Full Width 
Shortcuts 
Sign Up 
Log In 
Articles 
Reading List More from the singularity is nearer
This is not going to be a cakewalk like self driving cars. Most of comma’s competition is now out of business, taking billions and billions of dollars with it. Re: Tesla and FSD, we always expected Tesla to have the lead, but it’s not a winner take all market, it will look more like iOS vs Android. comma has been around for 10 years, is profitable, and is now growing rapidly. In self driving, most of the competition wasn’t even playing the right game. This isn’t how it is for ML frameworks. tinygrad’s competition is playing the right game, open source, and run by some quite smart people. But this is my second startup, so hopefully taking a bit more risk is appropriate. For comma to win, all it would take is people in 2016 being wrong about LIDAR, mapping, end to end, and hand coding, which hopefully we all agree now that they were. For tinygrad to win, it requires something much deeper to be wrong about software development in general. As it stands now, tinygrad is 14556 lines. Line count is not a perfect proxy for complexity, but when you have differences of multiple orders of magnitude, it might mean something. I asked ChatGPT to estimate the lines of code in PyTorch, JAX, and MLIR. JAX = 400k MLIR = 950k PyTorch = 3300k They range from one to two orders of magnitude off. And this isn’t even including all the libraries and drivers the other frameworks rely on, CUDA, cuBLAS, Triton, nccl, LLVM, etc…. tinygrad includes every single piece of code needed to drive an AMD RDNA3 GPU except for LLVM, and we plan to remove LLVM in a year or two as well. But so what? What does line count matter? One hypothesis is that tinygrad is only smaller because it’s not speed or feature competitive, and that if and when it becomes competitive, it will also be that many lines. But I just don’t think that’s true. tinygrad is already feature competitive, and for speed, I think the bitter lesson also applies to software. When you look at the machine learning ecosystem, you realize it’s just the same problems over and over again. The problem of multi machine, multi GPU, multi SM, multi ALU, cross machine memory scheduling, DRAM scheduling, SRAM scheduling, register scheduling, it’s all the same underlying problem at different scales. And yet, in all the current ecosystems, there are completely different codebases and libraries at each scale. I don’t think this stands. I suspect there is a simple formulation of the problem underlying all of the scheduling. Of course, this problem will be in NP and hard to optimize, but I’m betting the bitter lesson wins here. The goal of the tinygrad project is to abstract away everything except the absolute core problem in the cleanest way possible. This is why we need to replace everything. A model for the hardware is simple compared to a model for CUDA. If we succeed, tinygrad will not only be the fastest NN framework, but it will be under 25k lines all in, GPT-5 scale training job to MMIO on the PCIe bus! Here are the steps to get there: Expose the underlying search problem spanning several orders of magnitude. Due to the execution of neural networks not being data dependent, this problem is very amenable to search. Make sure your formulation is simple and complete. Fully capture all dimensions of the search space. The optimization goal is simple, run faster. Apply the state of the art in search. Burn compute. Use LLMs to guide. Use SAT solvers. Reinforcement learning. It doesn’t matter, there’s no way to cheat this goal. Just see if it runs faster. If this works, not only do we win with tinygrad, but hopefully people begin to rethink software in general. Of course, it’s a big if, this isn’t like comma where it was hard to lose. But if it wins… The main thing to watch is development speed. Our bet has to be that tinygrad’s development speed is outpacing the others. We have the AMD contract to train LLaMA 405B as fast as NVIDIA due in a year, let’s see if we succeed.
I signed up for Hinge. Holy shit with the boosts. How does someone who works on this wake up every morning and feel okay about themselves? Similarly with the tip screens, Uber algorithm, all the zero sum bullshit using all the tricks of psychology to extract a little bit more from every interaction in society. Nudge. Nudge. NUDGE. Want to partake in normal society like buying a coffee, going on a date, getting a ride, paying a friend. Oh, there’s a middle man now. An evil ominous middleman using state of the art AI algorithms to extract just a little bit more from you. But eventually the market will fix this, right? People will feel sick of being manipulated and move elsewhere? Ahhh, but they see that coming long before you do. They have dashboards. Quick Jeeves, tune the AI to make people feel less manipulated. Give them a little bit more for now, we have to think about maximizing lifetime customer value here. Oh the AI already did this on its own? Jeeves you’ve been replaced! People perpetually on the edge. You want to opt out of this all you say? Good luck running a competitive business! Every metric is now a target. You better maximize engagement or you will lose engagement this is a red queen’s race we can’t afford to lose! Burn all the social capital, burn all your values, FEED IT ALL TO MOLOCH! Someday, people will have to realize we live in a society. What will it take? A complete self cannibalization to the point you can’t eat your own mouth? It sure as hell isn’t going to be people opting out, that’s a collective action problem you can’t solve. Democracy, haha, you think the algorithms will let you vote to kill them? Your vote is as decoupled from action as the amount Uber pays the driver is decoupled from the fare that you pay. There’s no reform here, there’s only revolution. Will it simply be a huge financial collapse? Or do we need World War 3? And even World War 3 is on a spectrum. Will mass starvation fix this? Or will the attitude of thinking it’s okay to manipulate others at scale persist even past that? He’s got his, and I’ve got mine… If you open a government S&P 500 account for everyone with $1,000 at birth that’ll pay their social security cause it like…goes up…wait who’s creating this value again? It’s not okay. Advertising is not okay. Price discrimination is not okay. Using big data, machine learning, and psychology to manipulate others at scale is not okay. But you aren’t going to learn this lesson until you have fed a huge majority of your customers to Moloch. Modern capitialism is wireheading. Release the hypnodrones. How many cans of Pepsi did you want them to consume an hour again?
You know about Critical Race Theory, right? It says that if there’s an imbalance in, say, income between races, it must be due to discrimination. This is what wokism seems to be, and it’s moronic and false. The right wing has invented something equally stupid. Introducing Critical Trade Theory, stolen from this tweet. If there’s an imbalance in trade between countries, it must be due to unfair practices. (not due to the obvious, like one country is 10x richer than the other) There’s really only one way the trade deficits will go away, and that’s if trade goes to zero (or maybe if all these countries become richer than America). Same thing with the race deficits, no amount of “leg up” bullshit will change them. Why are all the politicians in America anti-growth anti-reality idiots who want to drive us into the poor house? The way this tariff shit is being done is another stupid form of anti-merit benefits to chosen groups of people, with a whole lot of grift to go along with it. Makes me just not want to play.
Intel is sitting on a huge amount of card inventory they can’t move, largely because of bad software. Most of this is a summary of the public #intel-hardware channel in the tinygrad discord. Intel currently is sitting on: 15,000 Gaudi 2 cards (with baseboards) 5,100 Intel Data Center GPU Max 1450s (without baseboards) If you were Intel, what would you do with them? First, starting with the Gaudi cards. The open source repo needed to control them was archived on Feb 4, 2025. There’s a closed source version of this that’s maybe still maintained, but eww closed source and do you think it’s really maintained? The architecture is kind of tragic, and that’s likely why they didn’t open source it. Unlike every other accelerator I have seen, the MMEs, which is where all the FLOPS are, are not controllable by the TPCs. While the TPCs have an LLVM port, the MME is not documented. After some poking around, I found the spec: It’s highly fixed function, looks very similar to the Apple ANE. But that’s not even the real problem with it. The problem is that it is controlled by queues, not by the TPCs. Unpacking habanalabs-dkms-1.19.2-32.all.deb you can find the queues. There is some way to push a command stream to the device so you don’t actually have to deal with the host itself for the queues. But that doesn’t prevent you having to decompose the network you are trying to run into something you can put on this fixed function block. Programmability is on a spectrum, ranging from CPUs being the easiest, to GPUs, to things like the Qualcomm DSP / Google TPU (where at least you drive the MME from the program), to this and the Apple ANE being the hardest. While it’s impressive that they actually got on MLPerf Training v4.0 training GPT3, I suspect it’s all hand coded, and if you even can deviate off the trodden path you’ll get almost no perf. Accelerators like this are okay for low power inference where you can adjust the model architecture for the target, Apple does a great job of this. But this will never be acceptable for a training chip. Then there’s the Data Center GPU Max 1450. Intel actually sent us a few of these. You quickly run into a problem…how do you plug them in? They need OAM sockets, 48V power, and a cooling solution that can sink 600W. As far as I can tell, they were only ever deployed in two systems, the Aurora Supercomputer and the Dell XE9640. It’s hard to know, but I really doubt many of these Dell systems were sold. Intel then sent us this carrier board. In some ways it’s helpful, but in other ways it’s not at all. It still doesn’t solve cooling or power, and you need to buy 16x MCIO cables (cheap in quantity, but expensive and hard to find off the shelf). Also, I never got a straight answer, but I really doubt Intel has many of these boards. And that board doesn’t look cheap to manufacturer more of. The connectors alone, which you need two of per GPU, cost $26 each. That’s $104 for just the OAM connectors. tiny corp was in discussions to buy these GPUs. How much would you pay for one of these on a PCIe card? The specs look great. 839 TFLOPS, 128 GB of ram, 3.3 TB/s of bandwidth. However…read this article. Even in simple synthetic benchmarks, the chip doesn’t get anywhere near its max performance, and it looks to be for fundamental reasons like memory latency. We estimate we could sell PCIe versions of these GPUs for $1,000; I don’t think most people know how hard it is to move non NVIDIA hardware. Before you say you’d pay more, ask yourself, do you really want to deal with the software? An adapter card has four pieces. A PCB for the card, a 12->48V voltage converter, a heatsink, and a fan. My quote from the guy who makes an OAM adapter board was $310 for 10+ PCBs and $75 for the voltage converter. A heatsink that can handle 600W (heat pipes + vapor chamber) is going to cost $100, then maybe $20 more for the fan. That’s $505, and you still need to assemble and test them, oh and now there’s tariffs. Maybe you can get this down to $400 in ~1000 quantity. So $200 for the GPU, $400 for the adapter, $100 for shipping/fulfillment/returns (more if you use Amazon), and 30% profit if you sell at $1k. tiny would net $1M on this, which has to cover NRE and you have risk of unsold inventory. We offered Intel $200 per GPU (a $680k wire) and they said no. They wanted $600. I suspect that unless a supercomputer person who already uses these GPUs wants to buy more, they will ride it to zero. tl;dr: there’s 5100 of these GPUs with no simple way to plug them in. It’s unclear if they worth the cost of the slot they go in. I bet they end up shredded, or maybe dumped on eBay for $50 each in a year like the Xeon Phi cards. If you buy one, good luck plugging it in! The reason Meta and friends buy some AMD is as a hedge against NVIDIA. Even if it’s not usable, AMD has progressed on a solid steady roadmap, with a clear continuation from the 2018 MI50 (which you can now buy for 99% off), to the MI325X which is a super exciting chip (AMD is king of chiplets). They are even showing signs of finally investing in software, which makes me bullish. If NVIDIA stumbles for a generation, this is AMD’s game. The ROCm “copy each NVIDIA repo” strategy actually works if your competition stumbles. They can win GPUs with slow and steady improvement + competition stumbling, that’s how AMD won server CPUs. With these Intel chips, I’m not sure who they would appeal to. Ponte Vecchio is cancelled. There’s no point in investing in the platform if there’s not going to be a next generation, and therefore nobody can justify the cost of developing software, therefore there won’t be software, therefore they aren’t worth plugging in. Where does this leave Intel’s AI roadmap? The successor to Ponte Vecchio was Rialto Bridge, but that was cancelled. The successor to that was Falcon Shores, but that was also cancelled. Intel claims the next GPU will be “Jaguar Shores”, but fool me once… To quote JazzLord1234 from reddit “No point even bothering to listen to their roadmaps anymore. They have squandered all their credibility.” Gaudi 3 is a flop due to “unbaked software”, but as much as I usually do blame software, nothing has changed from Gaudi 2 and it’s just a really hard chip to program for. So there’s no future there either. I can’t say that “Jaguar Shores” square instills confidence. It didn’t inspire confidence for “Joseph B.” on LinkedIn either. From my interactions with Intel people, it seems there’s no individuals with power there, it’s all committee like leadership. The problem with this is there’s nobody who can say yes, just many people who can say no. Hence all the cancellations and the nonsense strategy. AMD’s dysfunction is different. from the beginning they had leadership that can do things (Lisa Su replied to my first e-mail), they just didn’t see the value in investing in software until recently. They sort of had a point if they were only targeting hyperscalars. but it seems like SemiAnalysis got through to them that hyperscalars aren’t going to deal with bad software either. It remains to be seem if they can shift culture to actually deliver good software, but there’s movement in that direction, and if they succeed AMD is so undervalued. Their hardware is good. With Intel, until that committee style leadership is gone, there’s 0 chance for success. Committee leadership is fine if you are trying to maintain, but Intel’s AI situation is even more hopeless than AMDs, and you’d need something major to turn it around. At least with AMD, you can try installing ROCm and be frustrated when there are bugs. Every time I have tried Intel’s software I can’t even recall getting the import to work, and the card wasn’t powerful enough that I cared. Intel needs actual leadership to turn this around, or there’s 0 future in Intel AI.
More in programming
As search gets worse and “working code” gets cheaper, apps get easier to make from scratch than to find.
I’ve never published an essay quite like this. I’ve written about my life before, reams of stuff actually, because that’s how I process what I think, but never for public consumption. I’ve been pushing myself to write more lately because my co-authors and I have a whole fucking book to write between now and October. […]
Less known desktop UI frameworks Writing desktop software is hard. The UI technologies of Windows or MacOS are awful compared to web technology. What can trivially be done with HTML/CSS/JavaScript in few minutes can take hours using Windows’s win32 APIs or Mac’s Cocoa. That’s why the default technology for desktop apps, especially cross-platform, is Electron: a Chrome browser combined with Node runtime. The problem is that it’s bloaty: each app is a unique build of Chrome with a little bit of application code. Chrome is over 100MB so many apps ship less than 1MB of code in a 100M wrapper. People tried to address the problem of poor OS APIs by writing UI frameworks, often meant to be cross-platform. You’ve heard about QT, GTK, wxWindows. The problem with those is that they are also old, their APIs are not the greatest either and they are bloaty as well. There just doesn’t seem to be a good option. Writing your own framework seems impossible due to the size of task. But is it? I’ll show a couple of less-known UI frameworks written mostly be a single person, often done simply to enable writing an application. SWELL in WDL WDL is interesting. Justin Frankel, the guy who created Winamp, has a repository of C++ code he uses in different projects. After selling Winamp to AOL, a side quest of writing file sharing application, getting fired from AOL for writing file sharing application, he started a company building Reaper a digital audio workstation software for Windows. Winamp is a win32 API program and so is Reaper. At some point Justin decided to make a Mac version but by then he had a lot of code heavily using win32 APIs. So he did what anyone in his position would: he implemented win32 APIs for Mac OS and Linux and called it SWELL - Simple Windows Emulation Layer. Ok, actually no-one else would do it. It was an insane idea but it worked. It’s important to not over-state SWELL capabilities. It’s not Wine. You can’t take any win32 program and recompile for Mac with SWELL. Frankel is insanely pragmatic and so is his code. SWELL only implements the subset of APIs he uses in Reaper. At the same time Reaper is a big app so if SWELL works for Reaper, it could work for your app. WDL is open-source using permissive MIT license. Sublime Text For a few years Sublime Text was THE programmer’s editor. It was written by a single developer in C++ and he wrote a custom UI toolkit for it. Not open source but its existence shows it can be done. RAD Debugger RAD Debugger is an open-source Windows debugger for C/C++ apps written in C by mostly a single person. It implements a custom UI framework based on 3D renderer. The UI is integral part of the the app but the code is well structured so you probably can take just their UI / render code and use it in your own C / C++ app. Currently the app / UI is only for Windows but it’s designed to be cross-platform and they are working on porting the renderer to Mac OS / Linux. They use permissive MIT license and everything is written in C. Dear ImGUI Dear ImGui is a newer cross-platform, UI framework in C++. Open source, permissive MIT license. Written by mostly a single person. Ghostty Ghostty is a cross-platform terminal emulator and UI. It’s written in Zig by mostly a single person and uses it’s own low-level GPU renderer for the UI. You too can write your own UI framework At first the idea of writing your own UI framework seems impossibly daunting. What I’m hoping to show is that if you’re ambitious enough it’s possible to build cross platform desktop apps that are not just bloated 100MB Chrome wrappers around few kilobytes of custom code. I’m not saying it’s a simple thing, just that enough people did it that it’s possible. It shouldn’t be necessary but both Microsoft and Apple have tragically dropped the ball on providing decent, high-performance UI libraries for their OS. Microsoft even writes their own apps, like Teams, in web technologies. Thanks to open source you’re not at the staring line. You can just use Dear ImGUI or WDL’s SWELL. Or you can extract the UI code from RAD Debugger or Ghostty (if you write in Zig). Or you can look at how their implementation to speed up your own design and implementation.
I released Logic for Programmers exactly one year ago today. It feels weird to celebrate the anniversary of something that isn't 1.0 yet, but software projects have a proud tradition of celebrating a dozen anniversaries before 1.0. I wanted to share about what's changed in the past year and the work for the next six+ months. The Road to 0.1 I had been noodling on the idea of a logic book since the pandemic. The first time I wrote about it on the newsletter was in 2021! Then I said that it would be done by June and would be "under 50 pages". The idea was to cover logic as a "soft skill" that helped you think about things like requirements and stuff. That version sucked. If you want to see how much it sucked, I put it up on Patreon. Then I slept on the next draft for three years. Then in 2024 a lot of business fell through and I had a lot of free time, so with the help of Saul Pwanson I rewrote the book. This time I emphasized breadth over depth, trying to cover a lot more techniques. I also decided to self-publish it instead of pitching it to a publisher. Not going the traditional route would mean I would be responsible for paying for editing, advertising, graphic design etc, but I hoped that would be compensated by much higher royalties. It also meant I could release the book in early access and use early sales to fund further improvements. So I wrote up a draft in Sphinx, compiled it to LaTeX, and uploaded the PDF to leanpub. That was in June 2024. Since then I kept to a monthly cadence of updates, missing once in November (short-notice contract) and once last month (Systems Distributed). The book's now on v0.10. What's changed? A LOT v0.1 was very obviously an alpha, and I have made a lot of improvements since then. For one, the book no longer looks like a Sphinx manual. Compare! Also, the content is very, very different. v0.1 was 19,000 words, v.10 is 31,000.1 This comes from new chapters on TLA+, constraint/SMT solving, logic programming, and major expansions to the existing chapters. Originally, "Simplifying Conditionals" was 600 words. Six hundred words! It almost fit in two pages! The chapter is now 2600 words, now covering condition lifting, quantifier manipulation, helper predicates, and set optimizations. All the other chapters have either gotten similar facelifts or are scheduled to get facelifts. The last big change is the addition of book assets. Originally you had to manually copy over all of the code to try it out, which is a problem when there are samples in eight distinct languages! Now there are ready-to-go examples for each chapter, with instructions on how to set up each programming environment. This is also nice because it gives me breaks from writing to code instead. How did the book do? Leanpub's all-time visualizations are terrible, so I'll just give the summary: 1180 copies sold, $18,241 in royalties. That's a lot of money for something that isn't fully out yet! By comparison, Practical TLA+ has made me less than half of that, despite selling over 5x as many books. Self-publishing was the right choice! In that time I've paid about $400 for the book cover (worth it) and maybe $800 in Leanpub's advertising service (probably not worth it). Right now that doesn't come close to making back the time investment, but I think it can get there post-release. I believe there's a lot more potential customers via marketing. I think post-release 10k copies sold is within reach. Where is the book going? The main content work is rewrites: many of the chapters have not meaningfully changed since 1.0, so I am going through and rewriting them from scratch. So far four of the ten chapters have been rewritten. My (admittedly ambitious) goal is to rewrite three of them by the end of this month and another three by the end of next. I also want to do final passes on the rewritten chapters; as most of them have a few TODOs left lying around. (Also somehow in starting this newsletter and publishing it I realized that one of the chapters might be better split into two chapters, so there could well-be a tenth technique in v0.11 or v0.12!) After that, I will pass it to a copy editor while I work on improving the layout, making images, and indexing. I want to have something worthy of printing on a dead tree by 1.0. In terms of timelines, I am very roughly estimating something like this: Summer: final big changes and rewrites Early Autumn: graphic design and copy editing Late Autumn: proofing, figuring out printing stuff Winter: final ebook and initial print releases of 1.0. (If you know a service that helps get self-published books "past the finish line", I'd love to hear about it! Preferably something that works for a fee, not part of royalties.) This timeline may be disrupted by official client work, like a new TLA+ contract or a conference invitation. Needless to say, I am incredibly excited to complete this book and share the final version with you all. This is a book I wished for years ago, a book I wrote because nobody else would. It fills a critical gap in software educational material, and someday soon I'll be able to put a copy on my bookshelf. It's exhilarating and terrifying and above all, satisfying. It's also 150 pages vs 50 pages, but admittedly this is partially because I made the book smaller with a larger font. ↩
Translating user interface of SumatraPDF SumatraPDF is the best PDF/eBook/Comic Book viewer for Windows. It’s small, fast, full of features, free and open-source. It became popular enough that it made sense to translate the UI for non-English users. Currently we support 72 languages. This article describes how I designed and implemented a translation system in SumatraPDF, a native win32 C++ Windows application. Hard things about translating the UI There are 2 hard things about translating an application code for translation system (extracting strings to translate, translate strings from English to user’s language) translating them into many languages Extracting strings to translate from source code Currently there are 381 strings in SumatraPDF subject to translation. It’s important that the system requires the least amount of effort when adding new strings to translate. Every string that needs to be translated is marked in .cpp or .h file with one of two macros: _TRA("Rename") _TRN("Open") I have a script that extracts those strings from source files. Mine is written in Go but it could just as well be Python or JavaScript. It’s a simple regex job. _TR stands for “translation”. _TRA(s) expands into const char* trans::GetTranslation(const char* str) function which returns str translated to current UI language. We auto-detect language at startup based on Windows settings and allow the user to explicitly set UI language. For English we just return the original string. If a string to be translated is e.g. a part of const char* array[], we can’t use trans::GetTranslation(). For cases like that we have _TRN() which expands to English string. We have to write code to translate it at some point. Adding new strings is therefore as simple as wrapping them in _TRA() or _TRN() macros. Translating strings into many languages Now that we’ve extracted strings to be translated, we need to translate them into 72 languages. SumatraPDF is a free, open-source program. I don’t have a budget to hire translators. I don’t have a budget, period. The only option was to get help from SumatraPDF users. It was vital to make it very easy for users to send me translations. I didn’t want to ask them, for example, to download some translation software. Design and implementation of AppTranslator web app I couldn’t find a really simple software for crowd sourcing translations so I wrote my own: https://github.com/kjk/apptranslator You can see it in action: https://www.apptranslator.org/app/SumatraPDF I designed it to be generic but I don’t think anyone else is using it. AppTranslator is simple. Per https://tools.arslexis.io/wc/: 4k lines of Go server code 451 lines of html code a single dependency: bootstrap CSS framework (the project is old) It’s simple because I don’t want to spend a lot of time writing translation software. It’s just a side project in service of the goal of translating SumatraPDF. Login is exclusively via GitHub. It doesn’t even use a database. Like in Redis, changes are stored as a series of operations in an append-only log. We keep the whole state in memory and re-create it from the log at startup. Main operation is translate a string from English to language X represented as [kOpTranslation, english string, language, translation, user who provided translation]. When user provides a translation in the web UI, we send an API call to the server which appends the translation operation to the log. Simple and reliable. Because the code is written in Go, it’s very fast and memory efficient. When running it uses mere megabytes of RAM. It can comfortably run on the smallest 256 MB VPS server. I backup the log to S3 so if the server ever fails, I can re-install the program on a new server and re-download the translations from S3. I provide RSS feed for each language so that people who provide translations can monitor for new strings to be translated. Sending strings for translation and receiving translations So I have a web app for collecting translations and a script that extracts strings to be translated from source code. How do they connect? AppTranslator has an API for submitting the current set of strings to be translated in the simplest possible format: a line for each string (I ensure there are no newlines in the string itself by escaping them with \n) API is password protected because only I can submit the strings. The server compares the strings sent with the current set and records a difference in the log. It also sends a response with translations. Again the simplest possible format: AppTranslator: SumatraPDF 651b739d7fa110911f25563c933f42b1d37590f8 :%s annotation. Ctrl+click to edit. am:%s մեկնաբանություն: Ctrl+քլիք՝ խմբագրելու համար: ar:ملاحظة %s. اضغط Ctrl للتحرير. az:Qeyd %s. Düzəliş etmək üçün Ctrl+düyməyə basın. As you can see: a string to translate is on a line starting with : is followed by translations of that strings in the format: ${lang}: ${translation} An optimization: 651b739d7fa110911f25563c933f42b1d37590f8 is a hash of this response. If I submit this hash with my request and translations didn’t change on the server, the response is empty. Implementing C++ part of translation system So now I have a text file with translation downloaded from the server. How do I get a translation in my C++ code? As with everything in SumatraPDF, I try to do things in a simple and efficient way. The whole Translation.cpp is only 239 lines of code. The core of translation system is const char* trans::GetTranslation(const char* s); function. I embed the translations in exact the same format as received from AppTranslator in the executable as data file in resources. If the UI language is English, we do nothing. trans::GetTranslation() returns its argument. When we switch the language, we load the translations from resources and build an index: an array of English strings an array of corresponding translations Both arrays use my own StrVec class optimized for storing an array of strings. To find a translation we scan the first array to find an index of the string and return translation from the second array, at the same index. Linear scan seems like it would be slow but it isn’t. Resizing dialogs I have a few dialogs defined in SumatraPDF.rc file. The problem with dialogs is that position of UI elements is fixed. A translated string will almost certainly have a different size than the English string which will mess up fixed layout. Thankfully someone wrote DialogSizer that smartly resizes dialogs and solves this problem. The evolution of a solution No AppTranslator My initial implementation was simpler. I didn’t yet have AppTranslator so I stored the strings in a text file in repository in the same format as what I described above. People would download it, make changes using a text editor and send me the file via email which I would then checkin. It worked for a while but it became worse over time. More strings, more languages created more work for me to manually manage e-mail submissions. I decided to automate the process. Code generation My first implementation of C++ side used code generation instead of embedding the text file in resources. My Go script would generate C++ source code files with static const char* [] arrays. This worked well but I decided to improve it further by making the code use the text file with translations embedded in the app. The main motivation for the change was to open a possibility of downloading latest translations from the server to fix the problem of translations not being all ready when I build the release executable. I haven’t done that yet but it’s now easier to implement given that the format of strings embedded in the exe is the same as the one I can download from AppTranslator. Only utf-8 SumatraPDF started by using both WCHAR* Unicode strings and char* utf8 strings. For that reason the translation system had to support returning translation in both WCHAR* and char* version. Over time I refactored the code to use mostly utf8 and at some point I no longer needed to support WCHAR* version. That made the code even smaller and reduced memory usage. The experience I’m happy how things turned out. AppTranslator proved to be reliable and hassle free. It runs for many years now and collected 35440 string translations from users. I automated everything so that all I need to do is to periodically re-run the script that extracts strings from source code, uploads them to AppTranslator and downloads latest translations. One problem is that translations are not always ready in time for release so I make a release and then people start translating strings added since last release. I’ve considered downloading the latest translations from the server, in addition to embedding them in an executable at the time of building the app. Would I do the same today? While AppTranslator is reliable and doesn’t require on-going work, it would be better to not have to run a server at all. The world has changed since I started SumatraPDF. Namely: people are comfortable using GitHub and you can edit files directly in GitHub UI. It’s not a great experience but it works. One option would be to generate a translation text file for each language, in this format: :first untranslated string :second untranslated string :first translated string translation of first string :second translated string translation of second string Untranslated strings are listed at the top, to make it easier to find. A link would send a translator directly to edit this file in GitHub UI. When translator saves translations, it creates a PR for me to review and merge. The roads not taken But why did you re-invent everything? You should do X instead. All other X that I know about suck. Using per-language .rc resource files Traditional way of localizing / translating Window GUI apps is to store all strings and dialog definitions in an .rc file. Each language gets its own .rc file (or files) and the program picks the right resource based on a language. This doesn’t solve the 2 hard problems: having an easy way to add strings for translations having an easy way for users to provide translations XML horror show There was a dark time when the world was under the iron grip of XML fanaticism. Everything had to be an XML file even when it was the worst possible solution for the problem. XML doesn’t solve the 2 hard problems and a string storage format is an absolute nightmare for human editing. GNU gettext There’s a C library gettext that uses .po files. This is much saner solution than XML horror show. .po files are relatively simple text format. The code is already written. Warning: tooting my own horn. My format is better. It’s easier for people to edit, it’s easier to write code to parse it. This looks like many times more than 239 lines of code. Ok, gettext probably does a bit more than my code, but clearly nothing than I need. It also doesn’t solve the 2 hard problems. I would still have to write code to extract strings from source code and build a way to allow users to translate them easily.
