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AI has recently crossed a utility threshold, where cutting-edge models such as GPT-3, Codex, and DALL-E 2 are actually useful and can perform tasks computers cannot do any other way. The act of producing these models is an exploration of a new frontier, with the discovery of unknown capabilities, scientific progress, and incredible product applications as the rewards. And perhaps most exciting for me personally, because the field is fundamentally about creating and studying software systems, great engineers are able to contribute at the same level as great researchers to future progress. “A self-learning AI system.” by DALL-E 2. I first got into software engineering because I wanted to build large-scale systems that could have a direct impact on people’s lives. I attended a math research summer program shortly after I started programming, and my favorite result of the summer was a scheduling app I built for people to book time with the professor. Specifying every detail of how a program should work is hard, and I’d always dreamed of one day putting my effort into hypothetical AI systems that could figure out the details for me. But after taking one look at the state of the art in AI in 2008, I knew it wasn’t going to work any time soon and instead started building infrastructure and product for web startups. DALL-E 2’s rendition of “The two great pillars of the house of artificial intelligence” (which according to my co-founder Ilya Sutskever are great engineering, and great science using this engineering) It’s now almost 15 years later, and the vision of systems which can learn their own solutions to problems is becoming incrementally more real. And perhaps most exciting is the underlying mechanism by which it’s advancing — at OpenAI, and the field generally, precision execution on large-scale models is a force multiplier on AI progress, and we need more people with strong software skills who can deliver these systems. This is because we are building AI models out of unprecedented amounts of compute; these models in turn have unprecedented capabilities, we can discover new phenomena and explore the limits of what these models can and cannot do, and then we use all these learnings to build the next model. “Harnessing the most compute in the known universe” by DALL-E 2 Harnessing this compute requires deep software skills and the right kind of machine learning knowledge. We need to coordinate lots of computers, build software frameworks that allow for hyperoptimization in some cases and flexibility in others, serve these models to customers really fast (which is what I worked on in 2020), and make it possible for a small team to manage a massive system (which is what I work on now). Engineers with no ML background can contribute from the day they join, and the more ML they pick up the more impact they have. The OpenAI environment makes it relatively easy to absorb the ML skills, and indeed, many of OpenAI’s best engineers transferred from other fields. All that being said, AI is not for every software engineer. I’ve seen about a 50-50 success rate of engineers entering this field. The most important determiner is a specific flavor of technical humility. Many dearly-held intuitions from other domains will not apply to ML. The engineers who make the leap successfully are happy to be wrong (since it means they learned something), aren’t afraid not to know something, and don’t push solutions that others resist until they’ve gathered enough intuition to know for sure that it matches the domain. “A beaver who has humbly recently become a machine learning engineer” by DALL-E 2 I believe that AI research is today by far the most impactful place for engineers who want to build useful systems to be working, and I expect this statement to become only more true as progress continues. If you’d like to work on creating the next generation of AI models, email me (gdb@openai.com) with any evidence of exceptional accomplishment in software engineering.
The text of my speech introducing OpenAI Five at Saturday’s OpenAI Five Finals event, where our AI beat the world champions at Dota 2: “Welcome everyone. This is an exciting day. First, this is an historic moment: this will be the first time that an AI has even attempted to play the world champions in an esports game. OG is simply on another level relative to other teams we’ve played. So we don’t know what’s going to happen, but win or lose, these will be games to remember. And you know, OpenAI Five and DeepMind’s very impressive StarCraft bot This event is really about something bigger than who wins or loses: letting people connect with the strange, exotic, yet tangible intelligences produced by today’s rapidly progressing AI technology. We’re all used to computer programs which have been meticulously coded by a human programmer. Do one thing that the human didn’t anticipate, and the program will break. We think of our computers as unthinking machines which can’t innovate, can’t be creative, can’t truly understand. But to play Dota, you need to do all these things. So we needed to do something different. OpenAI Five is powered by deep reinforcement learning — meaning that we didn’t code in how to play Dota. We instead coded in the how to learn. Five tries out random actions, and learns from a reward or punishment. In its 10 months of training, its experienced 45,000 years of Dota gameplay against itself. The playstyle it has devised are its own — they are truly creative and dreamed up by our computer — and so from Five’s perspective, today’s games are going to its first encounter with an alien intelligence (no offense to OG!). The beauty of this technology is that our learning code doesn’t know it’s meant for Dota. That makes it general purpose with amazing potential to benefit our lives. Last year we used it to control a robotic hand that no one could program. And we expect to see similar technology in new interactive systems, from elderly care robots to creative assistants to other systems we can’t dream of yet. This is the final public event for OpenAI Five, but we expect to do other Dota projects in the future. I want to thank the incredible team at OpenAI, everyone who worked directly on this project or cheered us on. I want to thank those who have supported the project: Valve, dozens of test teams, today’s casters, and yes, even all the commenters on Reddit. And I want to give massive thanks today to our fantastic guests OG who have taken time out of their tournament schedule to be here today. I hope you enjoy the show — and just to keep things in perspective, no matter how surprising the AIs are to us, know that we’re even more surprising to them!”
This post is co-written by Greg Brockman (left) and Ilya Sutskever (right). We’ve been working on OpenAI for the past three years. Our mission is to ensure that artificial general intelligence (AGI) — which we define as automated systems that outperform humans at most economically valuable work — benefits all of humanity. Today we announced a new legal structure for OpenAI, called OpenAI LP, to better pursue this mission — in particular to raise more capital as we attempt to build safe AGI and distribute its benefits. In this post, we’d like to help others understand how we think about this mission. Why now? # The founding vision of the field of AI was “… to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it”, and to eventually build a machine that thinks — that is, an AGI. But over the past 60 years, progress stalled multiple times and people started thinking of AI as a field that wouldn’t deliver. Since 2012, deep learning has generated sustained progress in many domains using a small simple set of tools, which have the following properties: Generality: deep learning tools are simple, yet they apply to many domains, such as vision, speech recognition, speech synthesis, text synthesis, image synthesis, translation, robotics, and game playing. Competence: today, the only way to get competitive results on most “AI-type problems” is through the use of deep learning techniques. Scalability: good old fashioned AI was able to produce exciting demos, but its techniques had difficulty scaling to harder problems. But in deep learning, more computational power and more data leads to better results. It has also proven easy (if costly) to rapidly increase the amount of compute productively used by deep learning experiments. The rapid progress of useful deep learning systems with these properties makes us feel that it’s reasonable to start taking AGI seriously — though it’s hard to know how far away it is. The impact of AGI # Just like a computer today, an AGI will be applicable to a wide variety of tasks — and just like computers in 1900 or the Internet in 1950, it’s hard to describe (or even predict) the kind of impact AGI will have. But to get a sense, imagine a computer system which can do the following activities with minimal human input: Make a scientific breakthrough at the level of the best scientists Productize that breakthrough and build a company, with a skill comparable to the best entrepreneurs Rapidly grow that company and manage it at large scale The upside of such a computer system is enormous — for an illustrative example, an AGI following the pattern above could produce amazing healthcare applications deployed at scale. Imagine a network of AGI-powered computerized doctors that accumulates a superhuman amount of clinical experience, allowing it to produce excellent diagnoses, deeply understand the nuanced effect of various treatments in lots of conditions, and greatly reduce the human error factor of healthcare — all for very low cost and accessible to everyone. Risks # We already live in a world with entities that surpass individual human abilities, which we call companies. If working on the right goals in the right way, companies can produce huge amounts of value and improve lives. But if not properly checked, they can also cause damage, like logging companies that cut down rain forests, cigarette companies that get children smoking, or scams like Ponzi schemes. We think of AGI as being like a hyper-effective company, with commensurate benefits and risks. We are concerned about AGI pursuing goals misspecified by its operator, malicious humans subverting a deployed AGI, or an out-of-control economy that grows without resulting in improvements to human lives. And because it’s hard to change powerful systems — just think about how hard it’s been to add security to the Internet — once they’ve been deployed, we think it’s important to address AGI’s safety and policy risks before it is created. OpenAI’s mission is to figure out how to get the benefits of AGI and mitigate the risks — and make sure those benefits accrue to all of humanity. The future is uncertain, and there are many ways in which our predictions could be incorrect. But if they turn out to be right, this mission will be critical. If you’d like to work on this mission, we’re hiring! About us # Ilya: I’ve been working on deep learning for 16 years. It was fun to witness deep learning transform from being a marginalized subfield of AI into one the most important family of scientific advances in recent history. As deep learning was getting more powerful, I realized that AGI might become a reality on a timescale relevant to my lifetime. And given AGI’s massive upside and significant risks, I want to maximize the positive parts of this impact and minimize the negative. Greg: Technology causes change, both positive and negative. AGI is the most extreme kind of technology that humans will ever create, with extreme upside and downside. I work on OpenAI because making AGI go well is the most important problem I can imagine contributing towards. Today I try to spend most of my time on technical work, and also work to spark better public discourse about AGI and related topics.
The text of my speech introducing OpenAI Five at yesterday’s Benchmark event: “We’re here to watch humans and AI play Dota, but today’s match will have implications for the world. OpenAI’s mission is to ensure that when we can build machines as smart as humans, they will benefit all of humanity. That means both pushing the limits of what’s possible and ensuring future systems are safe and aligned with human values. We work on Dota because it is a great training ground for AI: it is one of the most complicated games, involving teamwork, real time strategy, imperfect information, and an astronomical combinations of heroes and items. We can’t program a solution, so Five learns by playing 180 years of games against itself every day — sadly that means we can’t learn from the players up here unless they played for a few decades. It’s powered by 5 artificial neural networks which act like an artificial intuition. Five’s neural networks are about the size of the brain of an ant — still far from what we all have in our heads. One year ago, we beat the world’s top professionals at 1v1 Dota. People thought 5v5 would be totally out of reach. 1v1 requires mechanics and positioning; people did not expect the same system to learn strategy. But our AI system can learn problems it was not even designed to solve — we just used the same technology to learn to control a robotic hand — something no one could program. The computational power for OpenAI Five would have been impractical two years ago. But the availability of computation for AI has been increasing exponentially, doubling every 3.5 months since 2012, and one day technologies like this will become commonplace. Feel free to root for either team. Either way, humanity wins.” I’m very excited to see where the upcoming months of OpenAI Five development and testing take us.
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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.
