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I took an amazing trip to SE Asia last month, including Angkor Wat. I had a hard time finding good reading or other resources to learn from before I went, in part because Amazon is awash in AI garbage. Here’s some books and podcasts I found useful about the Khmer empire in general and Angkor in particular: Ancient Angkor by Michael Freeman and Claude Jacques. The closest thing to a coffee-table book to preview what you will see. The practical information is outdated but the pictures and descriptions are good. Empire Podcast #185: The God Kings of Angkor Wat by William Dalrymple and Anita Anand. An entertaining and fully detailed account of the Khmer empire. It’s basically an excerpt from Dalrymple’s new book The Golden Road: How Ancient India Transformed the World. Fall of Civilizations Podcast #5: The Khmer Empire by Paul Cooper. Another history, not quite as magically well told as Dalrymple but full of good information. Angkor and the Khmer Civilization by Michael D. Coe. A highly recommended history of the Khmer region. Honestly I found this very dry and too detailed, but I did learn from it. Lonely Planet Pocket Guide: Siem Reap & the Temples of Angkor. We didn’t use this much but it seemed like a useful practical guide. OTOH it dates to 2018 so things have changed. My other advice for visiting Siem Reap and Angkor is: go. It is amazing. Plan for at least two full days of touristing there. Hire a private guide and driver if you can, it is absolutely worth it. (Email me for a recommendation.)
I’ve been doing Dry January this year. One thing I missed was something for apéro hour, a beverage to mark the start of the evening. Something complex and maybe bitter, not like a drink you’d have with lunch. I found some good options. Ghia sodas are my favorite. Ghia is an NA apéritif based on grape juice but with enough bitterness (gentian) and sourness (yuzu) to be interesting. You can buy a bottle and mix it with soda yourself but I like the little cans with extra flavoring. The Ginger and the Sumac & Chili are both great. Another thing I like are low-sugar fancy soda pops. Not diet drinks, they still have a little sugar, but typically 50 calories a can. De La Calle Tepache is my favorite. Fermented pineapple is delicious and they have some fun flavors. Culture Pop is also good. A friend gave me the Zero book, a drinks cookbook from the fancy restaurant Alinea. This book is a little aspirational but the recipes are doable, it’s just a lot of labor. Very fancy high end drink mixing, really beautiful flavor ideas. The only thing I made was their gin substitute (mostly junipers extracted in glycerin) and it was too sweet for me. Need to find the right use for it, a martini definitely ain’t it. An easier homemade drink is this Nonalcoholic Dirty Lemon Tonic. It’s basically a lemonade heavily flavored with salted preserved lemons, then mixed with tonic. I love the complexity and freshness of this drink and enjoy it on its own merits. Finally, non-alcoholic beer has gotten a lot better in the last few years thanks to manufacturing innovations. I’ve been enjoying NA Black Butte Porter, Stella Artois 0.0, Heineken 0.0. They basically all taste just like their alcoholic uncles, no compromise. One thing to note about non-alcoholic substitutes is they are not cheap. They’ve become a big high end business. Expect to pay the same for an NA drink as one with alcohol even though they aren’t taxed nearly as much.
The Trump administration has made aggressive threats against immigrants in the US. It’s not clear what’s coming, my biggest fear is a violent display of fascism. (Don’t call them camps!) But even if it’s a polite legal process it will be chaotic and disruptive to many neighbors. Back in 2018 I donated reactively to the Trump administration’s cruelty to immigrant families. This time I’m trying to get ahead of it. The need for the money is now, no matter what happens it is going to be a bad few years for immigrants in the US. To that end I asked on Metafilter about charities to donate to. I got back a remarkable reply listing 18 charities that all have some California focus. I donated to most of them. I want to highlight two groups in particular. One is RAICES. They work in Texas, not California, but they are well organized and effective. The other is KIND. They have a simple mission. They try to ensure every unaccompanied minor has legal representation in immigration court (something not guaranteed.) The other groups on the list are all also deserving of consideration.
LLMs are good search helpers. Here’s three search tools I use every day. All of these use an AI to synthesize answers but also provide an essential feature: specific web search results for you to verify and further research. I use these for conversational inquiries in addition to more traditional keyword searches. Phind is an excellent free LLM + search engine. The AI writes an answer to your query but is very careful to provide footnotes to a web-search-like list of links on the right. I use this mostly for directed search queries, things like “what’s an inexpensive TV streaming device?” where I might have used keyword search too. The Llama-70b LLM that powers the free version is quite good, sometimes I have general conversations with it or ask it to generate code. Bing CoPilot has a very similar output result to Phind. I find it a little less useful and the search result links are less prominent. But it’s a good second opinion. Bing has been a very good search engine for 10+ years, I’m grateful to Microsoft for continuing to invest in it. CoPilot results are sometimes volunteered on the main Bing page but you often have to click to get to the ChatGPT 4 Turbo enhanced pages. Kagi is what I use as my general search engine, my Google replacement. It mostly gives traditional keyword search results but sometimes it will volunteer a “Quick Answer” where Claude 3 Haiku synthesizes an answer with references. You can also request one. I think Phind and CoPilot do a better job but I appreciate when Kagi intercepts a keyword search I did and just gives me the right answer. Google has tried various versions of LLM-enhancement in search, I think the current version is called AI Overviews. It’s not bad but it’s also not as good as the others. Not mentioned here: ChatGPT or Claude. Those are general purpose LLMs but they don’t really give search results or specific references. In the old days they’d make up URLs if you asked but that’s improving.
8BitDo makes good game controllers. A wide variety of styles from retro to mainstream, with some unusual shapes. And wide compatibility with various systems: PC, Macs, Switch, Android. They’re well built, work right, and quite inexpensive. A far cry from the MadCatz-style junk we used to get. The new hotness is the Ultimate 2C, an Xbox-style wireless controller for the very low price of $30. But it works great, doesn’t feel cheap at all. The fancier mainstream choice is the Ultimate 2.4g at $50 which includes a charging stand and extra reprogrammability. But what’s really interesting to me are the odd layouts, often small or retro. The SN30 Pro is particularly interesting as a portable controller. SNES-styling but a full XBox style modern controller with two analog sticks, easy to throw in a suitcase. There’s a lot of fiddly details for this class of device. Controller type (XInput, DInput, switch, etc), wireless interface (Bluetooth or proprietary), etc. 8BitDo makes good choices and implementations for all that stuff I’ve tested. They seem to work well with Steam. They’re a popular brand so well tested. It helps that PC game controllers have mostly standardized around the Xbox layout and XInput. Steam can patch over any rough spots for older games.
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!
