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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!
I’ve been using the Irish energy provider Energia for 5 years or so (as of writing, 2026) and they used to have a useful insights dashboard that let me analyse my power usage. Well, they seem to have removed it so I built a handy dashboard that anyone can use. It’s at https://energy.chofter.com/ , try it out! You simply download your power usage information as a CSV file (a spreadsheet) from their site, currently at https://energyonline.energia.ie/my-account/half-hourly-usage/ . Then drop the file into the web app and it will: Show a useful overview of your usage for the full time period You can configure your current home setup This includes specifying your current tariff, whether or not you have a home battery or a car Compare usage versus last year Shows a heatmap of your usage by every 30 minutes Simulate the change in cost if you change your home setup Try out what would happen if you kept your consumption the same but changed your tariff, or added a battery or a car. This one is particularly useful.
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I listen to a lot of podcasts, and I like how they fit around other tasks. I press play, lock my phone, and put it down. I’m free to wash the dishes, fold the laundry, or shop for groceries. Unfortunately, more and more information is only published as a video. Technical talks, conference sessions, video essays – they don’t work in an audio-only podcast app. I could convert these videos to MP3 files, but that breaks down the moment a video isn’t pure spoken word. If a speaker says, “Look at this slide” or holds up a diagram, an audio-only file leaves me stranded. I don’t want to give up the podcast player I like, nor stare at a screen for an hour – but I do want the information in these videos. To solve this, I’m abusing my podcast player’s chapter support. This gives me the best of both worlds: I can listen to a video as audio-first, and glance at my lock screen if I need a moment of visual context. The idea: Chapters every few seconds MP3 files can have ID3 metadata, and ID3 metadata can include chapters. A chapter covers a particular time range, and it can have an associated title, description, and cover art. My podcast app of choice is Overcast, which can’t play videos, but it does have robust chapter support. I can jump between chapters, navigate a table of contents, and see per-chapter cover art. To get videos into Overcast, I’m creating MP3 files with a new chapter every few seconds, and the per-chapter cover art is a corresponding frame from the video. As I play the file, I get a slow, stop-motion-like rendition of the original video. If my phone is locked, I can glance at my lock screen and see the current frame in the Now Playing screen. Overcast is developed by Marco Arment, and I got this idea from Forecast, his app for adding chapters to podcasts. In particular, I was struck by its ability to create chapters that don’t display in the chapter list – ideal if I don’t want a table of contents with hundreds of entries. As I was developing my script, I compared my output to the output from Forecast to ensure I was creating the chapters correctly. The code: FFmpeg and Mutagen There are three steps in this process: Convert a video file to an MP3 Extract images from the video at a fixed interval Insert the images as hidden chapters in the MP3 file Let’s go through each in turn. 1. Convert a video file to an MP3 Converting a video file to an MP3 is a single FFmpeg command: ffmpeg -i video.mp4 audio.mp3 This is consistently the slowest step of the process, and I do wonder if I could use different settings or an alternative encoder to make it go faster – but it’s not slow enough to be worth further investigation. 2. Extract images from the video at a fixed interval Extracting images from a video needs a more complicated FFmpeg command: ffmpeg -i video.mp4 \ -vf 'fps=1/5,scale=iw*sar:ih,scale=min(iw\,945):min(ih\,945):force_original_aspect_ratio=decrease' \ thumbnail_%04d.jpg This extracts an image every 5 seconds, downscales any image larger than 945 pixels square (while preserving the original aspect ratio), and saves the results as sequentially numbered JPEG images (thumbnail_0001.png, thumbnail_0002.png, and so on). The key is the -vf flag, which defines two FFmpeg filters: The fps filter selects one frame every 5 seconds (fps=1/5). The first scale filter scales the width based on the sample aspect ratio (scale=iw*sar:ih). Without this filter, frames can be stretched and distorted. The second scale filter scales the input video, preserving the original aspect ratio (force_original_aspect_ratio=decrease), and ensuring the output images fit within 945×945px or the size of the input video, whichever is smaller. My limit is 945 pixels because that’s the largest size that cover art is shown on my iPhone. This filter still isn’t completely correct – it sometimes creates images from portrait videos that are smaller than I’m expecting – but it’s good enough. These are only thumbnails for glancing at, and if I want to change it later, I can always do the image resizing outside FFmpeg. 3. Insert the images as hidden chapters in the MP3 file Inserting the chapters into the MP3 file is more complicated. Although FFmpeg has basic support for ID3 metadata, as far as I know, it can’t insert chapters with per-chapter artwork. Instead, I’m going to reach for Python and the Mutagen library. Here’s the code to add a chapter to an MP3 file: from mutagen.id3 import APIC, CHAP, ID3, PictureType audio = ID3("audio.mp3") with open("thumbnail_0001.jpg", "rb") as f: img_data = f.read() image_frame = APIC(mime="image/jpeg", type=PictureType.OTHER, data=img_data) chapter_frame = CHAP( element_id="chp1", start_time=0, end_time=5 * 1000, sub_frames=[image_frame] ) audio.add(chapter_frame) audio.save() This creates a single chapter that lasts the first 5 seconds (0 to 5000 milliseconds), and the per-chapter cover art is thumbnail_0001.jpg. If we ran this in a loop, we could add images for every 5 second slice of the original video. This code is inserting two frames into the ID3 metadata: The CHAP (chapter) frame contains the timing information, and it can have subframes for metadata like title, chapter art, or associated URL. The APIC (attached picture) subframe contains information about a picture, which can either be a blob of image data or a URL to an image on the web. Normally, you’d also insert a CTOC frame which defines a table of contents, but I don’t want a TOC with hundreds of 5-second chapters, so I’m deliberately not doing this here. This is allowed by the ID3 spec – you’re not required to insert a CTOC frame if you’re using chapters, and you can have chapters that aren’t listed in your table of contents. To work out which frames I needed, I used Forecast to create some chapters by hand, and I inspected their frames. In particular, loading an MP3 and calling Mutagen’s pprint() method shows a human-readable list of frames, and then I could drill into the individual fields: from mutagen.id3 import ID3 audio = ID3("audio.mp3") print(audio.pprint()) I wrapped all this code in a project called glancecast, which allows you to convert a video file with a single command, with optional flags to set the frame length and chapter art size: $ python3 glancecast.py interesting_talk.mp4 interesting_talk.mp3 The process takes a minute or so to complete, most of which is spent transcoding the video file to MP3. The resulting MP3s are usually 40 to 50 MB in size, which is very reasonable. The outcome: How it looks in practice Here’s what one of these “glanceable” podcasts looks like in Overcast and on my lock screen: Maggie Appleton presented this talk over two years ago and it’s been on my “talks to watch” list ever since. Once I put it in Overcast? I listened to it in less than a day. It’s not a lot of extra information, but enough that I can quickly glance down and get the gist of what a speaker is saying. Both views update with a new frame every few seconds, or I can put my phone in my pocket and ignore the screen. I’ve used this approach for half a dozen videos so far, and I’m happy with the results. I expect to keep using it, because I have a long queue of videos I’ve been meaning to watch. If you’d like to try this, check out glancecast for the full code and instructions. [If the formatting of this post looks odd in your feed reader, visit the original article]
Andrew Baker, the current Group CIO at Capitec Bank wrote an interesting piece on AI and open source, and how these tools that generate code according to one’s specification may replace the general reliance on open source implementations done by contributors around the world. I’d really recommend reading it. I have great admiration and respectContinue reading "AI Isn’t Replacing Open Source"
A framework for thinking about when AI involvement is additive or a violation
Why we need richer, thicker interfaces and better boundary objects for collaborative planning with agents
A look at 10 foundational pillars that enable agents to operate more competently and more efficiently in any codebase.
