> Hour -> Int revenue: Day -> Hour -> USD But we want to keep all of the data in one place. That's where tables come in: an array-of-structs is isomorphic to a struct-of-arrays: AirportColumns( airport: Day -> Hour -> Str, flights: Day -> Hour -> Int, revenue: Day -> Hour -> USD, ) The table is a sort of both representations simultaneously. If this was a pandas dataframe, df["airport"] would get the airport column, while df.loc[day1] would get the first day's data. I don't think many table implementations support more than one axis dimension but there's no reason they couldn't. These are also possible transforms: Hour -> NamesAreHard( airport: Day -> Str, flights: Day -> Int, revenue: Day -> USD, ) Day -> Whatever( airport: Hour -> Str, flights: Hour -> Int, revenue: Hour -> USD, ) In my mental model, the heterogeneous struct acts as a "block" in the array. We can't remove it, we can only push an index into the fields or pull a shared column out. But there's no way to convert a heterogeneous table into an array. Actually there is a terrible way Most languages have unions or product types that let us say "this is a string OR integer". So we can make our airport data Day -> Hour -> AirportKey -> Int | Str | USD. Heck, might as well just say it's Day -> Hour -> AirportKey -> Any. But would anybody really be mad enough to use that in practice? Oh wait J does exactly that. J has an opaque datatype called a "box". A "table" is a function Dim1 -> Dim2 -> Box. You can see some examples of what that looks like here Misc Thoughts and Questions The heterogeneity barrier seems like it explains why we don't see multiple axes of table columns, while we do see multiple axes of array dimensions. But is that actually why? Is there a system out there that does have multiple columnar axes? The array x = [[a, b, a], [b, b, b]] has type 1..2 -> 1..3 -> {a, b}. Can we rearrange it to 1..2 -> {a, b} -> 1..3? No. But we can rearrange it to 1..2 -> {a, b} -> PowerSet(1..3), which maps rows and characters to columns with that character. [(a -> {1, 3} ++ b -> {2}), (a -> {} ++ b -> {1, 2, 3}]. We can also transform Row -> PowerSet(Col) into Row -> Col -> Bool, aka a boolean matrix. This makes sense to me as both forms are means of representing directed graphs. Are other function combinators useful for thinking about arrays? Does this model cover pivot tables? Can we extend it to relational data with multiple tables? Systems Distributed Talk (will be) Online The premier will be August 6 at 12 CST, here! I'll be there to answer questions / mock my own performance / generally make a fool of myself. Sacrilege! But it turns out in this context, it's easier to use 1-indexing than 0-indexing. In the years since I wrote that article I've settled on "each indexing choice matches different kinds of mathematical work", so mathematicians and computer scientists are best served by being able to choose their index. But software engineers need consistency, and 0-indexing is overall a net better consistency pick. ↩ This is right-associative: a -> b -> c means a -> (b -> c), not (a -> b) -> c. (1..3 -> 1..4) -> Int would be the associative array that maps length-3 arrays to integers. ↩ Technically it has type Num a => a -> a -> a, since (+) works on floats too. ↩ Notice that if each Airport had a unique name, we could pull it out into AirportName -> Airport(flights, revenue), but we still are stuck with two different values. ↩

3 days ago 9 votes
Our $100M Series B

We don’t want to bury the lede: we have raised a $100M Series B, led by a new strategic partner in USIT with participation from all existing Oxide investors. To put that number in perspective: over the nearly six year lifetime of the company, we have raised $89M; our $100M Series B more than doubles our total capital raised to date — and positions us to make Oxide the generational company that we have always aspired it to be. If this aspiration seems heady now, it seemed absolutely outlandish when we were first raising venture capital in 2019. Our thesis was that cloud computing was the future of all computing; that running on-premises would remain (or become!) strategically important for many; that the entire stack — hardware and software — needed to be rethought from first principles to serve this market; and that a large, durable, public company could be built by whomever pulled it off. This scope wasn’t immediately clear to all potential investors, some of whom seemed to latch on to one aspect or another without understanding the whole. Their objections were revealing: "We know you can build this," began more than one venture capitalist (at which we bit our tongue; were we not properly explaining what we intended to build?!), "but we don’t think that there is a market." Entrepreneurs must become accustomed to rejection, but this flavor was particularly frustrating because it was exactly backwards: we felt that there was in fact substantial technical risk in the enormity of the task we put before ourselves — but we also knew that if we could build it (a huge if!) there was a huge market, desperate for cloud computing on-premises. Fortunately, in Eclipse Ventures we found investors who saw what we saw: that the most important products come when we co-design hardware and software together, and that the on-premises market was sick of being told that they either don’t exist or that they don’t deserve modernity. These bold investors — like the customers we sought to serve — had been waiting for this company to come along; we raised seed capital, and started building. And build it we did, making good on our initial technical vision: We did our own board designs, allowing for essential system foundation like a true hardware root-of-trust and end-to-end power observability. We did our own microcontroller operating system, and used it to replace the traditional BMC. We did our own platform enablement software, eliminating the traditional UEFI BIOS and its accompanying flotilla of vulnerabilities. We did our own host hypervisor, assuring an integrated and seamless user experience — and eliminating the need for a third-party hypervisor and its concomitant rapacious software licensing. We did our own switch — and our own switch runtime — eliminating entire universes of integration complexity and operational nightmares. We did our own integrated storage service, allowing the rack-scale system to have reliable, available, durable, elastic instance storage without necessitating a dependency on a third party. We did our own control plane, a sophisticated distributed system building on the foundation of our hardware and software components to deliver the API-driven services that modernity demands: elastic compute, virtual networking, and virtual storage. While these technological components are each very important (and each is in service to specific customer problems when deploying infrastructure on-premises), the objective is the product, not its parts. The journey to a product was long, but we ticked off the milestones. We got the boards brought up. We got the switch transiting packets. We got the control plane working. We got the rack manufactured. We passed FCC compliance. And finally, two years ago, we shipped our first system! Shortly thereafter, more milestones of the variety you can only get after shipping: our first update of the software in the field; our first update-delivered performance improvements; our first customer-requested features added as part of an update. Later that year, we hit general commercial availability, and things started accelerating. We had more customers — and our first multi-rack customer. We had customers go on the record about why they had selected Oxide — and customers describing the wins that they had seen deploying Oxide. Customers starting landing faster now: enterprise sales cycles are infamously long, but we were finding that we were going from first conversations to a delivered product surprisingly quickly. The quickening pace always seemed to be due in some way to our transparency: new customers were listeners to our podcast, or they had read our RFDs, or they had perused our documentation, or they had looked at the source code itself. With growing customer enthusiasm, we were increasingly getting questions about what it would look like to buy a large number of Oxide racks. Could we manufacture them? Could we support them? Could we make them easy to operate together? Into this excitement, a new potential investor, USIT, got to know us. They asked terrific questions, and we found a shared disposition towards building lasting value and doing it the right way. We learned more about them, too, and especially USIT’s founder, Thomas Tull. The more we each learned about the other, the more there was to like. And importantly, USIT had the vision for us that we had for ourselves: that there was a big, important market here — and that it was uniquely served by Oxide. We are elated to announce this new, exciting phase of the company. It’s not necessarily in our nature to celebrate fundraising, but this is a big milestone, because it will allow us to address our customers' most pressing questions around scale (manufacturing scale, system scale, operations scale) and roadmap scope. We have always believed in our mission, but this raise gives us a new sense of confidence when we say it: we’re going to kick butt, have fun, not cheat (of course!), love our customers — and change computing forever.

3 days ago 11 votes