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While on a short road trip and then recovering from surgery, I hand quilted and hand embroidered a linen lilypad quilt. And yes, I did make this little quilt for my Frinos plush from Hades II to have a nice place to rest.
Like so many other hat knitters, I've knitted the classic WWII watch cap, which is also known as Beanie no. 212, more than a handful of times. (You can find it on Ravelry here and here, respectively.) It's a charming, quick knit that's easy to resize, but I've never really liked the crown shaping on the original. I've knit a lot of these hats, and only one's had the original crown shaping for the decreases. I'm sharing two crown shaping variations I keep going back to below. I always knit this hat in the round, and my crown shaping patterns assume you will be knitting in the round, too. Alternate crown shaping 1 This alternate crown shaping follows the 6x2 ribbing of the previous section for the bulk of each round so that the ribbing continues throughout the entire hat. Written out: Divide stitches on 3 double pointed needles with stitches evenly divided among the three needles or mark out even sections with stitch markers if decreasing across 2 circular needles or with magic loop. (k1, ssk or skp, k if previous row was knit or p if previous row was purl until 3 stitches are left on the needle/before the next marker, k2tog, k) x3. Repeat step 1 until 4 stitches remain on each needle/in each marked section. Cut and draw yarn through the remaining 12 stitches 2-3 times. Tie off and weave in the ends. Alternate crown shaping 2 This alternate shaping both maintains the ribbing throughout the crown shaping and accentuates it by creating a sort of three-pointed star with 3 of the 2-stitch-wide purl parts from the previous 6x2 ribbed section. This shaping really only makes sense when making a size hat where the number of stitches is evenly divisible by 24, so I typically only do it on size "small" hats (cast on 96 stitches). You could adjust the needle size and gauge for a yarn that works well at that gauge to make this work for different sizes. Written out: Stop the last 6x2 ribbed row 1 stitch early and divide stitches on 3 double pointed needles with stitches evenly divided among the three needles or mark out even sections with stitch markers if decreasing across 2 circular needles or with magic loop. Ensure that each needle transition or marker has a single purl stitch on each side. (p1, ssk or skp, k if previous row was knit or p if previous row was purl until 3 stitches are left on the needle/before the next marker, k2tog, p) x3. Repeat step 1 until 4 stitches remain on each needle/in each marked section. Cut and draw yarn through the remaining 12 stitches 2-3 times. Tie off and weave in the ends. Additional project notes Needle: US 7 - 4.5 mm Gauge: 19 stitches and 24 rows = 4 inches in stockinette Hats made for me should generally be a small (cast on 96 stitches), and hats made for my partner Matt should generally be a medium (cast on 104 stitches). Footnotes Either of these will create a left-leaning decrease, use whichever one you prefer.↩ Ibid.↩
After five different drivers, including a school bus driver, aggressively close-passed me in Midwood last night and two of them threatened me just for being on the street, I realized there's a huge gap in educational outreach about bike rules in NYC: drivers. I decided to make a mini-zine that demystifies the behavior of law-abiding bicyclists like myself and helps drivers better understand what to expect when sharing the road: All text and illustrations in the mini-zine are my own. Many thanks to Matt Denys for help proofreading! This zine is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, so you can copy and distribute this zine for noncommercial purposes in unadapted form as long as you give credit to me. Gas stations, car dealerships, auto repair shops, rental car offices, parking lots, book shops, restaurants, coffee shops, and other commercial establishments are welcome to give out these zines, too - as long as the zine itself is free! Check out the NYC Bike Rules for Drivers mini-zine on the web or download the pdf to print here!
Here's how to fold a mini-zine, featuring my mini-zine So you want to make a public comment! Fold the printed mini-zine page in half, then half in again, and then in half again as shown. When you open the piece of paper, each of the pages should fit fully within a set of folds. With the mini-zine page completely open, fold it in half with short ends touching ("hamburger style") so the text is on the outside. Using scissors, cut a across the dotted line. When you reopen your paper, there will be a slit in the middle of the sheet. Fold the paper in half lengthwise with long ends touching ("hotdog style"), hold the paper at either end, and fold the sheet into itself to form an 8-page booklet. Make sure the cover is on the front!
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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!
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.
The economics of software engineering have fundamentally changed.
