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Yesterday, I announced that I was joining Val.town, but that Placemark lived. And I haven’t really given an update on Placemark, the product and business, in a while. Writing about an operating business is a different thing that writing feature announcements or essays about technology: the ramifications of honesty, spin, or disclosure are so much more vast and unpredictable. This is why we often learn about people’s trials or a company’s struggles only years later, but at the time everyone exudes confidence. But, having drafted something about this a few times and edited myself into knots, I figure it’s better to just write something. The best writing always comes after I’ve scrapped a draft anyway. The company behind Placemark, Working Idea LLC, is still a bootstrapped, single-person entity. I’ve been building Placemark for the last two years. I had a few days of an engineer working on it, and a few hours of design review with some talented designers, but besides that it’s a solo...
over a year ago

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Reading Zanzibar

Google published Zanzibar: Google’s Consistent, Global Authorization System in 2019. It describes a system for authorization – enforcing who can do what – which maxes out both flexibility and scalability. Google has lots of different apps that rely on Zanzibar, and bigger scale than practically any other company, so it needed Zanzibar. The Zanzibar paper made quite a stir. There are at least four companies that advertise products as being inspired by or based on Zanzibar. It says a lot for everyone to loudly reference this paper on homepages and marketing materials: companies aren’t advertising their own innovation as much as simply saying they’re following the gospel. A short list of companies & OSS products I found: Companies WorkOS FGA Authzed auth0 FGA Ory Permify Open source Ory Keto (Go) Warrant (Go) probably the basis for WorkOS FGA, since WorkOS acquired Warrant. SpiceDB (Go) the basis for Authzed. Permify (Go) OpenFGA (Go) the basis of auth0 FGA. I read the paper, and have a few notes, but the Google Zanzibar Paper, annotated by AuthZed is the same thing from a real domain expert (albeit one who works for one of these companies), so read that too, or instead. Features My brief summary is that the Zanzibar paper describes the features of the system succinctly, and those features are really appealing. They’ve figured out a few primitives from which developers can build really flexible authorization rules for almost any kind of application. They avoid making assumptions about ID formats, or any particular relations, or how groups are set up. It’s abstract and beautiful. The gist of the system is: Objects: things in your data model, like documents Users: needs no explanation Namespaces: for isolating applications Usersets: groups of users Userset rewrite rules: allow usersets to inherit from each other or have other kinds of set relationships Tuples, which are like (object)#(relation)@(user), and are sort of the core ‘rule’ construct for saying who can access what There’s then a neat configuration language which looks like this in an example: name: "doc" relation { name: "owner"} relation { name: "editor" userset_rewrite { union { child { _this f } } child { computed_userset { relation: "owner" } } relation { name: "viewer" userset_rewrite { union { child {_this f} } child { computed_userset & relation: "editor" 3 } child { tuple_to_userset { tupleset { relation: "parent" } computed_userset { object: $TUPLE_USERSET_OBJECT # parent folder relation: "viewer" } } } } } } It’s pretty neat. At this point in the paper I was sold on Zanzibar: I could see this as being a much nicer way to represent authorization than burying it in a bunch of queries. Specifications & Implementation details And then the paper discusses specifications: how much scale it can handle, and how it manages consistency. This is where it becomes much more noticeably Googley. So, with Google’s scale and international footprint, all of their services need to be globally distributed. So Zanzibar is a distributed system, and it is also a system that needs good consistency guarantees so that it avoid the “new enemy” problem, nobody is able to access resources that they shouldn’t, and applications that are relying on Zanzibar can get a consistent view of its data. Pages 5-11 are about this challenge, and it is a big one with a complex, high-end solution, and a lot of details that are very specific to Google. Most noticeably, Zanzibar is built with Spanner Google’s distributed database, and Spanner has the ability to order timestamps using TrueTime, which relies on atomic clocks and GPS antennae: this is not standard equipment for a server. Even CockroachDB, which is explicitly modeled off of Spanner, can’t rely on having GPS & atomic clocks around so it has to take a very different approach. But this time accuracy idea is pretty central to Zanzibar’s idea of zookies, which are sort of like tokens that get sent around in its API and indicate what time reference the client expects so that a follow-up response doesn’t accidentally include stale data. To achieve scalability, Zanzibar is also a multi-server architecture: there are aclservers, watchservers, a Leopard indexing system that creates compressed skip list-based representations of usersets. There’s also a clever solution to the caching & hot-spot problem, in which certain objects or tuples will get lots of requests all at once so their database shard gets overwhelmed. Conclusions Zanzibar is two things: A flexible, relationship-based access control model A system to provide that model to applications at enormous scale and with consistency guarantees My impressions of these things match with AuthZed’s writeup so I’ll just quote & link them: There seems to be a lot of confusion about Zanzibar. Some people think all relationship-based access control is “Zanzibar”. This section really brings to light that the ReBAC concepts have already been explored in depth, and that Zanzibar is really the scaling achievement of bringing those concepts to Google’s scale needs. link And Zookies are very clearly important to Google. They get a significant amount of attention in the paper and are called out as a critical component in the conclusion. Why then do so many of the Zanzibar-like solutions that are cropping up give them essentially no thought? link I finished the paper having absorbed a lot of tricky ideas about how to solve the distributed-consistency problems, and if I were to describe Zanzibar, those would be a big part of the story. But maybe that’s not what people mean when they say Zanzibar, and it’s more a description of features? I did find that Permify has a zookie-like Snap Token, AuthZed/SpiceDB has ZedTokens, and Warrant has Warrant-Tokens. Whereas OpenFGA doesn’t have anything like zookies and neither does Ory Keto. So it’s kind of mixed on whether these Zanzibar-inspired products have Zanzibar-inspired implementations, or focus more on exposing the same API surface. For my own needs, zookies and distributed consistency to the degree described in the Zanzibar paper are overkill. There’s no way that we’d deploy a sharded five-server system for authorization when the main application is doing just fine with single-instance Postgres. I want the API surface that Zanzibar describes, but would trade some scalability for simplicity. Or use a third-party service for authorization. Ideally, I wish there was something like these products but smaller, or delivered as a library rather than a server.

3 months ago 22 votes
Recently

I watched a large part of All Watched Over By Machines of Loving Grace this month. This also counts as a “listening” item, because the theme song, “Baby Love Child” by Pizzicato Five, is also spectacular. Guitar Moves is a good series of interviews by Matt Sweeney, who I mostly know via his involvement in Bonnie Prince Billy. It’s a really cool format. I like how he interviews guitarists with recognizable sounds, and you get to see how little they need to play to sound just like themselves. The episode with St. Vincent is excellent too: she’s one of my guitar heroes: check out the guitar solo in Just The Same But Brand New, or her version of Dig a Pony. I also watched No Other Land. Everyone should watch No Other Land. AI thoughts roundup I don’t have a conclusion. Really, that’s my current state: ambivalence. I acknowledge that these tools are incredibly powerful, I’ve even started incorporating them into my work in certain limited ways (low-stakes code like POCs and unit tests seem like an ideal use case), but I absolutely hate them. I hate the way they’ve taken over the software industry, I hate how they make me feel while I’m using them, and I hate the human-intelligence-insulting postulation that a glorified Excel spreadsheet can do what I can but better. Nolan Lawson: AI ambivalence As I always say, the purpose of the system is what it does. Or, in this case, how I think about AI stuff is mostly affected by how people use AI stuff, and how people use AI stuff is a real mixed bag. There’s the tidal wave of spam, the aesthetic of fascism, the low-effort marketing materials with nonsense images, the non-consensual AI porn. I see all of the bad stuff every day both online and in the odd subway ad. The good stuff seems pretty theoretical, though: the press releases about AI-driven medical advances never seem to break into the real world. The stories about engineers 10x’ing their ability seem pretty mixed: we’re already at the hangover-and-regret phase with programmers bemoaning how they’ve generated so much slop and lost so much knowledge. Anyway, I’m mildly optimistic about the potential! But it’s a lot like crypto in that you could theoretically use the technology for something good but most people loudly used it for bad stuff, and people including me judged it based on what it did. AI has to start doing some good stuff soon. Potential isn’t enough. I think one thing chatGPT’s invention has revealed is how many people - including some very important people in society - find just basic reading and writing to be laborious and cumbersome to perform, and how oddly closely that type of strained literacy correlates with having other shitty opinions. From mtsw on Bluesky, about this story about Andrew Cuomo using ChatGPT to half-assedly write a policy platform. Right off the bat I should say that judging people for their level of literacy reeks of classism and so on. My own ability to read & write has a lot to do with my place in society: I went to good schools, had a stable home life, and smart parents. However, “the way that society was set up” kind of evened this out. Extremely social people with cultural capital and chiseled jawlines and biceps would get their rewards, and people like… myself, we would get rewarded for literacy and critical thinking. When one group needed the other, it was usually some kind of payment or partnership: Cuomo pays his scriptwriter, the TV show creator pays the actors. And some people can do both sides of the equation. But LLMs definitely indicate that people do not like this deal. Whew, they don’t like writing, but they also don’t like paying the writers or reading what they write. Maybe they could rejigger the system so that they could do it all. They have ideas for music and art but no interest in learning about music, practicing instruments, going to art school, or concentrating on a task for a long time, so why not generate it all? Why not, well - there are reasons, those reasons being that the generated output usually passes their own vibe check but once someone who looks closely at things or reads all the words encounters it, everyone points at the slop and it’s embarrassing. (Cuomo will never be embarrassed) Plus, you’re always going to get average results by asking a device that is incapable of creativity or thought. Also, you’ll miss out on the human experience of creating. And you’ll be indirectly feeding output data into training data for future LLMs, consequentially making their output worse. (Cuomo does not care about consequences) Colophon update I’ve moved the images for this website to Bunny (that’s an affiliate link, here’s a non-affiliate link if that’s what you prefer). When I initially moved my photos to this website, I set them up with Amazon S3 for storage and CloudFront to serve them with a CDN. Using AWS is painful for me, so I moved them to Cloudflare R2, which is Cloudflare’s equivalent to S3, and Cloudflare as a CDN. Thanks to owning my own domains, swapping out image hosts is pretty quick: switching to Bunny took all of five minutes. So what’s the deal with Bunny? Partly I’ve become a little more negative on Cloudflare and R2: I think Cloudflare’s technology is neat, but R2 has iffy reliability and Cloudflare has iffy politics. I’m also intrigued by diversifying my dependencies geographically. Bunny is a Slovenian company, and my email is from an Australian company. This probably won’t have any practical effect, but it feels kind of good for obvious reasons to even minutely hedge my bets here. So far Bunny has been great. They don’t support the S3 protocol but they do support SFTP, which works just as well for my purposes and works great with the beautiful Transmit app. Before, with R2, I was using the significantly less beautiful Cyberduck application because Cloudflare R2 doesn’t support all of the S3 protocol. It seems to be just as fast as Cloudflare was, too. And I’m somewhat reassured by the prospect of paying Bunny. I don’t like the feeling of getting “free” services like I can from Cloudflare. I want that customer relationship. Reading Then again, pop culture is powerful, and even the dumbest marketing both affects and reflects it. Busch Light’s can holder shaped like a cup that holds beer is dumb, which is fine, because most beer promos are. But the fact that the brand frames it as a functional, masculine alternative to Stanley’s H2.0 Flowstate affirms a similarly retrograde outlook on gender roles to the one that young American men are seeking out on the political right. From my friend Dave’s article about Busch Light’s weird attempt to riff on the Stanley Tumbler trend. I was once a loyal listener of the Chapo Trap House podcast, but fell off of it in 2020 when their support of Bernie Sanders led them to be jerks about Elizabeth Warren. But reading this Vanity Fair article about the cohosts of the podcast endeared them to me a bit. “Like to thank” is linguistic phlegm. “I’d like to thank the Academy.” They’d “like to thank” me. Well I’d like to be 6’3” and drive a G Wagon, thanks. I’d like you to accept my novella. I’d like to quit paying three dollars to Submittable every time I want to send a story out. The world is full of actions I would like to do. The most direct way to say thank you is just to say it: “thank you, name, for doing X.” “I’d like to thank” is a performative thanks, a thanks with a smirk and a blink, eyeing for extra credit. Just because people say it in their award show acceptance speeches doesn’t mean you should say it, too. In fact, that’s the reason you shouldn’t say it. Loved this article “Close reading my rejections” from friend of the blog Barrett Hathcock.

3 months ago 20 votes
Tidbyt without the company

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It’s a super low-resolution, internet-connected, wood-paneled display that I wrote a review of it back in 2022. It’s been on my shelf for years now, showing the time, weather, warning me when the UV is going to be high. In 2023 I used it as an excuse to learn some Rust, to render custom graphics. It’s a toy, a distraction, a worry stone for me to work on when I need something open-ended and low-stakes. Anyway, the company that made the Tidbyt is no more. They got acquihired by Modal, a company that makes serverless AI compute hosting. So, they aren’t making devices right now, and the blog post promises that their cloud services will keep working. I don’t hold anything against the Tidbyt team: in fact, our Val Town office was coincidentally right next to theirs in a WeWork, and we met in real life! They’re very nice folks, and were doing so much with a small team. Lots of respect to them. Modal made a smart choice acquiring Tidbyt. But realistically, it’s time to make sure my device doesn’t become e-waste. The Tidbyt is ready for this One of the biggest critiques of the Tidbyt was that it was just an LED matrix and an ESP chip. You could buy an LED matrix on Sparkfun, the ESP, a power supply, some wood for the enclosure, and you’d have your own DIY Tidbyt. Maybe you could do it for half the price! But that’s also a strength. The Tidbyt is not some custom SoC with an exotic custom software stack and boutique hardware. It is what it looks like: a neat combination of commonplace parts. That makes it kind of future-proof and flexible. The first step is to replace the firmware. Tidbyt’s stock firmware routes all of its requests through the Tidbyt company’s servers. I want to eliminate that hop. Replacing the firmware Thankfully, Tidbyt published their ‘HDK’, which is an open source version of their stock firmware. It’s remarkably simple: It connects to Wifi It downloads a WebP image from a URL It displays that WebP image The HDK contains the code to do this stuff. There’s very little code required, but it does drag in a WebP decoder, Wifi library, and a library for running the LED matrix. But, setting up the HDK I ran into issues both small and large: it had issues with HTTPS URLs and Wifi passwords that contain spaces. Plus nobody has been added as a contributor to the HDK repository, so Pull Requests aren’t being accepted and it hasn’t had a change in 7 months. But the community came to the rescue with tronbyt’s firmware-http, a fork of the HDK that fixes every issue I experienced. Open source works! So back in 2022 I included this chart of the Tidbyt network: With an updated HDK, this workflow is a lot simpler. Instead of sending images to the Tidbyt servers and those Tidbyt servers delivering them to my device, the device makes requests directly of the server that generates the images. Replacing pixlet The Tidbyt team wrote pixlet, a little framework for generating pixel graphics that the Tidbyt displays. It lets you define a React-like tree of components - some text in a stack, a rectangle, images, and so on - and does all of the layout and rendering. The tronbyt community also forked pixlet and are actively developing it, which is fantastic. But this part of the stack I really never liked. That’s why I spent so much time reimplementing it in Rust and JavaScript. Partly it’s the language - pixlet apps are written in starlark, which is kind of an outgrowth of the Bazel build system from Google. Starlark is sort of like Python, but isn’t actually compatible with anything in the Python ecosystem. It’s very niche, limited, and overall just weird. I think I understand why Tidbyt would choose Starlark - it’s fast and has hermetic execution - making it safe to run untrusted Starlark programs because they can’t access the filesystem, network, or even the system clock without being given explicit controlled APIs to do those things. If you’re building a cloud service that runs a lot of untrusted user code, dictating that code is all Starlark is a really good cheat code - I know firsthand how hard it is to run untrusted JavaScript. But I’m not building a cloud service full of untrusted code. People who are self-hosting their Tidbyt devices (dozens of us!) don’t benefit from the tradeoffs of the Starlark language. They’d be better off with something normal. I rewrote pixlet again It’s called indiepixel and it’s a Python reimplementation of pixlet. It supports almost the entire pixlet API, and comes with the added benefit of being Python. You can use Python modules! You can read from the filesystem, parse CSVs, do all of your usual Python stuff. You can embed it in a Python application to render some graphics. What does indiepixel do currently? Renders text in the glorious retro BDF pixel font format. Renders pixelated pie charts, rectangles, and boxes. Supports animation for its WebP outputs. Provides a nice UI for browsing your selection of screens. It’ll probably never be finished, but it works well enough to power my Tidbyt. I’m running indiepixel on a free Render server instance, but it should run pretty much the same on any Python-compatible hosting: the only tricky dependency is Pillow, which it uses for image parsing and rendering. My free time for computer-oriented side projects has been limited, due to other commitments and an intention to get offline on the weekends. I’ve been sewing, biking, and running more. So I really want a side project I can enjoy, and indiepixel has fit the bill. It’s really satisfying to implement a new widget and see it rendered in blocky 64x32 pixels. The Pillow image rendering library for Python is mostly wonderful and very powerful. Why Python? Why is indiepixel written in Python? Well - I learned from tidbyt-rs that Rust would be an awkward fit as a scripting language for rendering graphics. The well-known Rust complexities around memory management made simple things difficult for me, which would make them totally unacceptable for others. Besides the attraction of being able to compile a small binary that might be able to run on the Tidbyt itself, Rust didn’t have many other advantages. The Pillow module really is such an advantage for Python. JavaScript doesn’t have a real alternative: there’s sharp, a great module for image conversion, but nothing that has such a great canvas interface. node-canvas is fine, but it doesn’t support WebP or animation, which are critical features for this project. I also wanted a test out the amazing new Python tooling that Astral is cooking up, like uv. I now have a better grasp of the Python ecosystem than I did a few months ago, and it’s optimistic but mixed. uv is amazing, but Python has a lot of legacy cruft around packaging. People are critical of NPM, but I think it did benefit from being established after PyPI and learning from its lessons. Thank you Steven Loria for a PR that fixed everything and made it all work and saved me months of tweaking settings. The graphic I watercolored that Tidbyt a while while ago and have been seriously dragging my feet on finishing this blog post. Sometimes the watercolor-illustration wags the technical-blog-post dog’s tail? Anyway, it’s a callback to that little world, with some small tweaks: this time I thought it’d be nice to have it be both watercolored and interactive. That ‘cybernetic’ feel. The secret recipe: a nice palette from lospec, creating a black & white mask of areas in Affinity Photo and vectorizing it with potrace, and then just some JavaScript that recolors based on hover handling. If you’re using the Tidbyt or some similar pixel-displaying device, try out indiepixel! It’s niche and has required a silly amount of effort to generate a glorified weather clock in my apartment, but it was a fun time chasing another interest.

3 months ago 48 votes
Recently

Reading Whether it’s cryptocurrency scammers mining with FOSS compute resources or Google engineers too lazy to design their software properly or Silicon Valley ripping off all the data they can get their hands on at everyone else’s expense… I am sick and tired of having all of these costs externalized directly into my fucking face. Drew DeVault on the annoyance and cost of AI scrapers. I share some of that pain: Val Town is routinely hammered by some AI company’s poorly-coded scraping bot. I think it’s like this for everyone, and it’s hard to tell if AI companies even care that everyone hates them. And perhaps most recently, when a person who publishes their work under a free license discovers that work has been used by tech mega-giants to train extractive, exploitative large language models? Wait, no, not like that. Molly White wrote a more positive article about the LLM scraping problem, but I have my doubts about its positivity. For example, she suggests that Wikimedia’s approach with “Wikimedia Enterprise” gives LLM companies a way to scrape the site without creating too much cost. But that doesn’t seem like it’s working. The problem is that these companies really truly do not care. Harberger taxes represent an elegant theoretical solution that fails in practice for immobile property. Just as mobile home residents face exploitation through sudden ground rent increases, property owners under a Harberger system would face similar hold-up problems. This creates an impossible dilemma: pay increasingly burdensome taxes or surrender investments at below-market values. Progress and Poverty, a blog about Georgism, has this post about Herberger taxes, which are a super neat idea. The gist is that you would be in charge of saying how much your house is worth, but the added wrinkle is that by saying a price you are bound to be open to selling your house at that price. So if you go too low, someone will buy it, or too high, and you’re paying too much in taxes. It’s clever but doesn’t work, and the analysis points to the vital difference between housing and other goods: that buying, selling, and moving between houses is anything but simple. I’ve always been a little skeptical of the line that the AI crowd feels contempt for artists, or that such a sense is particularly widespread—because certainly they all do not!—but it’s hard to take away any other impression from a trend so widely cheered in its halls as AI Ghiblification. Brian Merchant on the OpenAI Studio Ghibli ‘trend’ is a good read. I can’t stop thinking that AI is in danger of being right-wing coded, the examples of this, like the horrifying White House tweet mentioned in that article, are multiplying. I feel bad when I recoil to innocent usage of the tool by good people who just want something cute. It is kind of fine, on the micro level. But with context, it’s so bad in so many ways. Already the joy and attachment I’ve felt to the graphic style is fading as more shitty Studio Ghibli knockoffs have been created in the last month than in all of the studio’s work. Two days later, at a state dinner in the White House, Mark gets another chance to speak with Xi. In Mandarin, he asks Xi if he’ll do him the honor of naming his unborn child. Xi refuses. Careless People was a good read. It’s devastating for Zuckerberg, Joel Kaplan, and Sheryl Sandberg, as well as a bunch of global leaders who are eager to provide tax loopholes for Facebook. Perhaps the only person who ends the book as a hero is President Obama, who sees through it all. In a March 26 Slack message, Lavingia also suggested that the agency should do away with paper forms entirely, aiming for “full digitization.” “There are over 400 vet-facing forms that the VA supports, and only about 10 percent of those are digitized,” says a VA worker, noting that digitizing forms “can take years because of the sensitivity of the data” they contain. Additionally, many veterans are elderly and prefer using paper forms because they lack the technical skills to navigate digital platforms. “Many vets don’t have computers or can’t see at all,” they say. “My skin is crawling thinking about the nonchalantness of this guy.” Perhaps because of proximity, the story that Sahil Lavingia has been working for DOGE seems important. It was a relief when a few other people noticed it and started retelling the story to the tech sphere, like Dan Brown’s “Gumroad is not open source” and Ernie Smith’s “Gunkroad”, but I have to nitpick on the structure here: using a non-compliant open source license is not the headline, collaborating with fascists and carelessly endangering disabled veterans is. Listening Septet by John Carroll Kirby I saw John Carroll Kirby play at Public Records and have been listening to them constantly ever since. The music is such a paradox: the components sound like elevator music or incredibly cheesy jazz if you listen to a few seconds, but if you keep listening it’s a unique, deep sound. Sierra Tracks by Vega Trails More new jazz! Mammoth Hands and Portico Quartet overlap with Vega Trails, which is a beautiful minimalist band. Watching This short video with John Wilson was great. He says a bit about having a real physical video camera, not just a phone, which reminded me of an old post of mine, Carrying a Camera.

4 months ago 40 votes
Personal tools

I used to make little applications just for myself. Sixteen years ago (oof) I wrote a habit tracking application, and a keylogger that let me keep track of when I was using a computer, and generate some pretty charts. I’ve taken a long break from those kinds of things. I love my hobbies, but they’ve drifted toward the non-technical, and the idea of keeping a server online for a fun project is unappealing (which is something that I hope Val Town, where I work, fixes). Some folks maintain whole ‘homelab’ setups and run Kubernetes in their basement. Not me, at least for now. But I have been tiptoeing back into some little custom tools that only I use, with a focus on just my own computing experience. Here’s a quick tour. Hammerspoon Hammerspoon is an extremely powerful scripting tool for macOS that lets you write custom keyboard shortcuts, UIs, and more with the very friendly little language Lua. Right now my Hammerspoon configuration is very simple, but I think I’ll use it for a lot more as time progresses. Here it is: hs.hotkey.bind({"cmd", "shift"}, "return", function() local frontmost = hs.application.frontmostApplication() if frontmost:name() == "Ghostty" then frontmost:hide() else hs.application.launchOrFocus("Ghostty") end end) Not much! But I recently switched to Ghostty as my terminal, and I heavily relied on iTerm2’s global show/hide shortcut. Ghostty doesn’t have an equivalent, and Mikael Henriksson suggested a script like this in GitHub discussions, so I ran with it. Hammerspoon can do practically anything, so it’ll probably be useful for other stuff too. SwiftBar I review a lot of PRs these days. I wanted an easy way to see how many were in my review queue and go to them quickly. So, this script runs with SwiftBar, which is a flexible way to put any script’s output into your menu bar. It uses the GitHub CLI to list the issues, and jq to massage that output into a friendly list of issues, which I can click on to go directly to the issue on GitHub. #!/bin/bash # <xbar.title>GitHub PR Reviews</xbar.title> # <xbar.version>v0.0</xbar.version> # <xbar.author>Tom MacWright</xbar.author> # <xbar.author.github>tmcw</xbar.author.github> # <xbar.desc>Displays PRs that you need to review</xbar.desc> # <xbar.image></xbar.image> # <xbar.dependencies>Bash GNU AWK</xbar.dependencies> # <xbar.abouturl></xbar.abouturl> DATA=$(gh search prs --state=open -R val-town/val.town --review-requested=@me --json url,title,number,author) echo "$(echo "$DATA" | jq 'length') PR" echo '---' echo "$DATA" | jq -c '.[]' | while IFS= read -r pr; do TITLE=$(echo "$pr" | jq -r '.title') AUTHOR=$(echo "$pr" | jq -r '.author.login') URL=$(echo "$pr" | jq -r '.url') echo "$TITLE ($AUTHOR) | href=$URL" done Tampermonkey Tampermonkey is essentially a twist on Greasemonkey: both let you run your own JavaScript on anybody’s webpage. Sidenote: Greasemonkey was created by Aaron Boodman, who went on to write Replicache, which I used in Placemark, and is now working on Zero, the successor to Replicache. Anyway, I have a few fancy credit cards which have ‘offers’ which only work if you ‘activate’ them. This is an annoying dark pattern! And there’s a solution to it - CardPointers - but I neither spend enough nor care enough about points hacking to justify the cost. Plus, I’d like to know what code is running on my bank website. So, Tampermonkey to the rescue! I wrote userscripts for Chase, American Express, and Citi. You can check them out on this Gist but I strongly recommend to read through all the code because of the afore-mentioned risks around running untrusted code on your bank account’s website! Obsidian Freeform This is a plugin for Obsidian, the notetaking tool that I use every day. Freeform is pretty cool, if I can say so myself (I wrote it), but could be much better. The development experience is lackluster because you can’t preview output at the same time as writing code: you have to toggle between the two states. I’ll fix that eventually, or perhaps Obsidian will add new API that makes it all work. I use Freeform for a lot of private health & financial data, almost always with an Observable Plot visualization as an eventual output. For example, when I was switching banks and one of the considerations was mortgage discounts in case I ever buy a house (ha 😢), it was fun to chart out the % discounts versus the required AUM. It’s been really nice to have this kind of visualization as ‘just another document’ in my notetaking app. Doesn’t need another server, and Obsidian is pretty secure and private.

4 months ago 49 votes

More in programming

The Framework Desktop is a beast

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!

19 hours ago 4 votes
Writing: Blog Posts and Songs

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

2 days ago 4 votes
Doing versus Delegating

A staff+ skill

2 days ago 7 votes
p-fast trie, but smaller

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.

2 days ago 4 votes
Software books I wish I could read

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 ↩

2 days ago 9 votes