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I strongly believe that fast iteration cycle is important for productivity. In programming fast iteration means: how quickly can I go from finishing writing the code to testing it. That’s why I don’t like languages that compile slowly: slow compilation puts a hard limit on your iteration cycle. That’s a general principle. How does it translate to actions? I’m glad you asked. Simulated errors I’m writing a web app and some code paths might fail e.g. server returns an error response. When that happens I want to display an error message to the user. I want to test that but server doesn’t typically return errors. I can modify the server to return error and restart it but in a compiled language like Go it’s a whole thing. Instead I can force error condition in the code. Because web dev typically offers hot reload of code, I can modify the code to pretend the request failed, save the code, reload the app and I’m testing the error handling. To make it less ad-hoc another strategy is to have debug flags on window object e.g.: window.debug.simulateError = false Then the code will have: try { if (window.debug.simulateError) { throw Error("fetch failed"); } let rsp = await fetch(...); } That way I can toggle window.debug.simulateError in dev tools console, without changing the code. I have to repeat this code for every fetch(). More principled approach is: async function myFetch(uri, opts) { if (window.debug.simulateError) { throw new Error("featch failed"); } return await fetch(uri, opts); } To go even further, we could simulate different kinds of network errors: Response.ok is false response is 404 or 500 failed to reach the server We can change simulateError from bool to a number and have: async function myFetch(uri, opts) { let se = window.debug.simulateError; if (se === 1) { // simulate Response.ok is false return ...; } if (se === 2) { // simulate 404 response return; } if (se === 3) { // simulate network offline } return await fetch(uri, opts); } Start by show dialog Let’s say I’m working on a new dialog e.g. rename dialog. To get to that dialog I have to perform some UI action e.g. use context menu and click the Rename note menu item. Not a big deal but I’m still working on the dialog so it’s a bit annoying to repeat that UI action every time I move the button to the right, to see how it’ll look. In Svelte we do: let showingRenameDialog = $state(false); function showRenameDialog() { showingRenameDialog = true; } {#if showingRenameDialog} <RenameDialog ...></RenameDialog> {'if} To speed up dev cycle: let showingRenameDialog = $state(true); // show while we're working ont While I’m still designing and writing code for the dialog, show it by default. That way app reloads due to changing code won’t require you to manually redo UI actions to trigger the dialog. Ad-hoc test code Let’s say you’re writing a non-trivial code server code to process a request. Let’s say the request is a POST with body containing zip file with images which the server needs to unzip, resize the files, save them to a file system. You want to test it as you implement the logic but iteration cycle is slow: you write the code recompile and restart the server go through UI actions to send the request Most of the code (unpacking zip file, resizing images) can be tested without doing the request. So you isolate the function: func resizeImagesInZip(zipData []byte) error { // the code you're writing on } Now you have to trigger it easily. The simplest way is ad-hoc test code: func main() { if true { zipData := loadTestZipFileMust() resizeImagesInZip(zipData); return; } } While you’re working on the code, the server just runs the test code. When you’re done, you switch it off: func main() { if false { // leave the code so that you can re-enable it // easily in the future zipData := loadTestZipFileMust() resizeImagesInZip(zipData); return; } } Those are few tactical tips to increase dev cycles. You can come up with more such ideas by asking yourself: how can I speed iteration cycle?
Stage Manager in Mac OS is not a secret, but I’ve only learned about it recently. It’s off by default so you have to enable it in system settings: It’s hard to describe in words, so you’ll have to try it. And experiment a bit because I didn’t get in the first hour. It’s a certain way to manage windows for less clutter. Imagine you have a browser, an editor and a terminal i.e. 3 windows. Might be annoying to have them all shown on screen. When Stage Manager is enabled, thumbnails of windows are on the left edge of the screen and you can switch to the window by clicking the thumbnail. By default Stage Manager shows each window by itself on screen. You can group them by dragging thumbnail onto screen. For example, when I’m developing, I’m using text editor and terminal so I will group them so they are both visible on screen at the same time, but not the other apps. So far I’m enjoying using Stage Manager.
Today I figured out how to setup Zed to debug, at the same time, my go server and Svelte web app. My dev setup for working on my web app is: go server is run with -run-dev arg go server provides backend apis and proxies requests it doesn’t handle to a vite dev server that does the serving of JavaScript etc. files from my Svelte code go server in -run-dev mode automatically launches vite dev go server runs on port 9339 It’s possible to setup Zed to debug both server and frontend JavaScript code. In retrospect it’s simple, but took me a moment to figure out. I needed to create the following .zed/debug.json: // Project-local debug tasks // // For more documentation on how to configure debug tasks, // see: https://zed.dev/docs/debugger [ { "adapter": "Delve", "label": "run go server", "request": "launch", "mode": "debug", "program": ".", "cwd": "${ZED_WORKTREE_ROOT}", "args": ["-run-dev", "-no-open"], "buildFlags": [], "env": {} }, { "adapter": "JavaScript", "label": "Debug in Chrome", "type": "chrome", "request": "launch", "url": "http://localhost:9339/", "webRoot": "$ZED_WORKTREE_ROOT/src", "console": "integratedTerminal", "skipFiles": ["<node_internals>/**"] } ] It’s mostly self-exploratory. First entry tells Zed to build go program with go build . and run the resulting executable under the debugger with -run-dev -no-open args. Second entry tells to launch Chrome in debug mode with http://localhost:9339/ and that files seen by Chrome come from src/ directory i.e. if browser loads /foo.js the source file is src/foo.js. This is necessary to be able to set breakpoints in Zed and have them propagate to Chrome. This eliminates the need for terminal so I can edit and debug with just Zed and Chrome. This is a great setup. I’m impressed with Zed.
When working on big JavaScript web apps, you can split the bundle in multiple chunks and import selected chunks lazily, only when needed. That makes the main bundle smaller, faster to load and parse. How to lazy import a module? let hljs = await import("highlight.js").default; is equivalent of: import hljs from "highlight.js"; Now: let libZip = await import("@zip.js/zip.js"); let blobReader = new libZip.BlobReader(blob); Is equivalent to: import { BlobReader } from "@zip.js/zip.js"; It’s simple if we call it from async function but sometimes we want to lazy load from non-async function so things might get more complicated: let isLazyImportng = false; let hljs; let markdownIt; let markdownItAnchor; async function lazyImports() { if (isLazyImportng) return; isLazyImportng = true; let promises = await Promise.all([ import("highlight.js"), import("markdown-it"), import("markdown-it-anchor"), ]); hljs = promises[0].default; markdownIt = promises[1].default; markdownItAnchor = promises[2].default; } We can run it from non-async function: function doit() { lazyImports().then( () => { if (hljs) { // use hljs to do something } }) } I’ve included protection against kicking of lazy import more than once. That means on second and n-th call we might not yet have the module loaded so hljs will be still undefined.
Svelte 5 just added a way to use async function in components. This is from Rich Harris talk The simplest component <script> async function multiply(x, y) { let uri = `/multiply?x=${x}&y=${y}` let rsp = await fetch(uri) let resp = await rsp.text(); return parseInt(resp); } let n = $state(2) </script> <div>{n} * 2 = {await multiply(n, 2)}</div> Previously you couldn’t do {await multiply(n, 2) because Svelte didn’t understand promises. Now you can. Aborting outdated requests Imagine getting search results from a server based on what you type in an input box. If you type foo, we first send request for f, then for fo then for foo at which point we don’t care about the results for f and fo. Svelte 5 can handle aborting outdated requests: <script> import { getAbortSignal } from "svelte"; let search = $state("") const API = "https://dummyjson.com/product/search"; const response = $derived(await fetch(`${API}?q=${search}`), { signal: getAbortSignal() }) </script> <input bind:value={search}> <svelte:boundary> <ul> {#each (await response.json().products as product)} <li>{product.title}</li> {/each} </ul>
More in programming
I always had a diffuse idea of why people are spending so much time and money on amateur radio. Once I got my license and started to amass radios myself, it became more clear.
What does it mean when someone writes that a programming language is “strongly typed”? I’ve known for many years that “strongly typed” is a poorly-defined term. Recently I was prompted on Lobsters to explain why it’s hard to understand what someone means when they use the phrase. I came up with more than five meanings! how strong? The various meanings of “strongly typed” are not clearly yes-or-no. Some developers like to argue that these kinds of integrity checks must be completely perfect or else they are entirely worthless. Charitably (it took me a while to think of a polite way to phrase this), that betrays a lack of engineering maturity. Software engineers, like any engineers, have to create working systems from imperfect materials. To do so, we must understand what guarantees we can rely on, where our mistakes can be caught early, where we need to establish processes to catch mistakes, how we can control the consequences of our mistakes, and how to remediate when somethng breaks because of a mistake that wasn’t caught. strong how? So, what are the ways that a programming language can be strongly or weakly typed? In what ways are real programming languages “mid”? Statically typed as opposed to dynamically typed? Many languages have a mixture of the two, such as run time polymorphism in OO languages (e.g. Java), or gradual type systems for dynamic languages (e.g. TypeScript). Sound static type system? It’s common for static type systems to be deliberately unsound, such as covariant subtyping in arrays or functions (Java, again). Gradual type systems migh have gaping holes for usability reasons (TypeScript, again). And some type systems might be unsound due to bugs. (There are a few of these in Rust.) Unsoundness isn’t a disaster, if a programmer won’t cause it without being aware of the risk. For example: in Lean you can write “sorry” as a kind of “to do” annotation that deliberately breaks soundness; and Idris 2 has type-in-type so it accepts Girard’s paradox. Type safe at run time? Most languages have facilities for deliberately bypassing type safety, with an “unsafe” library module or “unsafe” language features, or things that are harder to spot. It can be more or less difficult to break type safety in ways that the programmer or language designer did not intend. JavaScript and Lua are very safe, treating type safety failures as security vulnerabilities. Java and Rust have controlled unsafety. In C everything is unsafe. Fewer weird implicit coercions? There isn’t a total order here: for instance, C has implicit bool/int coercions, Rust does not; Rust has implicit deref, C does not. There’s a huge range in how much coercions are a convenience or a source of bugs. For example, the PHP and JavaScript == operators are made entirely of WAT, but at least you can use === instead. How fancy is the type system? To what degree can you model properties of your program as types? Is it convenient to parse, not validate? Is the Curry-Howard correspondance something you can put into practice? Or is it only capable of describing the physical layout of data? There are probably other meanings, e.g. I have seen “strongly typed” used to mean that runtime representations are abstract (you can’t see the underlying bytes); or in the past it sometimes meant a language with a heavy type annotation burden (as a mischaracterization of static type checking). how to type So, when you write (with your keyboard) the phrase “strongly typed”, delete it, and come up with a more precise description of what you really mean. The desiderata above are partly overlapping, sometimes partly orthogonal. Some of them you might care about, some of them not. But please try to communicate where you draw the line and how fuzzy your line is.
(Last week's newsletter took too long and I'm way behind on Logic for Programmers revisions so short one this time.1) In classical logic, two operators F/G are duals if F(x) = !G(!x). Three examples: x || y is the same as !(!x && !y). <>P ("P is possibly true") is the same as ![]!P ("not P isn't definitely true"). some x in set: P(x) is the same as !(all x in set: !P(x)). (1) is just a version of De Morgan's Law, which we regularly use to simplify boolean expressions. (2) is important in modal logic but has niche applications in software engineering, mostly in how it powers various formal methods.2 The real interesting one is (3), the "quantifier duals". We use lots of software tools to either find a value satisfying P or check that all values satisfy P. And by duality, any tool that does one can do the other, by seeing if it fails to find/check !P. Some examples in the wild: Z3 is used to solve mathematical constraints, like "find x, where f(x) >= 0. If I want to prove a property like "f is always positive", I ask z3 to solve "find x, where !(f(x) >= 0), and see if that is unsatisfiable. This use case powers a LOT of theorem provers and formal verification tooling. Property testing checks that all inputs to a code block satisfy a property. I've used it to generate complex inputs with certain properties by checking that all inputs don't satisfy the property and reading out the test failure. Model checkers check that all behaviors of a specification satisfy a property, so we can find a behavior that reaches a goal state G by checking that all states are !G. Here's TLA+ solving a puzzle this way.3 Planners find behaviors that reach a goal state, so we can check if all behaviors satisfy a property P by asking it to reach goal state !P. The problem "find the shortest traveling salesman route" can be broken into some route: distance(route) = n and all route: !(distance(route) < n). Then a route finder can find the first, and then convert the second into a some and fail to find it, proving n is optimal. Even cooler to me is when a tool does both finding and checking, but gives them different "meanings". In SQL, some x: P(x) is true if we can query for P(x) and get a nonempty response, while all x: P(x) is true if all records satisfy the P(x) constraint. Most SQL databases allow for complex queries but not complex constraints! You got UNIQUE, NOT NULL, REFERENCES, which are fixed predicates, and CHECK, which is one-record only.4 Oh, and you got database triggers, which can run arbitrary queries and throw exceptions. So if you really need to enforce a complex constraint P(x, y, z), you put in a database trigger that queries some x, y, z: !P(x, y, z) and throws an exception if it finds any results. That all works because of quantifier duality! See here for an example of this in practice. Duals more broadly "Dual" doesn't have a strict meaning in math, it's more of a vibe thing where all of the "duals" are kinda similar in meaning but don't strictly follow all of the same rules. Usually things X and Y are duals if there is some transform F where X = F(Y) and Y = F(X), but not always. Maybe the category theorists have a formal definition that covers all of the different uses. Usually duals switch properties of things, too: an example showing some x: P(x) becomes a counterexample of all x: !P(x). Under this definition, I think the dual of a list l could be reverse(l). The first element of l becomes the last element of reverse(l), the last becomes the first, etc. A more interesting case is the dual of a K -> set(V) map is the V -> set(K) map. IE the dual of lived_in_city = {alice: {paris}, bob: {detroit}, charlie: {detroit, paris}} is city_lived_in_by = {paris: {alice, charlie}, detroit: {bob, charlie}}. This preserves the property that x in map[y] <=> y in dual[x]. And after writing this I just realized this is partial retread of a newsletter I wrote a couple months ago. But only a partial retread! ↩ Specifically "linear temporal logics" are modal logics, so "eventually P ("P is true in at least one state of each behavior") is the same as saying !always !P ("not P isn't true in all states of all behaviors"). This is the basis of liveness checking. ↩ I don't know for sure, but my best guess is that Antithesis does something similar when their fuzzer beats videogames. They're doing fuzzing, not model checking, but they have the same purpose check that complex state spaces don't have bugs. Making the bug "we can't reach the end screen" can make a fuzzer output a complete end-to-end run of the game. Obvs a lot more complicated than that but that's the general idea at least. ↩ For CHECK to constraint multiple records you would need to use a subquery. Core SQL does not support subqueries in check. It is an optional database "feature outside of core SQL" (F671), which Postgres does not support. ↩
Omarchy 2.0 was released on Linux's 34th birthday as a gift to perhaps the greatest open-source project the world has ever known. Not only does Linux run 95% of all servers on the web, billions of devices as an embedded OS, but it also turns out to be an incredible desktop environment! It's crazy that it took me more than thirty years to realize this, but while I spent time in Apple's walled garden, the free software alternative simply grew better, stronger, and faster. The Linux of 2025 is not the Linux of the 90s or the 00s or even the 10s. It's shockingly more polished, capable, and beautiful. It's been an absolute honor to celebrate Linux with the making of Omarchy, the new Linux distribution that I've spent the last few months building on top of Arch and Hyprland. What began as a post-install script has turned into a full-blown ISO, dedicated package repository, and flourishing community of thousands of enthusiasts all collaborating on making it better. It's been improving rapidly with over twenty releases since the premiere in late June, but this Version 2.0 update is the biggest one yet. If you've been curious about giving Linux a try, you're not afraid of an operating system that asks you to level up and learn a little, and you want to see what a totally different computing experience can look and feel like, I invite you to give it a go. Here's a full tour of Omarchy 2.0.
In 2020, Apple released the M1 with a custom GPU. We got to work reverse-engineering the hardware and porting Linux. Today, you can run Linux on a range of M1 and M2 Macs, with almost all hardware working: wireless, audio, and full graphics acceleration. Our story begins in December 2020, when Hector Martin kicked off Asahi Linux. I was working for Collabora working on Panfrost, the open source Mesa3D driver for Arm Mali GPUs. Hector put out a public call for guidance from upstream open source maintainers, and I bit. I just intended to give some quick pointers. Instead, I bought myself a Christmas present and got to work. In between my university coursework and Collabora work, I poked at the shader instruction set. One thing led to another. Within a few weeks, I drew a triangle. In 3D graphics, once you can draw a triangle, you can do anything. Pretty soon, I started work on a shader compiler. After my final exams that semester, I took a few days off from Collabora to bring up an OpenGL driver capable of spinning gears with my new compiler. Over the next year, I kept reverse-engineering and improving the driver until it could run 3D games on macOS. Meanwhile, Asahi Lina wrote a kernel driver for the Apple GPU. My userspace OpenGL driver ran on macOS, leaving her kernel driver as the missing piece for an open source graphics stack. In December 2022, we shipped graphics acceleration in Asahi Linux. In January 2023, I started my final semester in my Computer Science program at the University of Toronto. For years I juggled my courses with my part-time job and my hobby driver. I faced the same question as my peers: what will I do after graduation? Maybe Panfrost? I started reverse-engineering of the Mali Midgard GPU back in 2017, when I was still in high school. That led to an internship at Collabora in 2019 once I graduated, turning into my job throughout four years of university. During that time, Panfrost grew from a kid’s pet project based on blackbox reverse-engineering, to a professional driver engineered by a team with Arm’s backing and hardware documentation. I did what I set out to do, and the project succeeded beyond my dreams. It was time to move on. What did I want to do next? Finish what I started with the M1. Ship a great driver. Bring full, conformant OpenGL drivers to the M1. Apple’s drivers are not conformant, but we should strive for the industry standard. Bring full, conformant Vulkan to Apple platforms, disproving the myth that Vulkan isn’t suitable for Apple hardware. Bring Proton gaming to Asahi Linux. Thanks to Valve’s work for the Steam Deck, Windows games can run better on Linux than even on Windows. Why not reap those benefits on the M1? Panfrost was my challenge until we “won”. My next challenge? Gaming on Linux on M1. Once I finished my coursework, I started full-time on gaming on Linux. Within a month, we shipped OpenGL 3.1 on Asahi Linux. A few weeks later, we passed official conformance for OpenGL ES 3.1. That put us at feature parity with Panfrost. I wanted to go further. OpenGL (ES) 3.2 requires geometry shaders, a legacy feature not supported by either Arm or Apple hardware. The proprietary OpenGL drivers emulate geometry shaders with compute, but there was no open source prior art to borrow. Even though multiple Mesa drivers need geometry/tessellation emulation, nobody did the work to get there. My early progress on OpenGL was fast thanks to the mature common code in Mesa. It was time to pay it forward. Over the rest of the year, I implemented geometry/tessellation shader emulation. And also the rest of the owl. In January 2024, I passed conformance for the full OpenGL 4.6 specification, finishing up OpenGL. Vulkan wasn’t too bad, either. I polished the OpenGL driver for a few months, but once I started typing a Vulkan driver, I passed 1.3 conformance in a few weeks. What remained was wiring up the geometry/tessellation emulation to my shiny new Vulkan driver, since those are required for Direct3D. Et voilà, Proton games. Along the way, Karol Herbst passed OpenCL 3.0 conformance on the M1, running my compiler atop his “rusticl” frontend. Meanwhile, when the Vulkan 1.4 specification was published, we were ready and shipped a conformant implementation on the same day. After that, I implemented sparse texture support, unlocking Direct3D 12 via Proton. …Now what? Ship a great driver? Check. Conformant OpenGL 4.6, OpenGL ES 3.2, and OpenCL 3.0? Check. Conformant Vulkan 1.4? Check. Proton gaming? Check. That’s a wrap. We’ve succeeded beyond my dreams. The challenges I chased, I have tackled. The drivers are fully upstream in Mesa. Performance isn’t too bad. With the Vulkan on Apple myth busted, conformant Vulkan is now coming to macOS via LunarG’s KosmicKrisp project building on my work. Satisfied, I am now stepping away from the Apple ecosystem. My friends in the Asahi Linux orbit will carry the torch from here. As for me? Onto the next challenge!
