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TLDR: Download the best disk cleanup app in existence from diskspace.io. A fun experiment in writing the same app three times (well kind of 4 times) with AI, and being as optimised and OS native as possible. My Mac recently filled its 1TB drive and my quest to find out where the space had gone was very frustrating. The existing tools for tracking down disk space were slow, clunky and it was impossible to use them until their 30 minute crawl of my hard drive completed. I decided to fix this by building a new app called Disk Space, which takes the great features of my favourite old time app Disk Inventory X, makes it blazingly fast and adds much improved file/folder deletion aesthetics so that you can clean up safely and quickly, as well as highlighting recently created files so you can find what changed more quickly. Let’s get nerdy The three versions, Mac, Windows and Linux, are all mostly independent, with just a little shared C++ code. My goal was to make each app as small, fast, as native to its environment as possible. This meant not using any of the more common cross platform libraries, and leaning on Claude to do the work. MacOS The MacOS version of Disk Space is written fully in Swift, with no dependencies on other libraries. This was the first version I built. It detects how many CPU cores your machine has and optimises itself to maximise the throughput. The size of the installed app is just under 1MB, with about 300KB of that being multiple sizes of the icon, so the app itself is just over 600KB in size. Not bad! Releasing desktop apps for the Mac is actually not too bad an experience. You can choose to put it in the App Store, but then it can be a pain to release a new version. Apple offer a free notarisation service that signs the built app with your Apple Developer credentials, and it works well. For this reason, you can simply download the MacOS version of Disk Space from the site. Windows The Windows version is a direct port of the Swift code to C++. Similar to the MacOS version it optimises its operation based on the number of cores, but it also checks if the drive being scanned is a spinning disk or a solid state drive. If it’s a spinning disk there’s no point running many threads against it, it’s physically incapable of responding, so it caps the number of threads. Since it it just pure C++ with no dependencies pulled in, the installer is just about 600KB, pretty cool. Releasing apps on Windows these days generally means you are forced to either release through the Microsoft Store or pay for quite expensive yearly fees to have your app notarized. Without this, the user will be shown very scary warning dialogs, making the app very hostile to use. Since this is a small free app, I went the Microsoft Store route. Linux The Linux version shares some of the C++ with the Windows version, especially the code that draws the multi-coloured tree map on the right. Similar to the Windows version, it checks the hardware of your storage to best optimize itself and otherwise builds the UI using Linux native code with almost not dependencies. The first version Claude recommended depended on the GTK libraries for the UI, which was convenient, but it meant that using the app on any system that didn’t include those libraries would force the user to download hundreds of megabytes just to get a 500KB app running. Luckily, within an hour Claude had completely rewritten the app to be almost fully self contained. This means that the Linux app, which is packaged as an AppImage file, is just about 600KB all in, and you can simply download it from the site. Epilogue This was a fun experiment in building an identical app for all three operating systems while keeping it as native and optimised as possible. The hardest part was the hardware setup required for testing. I now have on my desk: My MacBook Pro (my primary machine). I do most of my work on this, run Claude and do all testing of the MacOS app. A small but powerful Windows Desktop. This is pretty great, as not only can Claude build and test Windows apps on it, it can also build and test (to some degree) Linux apps too. I use this as the main machine for those two operating systems. An ancient, 2009 MacBook Pro 17″ that I installed Linux on just for building this app. I use this for testing the Linux version on a real machine, not just on Windows WSL. It works relatively well, but with just 4GB of memory I won’t be doing any development on it any time soon. Still, it’s great to make use of the old hardware instead of throwing it away – I knew there was a reason I hung on to it! … a lot of messy crap I need to tidy up. Any day now…. I can’t believe you read this far, thanks! Now go get Disk Space from diskspace.io, your hard drive will thank you
One of the absolutely coolest features of Kidz Fun Art is the ability to create Animations. This was initially inspired by watching my nieces creating an animation on another Android app, so I focused purely on the creation case. This worked well, where most animations were under 50 frames in length, as it takes time to create them. I later added the ability to import Gif images, since it was a relatively simple change – parse the Gif image into frames, save them and boom, you can edit and re-export it. However this exposed a problem: Gifs can be huge, and Kidz Fun Art didn’t work well with thousands of animation frames of data. I wasn’t sure where the bottlenecks were, but at some point the app would just crash if the Gif was big enough, of if the user created an animation over 100 frames or so. This is all now fixed, and the app comfortably imports multi-Megabyte Gif images and provides a better user experience when some operations (like deleting hundreds of frames) are not instant. Using AI to find performance issues & subtle bugs While I have always written the vast majority of the code for Kidz Fun Art by hand (using AI for more complex things like WebGL shaders and C++ based paint brush simulation), it’s been invaluable recently for reviewing and testing the code. I asked Claude Opus 4.8 (the current frontier model as of July 2026) to identify the bottlenecks and it did a great job. There were multiple places where I initially wrote a function to take an action on a single frame that would then save the full animation, but later reused this function in a loop over all the frames. This caused the full animation to be saved hundreds of times in a few seconds, crashing the app. The list of frame thumbnails at the bottom of the screen rendered all thumbnails up front. This is fine for 50 but not for 1000. When saving a Gif, the file was far too large. This is because it wrote each frame in its entirety to the Gif image. The Gif standard obviously supports just writing the pixels that changed from the previous frame, and I wasn’t doing that. Deleting a large animation would take multiple seconds, with not user feedback. This was fine for other media types as they are more or less instant, but in this case it let the user click around the app, then have unexpected things happen 5 seconds later. It made the app feel broken. Gifs that stored some frames with the option to simply restore the previous frame were not handled, making the import of some images be inaccurate. There were a number of places where race conditions between multiple asynchronous actions could cause bugs. The AI was very good at finding places where my code should have been waiting for one to complete before beginning the second. There were multiple places where memory leaks occurred, specifically with not cleaning up event listeners. It found them all. When leaving the app open for days or weeks at a time without a reload, this could have been a real problem. What is better now? Animations now scale up to very large sizes, with instant access to all frames. You should be able to import basically any reasonable Gif image, and it will be exported in a highly optimized manner, with perfect colour matching per pixel. When deleting a large animation, you are told it is being deleted immediately, so it’s obvious the app is doing what you asked it to do. When importing a large Gif, you are shown a dialog telling you that it is happening, and blocking you doing other work until that completes. Many subtle bugs fixed. The frame list is fully virtualized, and scales up to essentially any size of animation. We have tests now! Another great use of AI is for writing tests, in this case laboriously creating dozens of large integration tests. These were invaluable in both validating the deep changes being made and in finding more edge cases and race conditions. Kidz Fun Art now has full end-to-end tests covering animations, layers, comics, cards, drawing, colouring, handwriting, maths and puzzles. I hate writing these, but AI doesn’t get bored, and I look forward to adding more and more regression tests in the future to keep quality high for all the world’s young artists out there.
For a long time I’ve wanted to add Spirographs to my (awesome ) drawing app for kids, Kidz Fun Art, and today it’s ready! There was quite a bit of fun mathematics in getting it to feel natural and work with all sizes of circles, but it seems to have worked out very well! You can move the Spirograph around, change the size of the outer and inner circles, and draw in any colours you like. Read more about it on the main blog post here, try it out on the web at https://kidzfun.art , get it for iPad here, or download for Microsoft Windows here.
Way, waaayyy back in 2010, I built a fun little game for the Palm WebOS series of phones called Mazer. I was happy with it, loads of people downloaded and played it, and then WebOS died. I recently found the source code again, and with the help of Claude AI I rewrote it to run on iOS and iPad! Get it for free today from the iOS App Store. (Android version coming soon) There are four different game types You can find your way around a simple maze, or race a terrifying fiery ball to the finish. Over 120 hand crafted obstacle courses to get around with worm holes, force fields, evil fiery balls, and more. My personal favourite, a Pacman like maze where the four ghosts chase your little ball around as you try to open the portal and get outta there!
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The Tetris effect is one of psychology’s most easy to reproduce experiments. Simply spend a bit of time playing the eponymous game every day for a few weeks. After a little while, you’ll start recognizing familiar Tetromino shapes in clouds, buildings, and everyday objects. You might even see them appear before your eyes when you start falling asleep. Tom Tang Attention hijacking There’s one lesson the Tetris effect teaches us: whatever you focus on long enough will end up shaping your thoughts. This can be a good thing since it’s how we learn new skills and discover new ideas. Sadly, less and less of our attention is focused intentionally. Instead of picking what we want to see we let other people decide what is supposed to be good for us. Do you want to watch a video? YouTube knows you like cooking and art streams. But why not also recommend a few clips about the stock market bubble, global warming, and the war in Iran. Doomscrolling will make you stay longer and click on a few more ads. Do you want to listen to music? Just open a Spotify playlist and let the algorithm figure out what you like. Please ignore the AI slop they will insert in between real songs to avoid paying royalties to real artists. Do you want to know how your colleagues are doing? Too bad, LinkedIn will bury any relevant career news between the opinion of complete strangers. It is surely just a coincidence that those strangers happen to be shilling whatever Microsoft is invested in at the moment. Do you want the opinion of strangers on a product? Well those Redditors you wanted to ask are probably just a bunch of LLMs talking to a bunch of Russian trolls now. I hope you didn’t value their opinion too much. If, like me and most people, you spend the major part of your day focused on your device, there’s no doubt it’s affecting you. And when you let someone else dictate what appears on your screen, it’s the same as giving them the key to your brain. New York Said Back to an intentional internet The internet wasn’t always like that. Before recommendation algorithms where a thing, you had to decide what you would be doing on the computer. You didn’t really have one big app that you could open and order it to entertain you. Instead, you had a few dozen of bookmarks to websites, each with a specific idea in mind. A site for video game news, that one website with lots of tutorials, a blog about anime that didn’t update often enough, a wiki about a TV show from the 90s… Of course awful things existed on the web. We had Encyclopedia Dramatica and Rotten.com, but you actually had to put the effort to go there if you wanted. Nobody was going to put pictures of dead kids and far-right propaganda as a suggestion after a pancake recipe or a cat video. The good thing is that this intentional internet is still around. It has just been a bit buried below the corporate web, but it’s not very hard to find. After all you’re on this blog, so you probably already have a good idea about it. The main difference between this time and now is you. When you want to get back to reading blogs, RSS feeds, and finish that tutorial instead of doomscrolling shorts, you have to get used to a slower internet. One where content is not infinite and doesn’t get updated every click. But like every habit, the only thing you have to do is to keep at it. And if you pay enough attention to it, something will click in your brain.
One of the interesting challenges of the AI ecosystem in 2026 is that new, effective patterns emerge faster than I can adopt them. I’ll find a handful, get back to work, and realize a month later that I’d missed four or five more. The adoption cycle for Imprint this year has been something like: January: get every engineer onto Claude Code every single day March: ok, let’s also get everyone else onto Claude Code or Claude Cowork every single day April: local development is bottlenecked on checkout and worktree model, instead create ~10 local workspaces which each have an independent checkout of every repository, and operate at the workspace level, not at the repository level, so it can generate cross-repository pull requests across frontend, backend, infrastructure and data monorepos June: oh boy, agent-driven development is heavily constrained by lack of a common task management system with higher visibility and less permission complexity than Jira, so let’s migrate the entire company over to Linear and hard stop on Jira July: yikes, now we have visibility into all these tickets, many of them are trivial but managing them through local development isn’t scaling, let’s roll out an orchestrated harness which internally we call “Agent Fleet”, along the lines of Stripe’s Minions The most recent question for me has been figuring out how to adopt the software factory pattern. (After some light research, the specific AI-context origin of this term is slightly messy to attribute, but I think it might be Justin McCarthy in February 2026’s Software Factories And The Agentic Moment.) The software factory pattern is looping on a broad goal, and then relying on the harness to drive progress towards that goal. Our first pass at implementation is fairly basic: An agent skill /linear-project-loop which reads in a Linear project and starts by auditing that project’s goal definition on these dimensions: An RFC in Notion that describes the project’s goals, how those goals are measured, and the general approach A Datadog dashboard or Snowflake queries that measure progress against those goals If those are missing, or the Linear project is missing in its entirety, it iterates with you on creating those missing tools. Then it reviews the state of the metrics and issues for the project. If new work is identified, it adds those issues to the project. It updates the state of issues that have moved. It works on the non-blocked tasks based on the project’s current state. This is often writing a pull request, updating a pull request, pinging for review, asking a clarifying question, etc. When a task completes, if the project description is fresh, it takes on the next task. If the description hasn’t been updated in a while, it reruns the loop starting with the first step. Right now I am running this locally in a local harness, but it’s working well enough that I anticipate moving the behavior to be driven by the same orchestrated harness that we assign one-off tasks to. What I particularly like about the factory pattern is that it parallels very closely how I’ve been working locally, while forcing me to recognize the places where I was accidentally hording parts of the state for myself regarding the goals of the project. I was already asking agents to iterate on specific Linear projects, but they didn’t have the ability to evaluate if they were going in the right direction, or if it was missing necessary tasks. Now it does. The other place this has been extremely helpful for me is checking in on projects post release. For example, I shipped our passkeys implementation earlier this year, but some months go by without my checking in on how it’s going. If we saw adoption spike, or error rates start to turn, I might miss it, but running the factory in a less frequent post-release mode would catch it immediately. The final thought that’s been interesting to me is how much all of the pieces here compound only to the extent that you have the other pieces. For example, this factory pattern depends on having Datadog MCP and Snowflake access available to manage goal-tracking, but it also depends on Linear being the single source of state for the company’s work, and an orchestrated harness that can perform work independently from your laptop. Keeping up with this many migrations is a fascinating industry moment.
I owe a lot of my professional identity and success to CSS-Tricks. CSS-Tricks repeatedly gave me the opportunity to write for them. In doing so, they helped to both socialize and normalize accessibility as a mainstream frontend concern. I’m deeply thankful to them for this. The team was also a joy to work with, notably Geoff Graham. He’s a mensch, and one of the nicest people you can interact with in the frontend web space. If you have not been following the news about the site, Kevin Powell has a good video about the whole situation: Content skipped. I’m not speaking on behalf of Geoff, Chris, or others involved with running the current version of CSS-Tricks. I’ve got skin in the game as an author. This is my personal opinion, born of my feelings and beliefs. I think a lot of the web’s infrastructure should be co-ops, and CSS-Tricks is knowledge infrastructure. To that point, I should also point out that the website covers far more than just CSS. The corporate model of ownership can be a risk. If infrastructure is not part of a corporation’s core strategy, it is not a priority. As Kevin’s video touched on, it seems like promotion via owning the frontend content space isn’t part of Digital Ocean’s strategy anymore. It is not that CSS-Tricks does not have value. It is that Digital Ocean cannot see it. It is deeply, tragically ironic to me that Digital Ocean allowed this to transpire. This is because I know for a fact that the techniques and philosophies shared by CSS-Trick authors helped to shape iterations of their product’s UI. Some may be quick to point out that this knowledge now—illegally—exists inside of LLM training data, so the risk of the website going away is mitigated. To this, know that we should be striving to keep resources like CSS-Tricks going. Human creativity is the force that creates new techniques, strategies, and technologies. The web will calcify without voices sharing what they know, forever locking us into endless permutations of a fixed point in time. Unlike corporations, co-ops don’t have to be motivated by profit. By not needing to prioritize growth at all costs it means co-ops can instead prioritize and incentivise things like preservation and cultivation. It is also a successful model of operation, one that even already exists, and flourishes in the tech space. Collective ownership can also serve as checks and balances for, and protection against hierarchical decision-making. I only need to point to the chaotic and aberrant decisions many CEOs in the technology space have been making as of late to demonstrate the value of this approach. Paddy Srinivasan, if you somehow wind up reading this: Save some face and take a big swing. Give CSS-Tricks back to the people who love it.
How can something that “just works” be so annoying? situation We live in Cambridge off a little road down a drive in shared ownership between us and our neighbouring houses. All the utilities are buried under this drive, including the phone line. anticipation Over the last few years we have been canvassed repeatedly by CityFibre saying that they can deliver fibre all way to our house. I saw them digging trenches and leaving tails of purple fibre cladding along nearby roads, ready to hook up all the houses. I thought they would need to do something similar to deliver fibre to us. So when they turned up and knocked on our door, I talked to their salesbods and walked them up and down the drive and pointed out where the existing BT line goes. Then they gave up trying to sell to us. This happened about three times. disaffection We were not eager enough for an upgrade to deal with these impediments. notification A few months ago we were told that CityFibre would soon come and do the upgrade, since there’s a nationwide deadline for turning off the copper phone network at the end of the year. We expected that this would force them to actually plan some digging works, so we talked to our neighbours about it. We were all ready for some huge faff to follow the next visit by the CityFibre bods. installation CityFibre turned up on the promised morning bright and early. To our enormous surprise, a brown fibre housing was already poking out of the ground next to our copper phone line. It had been fed through 50 metres of 5cm duct without us being aware they were even working on the street. Within a couple of hours, the technicians had drilled through our wall, installed the ONT, blown fibre through the unexpected pipe, plugged in the CPE (superficially identical to the old one), and left telling us to anticipate that it might not work properly until tomorrow. activation Around lunch time, the copper phone line stopped working completely. Some faff ensued, switching all our devices over to the new WiFi network. For a while we thought this was the death of our land line, but in the course of debugging other issues, I realised that the router has a built-in VoIP adapter (I don’t think we were told it has a built-in VoIP adapter) so I plugged the phone in and it Just Worked: they had ported our phone number across and everything. Flawless. I was seriously impressed. rumination It has been a few weeks since the switchover, and apart from a couple of horrible Clown-afflicted IoT devices, it has been fairly smooth. What prompted me to write this up was realising that we delayed this upgrade for years because the sales people were not given enough technical information about how the installation process works: the fact that houses typically have a 5cm duct containing the copper lines (probably standard for the last 40 years) and the fact that fibre can be shoved through a few tens of metres without difficulty. And worse, the sales people didn’t have an esclation path for difficult cases: they just gave up instead. From a technical point of view, the installation was impeccable. (I guess the loose 24 hour window for the cutover time was because OpenReach and CityFibre don’t have tight requirements on ISP reconfiguration schedules.) From the sales point of view, it was crap. Maybe it would have gone faster if we offered to switch early without asking if the drive would be a problem? But I guess the difference between “yes!” and “yes, but will this be a problem?” is too much to expect from a minimum-wage door-to-door salesbod whose employer didn’t give them enough information or any escalation path.