Posts in Business (20 found)

Double opt-in PSA

As of today, I run three different newsletters, all powered by Buttondown: there’s my recently announced Dealgorithmed , my outdoors-focused From the Summit , and the People and Blogs series. I also send my blog posts via email , if you prefer to consume content that way. They all require double opt-in. Which means that if you signed up for one of them, you should have received a second email, asking you to click a link to confirm your subscription. Sometimes those land in the spam folder, sometimes they don’t arrive at all. That’s just the unfortunate reality of emails in 2025. I just checked, and a solid 10% of the people who have signed up for Dealgorithmed have not confirmed their address. This is a reminder to check your inbox and click the confirmation link otherwise, you will not receive the first edition when it goes out on January 1st. Thank you for keeping RSS alive. You're awesome. Email me :: Sign my guestbook :: Support for 1$/month :: See my generous supporters :: Subscribe to People and Blogs

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pabloecortez 2 days ago

Black Friday for You and Me

Yesterday it was Thanksgiving and I had the privilege of spending the holiday with my family. We have a tradition of doing a toast going around the table and sharing at least one thing for which we are grateful. I want to share with you a story that started last year, in January of 2024, when a family friend named Germán reached out to me for help with a website for his business. Germán is in his 50s, he went to school for mechanical engineering in Mexico and about twenty years ago he moved to the United States. Today he owns a restaurant in Las Vegas with his wife and also runs a logistics company for distributing produce. We met the last week of January, he told me that he was looking to build a website for his restaurant and eventually build up his infrastructure so most of his business could be automated. His current workflow required his two sons to run the business along with him. They managed everything manually on expensive proprietary software. There were lots of things that could be optimized, so I agreed to jump on board and we have been collaborating ever since. What I assumed would be a developer type of position instead became more of a peer-mentorship relationship. Germán is curious, intelligent, and hard working. It didn't take long for me to notice that he didn't just want to have software or services running "in the background" while he occupied himself with other tasks. He wanted to have a thorough understanding of all the software he adopted. "I want to learn but I simply don't have the patience," he told me during one of our first meetings. At first I admit I thought this was a bit of a red flag (sorry Germán haha) but it all began to make sense when he showed me his books. He had paid thousands of dollars for a Wordpress website that only listed his services and contact information. The company he had hired offered an expensive SEO package for a monthly fee. My time in open source and the indieweb had blinded me to how abusive the "web development" industry had become. I'm referring to those local agencies that take advantage of unsuspecting clients and charge them for every little thing. I began making Germán's website and we went back and forth on assets, copy, menus, we began putting together a project and everything went smoothly. He was happy that he got to see how I built things. During this time I would journal through my work on his project and e-mail my notes to him. He loved it. Next came a new proposition. While the static site was nice to have an online presence, what he was after was getting into e-commerce. His wife, Sarah, makes artisanal beauty products and custom clothes. Her friends would message her on Facebook to ask what new stuff she was working on and she would send pictures to them from her phone. She would have benefitted from having a website, but after the bad experience they had had with the agency, they weren't too enthused about the prospect of hiring them for another project. I met with both of them again for this new project and we talked for hours, more like coworkers this time around. We eventually came to the conclusion that it would be more rewarding for them to really learn how to put their own shop together. I acted more as a coach or mentor than a developer. We'd sit together and activate accounts, fill out pages, choose themes. I was providing a safe space for them to be curious about technology, make mistakes, learn from them, and immediately get feedback on technical details so they could stay on a safe path. I'm so grateful for that opportunity afforded to me by Germán and his family. I've thought about how that approach would look if applied to the indieweb. It's always so exciting for me to see what the friends I've made here are working on. I know the open web becomes stronger when more independent projects are released, as we have more options to free ourselves from the corporate web that has stifled so much of the creativity and passion that I love and miss from the internet. I want to keep doing this. If you are building something on your own, have been out of the programming world for a while but want to start again, or maybe you are almost done and need a little boost in confidence (or accountability!) to reach the finish line and ship, I'm here to help. Check out my coaching page to find out more. I'm excited about the prospect of a community of builders who care about self-reliance and releasing software that puts people first. Perhaps this Black Friday you could choose to invest in yourself :-)

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Stone Tools 2 days ago

Bank Street Writer on the Apple II

Stop me if you're heard this one . In 1978, a young man wandered into a Tandy Radio Shack and found himself transfixed by the TRS-80 systems on display. He bought one just to play around with, and it wound up transforming his life from there on. As it went with so many, so too did it go with lawyer Doug Carlston. His brother, Gary, initially unimpressed, warmed up to the machine during a long Maine winter. The two thus smitten mused, "Can we make money off of this?" Together they formed a developer-sales relationship, with Doug developing Galactic Saga and third brother Don developing Tank Command . Gary's sales acumen brought early success and Broderbund was officially underway. Meanwhile in New York, Richard Ruopp, president of Bank Street College of Education, a kind of research center for experimental and progressive education, was thinking about how emerging technology fit into the college's mission. Writing was an important part of their curriculum, but according to Ruopp , "We tested the available word processors and found we couldn’t use any of them." So, experts from Bank Street College worked closely with consultant Franklin Smith and software development firm Intentional Educations Inc. to build a better word processor for kids. The fruit of that labor, Bank Street Writer , was published by Scholastic exclusively to schools at first, with Broderbund taking up the home distribution market a little later. Bank Street Writer would dominate home software sales charts for years and its name would live on as one of the sacred texts, like Lemonade Stand or The Oregon Trail . Let's see what lessons there are to learn from it yet. 1916 Founded by Lucy Sprague Mitchell, Wesley Mitchell, and Harriet Johnson as the “Bureau of Educational Experiments” (BEE) with the goal of understanding in what environment children best learn and develop, and to help adults learn to cultivate that environment. 1930 BEE moves to 69 Bank Street. (Will move to 112th Street in 1971, for space reasons.) 1937 The Writer’s Lab, which connects writers and students, is formed. 1950 BEE is renamed to Bank Street College of Education. 1973 Minnesota Educational Computing Consortium (MECC) is founded. This group would later go on to produce The Oregon Trail . 1983 Bank Street Writer, developed by Intentional Educations Inc., published by Broderbund Software, and “thoroughly tested by the academics at Bank Street College of Education.” Price: $70. 1985 Writer is a success! Time to capitalize! Bank Street Speller $50, Bank Street Filer $50, Bank Street Mailer $50, Bank Street Music Writer $50, Bank Street Prewriter (published by Scholastic) $60. 1986 Bank Street Writer Plus $100. Bank Street Writer III (published by Scholastic) $90. It’s basically Plus with classroom-oriented additions, including a 20-column mode and additional teaching aides. 1987 Bank Street Storybook, $40. 1992 Bank Street Writer for the Macintosh (published by Scholastic) $130. Adds limited page layout options, Hypercard-style hypertext, clip art, punctuation checker, image import with text wrap, full color, sound support, “Classroom Publishing” of fliers and pamphlets, and electronic mail. With word processors, I want to give them a chance to present their best possible experience. I do put a little time into trying the baseline experience many would have had with the software during the height of its popularity. "Does the software still have utility today?" can only be fairly answered by giving the software a fighting chance. To that end, I've gifted myself a top-of-the-line (virtual) Apple //e running the last update to Writer , the Plus edition. You probably already know how to use Bank Street Writer Plus . You don't know you know, but you do know because you have familiarity with GUI menus and basic word processing skills. All you're lacking is an understanding of the vagaries of data storage and retrieval as necessitated by the hardware of the time, but once armed with that knowledge you could start using this program without touching the manual again. It really is as easy as the makers claim. The simplicity is driven by very a subtle, forward-thinking user interface. Of primary interest is the upper prompt area. The top 3 lines of the screen serve as an ever-present, contextual "here's the situation" helper. What's going on? What am I looking at? What options are available? How do I navigate this screen? How do I use this tool? Whatever you're doing, whatever menu option you've chosen, the prompt area is already displaying information about which actions are available right now in the current context . As the manual states, "When in doubt, look for instructions in the prompt area." The manual speaks truth. For some, the constant on-screen prompting could be a touch overbearing, but I personally don't think it's so terrible to know that the program is paying attention to my actions and wants me to succeed. The assistance isn't front-loaded, like so many mobile apps, nor does it interrupt, like Clippy. I simply can't fault the good intentions, nor can I really think of anything in modern software that takes this approach to user-friendliness. The remainder of the screen is devoted to your writing and works like any other word processor you've used. Just type, move the cursor with the arrow keys, and type some more. I think most writers will find it behaves "as expected." There are no Electric Pencil -style over-type surprises, nor VisiCalc -style arrow key manipulations. What seems to have happened is that in making a word processor that is easy for children to use, they accidentally made a word processor that is just plain easy. The basic functionality is drop-dead simple to pick up by just poking around, but there's quite a bit more to learn here. To do so, we have a few options for getting to know Bank Street Writer in more detail. There are two manuals by virtue of the program's educational roots. Bank Street Writer was published by both Broderbund (for the home market) and Scholastic (for schools). Each tailored their own manual to their respective demographic. Broderbund's manual is cleanly designed, easy to understand, and gets right to the point. It is not as "child focused" as reviews at the time might have you believe. Scholastic's is more of a curriculum to teach word processing, part of the 80s push for "computers in the classroom." It's packed with student activities, pages that can be copied and distributed, and (tellingly) information for the teacher explaining "What is a word processor?" Our other option for learning is on side 2 of the main program disk. Quite apart from the program proper, the disk contains an interactive tutorial. I love this commitment to the user's success, though I breezed through it in just a few minutes, being a cultured word processing pro of the 21st century. I am quite familiar with "menus" thank you very much. As I mentioned at the top, the screen is split into two areas: prompt and writing. The prompt area is fixed, and can neither be hidden nor turned off. This means there's no "full screen" option, for example. The writing area runs in high-res graphics mode so as to bless us with the gift of an 80-character wide display. Being a graphics display also means the developer could have put anything on screen, including a ruler which would have been a nice formatting helper. Alas. Bank Street offers limited preference settings; there's not much we can do to customize the program's display or functionality. The upshot is that as I gain confidence with the program, the program doesn't offer to match my ability. There is one notable trick, which I'll discuss later, but overall there is a missed opportunity here for adapting to a user's increasing skill. Kids do grow up, after all. As with Electric Pencil , I'm writing this entirely in Bank Street Writer . Unlike the keyboard/software troubles there, here in 128K Apple //e world I have Markdown luxuries like . The emulator's amber mode is soothing to the eyes and soul. Mouse control is turned on and works perfectly, though it's much easier and faster to navigate by keyboard, as God intended. This is an enjoyable writing experience. Which is not to say the program is without quirks. Perhaps the most unfortunate one is how little writing space 128K RAM buys for a document. At this point in the write-up I'm at about 1,500 words and BSW's memory check function reports I'm already at 40% of capacity. So the largest document one could keep resident in memory at one time would run about 4,000 words max? Put bluntly, that ain't a lot. Splitting documents into multiple files is pretty much forced upon anyone wanting to write anything of length. Given floppy disk fragility, especially with children handling them, perhaps that's not such a bad idea. However, from an editing point of view, it is frustrating to recall which document I need to load to review any given piece of text. Remember also, there's no copy/paste as we understand it today. Moving a block of text between documents is tricky, but possible. BSW can save a selected portion of text to its own file, which can then be "retrieved" (inserted) at the current cursor position in another file. In this way the diskette functions as a memory buffer for cross-document "copy/paste." Hey, at least there is some option available. Flipping through old magazines of the time, it's interesting just how often Bank Street Writer comes up as the comparative reference point for home word processors over the years. If a new program had even the slightest whiff of trying to be "easy to use" it was invariably compared to Bank Street Writer . Likewise, there were any number of writers and readers of those magazines talking about how they continued to use Bank Street Writer , even though so-called "better" options existed. I don't want to oversell its adoption by adults, but it most definitely was not a children-only word processor, by any stretch. I think the release of Plus embraced a more mature audience. In schools it reigned supreme for years, including the Scholastic-branded version of Plus called Bank Street Writer III . There were add-on "packs" of teacher materials for use with it. There was also Bank Street Prewriter , a tool for helping to organize themes and thoughts before committing to the act of writing, including an outliner, as popularized by ThinkTank . (always interesting when influences ripple through the industry like this) Of course, the Scholastic approach was built around the idea of teachers having access to computers in the classroom. And THAT was build on the idea of teachers feeling comfortable enough with computers to seamlessly merge them into a lesson-plan. Sure, the kids needed something simple to learn, but let's be honest, so did the adults. There was a time when attaching a computer to anything meant a fundamental transformation of that thing was assured and imminent. For example, the "office of the future" (as discussed in the Superbase post ) had a counterpart in the "classroom of tomorrow." In 1983, Popular Computing said, "Schools are in the grip of a computer mania." Steve Jobs took advantage of this, skating to where the puck would be, by donating Apple 2s to California schools. In October 1983, Creative Computing did a little math on that plan. $20M in retail donations brought $4M in tax credits against $5M in gross donations. Apple could donate a computer to every elementary, middle, and high school in California for an outlay of only $1M. Jobs lobbied Congress hard to pass a national version of the same "Kids Can't Wait" bill, which would have extended federal tax credits for such donations. That never made it to law, for various political reasons. But the California initiative certainly helped position Apple as the go-to system for computers in education. By 1985, Apple would dominate fully half of the education market. That would continue into the Macintosh era, though Apple's dominance diminished slowly as cheaper, "good enough" alternatives entered the market. Today, Apple is #3 in the education market, behind Windows and Chromebooks . It is a fair question to ask, "How useful could a single donated computer be to a school?" Once it's in place, then what? Does it have function? Does anyone have a plan for it? Come to think of it, does anyone on staff even know how to use it? When Apple put a computer into (almost) every school in California, they did require training. Well, let's say lip-service was paid to the idea of the aspiration of training. One teacher from each school had to receive one day's worth of training to attain a certificate which allowed the school to receive the computer. That teacher was then tasked with training their coworkers. Wait, did I say "one day?" Sorry, I meant about one HOUR of training. It's not too hard to see where Larry Cuban was coming from when he published Oversold & Underused: Computers in the Classroom in 2001. Even of schools with more than a single system, he notes, "Why, then, does a school's high access (to computers) yield limited use? Nationally and in our case studies, teachers... mentioned that training in relevant software and applications was seldom offered... (Teachers) felt that the generic training available was often irrelevant to their specific and immediate needs." From my perspective, and I'm no historian, it seems to me there were four ways computers were introduced into the school setting. The three most obvious were: I personally attended schools of all three types. What I can say the schools had in common was how little attention, if any, was given to the computer and how little my teachers understood them. An impromptu poll of friends aligned with my own experience. Schools didn't integrate computers into classwork, except when classwork was explicitly about computers. I sincerely doubt my time playing Trillium's Shadowkeep during recess was anything close to Apple's vision of a "classroom of tomorrow." The fourth approach to computers into the classroom was significantly more ambitious. Apple tried an experiment in which five public school sites were chosen for a long-term research project. In 1986, the sites were given computers for every child in class and at home. They reasoned that for computers to truly make an impact on children, the computer couldn't just be a fun toy they occasionally interacted with. Rather, it required full integration into their lives. Now, it is darkly funny to me that having achieved this integration today through smartphones, adults work hard to remove computers from school. It is also interesting to me that Apple kind of led the way in making that happen, although in fairness they don't seem to consider the iPhone to be a computer . America wasn't alone in trying to give its children a technological leg up. In England, the BBC spearheaded a major drive to get computers into classrooms via a countrywide computer literacy program. Even in the States, I remember watching episodes of BBC's The Computer Programme on PBS. Regardless of Apple's or the BBC's efforts, the long-term data on the effectiveness of computers in the classroom has been mixed, at best, or even an outright failure. Apple's own assessment of their "Apple Classrooms of Tomorrow" (ACOT) program after a couple of years concluded, "Results showed that ACOT students maintained their performance levels on standard measures of educational achievement in basic skills, and they sustained positive attitudes as judged by measures addressing the traditional activities of schooling." Which is a "we continue to maintain the dream of selling more computers to schools" way of saying, "Nothing changed." In 2001, the BBC reported , "England's schools are beginning to use computers more in teaching - but teachers are making "slow progress" in learning about them." Then in 2015 the results were "disappointing, "Even where computers are used in the classroom, their impact on student performance is mixed at best." Informatique pour tous, France 1985: Pedagogy, Industry and Politics by Clémence Cardon-Quint noted the French attempt at computers in the classroom as being, "an operation that can be considered both as a milestone and a failure." Computers in the Classrooms of an Authoritarian Country: The Case of Soviet Latvia (1980s–1991) by Iveta Kestere, Katrina Elizabete Purina-Bieza shows the introduction of computers to have drawn stark power and social divides, while pushing prescribed gender roles of computers being "for boys." Teachers Translating and Circumventing the Computer in Lower and Upper Secondary Swedish Schools in the 1970s and 1980 s by Rosalía Guerrero Cantarell noted, "the role of teachers as agents of change was crucial. But teachers also acted as opponents, hindering the diffusion of computer use in schools." Now, I should be clear that things were different in the higher education market, as with PLATO in the universities. But in the primary and secondary markets, Bank Street Writer 's primary demographic, nobody really knew what to do with the machines once they had them. The most straightforwardly damning assessment is from Oversold & Underused where Cuban says in the chapter "Are Computers in Schools Worth the Investment?", "Although promoters of new technologies often spout the rhetoric of fundamental change, few have pursued deep and comprehensive changes in the existing system of schooling." Throughout the book he notes how most teachers struggle to integrate computers into their lessons and teaching methodologies. The lack of guidance in developing new ways of teaching means computers will continue to be relegated to occasional auxiliary tools trotted out from time to time, not integral to the teaching process. "Should my conclusions and predictions be accurate, both champions and skeptics will be disappointed. They may conclude, as I have, that the investment of billions of dollars over the last decade has yet to produce worthy outcomes," he concludes. Thanks to my sweet four-drive virtual machine, I can summon both the dictionary and thesaurus immediately. Put the cursor at the start of a word and hit or to get an instant spot check of spelling or synonyms. Without the reality of actual floppy disk access speed, word searches are fast. Spelling can be performed on the full document, which does take noticeable time to finish. One thing I really love is how cancelling an action or moving forward on the next step of a process is responsive and immediate. If you're growing bored of an action taking too long, just cancel it with ; it will stop immediately . The program feels robust and unbreakable in that way. There is a word lookup, which accepts wildcards, for when you kinda-sorta know how to spell a word but need help. Attached to this function is an anagram checker which benefits greatly from a virtual CPU boost. But it can only do its trick on single words, not phrases. Earlier I mentioned how little the program offers a user who has gained confidence and skill. That's not entirely accurate, thanks to its most surprising super power: macros. Yes, you read that right. This word processor designed for children includes macros. They are stored at the application level, not the document level, so do keep that in mind. Twenty can be defined, each consisting of up to 32 keystrokes. Running keystrokes in a macro is functionally identical to typing by hand. Because the program can be driven 100% by keyboard alone, macros can trigger menu selections and step through tedious parts of those commands. For example, to save our document periodically we need to do the following every time: That looks like a job for to me. 0:00 / 0:23 1× Defining a macro to save, with overwrite, the current file. After it is defined, I execute it which happens very quickly in the emulator. Watch carefully. If you can perform an action through a series of discrete keyboard commands, you can make a macro from it. This is freeing, but also works to highlight what you cannot do with the program. For example, there is no concept of an active selection, so a word is the smallest unit you can directly manipulate due to keyboard control limitations. It's not nothin' but it's not quite enough. I started setting up markdown macros, so I could wrap the current word in or for italic and bold. Doing the actions in the writing area and noting the minimal steps necessary to achieve the desired outcome translated into perfect macros. I was even able to make a kind of rudimentary "undo" for when I wrap something in italic but intended to use bold. This reminded me that I haven't touched macro functionality in modern apps since my AppleScript days. Lemme check something real quick. I've popped open LibreOffice and feel immediately put off by its Macros function. It looks super powerful; a full dedicated code editor with watched variables for authoring in its scripting language. Or is it languages? Is it Macros or ScriptForge? What are "Gimmicks?" Just what is going on? Google Docs is about the same, using Javascript for its "Apps Script" functionality. Here's a Stack Overflow post where someone wants to select text and set it to "blue and bold" with a keystroke and is presented with 32 lines of Javascript. Many programs seem to have taken a "make the simple things difficult, and the hard things possible" approach to macros. Microsoft Word reportedly has a "record" function for creating macros, which will watch what you do and let you play back those actions in sequence. (a la Adobe Photoshop's "actions") This sounds like a nice evolution of the BSW method. I say "reportedly" because it is not available in the online version and so I couldn't try it for myself without purchasing Microsoft 365. I certainly don't doubt the sky's the limit with these modern macro systems. I'm sure amazing utilities can be created, with custom dialog boxes, internet data retrieval, and more. The flip-side is that a lot of power has has been stripped from the writer and handed over to the programmer, which I think is unfortunate. Bank Street Writer allows an author to use the same keyboard commands for creating a macro as for writing a document. There is a forgotten lesson in that. Yes, BSW's macros are limited compared to modern tools, but they are immediately accessible and intuitive. They leverage skills the user is already known to possess . The learning curve is a straight, flat line. Like any good word processor, user-definable tab stops are possible. Bringing up the editor for tabs displays a ruler showing tab stops and their type (normal vs. decimal-aligned). Using the same tools for writing, the ruler is similarly editable. Just type a or a anywhere along the ruler. So, the lack of a ruler I noted at the beginning is now doubly-frustrating, because it exists! Perhaps it was determined to be too much visual clutter for younger users? Again, this is where the Options screen could have allowed advanced users to toggle on features as they grow in comfort and ambition. From what I can tell in the product catalogs, the only major revision after this was for the Macintosh which added a whole host of publishing features. If I think about my experience with BSW these past two weeks, and think about what my wish-list for a hypothetical update might be, "desktop publishing" has never crossed my mind. Having said all of that, I've really enjoyed using it to write this post. It has been solid, snappy, and utterly crash free. To be completely frank, when I switched over into LibreOffice , a predominantly native app for Windows, it felt laggy and sluggish. Bank Street Writer feels smooth and purpose-built, even in an emulator. Features are discoverable and the UI always makes it clear what action can be taken next. I never feel lost nor do I worry that an inadvertent action will have unknowable consequences. The impression of it being an assistant to my writing process is strong, probably more so than many modern word processors. This is cleanly illustrated by the prompt area which feels like a "good idea we forgot." (I also noted this in my ThinkTank examination) I cannot lavish such praise upon the original Bank Street Writer , only on this Plus revision. The original is 40-columns only, spell-checking is a completely separate program, no thesaurus, no macros, a kind of bizarre modal switch between writing/editing/transfer modes, no arrow key support, and other quirks of its time and target system (the original Apple 2). Plus is an incredibly smart update to that original, increasing its utility 10-fold, without sacrificing ease of use. In fact, it's actually easier to use, in my opinion than the original and comes just shy of being something I could use on a regular basis. Bank Street Writer is very good! But it's not quite great . Ways to improve the experience, notable deficiencies, workarounds, and notes about incorporating the software into modern workflows (if possible). AppleWin 32bit 1.31.0.0 on Windows 11 Emulating an Enhanced Apple //e Authentic machine speed (enhanced disk access speed) Monochrome (amber) for clean 80-column display Disk II controller in slot 5 (enables four floppies, total) Mouse interface in slot 4 Bank Street Writer Plus At the classroom level there are one or more computers. At the school level there is a "computer lab" with one or more systems. There were no computers. Hit (open the File menu) Hit (select Save File) Hit three times (stepping through default confirmation dialogs) I find that running at 300% CPU speed in AppleWin works great. No repeating key issues and the program is well-behaved. Spell check works quickly enough to not be annoying and I honestly enjoyed watching it work its way through the document. Sometimes there's something to be said about slowing the computer down to swift human-speed, to form a stronger sense of connection between your own work and the computer's work. I did mention that I used a 4-disk setup, but in truth I never really touched the thesaurus. A 3-disk setup is probably sufficient. The application never crashed; the emulator was rock-solid. CiderPress2 works perfectly for opening the files on an Apple ][ disk image. Files are of file extension, which CiderPress2 tries to open as disassembly, not text. Switch "Conversion" to "Plain Text" and you'll be fine. This is a program that would benefit greatly from one more revision. It's very close to being enough for a "minimalist" crowd. There are four, key pieces missing for completeness: Much longer document handling Smarter, expanded dictionary, with definitions Customizable UI, display/hide: prompts, ruler, word count, etc. Extra formatting options, like line spacing, visual centering, and so on. For a modern writer using hyperlinks, this can trip up the spell-checker quite ferociously. It doesn't understand, nor can it be taught, pattern-matching against URLs to skip them.

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Raph Koster 4 days ago

Recommended posts, 2017-2025

It’s been over five years since the last time I gathered up recommended posts in one place and added them to the menu above. I figured I was due. As usual this will include talks and interviews as well as articles. Just think of it as “my take on what the best stuff to look at on the site is.” Previous collections of recommended posts can be found under the Blog heading on the menu bar or at these links: All of these posts are about game design in general and tend to cover big swaths of similar territory. These posts are all specifically about dealing with multiplayer game dynamics. These posts are about the games business and how we as developers get along within it. As always, there was a decent amount of stuff posted to the blog that answered questions about older games. I should probably specifically call out that my book Postmortems came out during this time period and collects 700+ pages of virtual world history. There were a bunch for Ultima Online specifically: And there’s a few more things that are of historical interest: These posts were basically written as marketing for Playable Worlds and our game Stars Reach. However, they also serve as a pretty good manifesto for what I think MMOs ought to be like. There was a huge boom in interest in the Metaverse during this time period. And frankly, most people had no clue what the hell they were talking about, or had very little idea how online worlds and therefore metaverses would even work. Sooooo… First was a series on how they work from a data point of view, aimed particular at folks who thought blockchains solved all the issues. Hint: they don’t. Then there were a series of panels, talks, and podcasts which touched on everything from governance in a metaverse to the hard realities of building one. Trivia: did you know the first one was built during the 90s? I built one during the 2000’s! I did a fair bit of work on emulators during this period. There is much more than just these three things, but these seemed like the most worthy of calling out. Recommended posts 1998-2012 Recommended posts 2012-2017 Reconciling Games , a talk which walks through the process of game design using a fish aquarium as a lens Disassembling Games also walks through high level views of game design, but with special attention to UX design Revisiting Fun: 20 Years of A Theory of Fun is a talk from GDC updating Theory of Fun Game design is simple, actually is now the most popular post on the site, and is an overview of the entire scope of game design as a field Consent systems , a post basically about tandem emotes between two players What old tennis players teach us , which is about overturning static leaderboards The Trust Spectrum is a whitepaper based on Google ATAP research on how trust operates within games, complete with ways to measure your game design for how much trust it demands and how that affects your audience From 1 to n : Multiplayer Game Design is a talk covering all sorts of multiplayer design including trust and social mechanics Depth and Design: Contrasting Human and AI Understandings is about what depth is and whether AI can see it. This talk is mathy and crunchy. Tabletop Game Grammar is a talk applying game grammar principles to poker in particular to see how the concepts translate Start with the Sim is a microtalk about balancing system design versus experience design What drives retention is a post listing the key drivers of retention in games Best practices for community engagement is a post that covers what the title says Why NYT’s Connections makes you feel bad is a crunchy game design breakdown of Connections Sandbox vs themepark is a historical overview of where these two terms come from and what they mean Some current game economics is a response to gamer questions about why the business of games looks like it does. Even though it is years old, everything in there is pretty much still true. The cost of games is a detailed breakdown of the costs involved in game development using data from 1985 to today, which then draws conclusions about what that means for players and for developers. Industry Lifecycles is a talk version of the above, which also describes the the cyclical nature of game platforms. The evolution of ‘gamers’ is a description of the way the term “gamer” and what target market is references has evolved from the 1970s to today. Classic Game Postmortem: Ultima Online at 20 , a talk given for that anniversary Ultima Online’s influence is a post about specifically what UO did to impact games in general Ultima Online’s 25th Anniversary is about Ultima Online terrain Spam Spam Spam Humbug Interview Looking back at a pandemic simulator describes the COVID public health simulator I designed in the midst of the pandemic Classic Game Postmortem: Star Wars Galaxies is a talk given at the 20th anniversary of the game’s launch Ten Lessons Learned is more of a career retrospective talk, ten pieces of advice I would give to any game developer starting now The Evolution of Online Worlds is an hour-long video survey of the history of online worlds The Future of Online Worlds is a manifesto on what living online worlds can be like Thinking long-term is about what makes them last Designing for Social Play gives guidelines on building proper communities and societies within online worlds Player-driven economies touches specifically on how economies play into that Revealing Playable Worlds Technology describes our technical platform But First, the Game is about why that tech, and how it impacts what we can make How Virtual Worlds Work, part one How Virtual Worlds Work, part two Digital Objects: How Virtual Worlds Work part 3 Object Behaviors: How Virtual Worlds Work part 4 Ownership: How Virtual Worlds Work, part 5 Building the Metaverse session Five Big Metaverse Questions covers similar ground to the How Virtual Worlds Work series, but extends it to governance and the question of the Internet as existential threat. Real Talk About a Real Metaverse gives a pile of historical context to it all The Metacast by Naavik: Mastering Digital Worlds Microvision Emulator Release Atari 8-bit Guide for lr-atari800 and Retropie Guide to Retroarch System, Emulator, Core, and ROM Config Files

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Jim Nielsen 4 days ago

Notes From an Interview With Jony Ive

Patrick Collison, CEO of Stripe, interviewed Jony Ive at Stripe Sessions . Below are my notes from watching the interview. I thought about packaging these up into a more coherent narrative, but I just don’t have the interest. However, I do want to keep these notes for possible reference later, so here’s my brain dump in a more raw form. On moving fast and breaking things: breaking stuff and moving on quickly leaves us surrounded by carnage. There’s an intriguing part in the interview where Ive reflects on how he obsessed over a particular detail about a cable’s packaging. He laughs at the story, almost seemingly embarrassed, because it seems so trivial to care about such a detail when he says it out loud. But Collison pushes him on it, saying there’s probably a utilitarian argument about how if you spend more time making the packaging right, some people mights save seconds of time and when you multiply that across millions of people, that's a lot of savings. But Collison presumes Ive isn’t interested in that argument — the numbers, the calculation, etc. — so there must be something almost spiritual about investing in something so trivial. Ive’s response: I believe that when somebody unwrapped that box and took out that cable, they thought “Somebody gave a shit about me.” I think that’s a nice sentiment. I do. But I also think there’s a counter argument here of: “They cared when they didn’t have to, but they were getting paid to spend their time that way. And now those who can pay for the result of that time spent get to have the feeling of being cared for.” Maybe that’s too cynical. Maybe what I’m getting at is: if you want to experience something beautiful, spend time giving a shit about people when you don’t stand to profit from it. To be fair, I think Ive hints at this with his use of “privilege” here: I think it’s a privilege if we get to practice and express our concern and care for one another [by making things for one another at work] People say products are a reflection of an organization’s communication structure. Ive argues that products are a function of the interpersonal relationships of those who make them: To be joyful and optimistic and hopeful in our practice, and to be that way in how we relate to each other and our colleagues, [is] how the products will end up. Ive talking about how his team practiced taking their design studio to someone’s house and doing their work there for a day: [Who] would actually want to spend time in a conference room? I can’t think of a more soulless and depressing place…if you’re designing for people and you’re in someone’s living room, sitting on their sofa or floor and your sketchbook is on their coffee table, of course you think differently. Of course your preoccupation, where your mind wanders, is so different than if you’re sitting in a typical corporate conference room. Everybody return to the office! Ive conveying an idea he holds that he can’t back up: I do believe, and I wish that I had empirical evidence What is the place for belief in making software? Ive speaks about how cabinet makers who care will finish the inside parts of the cabinet even if nobody sees them: A mark of how evolved we are as people is what we do when no one sees. It’s a powerful marker of who we truly are. If you only care about what's on the surface, then you are, by definition, superficial. Reply via: Email · Mastodon · Bluesky

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Tech predictions for 2026 and beyond

We’ve caught glimpses of a future that values autonomy, empathy, and individual expertise. Where interdisciplinary cooperation influences discovery and creation at an unrelenting pace. In the coming year, we will begin the transition into a new era of AI in the human loop, not the other way around. This cycle will create massive opportunities to solve problems that truly matter.

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Anton Sten 1 weeks ago

The hidden cost of shipping too fast

Speed is the default expectation in most startups. If you are not shipping fast, it can feel like you are falling behind. After 25 years of working with a wide range of teams, I have noticed a pattern: speed often gets treated as progress even when no one has agreed on what progress actually means. Speed works beautifully when there is clarity. When clarity is missing, moving quickly usually creates work that takes months to untangle. The common scenario is easy to recognize. Someone says, "We already know what to build," and the team moves straight into execution. Design gets a ticket. Engineering starts estimating. Leadership pushes for timelines. Everyone wants motion because motion feels productive. But without a shared understanding of the problem, everything becomes a matter of opinion. Instead of "Here is what our customers need," the conversation becomes "Here is what I think we should build" and "Here is what you think we should build." Once that happens, things slip quickly. The first thing to break isn't the product. It is decision making. When the problem is not clearly defined, decisions naturally end up with the most senior person in the room. It is rarely intentional. It is simply how companies behave under time pressure. What follows is a quiet shift in the team's energy. Designers stop exploring alternatives and choose the first workable idea. Engineers steer toward the easiest approach, not the best one. Meetings get quieter. The team moves from momentum to maintenance. This is not about people being unmotivated. It is what happens when the path forward is unclear. Humans conserve energy. And at the same time, leaders often find themselves lowering their own standards without noticing it at first. You adapt to the room. Not because you want to, but because that is what groups do. The worst moment is not when a bug ships. It is when you look at the work and realize it is no longer something you are proud of. Speed has not just weakened the product. It has weakened the craft. I have seen teams move so fast that they spend the next six months repairing what should have been built thoughtfully the first time. In those cases, the fast approach becomes far slower. This is why I appreciate the distinction between speed and velocity. At Summer Health, one of our values is to optimize for velocity, not speed. Speed is simply movement. Velocity is movement with direction. When a team shares the problem and the purpose, moving quickly becomes energizing rather than chaotic. The reset is simple and almost always effective. Before building anything, pause long enough to ask, "What problem am I solving, and for whom?" It sounds basic, but this question forces alignment. It replaces assumptions with clarity and shifts attention back to the user instead of internal preferences. When teams do this consistently, the entire atmosphere changes. Decisions become easier. Roadmaps make more sense. People contribute more of themselves. You can feel momentum return. The biggest pattern I have seen across startups is that skipping clarity never saves time. It costs time. The fastest teams are not the ones shipping the most. They are the ones who understand why they are shipping. That is the difference between moving for the sake of movement and moving with purpose. It is the difference between speed and true velocity.

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DYNOMIGHT 1 weeks ago

Make product worse, get money

I recently asked why people seem to hate dating apps so much. In response, 80% of you emailed me some version of the following theory: The thing about dating apps is that if they do a good job and match people up, then the matched people will quit the app and stop paying. So they have an incentive to string people along but not to actually help people find long-term relationships. May I explain why I don’t find this type of theory very helpful? I’m not saying that I think it’s wrong, mind you. Rather, my objection is that while the theory is phrased in terms of dating apps, the same basic pattern applies to basically anyone who is trying to make money by doing anything. For example, consider a pizza restaurant. Try these theories on for size: The thing about pizza restaurants is that if they use expensive ingredients or labor-intensive pizza-making techniques, then it costs more to make pizza. So they have an incentive to use low-cost ingredients and labor-saving shortcuts. The thing about pizza restaurants is that if they have nice tables separated at a comfortable distance, then they can’t fit as many customers. So they have an incentive to use tiny tables and cram people in cheek by jowl. The thing about pizza restaurants is that if they sell big pizzas, then people will eat them and stop being hungry, meaning they don’t buy additional pizza. So they have an incentive to serve tiny low-calorie pizzas. See what I mean? You can construct similar theories for other domains, too: The thing about automakers is that making cars safe is expensive. So they have an incentive to make unsafe cars. The thing about video streaming is that high-resolution video uses more expensive bandwidth. So they have an incentive to use low-resolution. The thing about bloggers is that research is time-consuming. So they have an incentive to be sloppy about the facts. The thing about {lightbulb, car, phone, refrigerator, cargo ship} manufacturing is that if you make a {lightbulb, car, phone, refrigerator, cargo ship} that lasts a long time, then people won’t buy new ones. So there’s an incentive to make {lightbulbs, cars, phones, refrigerators, cargo ships} that break quickly. All these theories can be thought of as instances of two general patterns: Make product worse, get money The thing about selling goods or services is that making goods or services better costs money. So people have an incentive to make goods and services worse. Raise price, get money The thing about selling goods and services is that if you raise prices, then you get more money. So people have an incentive to raise prices. Are these theories wrong? Not exactly. But it sure seems like something is missing. I’m sure most pizza restauranteurs would be thrilled to sell lukewarm 5 cm cardboard discs for $300 each. They do in fact have an incentive to do that, just as predicted by these theories! Yet, in reality, pizza restaurants usually sell pizzas that are made out of food. So clearly these theories aren’t telling the whole story. Say you have a lucrative business selling 5 cm cardboard discs for $300. I am likely to think, “I like money. Why don’t I sell pizzas that are only mostly cardboard, but also partly made of flour? And why don’t I sell them for $200, so I can steal Valued Reader’s customers?” But if I did that, then someone else would probably set prices at only $100, or even introduce cardboard-free pizzas, and this would continue until hitting some kind of equilibrium. Sure, producers want to charge infinity dollars for things that cost them zero dollars to make. But consumers want to pay zero dollars for stuff that’s infinitely valuable. It’s in the conflict between these desires that all interesting theories live. This is why I don’t think it’s helpful to point out that people have an incentive to make their products worse. Of course they do. The interesting question is, why are they able to get away with it? So let’s consider some common reasons stuff is bad. First reason stuff is bad: People are cheap Why are seats so cramped on planes? Is it because airlines are greedy? Sure. But while they might be greedy, I don’t think they’re dumb. If you do a little math, you can calculate that if airlines were to remove a single row of seats, they could add perhaps 2.5 cm (1 in) of extra legroom for everyone, while only decreasing the number of paying customers by around 3%. (This is based on a 737 with single-class, but you get the idea.) So why don’t airlines rip out a row of seats, raise prices by 3% and enjoy the reduced costs for fuel and customer service? The only answer I can see is that people, on average, aren’t actually willing to pay 3% more for 2.5 cm more legroom. We want a worse but cheaper product, and so that’s what we get. I think this is the most common reason stuff is “bad”. It’s why Subway sandwiches are so soggy, why video games are so buggy, why IKEA furniture and Primark clothes fall apart so quickly. It’s good when things are bad for this reason. Or at least, that’s the premise of capitalism: “When companies cut costs, that’s the invisible hand redirecting resources to maximize social value”, or whatever. Companies may be motivated by greed. And you may not like it, since you want to pay zero dollars for infinite value. But this is markets working as designed. Second reason stuff is bad: Information asymmetries Why is it that almost every book / blog / podcast about longevity is such garbage? Well, we don’t actually know many things that will reliably increase longevity, and those things are mostly all boring/hard/non-fun. And even if you do all of them, it probably only adds a couple years in expectation. And telling people those facts is not a good way to find suckers who will pay you lots of money for your unproven supplements / seminars / etc. True! But it doesn’t explain why all longevity stuff is so bad. Why don’t honest people tell the true story and drive all the hucksters out of business? I suspect the answer is that unless you have a lot of scientific training and do a lot of research, it’s basically impossible to figure out just how huckstery all the hucksters really are. I think this same basic phenomenon explains why some supplements contain heavy metals, why some food contain microplastics, why restaurants use so much butter and salt, why rentals often have crappy insulation, and why most cars seem to only be safe along dimensions included in crash test scores . When consumers can’t tell good from evil, evil triumphs. Third reason stuff is bad: People have bad taste Sometimes stuff is bad because people just don’t appreciate the stuff you consider good. Examples are definitionally controversial, but I think this includes restaurants in cities where all restaurants are bad, North American tea , and travel pants. This reason has a blurry boundary with information asymmetries, as seen in ultrasonic humidifiers or products that use Sucralose instead of aspartame for “safety”. Fourth reason stuff is bad: Pricing power Finally, sometimes stuff is bad because markets aren’t working. Sometimes a company is selling a product but has some kind of “moat” that makes it hard for anyone else to compete with them, e.g. because of some technological or regulatory barrier, control of some key resource or location, some intellectual property, some beloved brand, or because of network effects. If that’s true then those companies don’t have to worry much about someone else stealing their business, and so (because everyone is axiomatically greedy) they will find ways to make their product cheaper and/or raise their prices up until it’s equal to the full value it provides to the marginal consumer. Why is food so expensive at sporting events? Yes, people have no alternatives. But people know food is expensive at sporting events. And they don’t like it. Instead of selling water for $17, why don’t venues sell water for $2 and raise ticket prices instead? I don’t know. Probably something complicated, like that expensive food allows you to extract extra money from rich people without losing business from non-rich people. So of course dating apps would love to string people along for years instead of finding them long-term relationships, so they keep paying money each month. I’d bet that some people at those companies have literally thought, “Maybe we should string people along for years instead of finding them long-term relationships, so they keep paying money each month. I love money so much.” But if they are actually doing that (which is unclear to me) or if they are bad in some other way, then how do they get away with it? Why doesn’t someone else create a competing app that’s better and thereby steal all their business? It seems like the answer has to be either “because that’s impossible”, or “because people don’t really want that”. That’s where the mystery begins.

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Wreflection 1 weeks ago

Command Lines

In the early 1950s, Grace Hopper coined the term “compiler” and built one of the first versions with her A-0 system 1 . The compilers that followed abstracted away machine code, letting programmers focus on higher-level logic instead of lower-level hardware details. Today, AI coding assistants 2 are enabling a similar change, letting software engineers focus on higher-order work by generating code from natural language prompts 3 . Everyone from big tech to well-funded startups is competing to capture this shift. Yesterday Google announced Antigravity, their new AI coding assistant, and the day before, AWS announced the general availability of their AI coding tool, Kiro. Last week, Cursor, the standout startup in this space, raised $2.3B in their series-D round at a valuation of $29.3B. Two lines in Cursor’s press release stood out to me. The first: We’ve also crossed $1B in annualized revenue, counting millions of developers. This disclosure means Anysphere Inc. (Cursor’s parent company) is the fastest company in history to reach $1B in annual recurring revenue (ARR). Yes, faster than OpenAI, and faster than Anthropic 4 . Source: Yuchen Jin, Twitter/X, 2025 Engineers are trying every new AI coding tool. As a result, the AI-coding tool market is growing exponentially (+5x in just over a year) 5 . But it’s still early. As I wrote in Why Some AI Wrappers Build Billion-dollar Businesses , companies spend several hundred billion dollars a year on software engineering, and AI has the potential to unlock productivity gains across that entire spend. Software developers represent roughly 30% of the workforce at the world’s five largest market cap companies, all of which are technology firms as of October 2025. Development tools that boost productivity by even modest percentages unlock billions in value. In my view, this nascent market is splitting based on three types of users. Source: Command Lines, wreflection.com, 2025 On one end is Handcrafted Coding . These are engineers who actively decline to use LLMs, either because of skepticism about quality or insistence on full control of every code. They argue that accepting AI suggestions creates technical debt you cannot see until it breaks in production. This segment continues to decline as the quality of AI coding models improves. The opposite end is Vibe Coding . These are typically non-engineers, who use AI to build concepts and prototypes. They prompt the model hoping for an end-to-end solution, accept the output with minimal review, and trust that it works. The user describes what they want and lets the model figure out the implementation details of how to build it. In the middle sits Architect + AI Coding . The engineer uses the AI/LLM as a pair programmer exploring system designs, analyzing data models, and reviewing API details. When the work is something entirely new or something that needs careful handling, the human programmer still codes those pieces by hand. But for boilerplate code, package installations, generic User Interface (UI) components, and any kind of code that is typically found on the internet, they assign it to the model 6 . The engineer stays in command of what is important to them and delegates what is not. Based on the user types, I think, the AI coding market splits into two. Source: wreflection.com based on SemiAnalysis estimate, 2025 Hands-off: Non-engineers (product managers, designers, marketers, other internal employees) use these tools to vibe code early product concepts. They look to AI as the lead engineer to spin-up concepts/prototypes of apps, websites, and tools by simply prompting the AI to make something for them. Lovable, Vercel, Bolt, and Figma Make fit here 7 . Code from these users, as of now, are not typically pushed to prod. Hands-on: Professional software engineers use these tools in their existing workflow to ship production code. They use AI as an assistant to write boilerplate code, refactor existing services, wire new features or UI screens, and triage bugs in codebases. Cursor, Claude Code, OpenAI Codex, Github Copilot, Cline, AWS Kiro play here. These products live where the work is done , and integrate into the engineer’s workflow. This is, at least as of now, the bigger market segment. To see an evaluation of all the major AI coding tools currently in the market, checkout this breakdown by Peter Yang, who runs the newsletter Behind The Craft . That brings me to the second thing in Cursor’s press release that stood out to me: Our in-house models now generate more code than almost any other LLMs in the world. While I am not convinced about that claim 8 , what I am convinced about is that Cursor is still growing despite its previous reliance on foundation models. From Why Some AI Wrappers Build Billion-dollar Businesses again: But Cursor and other such tools depend almost entirely on accessing Anthropic, OpenAI and Gemini models, until open-source open-weight and in-house models match or exceed frontier models in quality. Developer forums are filled with complaints about rate limits from paying subscribers. In my own projects, I exhausted my Claude credits in Cursor mid-project and despite preferring Cursor’s user interface and design, I migrated to Claude Code (and pay ten times more to avoid rate limits). The interface may be better, but model access proved decisive. Cursor’s new in-house model Composer-2, which just launched last month, is a good example of how this model versus application competition is evolving. Cursor claims (without any external benchmarks, I must say) that Composer-2 is almost as good as frontier models but 4x faster. It’s still early to say how true that is. Open-source models have not yet come close to the top spots in SWE-bench verified or in private evals 9 . Source : Introducing Claude Sonnet 4.5, Anthropic, 2025. To me, model quality is the most decisive factor in these AI coding wars. And in my view, that’s why Claude Code has already overtaken Cursor, and OpenAI’s Codex is close behind, despite both having launched a year or so later. Source : SemiAnalysis, 2025 Even though the newcomers Cursor, Claude Code, and OpenAI Codex are the talk of the (developer) town, incumbents such as Microsoft with Github Copilot, AWS with Kiro, and Google with Antigravity, can utilize their existing customer relationships, bundle their offerings with their existing suites, and/or provide their option as the default in their tech stack to compete. As an example, Cursor charges $20–$40 monthly per user for productive usage, while Google Antigravity launched free with generous limits for individual users. Github Copilot still leads this market, proving once again that enterprise bundling and distribution has structural advantages. This is the classic Microsoft Teams vs. Slack Dynamic 10 . One way for startups to compete is by winning individual users who may use a coding tool with or without formal approval, and then be the tool’s advocate inside the organization. That organic interest and adoption eventually forces IT and security teams to officially review the tool and then eventually sanction its usage. Yet, even as these newer tools capture developer mindshare, the underlying developer tools market is changing. Both the IDEs developers choose and the resources they we consult have changed dramatically. StackOverflow, once the default for programmers stuck on a programming issue, has seen its traffic and number of questions decline dramatically since ChatGPT’s launch, suggesting that AI is already replacing some traditional developer resources. Source : Developer Tools 2.0, Sequoia, 2023 Just as compilers freed programmers from writing assembly code, AI tools are freeing software engineers from the grunt work of writing boilerplate and routine code, and letting them focus on higher-order thinking. Eventually, one day, AI may get so good that it will generate applications on demand and create entire software ecosystems autonomously. Both hands-off and hands-on AI coding tools, as well as incumbents and newcomers, see themselves as the path to that fully autonomous software generation, even if they are taking different approaches. The ones who get there will be those who deliver the best model quality that ships code reliably, go deep enough to ship features that foundation models can’t care enough to replicate, and become sticky enough that users will not leave even when they can 11 . If you enjoyed this post, please consider sharing it on Twitter/X or LinkedIn , and tag me when you do. Thanks for reading Wreflection! Subscribe for free to receive new posts and support my work. Hopper’s A-0 system and her definition of the term compiler is different from what we consider a compiler today, but it established the foundational concept. In the context of coding assistants, most products labeled as AI tools are powered by LLMs, and so I use AI and LLM interchangeably in this article despite the actual difference. https://x.com/karpathy/status/1617979122625712128 A better comparison might be at the product level rather than company level. In that case, ChatGPT and Claude both reached $1B faster than Cursor did. https://newsletter.semianalysis.com/p/microsofts-ai-strategy-deconstructed I would argue that the vast majority of productive code is hidden behind company firewalls. Current foundation models are trained on publicly available data on the internet, and do not have access to proprietary codebases. We are yet to see breakthrough solutions where a company augments their confidential private data to generate production-ready code using current LLMs. While Retrieval-Augmented Generation has shown some promise, it has not yet delivered transformative results. Companies such as Glean are actively working on this problem. Replit and Cognition probably appeal to both segments. To me, Replit leans hands-off with its rapid prototyping focus. Cognition’s agent-based approach, though hands-off, lets engineers still control the code directly, making it lean hands-on. I was curious how Cursor knows how much code is generated by other LLMs outside Cursor? When I asked this on hackernews, swyx suggested that they “ can pretty much triangulate across openrouter x feedback from the top 3 model labs to compare with internal usage and figure that out ”. To me, triangulation makes sense for internal estimates. but for external publication, I’m surprised Cursor didn’t include “we estimate” or similar qualifying language. My understanding is that FTC policy requires substantiation before making definitive comparative claims (like more than, better than etc). All that to say, I’m not fully convinced about their claims. SWE-bench is a benchmark for evaluating large language models (LLMs) on real world software engineering tasks and issues collected from GitHub. Performance against public benchmarks can be gamed by the model builders. Currently after any new model launch, we see people using the model in the wild and forming a consensus around how the model performs which is a better indicator than these benchmarks. Microsoft bundled Teams into Office 365 subscriptions at no extra cost, using its dominant enterprise distribution to surpass Slack’s paid standalone product within three years despite Slack’s earlier launch and product innovation. See https://venturebeat.com/ai/microsoft-teams-has-13-million-daily-active-users-beating-slack Natasha Malpani , Twitter/X, 2025 Source: Yuchen Jin, Twitter/X, 2025 Engineers are trying every new AI coding tool. As a result, the AI-coding tool market is growing exponentially (+5x in just over a year) 5 . But it’s still early. As I wrote in Why Some AI Wrappers Build Billion-dollar Businesses , companies spend several hundred billion dollars a year on software engineering, and AI has the potential to unlock productivity gains across that entire spend. Software developers represent roughly 30% of the workforce at the world’s five largest market cap companies, all of which are technology firms as of October 2025. Development tools that boost productivity by even modest percentages unlock billions in value. In my view, this nascent market is splitting based on three types of users. Source: Command Lines, wreflection.com, 2025 On one end is Handcrafted Coding . These are engineers who actively decline to use LLMs, either because of skepticism about quality or insistence on full control of every code. They argue that accepting AI suggestions creates technical debt you cannot see until it breaks in production. This segment continues to decline as the quality of AI coding models improves. The opposite end is Vibe Coding . These are typically non-engineers, who use AI to build concepts and prototypes. They prompt the model hoping for an end-to-end solution, accept the output with minimal review, and trust that it works. The user describes what they want and lets the model figure out the implementation details of how to build it. In the middle sits Architect + AI Coding . The engineer uses the AI/LLM as a pair programmer exploring system designs, analyzing data models, and reviewing API details. When the work is something entirely new or something that needs careful handling, the human programmer still codes those pieces by hand. But for boilerplate code, package installations, generic User Interface (UI) components, and any kind of code that is typically found on the internet, they assign it to the model 6 . The engineer stays in command of what is important to them and delegates what is not. The Market Split Based on the user types, I think, the AI coding market splits into two. Source: wreflection.com based on SemiAnalysis estimate, 2025 Hands-off: Non-engineers (product managers, designers, marketers, other internal employees) use these tools to vibe code early product concepts. They look to AI as the lead engineer to spin-up concepts/prototypes of apps, websites, and tools by simply prompting the AI to make something for them. Lovable, Vercel, Bolt, and Figma Make fit here 7 . Code from these users, as of now, are not typically pushed to prod. Hands-on: Professional software engineers use these tools in their existing workflow to ship production code. They use AI as an assistant to write boilerplate code, refactor existing services, wire new features or UI screens, and triage bugs in codebases. Cursor, Claude Code, OpenAI Codex, Github Copilot, Cline, AWS Kiro play here. These products live where the work is done , and integrate into the engineer’s workflow. This is, at least as of now, the bigger market segment.

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How to Be Better at Talking About 'Tech Debt'

Tech Debt started out as a useful metaphor. You 'borrow' from the future to achieve an outcome today. Much in the same way you can achieve home ownership earlier by taking out a mortgage after saving enough for a down payment. However, the way it's used now is long past its usefulness as an explanation for anything. You might as well blame the phase of the moon. It's about as edifying. You wouldn't tell the doctor your problem is "pain" without giving more details, yet I hear 'tech debt' as the entire explanation way too often. Part of your job as a leader is to make technical problems understandable by non-technical stakeholders. Analogies can serve you well here: I've said it before, but at its core, management is the art of efficiently deploying capital for the highest return on investment. In a software company most of that capital is in the form of engineering time. To make the best decisions we need to be more precise than just saying we want to use it to "pay down tech debt". That doesn't sell because it is a statement devoid of useful information. It doesn't say anything about what kind of return on investment you might be getting. While specifically quantifying the ROI of preventative maintenance or restorative work in pure dollar terms can be hard or even impossible, using an analogy that most people understand can help make your case. First things first, you need to understand what kind of debt you're dealing with. When you're talking about intentional tech debt, the shortcuts you took to get a product out the door, talk about the debt in concrete terms. Discuss the specific shortcuts you took and what the 'interest payments' are on them. For example: This is the kind of debt you sometimes should take on but when you're deliberate about it and clear about why you're doing it and what you get out of it, you can also be clear about what's needed to pay it off and what the costs of not doing so are. You can even do all of that without saying the words "technical debt", which is becoming a term that makes people stop listening. This kind of debt doesn't really need metaphors to explain, as long as you're clear about what the trade-offs and ongoing costs are. This is probably the most common form and it's where the debt metaphor is often the least useful. You can build the best system you possibly could with the information available and the world can change in ways that your previously well-designed system simply can't handle. Many seemingly good decisions can prove to have been the wrong one in retrospect. This has a lot of the same characteristics as deliberate debt. You've got ongoing costs related to previous decisions where a project to 'pay down' the debt would remedy the situation. However because it was not taken on deliberately it can be harder to nail down the source of the pain and make a clear case for the remedy. This is especially true when the decisions were in the long ago past and nobody knows or remembers why they were made. To make matters worse, when there's a lot of it it can just sort of feel like a "big blob of yuck" This is the kind of situation that leads to people saying "we should just rewrite it" (Don't). Now, the inability to clearly articulate the problem can be a problem in and of itself . It's entirely possible that the understanding of the full shape of the system is lacking to the point where nobody can effectively diagnose things in specific enough terms to be useful. This is common in high-turnover organizations or ones that never developed good habits of documenting their decisions. Whatever the cause, it should go on your list of things to remedy. When you've got these kinds of problems, the age old technique of elephant-eating (one bite at a time) is the solution. Try to categorize, break down, and understand in more detail the types of problems you have. If you must stick with the debt metaphor you can categorize it by 'interest rate': Doing this breakdown and classification is probably best done as a group effort. One person's payday loan might be another person's mortgage debt. It's best to understand it in the broadest business context you can and try to be as scientific as you can be. There's bound to be some subjectivity to it and biases and preferences can skew the perception of how bad a problem truly is. Many eyes make this kind of planning more effective. If your product is built on a framework that's still maintained, and the version you're on is going end-of-life because you haven't kept up, that's not tech debt . You've neglected basic maintenance. If your porch is falling apart because the stain protecting the wood has been gone for years and water is rotting the boards that's not increasing the size of your mortgage. If it is debt, it's money you borrowed from the mafia and now they're on their way to break your legs. As the saying goes, code doesn't age like wine, it ages like milk. With that comes risks that need to be managed. There is often pressure to ignore those risks in favour of new features or other, more exciting things. Who wouldn't rather buy a new flashy electronic gadget than paint the porch again? You know that's irresponsible and so is neglecting basic maintenance of your software systems. This is one of the reasons it's critically important to align the incentives of the EM and PM partners . If only the EM has the incentive to keep the porch in good shape and only the PM has the incentive to buy new toys, you're gonna have a bad time. The best way to deal with this type of problem is to not get into it in the first place. The 'good news' is that if you leave it long enough the problem will become so obvious that you won't have to justify fixing it. The bad news is that the fix is going to be way more expensive than if you'd done the basic maintenance that could have prevented it. Don't borrow money from loan sharks! As engineering leaders, we have the responsibility to frame the necessity to do maintenance work and to pay down both deliberate and unintentional technical debt in business terms. Often the problem is that nobody is able to do that successfully until it becomes a crisis. Taking on deliberate debt with a full understanding and plan to pay it off can get you the software company equivalent of home ownership sooner with manageable interest payments. Being able to make business case for 'engineering work' or things that aren't just new features is a critical skill for engineering leaders to develop. And whenever you can, make sure your Product counterpart shares both the pain and the glory of having a well-functioning product. Once y0u develop that skill, you can use it to avoid taking on those payday loans with the highest interest payments and spend your capital on what's best for the business without saddling your future self or your successor with big bills. " Debt Consolidation, Circa 1948 " by Orin Zebest is licensed under CC BY 2.0 . Like this? Please feel free to share it on your favourite social media or link site! Share it with friends! Hit subscribe to get new posts delivered to your inbox automatically. Feedback? Get in touch ! We hung up a sheet of plastic instead of a door when we built the garage so we could start parking the car inside. We need to install the door now. We need to change the oil on the car now so the engine doesn't seize later. We should patch the leaky basement so that we don't need to deal with the foundation collapsing. "We shipped this in June to get fast customer feedback knowing it would need to be hardened before Black Friday in November. We need to make these specific improvements before then or we won't make it through the holidays without major downtime." "We knew this code wasn't optimized and we deployed it on bigger servers to make up for it. If we spend a couple weeks on performance improvements we can save X thousand dollars over the next year by redeploying it on smaller, less expensive machines." Your traffic or account sizes could vastly exceed your expectations. A library, component, or framework you depended on can become abandoned. You made a trade off without realizing it. You didn't fully know what you were doing when you wrote it in the first place. Payday Loan Debt (emergency): This is the stuff that's hurting you daily and costing you the most. You're coping, not solving anything . Pay it off ASAP. Credit Card Debt (not good): This stuff is a major drag on velocity and there's a clear case to be made for paying it off. Make that case, focus on the ROI. Mortgage debt (might be ok): This stuff might or might not have a business case for paying it off. It might bug you a little but it might be worth putting up with. But only by understanding it can you make that call.

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Rik Huijzer 1 weeks ago

Ondernemer op Reddit over subsidies

> De enige vorm van “subsidie” die ik als ondernemer concreet ervaar, zijn de belastingen die ik betaal. Het lijkt – en ik zeg nadrukkelijk lijkt – alsof je reactie komt vanuit een positie waarin men niet hoeft te dragen wat ondernemers dagelijks moeten dragen. Zonder een gezond bedrijfsleven bestaat er geen economische ruimte voor sociale voorzieningen of luxe waar we als samenleving allemaal van profiteren. > > Ik zeg niet dat vermogenden nóg rijker moeten worden, maar ik kan je verzekeren dat het tegenwoordig voor veel ondernemers, zelfs met een goedlopend bedrijf, buitengewoon moe...

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Bill Mill 2 weeks ago

Licensing will not save us

I enjoy this piece by Erlend Sogge Heggen which argues that we, the open source developers, ought not to freely give away our work because it advantages the capitalists and fascists that are leveraging the fruits of our labor to make untold millions for themselves without giving back to the community they're building off or the world at large. I've been using the computer long enough to remember how hard it was to get your hands on software that was interesting and useful. Without open source software, I'm certain that computing would not have progressed as far as it has, and that I and many others would not have the careers we enjoy because we wouldn't have found a way in. The class of business leaders who have built on open source software (and who often started as developers themselves) has taken a heavy toll on the world without returning the value they owe, but I also fear returning to a world where a privileged class of people has access to the source code for every important application, and interested people have to choose whether to break the law to satisfy their curiosity. More and more developers are playing around with licensing to try and defend themselves from the predatory practices of the tech elite, and I'm here for it - but that cannot be the whole solution. Only organization and community can protect the fruits of OSS (or a future OSS-like?) labor from explotation. It can't be one community, because there are many different aims, cultures, and viewpoints, but there can be many interlinked communities sharing tools, knowledge and practice. The sooner we start building the practices, habits and (less importantly) software to make it easy to build, maintain, and own communities of practice, the sooner we will make it possible to share the fruits of our labor in a less-destructive manner. Licensing alone won't save us, we need to build stable social organizations and learn how to empower them.

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Premium: The Hater's Guide To The AI Bubble Vol. 2

We’re approaching the most ridiculous part of the AI bubble, with each day bringing us a new, disgraceful and weird headline. As I reported earlier in the week, OpenAI spent $12.4 billion on inference between 2024 and September 2025 , and its revenue share with Microsoft heavily suggests it made at least $2.469 billion in 2024 ( when reports had OpenAI at $3.7 billion for 2024 ), with the only missing revenue to my knowledge being the 20% Microsoft shares with OpenAI when it sells OpenAI models on Azure, and whatever cut Microsoft gives OpenAI from Bing.  Nevertheless, the gap between reported figures and what the documents I’ve seen said is dramatic. Despite reports that OpenAI made, in the first half of 2025, $4.3 billion in revenue on $2.5 billion of “cost of revenue,” what I’ve seen shows that OpenAI spent $5.022 billion on inference (the process of creating an output using a model) in that period, and made at least $2.2735 billion. I, of course, am hedging aggressively, but I can find no explanation for the gaps. I also can’t find an explanation for why Sam Altman said that OpenAI was “profitable on inference” in August 2025 , nor how OpenAI will hit “$20 billion in annualized revenue” by end of 2025 , nor how OpenAI will do “well more” than $13 billion this year . Perhaps there’s a chance that for some 30 day period of this year OpenAI hits $1.66 billion in revenue (AKA $20 billion annualized), but even that would leave it short of its stated target revenue The very same day I ran that piece, somebody posted a clip of Microsoft CEO Satya Nadella saying , who had this to say when asked about recent revenue projections from AI labs:  I don’t know Satya, not fucking make shit up? Not embellishing? Is it too much to ask that these companies make projections that adhere to reality, rather than whatever an investor would want to hear? Or, indeed, projections that perpetuate a myth of inevitability, but fly in the face of reality?  I get that in any investment scenario you want to sell a story, but the idea that the CEO of a company with a $3.8 trillion market cap is sitting around saying “what do you expect them to do, tell the truth? They need money for compute!” is fucking disgraceful.  No, I do not believe a company should make overblown revenue projections, nor do I think it’s good for the CEO of Microsoft to encourage the practice. I also seriously have to ask why Nadella believes that this is happening, and, indeed, who he might be specifically talking about, as Microsoft has particularly good insights into OpenAI’s current and future financial health .  However, because Nadella was talking in generalities, this could refer to Anthropic, and it kinda makes sense, because Anthropic just received near-identical articles about its costs from both The Information and The Wall Street Journal , with The Information saying that Anthropic “projected a positive free cash flow as soon as 2027,” and the Wall Street Journal saying that Anthropic “anticipates breaking even by 2028,” with both pieces featuring the cash burn projections of both OpenAI and Anthropic based on “documents” or “investor projections” shared this summer. Both pieces focus on free cash flow, both pieces focus on revenue, and both pieces say that OpenAI is spending way more than Anthropic, and that Anthropic is on the path to profitability. The Information also includes a graph involving Anthropic’s current and projected gross margins, with the company somehow hitting 75% gross margins by 2028.  How does any of this happen? Nobody seems to know!  Per The Journal: …hhhhooowwwww????? I’m serious! How?  The Information tries to answer: Is…that the case? Are there any kind of numbers to back this up? Because Business Insider just ran a piece covering documents involving startups claiming that Amazon’s chips had "performance challenges,” were “plagued by frequent service disruptions,” and “underperformed” NVIDIA H100 GPUs on latency, making them “less competitive” in terms of speed and cost.” One startup “found Nvidia's older A100 GPUs to be as much as three times more cost-efficient than AWS's Inferentia 2 chips for certain workloads,” and a research group called AI Singapore “determined that AWS’s G6 servers, equipped with NVIDIA GPUs, offered better cost performance than Inferentia 2 across multiple use cases.” I’m not trying to dunk on The Wall Street Journal or The Information, as both are reporting what is in front of them, I just kind of wish somebody there would say “huh, is this true?” or “will they actually do that?” a little more loudly, perhaps using previously-written reporting.  For example, The Information reported that Anthropic’s gross margin in December 2023 was between 50% and 55% in January 2024 , CNBC stated in September 2024 that Anthropic’s “aggregate” gross margin would be 38% in September 2024, and then it turned out that Anthropic’s 2024 gross margins were actually negative 109% (or negative 94% if you just focus on paying customers) according to The Information’s November 2025 reporting . In fact, Anthropic’s gross margin appears to be a moving target. In July 2025, The Information was told by sources that “Anthropic recently told investors its gross profit margin from selling its AI models and Claude chatbot directly to customers was roughly 60% and is moving toward 70%,” only to publish a few months later (in their November piece) that Anthropic’s 2025 gross margin would be…47%, and would hit 63% in 2026. Huh? I’m not bagging on these outlets. Everybody reports from the documents they get or what their sources tell them, and any piece you write comes with the risk that things could change, as they regularly do in running any kind of business. That being said, the gulf between “38%” and “ negative 109%” gross margins is pretty fucking large, and suggests that whatever Anthropic is sharing with investors (I assume) is either so rapidly changing that giving a number is foolish, or made up on the spot as a means of pretending you have a functional business. I’ll put it a little more simply: it appears that much of the AI bubble is inflated on vibes, and I’m a little worried that the media is being too helpful. These companies are yet to prove themselves in any tangible way, and it’s time for somebody to give a frank evaluation of where we stand. if I’m honest, a lot of this piece will be venting, because I am frustrated. When all of this collapses there will, I guarantee, be multiple startups that have outright lied to the media, and done so, in some cases, in ways that are equal parts obvious and brazen. My own work has received significantly more skepticism than OpenAI or Anthropic, two companies worth alleged billions of dollars that appear to change their story with an aloof confidence borne of the knowledge that nobody read or thought too deeply about what it is that their CEOs have to say, other than “wow, Anthropic said a new number !”  So I’m going to do my best to write about every single major AI company in one go. I am going to pull together everything I can find and give a frank evaluation of what they do, where they stand, their revenues, their funding situation, and, well, however else I feel about them.  And honestly, I think we’re approaching the end. The Information recently published one of the grimmest quotes I’ve seen in the bubble so far: Hey, what was that? What was that about “growing concerns regarding the costs and benefits of AI”? What “capital shift”? The fucking companies are telling you, to your face, that they know there’s not a sustainable business model or great use case, and you are printing it and giving it the god damn thumbs up. How can you not be a hater at this point? This industry is loathsome, its products ranging useless to niche at best, its costs unsustainable, and its futures full of fire and brimstone.  This is the Hater’s Guide To The AI Bubble Volume 2 — a premium sequel to the Hater’s Guide from earlier this year — where I will finally bring some clarity to a hype cycle that has yet to prove its worth, breaking down industry-by-industry and company-by-company the financial picture, relative success and potential future for the companies that matter. Let’s get to it.

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annie's blog 2 weeks ago

My new business + tech podcast

After reading 1 the recent news about the unsurprising lack of diversity in podcasting — 64% of the hosts of the most popular US podcasts of 2024 were men…Shows with video are more likely to have male hosts; the worst gender balance is with business and technology podcasts, where men host 92% of shows. — I have decided to start my own business and technology podcast (with video) to help balance this dreadful imbalance 2 . Please enjoy. Show transcript available upon request 3 . Don’t forget to like, subscribe, share, burn it all down, etc. Thanks to Chris for sharing this , which I otherwise would never have seen because I don’t follow podcasting at all but I am sucker for reports  about anything especially when I am procrastinating on actual work I should be doing which is really what this entire post is all about. I can only help with the gender aspect. Better than nothing, I guess. Transcript: Dramatic intro music. Eyes. Nodding authoritatively. Pause. Thump. Coffee slurp. Coffee sigh. “Today in business-tech podcast we’ll look at the state of business and tech. Business: bad. That’s right. Tech: Also not good. Tune in next time. “ Thanks to Chris for sharing this , which I otherwise would never have seen because I don’t follow podcasting at all but I am sucker for reports  about anything especially when I am procrastinating on actual work I should be doing which is really what this entire post is all about. I can only help with the gender aspect. Better than nothing, I guess. Transcript: Dramatic intro music. Eyes. Nodding authoritatively. Pause. Thump. Coffee slurp. Coffee sigh. “Today in business-tech podcast we’ll look at the state of business and tech. Business: bad. That’s right. Tech: Also not good. Tune in next time. “

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Exclusive: Here's How Much OpenAI Spends On Inference and Its Revenue Share With Microsoft

As with my Anthropic exclusive from a few weeks ago , though this feels like a natural premium piece, I decided it was better to publish on my free one so that you could all enjoy it. If you liked or found this piece valuable, please subscribe to my premium newsletter — here’s $10 off the first year of an annual subscription . I have put out over a hundred thousand words of coverage in the last three months, most of which is on my premium, and I’d really appreciate your support. I also did an episode of my podcast Better Offline about this. Before publishing, I discussed the data with a Financial Times reporter. Microsoft and OpenAI both declined to comment to the FT. If you ever want to share something with me in confidence, my signal is ezitron.76, and I’d love to hear from you. What I’ll describe today will be a little more direct than usual, because I believe the significance of the information requires me to be as specific as possible.  Based on documents viewed by this publication, I am able to report OpenAI’s inference spend on Microsoft Azure, in addition to its payments to Microsoft as part of its 20% revenue share agreement, which was reported in October 2024 by The Information . In simpler terms, Microsoft receives 20% of OpenAI’s revenue. I do not have OpenAI’s training spend, nor do I have information on the entire extent of OpenAI’s revenues, as it appears that Microsoft shares some percentage of its revenue from Bing, as well as 20% of the revenue it receives from selling OpenAI’s models.  According to The Verge : Nevertheless, I am going to report what I’ve been told. One small note — for the sake of clarity, every time I mention a year going forward, I’ll be referring to the calendar year, and not Microsoft’s financial year (which ends in June).  These numbers in this post differ to those that have been reported publicly. For example, previous reports had said that OpenAI had spent $2.5 billion on “cost of revenue” - which I believe are OpenAI’s inference costs - in the first half of CY2025 .  According to the documents viewed by this newsletter, OpenAI spent $5.02 billion on inference alone with Microsoft Azure in the first half of Calendar Year CY2025.  This is a pattern that has continued through the end of September. By that point in CY2025 — three months later — OpenAI had spent $8.67 billion on inference.  OpenAI’s inference costs have risen consistently over the last 18 months, too. For example, OpenAI spent $3.76 billion on inference in CY2024, meaning that OpenAI has already doubled its inference costs in CY2025 through September. Based on its reported revenues of $3.7 billion in CY2024 and $4.3 billion in revenue for the first half of CY2025 , it seems that OpenAI’s inference costs easily eclipsed its revenues.  Yet, as mentioned previously, I am also able to shed light on OpenAI’s revenues, as these documents also reveal the amounts that Microsoft takes as part of its 20% revenue share with OpenAI.  Concerningly, extrapolating OpenAI’s revenues from this revenue share does not produce numbers that match those previously reported.  According to the documents, Microsoft received $493.8 million in revenue share payments in CY2024 from OpenAI — implying revenues for CY2024 of at least $2.469 billion, or around $1.23 billion less than the $3.7 billion that has been previously reported .  Similarly, for the first half of CY2025, Microsoft received $454.7 million as part of its revenue share agreement, implying OpenAI’s revenues for that six-month period were at least $2.273 billion, or around $2 billion less than the $4.3 billion previously reported . Through September, Microsoft’s revenue share payments totalled $865.9 million, implying OpenAI’s revenues are at least $4.329 billion. According to Sam Altman, OpenAI’s revenue is “well more” than $13 billion . I am not sure how to reconcile that statement with the documents I have viewed. The following numbers are calendar years. I will add that, where I have them, I will include OpenAI’s leaked or reported revenues. In some cases, the numbers match up. In others they do not. Though I do not know for certain, the only way to reconcile this would be some sort of creative means of measuring “annualized” or “recurring” revenue. I am confident in saying that I have read every single story about OpenAI’s revenue ever written, and at no point does OpenAI (or the documents reporting anything) explain how the company defines “annualized” or “annual recurring revenue.”  I must be clear that the following is me speaking in generalities, and not about OpenAI specifically, but you can get really creative with annualized revenue or annual recurring revenue. You can say 30 days, 28 days, and you can even choose a period of time that isn’t a calendar month too — so, say, the best 30 days of your company’s existence across two different months. I have no idea how OpenAI defines this metric, and default to saying that “annualized” or “ARR” means $Xnumber divided by 12. The Financial Times reported on February 9 2024 that OpenAI’s revenues had “surpassed $2 billion on an annualised basis” in December 2023, working out to $166.6 million in a month: The Information reported on June 12 2024 that OpenAI had “more than doubled its annualized revenue to $3.4 billion in the last six months or so,” working out to around $283 million in a month, likely referring to this period. On September 27 2024, the New York Times reported that “OpenAI’s monthly revenue hit $300 million in August…and the company expects about $3.7 billion in annual sales [in 2024],” according to a financial professional’s review of documents. On June 9, 2025, an OpenAI spokesperson told CNBC that it had hit “$10 billion annual recurring revenue,” excluding licensing revenue from OpenAI’s 20% revenue share and “large, one-time deals.” $10bn annualized revenue works out to around $833 million in a month. These numbers are inclusive of OpenAI’s revenue share payments to Microsoft and OpenAI’s inference spend. There could be potentially royalty payments made to OpenAI as part of its deal to receive 20% of Microsoft’s sales of OpenAI’s models, or other revenue related to its revenue share with Bing.  Due to the sensitivity and significance of this information, I am taking a far more blunt approach with this piece. Based on the information in this piece, OpenAI’s costs and revenues are potentially dramatically different to what we believed. The Information reported in October 2024 that OpenAI’s revenue could be $4 billion, and inference costs $2 billion based on documents “which include financial statements and forecasts,” and specifically added the following: I do not know how to reconcile this with what I am reporting today. In the first half of CY2024, based on the information in the documents, OpenAI’s inference costs were $1.295 billion, and its revenues at least $934 million.  Indeed, it is tough to reconcile what I am reporting with much of what has been reported about OpenAI’s costs and revenues.  OpenAI’s inference spend with Microsoft Azure between CY2024 and Q3 CY2025 was $12.43 billion. That is an astonishing figure, one that dramatically dwarfs any and all reporting, which, based on my analysis, suggested that OpenAI spent $2 billion on inference in 2024 and $2.5 billion through H1 CY2025. In other words, inference costs are nearly triple that reported elsewhere.  Similarly, OpenAI’s extrapolated revenues are dramatically different to those reported.  While we do not have a final tally for 2024, the indicators presented in the documents viewed contrast starkly with the reported predictions from that year.  Both reports of OpenAI’s 2024 revenues ( CNBC , The Information ) are from the same year and are projections of potential final totals, though The Information’s story about OpenAI’s H1 CY2025 revenues said that “OpenAI generated $4.3 billion in revenue in the first half of 2025, about $16% more than it generated all of last year,” which would bring us to $3.612 billion in revenue, or $1.145 billion more than are implied by OpenAI’s revenue share numbers paid to Microsoft. I do not have an answer for inference, other than I believe that OpenAI is spending far more money on inference than we were led to believe, and that the current numbers reported do not resemble those in the documents.  Based on these numbers, it appears that OpenAI may be the single-most cash intensive startup of all time, and that the cost of running large language models may not be something that can be supported by revenues. Even if revenues were to match those that had been reported, OpenAI’s inference spend on Azure consumes them, and appears to scale linearly above revenue.  I also cannot reconcile these numbers with the reporting that OpenAI will have a cash burn of $9 billion in CY2025 . On inference alone, OpenAI has already spent $8.67 billion through Q3 CY2025.  Similarly, I cannot see a path for OpenAI to hit its projected $13 billion in revenue by the end of 2025, nor can I see on what basis Mr. Altman could state that OpenAI will make “well more” than $13 billion this year .  I cannot and will not speak to the financial health of OpenAI in this piece, but I will say this: these numbers are materially different to what has been reported, and the significance of OpenAI’s inference spend alone makes me wonder about the larger cost picture for generative AI. If it costs this much to run inference for OpenAI, I believe it costs this much for any generative AI firm to run on OpenAI’s models. If it does not, OpenAI’s costs are dramatically higher than the prices it is charging its customers, which makes me wonder whether price increases could be necessary to begin making more money, or at the very least losing less. Similarly, if OpenAI’s costs are this high, it makes me wonder about the margins of any frontier model developer.  Inference: $546.8 million Microsoft Revenue Share: $77.3 million Implied OpenAI revenue: at least $386.5 million Inference: $748.3 million Microsoft Revenue Share: $109.5 million Implied OpenAI Revenue: at least $547.5 million Inference: $1.005 billion Microsoft Revenue Share: $139.2 million Implied OpenAI Revenue: at least $696 million Inference: $1.467 billion Microsoft Revenue Share: $167.8 million Implied OpenAI Revenue: at least $839 million Total inference spend for CY2024: $3.767 billion Total implied revenue for CY2024: at least $2.469 billion Reported (projected) revenue for CY2024: $3.7 billion, per CNBC in September 2024. The Information also reported that expected revenue could be as high as $4 billion in a piece from October 2024. Reported inference costs for CY2024: $2 billion, per The Information .  Inference: $2.075 billion Microsoft Revenue Share: $206.4 million Implied OpenAI Revenue: $1.032 billion Inference: $2.947 billion Microsoft Revenue Share: $248.3 million Implied OpenAI Revenue: $1.241.5 billion H1 CY2025 Inference: $5.022 billion H1 CY2025 Revenue: at least $2.273 billion Reported H1 CY2025 Revenue: $4.3 billion ( per The Information ) Reported H1 CY2025 “Cost of Revenue”: $2.5 billion ( per The Information ) Inference: $3.648 billion Microsoft Revenue Share: $411.1 million Implied OpenAI Revenue: at least $2.056 billion

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James O'Claire 2 weeks ago

AI Companies are raising crazy amounts of money so why not use their free tiers?

While some people (my friends included) are out there paying $200 a month to OpenAI and Anthropic, I’d just like to share that if you need to save some money now is the time to cash in on the high valuations and free tiers that all the major LLMs provide. Every day I bounce between most major LLMs, maybe just Grok and Qwen a bit less. I use the browser tabs and usually have one for quick lookups / research, and another for the main larger task I’m working on. I find running in this style, it’s very hard to ever hit noticeable limits. Especially if you use one LLM for spammy quick look ups (ie “git cherry pick syntax”, where it’s basically just returning a quick one liner you forgot how to run). It’s always best to be skeptical of the AIs, so I often take the output of one and directly send it to another to check. This isn’t usually a big change, but it might catch issues and gives me time to read the code more closely as I think about how / if I will incorporate the changes. I heard this first from I think the CTO of Anthropic. And apparently the idea isn’t going away, but you can still get that flow from cheaper tools like Cursor/CoPilot for $20 a month. When I’ve talked to friends about this, they’re ‘sure’ they’re maxing out or using it to it’s fullest, but I have a sneaking suspicion that if they were to try a cheaper / free tier setup they would probably be mostly fine. So, if you have the money and enjoy it, continue on, but if you’ve been looking for a way to save $200-$180 a month, try the free tiers, they’re really just as good. At $200 a month for two years you could buy yourself a homelab PC and a graphics card and run models locally.

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Takuya Matsuyama 3 weeks ago

My plan to avoid hyperfocus and keep a healthy work pace (as a dad of two)

Hi, it's Takuya here. As I wrote in my previous post , I recently pushed myself too hard with my indie SaaS dev work and ended up burning out. One of the main reasons was my tendency to hyperfocus. When I get absorbed in something, I tend to lose sight of everything else — sometimes even my personal life. To avoid repeating the same mistake, I’ve decided to redesign how I work. As a freelancer and indie developer, I have full control over how I spend my time. That’s a double-edged sword, where I can work as much as I want, but there’s no one to stop me when I go too extremely. My wife works in a different field, so she can’t really tell how intense my work pace is. If I say I’m tired, she’ll tell me to take a break, but she doesn’t interfere with how I manage my time. That means I can’t rely on anyone else to pull the brakes — I have to be my own guardrail. (Though I’m considering to have a therapist to get some professional perspective regularly.) It feels great to organize your day down to the minute — I used to love that. But now, with my second daughter recently born, keeping a perfectly structured schedule just isn’t realistic. So instead of sticking to rigid time blocks like “15 minutes of X after waking up,” I’m choosing flexibility. As a father of two, I need to adapt my day around the family’s rhythm. After burning out, I realized how crucial health is. Now in my 40s, I can’t rely on youth anymore. If I want to keep doing indie development for as long as I can, I need to prioritize health and stamina. I’ve had atopic dermatitis for years, and I’ve noticed it improves when I walk a lot. Luckily, my legs are fine, my eyes work, and my hearing is good — so I’ve decided to make 10,000 steps my daily goal. Walking also helps my mental state — it relaxes me, sparks new ideas, boosts serotonin, and improves sleep. So my rule is simple: if the destination is within 30 minutes, I’ll walk. So, I’d like to go places on foot if the destination is within 30 minutes. In the mornings, I walk around the park and head to a café in the next town. I even go to the supermarket on foot instead of by bike. From experience, I’ve learned that if I don’t hit 5,000 steps by noon, the afternoon gets tough — pacing matters. Playing with kids or filming videos both require physical strength. So I do light strength training daily. Here is my typical routine: Morning routine: Before bed, I add: There’s no deep reason behind these numbers — it’s just what feels sustainable. Also, excercise helps distract me from itching when my skin flares up. With the basics set, let’s plan out how I spend my day. After dropping my daughter off at kindergarten, I start by walking around the park to take in the season — about 2,000 steps. After dropping my daughter off at kindergarten, I start by walking around the park, taking in the sights and sounds of the season — about 2,000 steps. Then I grab my laptop or iPad mini and walk 15–20 minutes to a café (usually Starbucks). Cafés are great for creativity, because the people, the sounds — they make me feel connected to the world. Even a short chat with the barista can lift my mood. So in the mornings, I focus on marketing tasks, content ideas, or just free exploration. No strict plans — I follow my curiosity. Reading a book would be also nice. I'd like to work on tasks that require focus and energy in the afternoon, like coding, customer support, and other tasks that need deep concentration. Home is the best place for that. I can sing out loud if I feel like during the tasks. I usually work until around 5 p.m., when my daughter gets home from preschool. Sometimes I wrap up early to take a short walk — it helps cool my head before family time. I don’t want to bring that “wired” energy into the evening. I’m in charge of cooking, so I make dinner — usually something simple based on what I feel like eating. I try to keep it to one soup and one dish, a meal style called Ichiju-issai(一汁一菜). Both cooking and grocery shopping help me unwind and prevent hyperfocus. Right before I burned out, what I lacked most was mental space. I poured all my energy into app development and had no time to pause and reflect. So now, I’m setting aside one day a week to do things unrelated to the app — basically, a day off. Weekends are for family, so I’ll take a solo day off during the week — probably Wednesday for now. But I’ll stay flexible. This day is like watering a plant — giving myself time to breathe. It’s fine to do nothing at all. I might go on a short day camping trip, visit a museum, or just watch videos and read. Looking outward like this helps prevent tunnel vision and burnout. Development isn’t everything — I’ve always dedicated about half of my time to marketing anyway. So overall, the weekly balance won’t change much. What does “development pace” mean in the first place? I’d rather release high-quality updates at a sustainable rhythm than rush something out while feeling anxious and narrow-minded. This is all about building a lifestyle that lets me keep doing this long-term. Ultimately, my app users will decide if it works or not. I’m building a note-taking app surrounded by big competitors. As an indie developer, I can’t win by competing the same way large companies do. The key is to find areas they wouldn't do — to differentiate by not fighting the same battles. That requires keeping my mind open and constantly exploring new things. My wife is still in the hospital as of this writing, and I am in the midst of a chaotic one-person parenting situation. Also, since it will be a special operation even during my family visit, I cannot spend my days as described above. However, the most important thing is to work at a healthy pace and to enjoy doing it while maintaining mental and physical health. If I can maintain that, anything is fine. I should be flexible. Thanks for reading. Inkdrop is a Markdown-focused note-taking app for developers. I’ve been developing it for over eight years now. If you’re looking for a notes app, check it out: 50 push-ups 20 dumbbell curls 20 shoulder presses 10 scap raises 50 leg raises (abs) 100 back extensions

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Premium: OpenAI Burned $4.1 Billion More Than We Knew - Where Is Its Money Going?

Soundtrack: Queens of the Stone Age - Song For The Dead Editor's Note: The original piece had a mathematical error around burnrate, it's been fixed. Also, welcome to another premium issue! Please do subscribe, this is a massive, 7000-or-so word piece, and that's the kind of depth you get every single week for your subscription. A few days ago, Sam Altman said that OpenAI’s revenues were “well more” than $13bn in 2025 , a statement I question based on the fact, based on other outlets’ reporting , OpenAI only made $4.3bn through the first half of 2025, and likely around a billion a month, which I estimate means the company made around $8bn by the end of September. This is an estimate. If I receive information to the contrary, I’ll report it. Nevertheless, OpenAI is also burning a lot of money. In recent public disclosures ( as reported by The Register ), Microsoft noted that it had funding commitments to OpenAI of $13bn, of which $11.6bn had been funded by September 30 2025.  These disclosures also revealed that OpenAI lost $12bn in the last quarter — Microsoft’s Fiscal Year Q1 2026, representing July through September 2025. To be clear, this is actual, real accounting, rather than the figures leaked to reporters. It’s not that leaks are necessarily a problem — it’s just that anything appearing on any kind of SEC filing generally has to pass a very, very high bar. There is absolutely nothing about these numbers that suggests that OpenAI is “profitable on inference” as Sam Altman told a group of reporters at a dinner in the middle of August . Let me get specific.  The Information reported that through the first half of 2025, OpenAI spent $6.7bn on research and development, “which likely include[s] servers to develop new artificial intelligence.” The common refrain here is that OpenAI “is spending so much on training that it’s eating the rest of its margins,” but if that were the case here, it would mean that OpenAI spent the equivalent of six months’ training in the space of three. I think the more likely answer is that OpenAI is spending massive amounts of money on staff, sales and marketing ($2bn alone in the first half of the year), real estate, lobbying , data, and, of course, inference.  According to The Information , OpenAI had $9.6bn in cash at the end of June 2025. Assuming that OpenAI lost $12bn at the end of calendar year Q3 2025, and made — I’m being generous — around $3.3bn (or $1.1bn a month) within that quarter, this would suggest OpenAI’s operations cost them over $15bn in the space of three months. Where, exactly, is this money going? And how do the numbers published actually make sense when you reconcile them with Microsoft’s disclosures?  In the space of three months, OpenAI’s costs — if we are to believe what was leaked to The Information (and, to be clear, I respect their reporting) — went from a net loss of $13.5bn in six months to, I assume, a net loss of $ 12bn in three months.   Though there are likely losses related to stock-based compensation, this only represented a cost of $2.5bn in the first half of 2025. The Information also reported that OpenAI “spent more than $2.5 billion on its cost of revenue,” suggesting inference costs of…around that?  I don’t know. I really don’t know. But something isn't right, and today I'm going to dig into it. In this newsletter I'm going to reveal how OpenAI's reported revenues and costs don't line up - and that there's $4.1 billion of cash burn that has yet to be reported elsewhere.

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