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Warming up the Puma master before it forks

from 37signals Dev [alt+shift+b] in programming

Basecamp 5 runs on Puma in cluster mode: one master process with preload_app! and 63 single-threaded workers per host, deployed as a Docker container with Kamal. We serve Basecamp from several sites. Each site has its own web hosts and a read replica of the database, and writes go to a single primary database in one of them. On our busiest hosts, each deploy left up to 2,000 requests waiting while the new workers warmed up. We reduced those queues by running signed-in requests through the app in the Puma master, before it forked the workers. Why 63 single-threaded workers? Basecamp has always served web requests from processes rather than threads. It ran on Unicorn, which only does processes, until we moved to Puma in January 2025, and we kept the same setup: workers (Concurrent.physical_processor_count * 1.3).ceil threads 1, 1 preload_app! On a 48-core host that’s 63 workers, each handling one request at a time. We chose 1.3 after benchmarking HEY in 2023, when we moved our apps out of the cloud and onto our own hardware. We tested several combinations of workers and threads with a mix of GET and POST requests on a 32-vCPU VM. Every multithreaded configuration we tested was slower and handled fewer requests than single-threaded workers. Adding workers beyond about 1.2 to 1.3 per vCPU brought little benefit. The threaded workers spent a lot of their time waiting for Ruby’s global VM lock. That made single-threaded workers a good fit for this workload, and we use the same setup for Basecamp. An app that spends more time waiting on its database or other services may benefit from more threads, so benchmark your own app. The other reason is the app itself. Basecamp has class-level state in places and has never needed to be thread-safe. With one request per process, it still doesn’t. Processes do use more memory than threads, and preload_app! reduces the difference. The master loads the app once and the workers share its memory through copy-on-write until they write...
1st Oct 2026

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Lexxy 1.0 is here

Today we are releasing the version 1.0 of Lexxy. Lexxy is a rich text editor for Rails built on Lexical. It already powers Basecamp, Fizzy and many others, and it will become the default editor in Rails. I recently presented it in Rails World (slides, video coming soon). This is the article version of my talk. Trix hit a wall Trix has been our editor since 2015, and every Rails app’s editor since Action Text shipped in Rails 6. It’s small and reliable, and it has served millions of people for a decade. But in the last few years our customers kept asking for features like tables or code highlighting, and we kept struggling to deliver them. The reason is the Trix document model. A Trix document is a flat list of blocks. A block is a line of text with some labels attached, like quote, bullet list, bullet. There is no tree, and a block can never contain another block. Nesting is an illusion: at render time, adjacent blocks with the same labels get wrapped together. A flat list of blocks with very limited extensibility options That design bought a lot of simplicity, but you can’t express something like a table with it. Two cells next to each other would carry exactly the same labels, so Trix would merge them into one. The model can say “this bullet is one level deeper”. It cannot say “this cell is different from the cell next to it”. A tables issue has been opened since 2015! The model just can’t do it. The second problem was maintenance. An editor built on contenteditable behaves differently in every browser and even changes from time with operating system releases. In 2024, three iOS releases in a row broke typing, dictation or the caret in Trix, and each one cost us real effort to work around. Check this one as an example. Why Lexical This was a conversation we had at 37signals for years: 2022. We started a project to add tables to Trix. We gave up after a week. The document model can’t represent two-dimensional things. 2023. I built a proof of concept with Tiptap inside HEY. We liked it, but we never started a serious project with it. 2024. We built House, our own Markdown editor, for Writebook. Not WYSIWYG, but WYSIWYM: what you see is what you mean. A wonderful editor for long-form writing like books or technical documentation. 2025. We tried House in another product, and it didn’t fit. For most apps, WYSIWYG was just the right answer. 2025. We had the discussion again, and this time we looked at the whole field. Four years of the same conversation David ruled out Tiptap, CKEditor and the other commercial editors: an open source core with features kept proprietary, and a sales team behind them. We didn’t want our editor to depend on somebody else’s licensing decisions. Then we found Lexical: MIT, from Meta, and very powerful. I spent two weeks evaluating it, and in May we made the call to go with it. A tiny core. Pick the rest. Lexical’s core has zero dependencies and weighs forty-two kilobytes. In a way, it validates the approach that Trix pioneered. The document is an immutable state you never mutate directly, you just get new snapshots when performing updates; contenteditable is an input device and a rendering surface, never the source of truth. It has a DOM reconciler to update the actual DOM very efficiently, and other primitives to deal with handling commands and node transformations. Everything else, from lists to tables to markdown, is a package in the orbit. Lexxy uses thirteen of them. Lexical solved the maintenance problem too. Meta’s products like Facebook or Instagram use Lexical and their user count is in the hundreds of millions. This means that even small issues with new keyboards and devices are fixed fast by the dedicated Meta team that maintains it. Furthermore, Meta’s business is the products, not the editor. We much rather liked this structure of incentives for the long-term investment an editor represents. The iceberg The plan was simple. Pick Lexical, wire it up to Action Text, add a toolbar and ship it. Well, it didn’t go exactly like that. What you envision, and what's under the water Lexxy today is thirteen thousand lines of vanilla JavaScript on top of Lexical. A great editing experience is very hard to get right. An editor is a machine where the user can change the state in a thousand different ways. For example. you have two images one after the other and want to put the cursor between them, but there is nothing there to put a cursor in. Or somebody pastes from Google Docs, and you have to turn a pile of inline styles and empty spans into clean markup. And then Safari, and Android keyboards, and the clipboard, and undo, and… From the first pull request to Basecamp 5 The first pull request landed in May 2025. Fizzy launched with Lexxy in December, and Basecamp 5 in May this year. Basecamp was the real test: twenty years of content written with Trix, and people who use the editor all day, every day. Zoltán Hosszú and Samuel Péchèr were the key people who made this happen. The took a very green version of Lexxy, added a ton of features (including Tables) and polish, and they fixed countless bugs. They also pulled off a remarkable milestone: seamlessly switching millions of Basecamp users from Trix to Lexxy. What’s included? We didn’t want a to build Trix with tables. We had Lexical and we had agents to help, so we wanted to be ambitious here. We went for the whole package. Features In terms of major features: A color highlighter, built in instead of this being a custom Basecamp extension, as it was with Trix. Tables, with an interface we worked hard to keep simple and accessible. Markdown. You type it, you get rich text. Code blocks with syntax highlighting as you type, in more than twenty languages. Image galleries you can navigate and reorder with the keyboard. Prompts. Type a character, get a menu: mentions, emoji, or whatever your app needs. Links by pasting a URL over selected text. Previews of attachments like videos and PDFs, rendered as your app renders them. Action Text Native Lexxy is also Action Text native. Action Text stores attachments in a canonical format that Trix doesn’t speak, so it translates on save and again on render. We taught Lexxy to emit exactly that markup. What you see in the editor is what gets saved, and what gets saved is what your app renders. Your existing content, attachments and views keep working. That opened another door. Action Text now talks to an editor adapter, with an implementation for Trix and one for Lexxy, so switching is one line: config.action_text.editor = :lexxy. Here the credit goes to Sean Doyle, who started that pull request before Lexxy existed and took it to the finish line with our input. It ships with Rails 8.2, and we hope other editors will use it too. Extensibility And you can extend Lexxy. Extensions are built on Lexical’s own mechanism, and this is not a second-class API: Lexxy itself is thirteen extensions, tables included, and Basecamp has nine more. class MyExtension extends Lexxy.Extension { get enabled() { … } get allowedElements() { … } get lexicalExtension() { return this.defineExtension({ name: "my-extension", nodes: [ … ], register(editor) { … } }) } initializeToolbar(toolbar) { … } dispose() { … } } Lexxy.configure({ global: { extensions: [ MyExtension ] } }) My favorite of how extensible is Lexxy are voice notes in Basecamp: you record, you see the waveform while you talk, and it becomes a player inside the document. About a thousand lines, without forking or patching anything. Performance Lexxy is fast, because Lexical is fast. In a ten thousand word document, Trix takes thirty-eight milliseconds to process a keystroke. Lexxy takes four. Above fifty milliseconds, the editor starts feeling sluggish. Compared to Trix, Lexxy brought a whole new performance regime. Milliseconds per keystroke by document size Accessibility Accessibility in Trix was not great. In general, building accessible experiences for rich text editors built on top of contenteditable is quite hard. We had a dream team to help with Lexxy accessibility. Bruno Prieto worked with Michael Berger, our accessibility champion at 37signals, to bring the bar to where we wanted it to be. Bruno is an outstanding programmer who happens to be blind, so he knows one thing or two about accessibility, and he delivered. As a result, in Lexxy everything is reachable with the keyboard. The editor announces itself properly to screen readers, and it gets a thousand details right so that the editing experience using a screen reader is fantastic. You can learn more about accessibility in our docs. Security The latest AI models have resulted in an unprecedented explosion of vulnerabilities found, and we took this thread quite seriously. Lexxy counted with programmers of the caliber of Jeremy Daer and Mike Dalessio helping to make it more secure. We have put a lot of attention to sanitizing the editor contents, validating attachment URLs and making sure that the types of attachments and nodes the editor support are allow-listed. Lexxy also comes with preliminary Trusted Types support, to offer CSP-level control over certain DOM manipulation APIs. The trusted types policy is there, but we are not enforcing it everywhere yet. Agents We started Lexxy using Claude since day one. A main lesson was that an agent needs to drive the editor like a user does. The best decision we made in this project was moving the system tests from Capybara to Playwright: three browsers instead of one, a suite that runs in seconds, and a much more faithful clipboard, keyboard and focus. This represented a tremendous improvement in how agents could close the loop by themselves. Write a test, see it fail, fix it, see it pass. We have more than six hundred browser tests today. Moving the suite to Playwright changed how fast we could write tests With a solid testing foundation in place, we could start fixing bugs in large batches. As mentioned, getting a text editor right implies a ton of work, and the kind of backlog we got at some point would have have buried us in pre-agent times. We also used agents to validate the fixes: an agent reproduces the bug in the public Lexxy sandbox, checks that it’s gone with the branch applied, and labels the pull request. Agents were essential to get Lexxy done with the people and the deadlines we had: we are a small company, and the same people were shipping two products in parallel. 275 cards closed The new Rails default We believe Lexxy is the best rich text editor out there right now, and we are going to make it the default editor in Rails next. If you’re starting a Rails application today, use Lexxy. If you’re using Action Text with a standard configuration, switch. It’s one line, and we’ve worked hard to make it seamless.

28th Sep 2026 • 1 votes
Lexxy: A new rich text editor for Rails

Today, we are introducing Lexxy, a new rich text editor for Action Text. It’s based on Lexical — Meta’s text editing framework — and it brings a much better text editing experience to Rails: Good HTML semantics. Paragraphs are real <p> tags, as they should be. Markdown support: shortcuts, auto-formatting on paste. Real-time code syntax highlighting. Create links by pasting URLs on selected text. Configurable prompts. Support for mentions and other interactive prompts with multiple loading and filtering strategies. Preview attachments like PDFs and Videos in the editor. Works seamlessly with Action Text and Active Storage. We created Lexxy because Trix was falling short of expectations in certain areas, and we encountered technical barriers when attempting to offer the experience we wanted. Lexxy comes with a bunch of juicy improvements, but more than that, we now have a fantastic foundation to build on top of. Lexxy will also bring a great improvement to Action Text: we will let you configure the editor in Action Text just like you configure the database in Active Record. This will open the door to integrating other editors in Rails. Text editing is central to our products. We believe Lexxy will let us deliver the editing experience we want. We are launching an early beta today, give it a try!

4th Sep 2025 • 1 votes
Introducing Action Push Native

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18th Aug 2025 • 1 votes
Announcing Hotwire Native 1.2

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23rd Apr 2025 • 71 votes

More in programming

Tyding up my side projects

Being focused is about doing less things, and giving more space to the stuff that matters. I am spread out on too many projects and I can’t give enough attention to all of them. It’s time to clearly sort out which one I will keep maintaining and which I should drop entirely. Chuttersnap Mikochi Mikochi is a self-hostable remote file manager I’ve started in 2023. It’s my most successful open source project. I personally use it every day, and thousands of people have adopted it and started using it on their own servers. This year the project has been receiving more and more attention. Maybe it just reached a critical mass of users. In practice, that means a bit more maintenance work, since people have been pointing out bugs and suggesting new features. At the same time, working on Mikochi is fairly enjoyable. It’s a small, self-contained project made with technologies I enjoy working with. I’ve got no complaints about spending some time working on it. Verdict: Keep maintaining Mikochi and actively continue improving it. Itako Itako is an investment portfolio tracking SaaS. I built it for the most part between 2023 and 2025. In terms of scope, it’s the largest project I’ve done entirely by myself. Is it a success? Not really. There are definitely a lot of people who tried Itako, and some who’ve kept using it a bit. But it’s not engaging enough for anyone to consider getting the premium plan. And honestly I don’t find myself using it that much. My view of investing and portfolio management has significantly shifted since I started this project. While I used to think that more sophisticated analysis would lead to better returns, I now realize that the only strategies that consistently make money are honestly stupidly simple. Or maybe it’s a U curve: on one end you’ve got a monkey throwing darts, on the other end you’ve got Jane Street, and everyone in-between fails to beat the market. Anyway, the biggest issue with Itako is how dull working on it is. The interesting parts of the projects are working on statistics and data visualization. But in practice, 90% of the time is spent tediously fixing data, understanding why some German stocks have an exchange labelled XETRA instead of XETR, why some Belgian stocks still have quotes while Euronext shows them as delisted… Verdict: Shut down Itako today. Milou Milou is static site generator for press kits. It started as a fork of presskit.html which was completely abandoned at the time. It’s a very useful tool. At Tsukumogami, it’s part of our Cloudflare pipeline which builds our website in a few minutes whenever we need to change something. It seems like a few other game studios have picked it up, although I rarely get any feedback about it. I’ve not spent too much time working on it in 2026. I think it could be interesting to come back to it and add a feature or two. Better support for multiple languages could be an idea. At least, I need to make sure we don’t fall into the good old Node.js dependency rot. Verdict: Spend some time on Milou. Luluka Earlier this year, I created Luluka. It’s a CLI that allows previewing the output of Ebitengine shaders. I do still think Ebitengine is a great engine and Go is better suited for game development than most people realize. However, after quite a long period of experimentation around it, we’ve decided to remain a Godot studio. On top of having quite a large sunk cost in infrastructure surrounding Godot, the tooling and possibility of doing 3D games still makes it a more attractive engine for our use cases. And I’ve been doing a lot of 3D work lately, so better put those hours to use. Verdict: Archive the project. The rest I’ve had a lot of good ideas that stayed as almost empty repositories in my GitHub. For example, Hakumei was a terminal companion to Mikochi. I made a few commits on this project and never found the time to work on it again. I have to admit I’m probably never going to have the time to work on all those repositories. Time to archive them all!

10 hours ago • 1 votes
Not everybody is an AI expert - and that’s fine

This post was previously published through my newsletter on November 9, 2026. From time to time, I will republish newsletter issues on my blog here if I think people (and search engines) might benefit from it. If you want to read everything I’ve written once it is posted, I recommend signing up for the newsletter. If you’re doing anything to stay on top of what is happening in the world of software testing and test automation, like browsing or even posting on LinkedIn, which I stopped doing for the foreseeable future, or reading blog posts - my preferred way of keeping up these days, and I highly recommend Dawid Dylowicz’s excellent Software Testing Weekly aggregator mailing list if you prefer longer-form reading, too you’d be thinking that AI has consumed our entire field, and that everybody (or at least those who post or write in public) is an AI expert these days. The reality, though, is far from that. Yes, some people use a lot of AI, even when I would only call a small fraction of them ‘experts’. I definitely do not classify myself as an ‘AI expert’, by the way. A much larger group uses AI on a less frequent basis, and they’re getting some value out of it. Sometimes more, sometimes less. That’s the group I’m probably in. And then there’s a group of people who haven’t really been using a lot of AI at all so far, beyond maybe the odd prompt to ChatGPT or Claude. I don’t have the actual numbers, but I suspect that this group is a lot larger than you would think from browsing your feed or surfing the blogosphere. You just don’t see them, because they’re typically not the people who do a lot of work in public. A few weeks ago, I started working with a small group of testers to teach them how to use AI in their daily work. This work covers both test automation as well as broader software testing tasks, as some of them contribute to automation on a daily basis, but not all of them do. I’m working with them one day per week, on average, for three months, and we’re making small steps towards making AI a more prominent tool in their tool belt. So, what are we working on right now? I’m pulling ideas from my ‘Valuable Feedback, Fast with AI’ course, and so far, we’ve spent some time setting up Claude, going over some important concepts such as context windows, models, tokens, and (of course!) the probabilistic nature of LLMs, and what that means for the results they provide. We have gone through several initial conversations, using Claude to learn more about and review specific user stories, which was an excellent segue into using talking about MCPs and setting up a connection to both Jira and Confluence using the Atlassian MCP. Last week, we also started experimenting with writing straightforward skills to make it easier for Claude to repeat certain actions in a structured way. These actions ranged from reviewing stories and acceptance criteria to refactoring small chunks of code in their (Robot Framework-based) test automation suite. What I loved about this is the avalanche of ideas that popped up in their heads when they saw how easy it was to use Claude. We made a short to-do list of ideas to try out for the next couple of weeks, and I’m looking forward to seeing them getting more confident around using Claude. One thing I have noticed is that their level of comfort around using AI varies quite a bit. Some of them are naturally quite cautious, which means they’ll ask ‘can we really do this?’ and ‘should we really do this?’ frequently before even submitting a single prompt. This is a good habit, I think, but we’ll have to do some work to get them more comfortable with using LLMs, without losing too much of that caution. Others in the group are the opposite: I need to hold them back a little, because otherwise they’ll create a beautiful mess within days, simply because they see so many opportunities to use Claude in their work. That, too, isn’t inherently bad, but I do want to teach them not just how to get the most out of an LLM, but also (more so) how to use AI prudently. Over the next couple of months, we’ll dive deeper into writing better prompts, creating and structuring skills, better context management, and creating and using agents, and probably a few more things, too. I’m looking forward to seeing these people grow more comfortable around using AI in their daily work, and figuring out where AI genuinely adds value and where it doesn’t in their own context. Is there a deeper message in this newsletter? Maybe not, and I’m just happy to tell you about this project, and how it is a great example of a what I call ‘corporate mentoring’, a very efficient way of teaching a small group of engineers new skills, within their own context. But maybe it’s also to show you that, despite what you might see on the Internet, especially those places featuring a lot of self-congratulation (I’m looking at you, LinkedIn), not everybody out there is an AI expert. At least not yet. So, if you feel like you’re ‘behind’ simply because you’re not running at least 17 agents continuously, creating 5400 merge requests per day, know that you’re not alone. Quite far from it, I would say.

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