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button table { margin: 0; } .fa-arrow-left::before {content:"←"} Bad code <button type="button" onclick="window.open('https://example.com/other-page')"> <table> <tbody> <tr> <td> <i class="fa fa-arrow-left" aria-hidden="true"></i> </td> <td align="left">Back</td> </tr> </tbody> </table> </button> Back Issues and how to fix them The HTML isn't valid because the table element isn't allowed as a child of the button element. Based on the content categories listed on MDN, the table element qualifies as flow content and can only be placed inside an element that expects flow content. The button element, on the other hand, expects phrasing content, not flow content. The semantic information of the table doesn't cause any issues because the button's content is presentational See Buttons Role on W3C, Presentational Children. Keep it simple and structure the button element using CSS, such as Flexbox. Instead of using tables to structure the button element, use phrasing content elements, such as span, img, or SVG. Good code <button type="button" onclick="alert('Hello World!')"> Back </button> Resources <button> on MDN <table> on MDN Content categories on MDN Buttons Role on W3C <button> on W3Schools <table> on W3Schools Presentational Children
by Rian Rietveld It's a common pattern to use a logo in the header as a link to the homepage. Fun fact: the alt text of the image inside a link, will be added to the link text. The problem with linking a logo is that it serves 2 purposes: a logo, that tells you which site you are visiting; a link, that leads to the homepage. So add both that info in the alt text. Explain what's on the image and where the link leads to: "Site name logo, to the homepage". Note: Start the alt text with the visible text, then the link will be easier to target for people using voice recognition software. Let's take as example the logo of my home town Leidschendam-Voorburg. In code (simplified): <a href="/"> <img alt="Leidschendam-Voorburg logo, to the homepage" src="logo-lv.svg" /> </a> Generated HTML: VoiceOver in Safari will announce this as: All info is there. There is an advantage of using the alt text instead of an aria-label solution. When the connection is slow, the alt text will show up before the image does and already informs all users of the site name and link destination. It's quite robust. Discussion Do I need the word logo? Isn't that redundant, like the word image or picture? Yes, in this case the fact that this is a logo tells important info. Not only for blind users, but for all screenreader users. If you are visually impaired but not totally blind, you may see the image vaguely. With the addition of the word logo, you also know what the image is. The site name is required, but the logo gives extra context. Isn't this a violation of 2.5.3 Label in Name? No, the visible name is in the alt text, which becomes the accessible name of the link. Adding extra context to the link is common practice. Compare this with the notorious "Read more" link in a card. A common pattern is to use a sr-only CSS class (or equivalent) with additional text: <a href="url">Read more<span class="sr-only"> about cute kitten</span></a> . You see Read more. You hear Read more about cute kitten. In our case: You see the site name and logo; the visual position tells the link destination. You hear the site name, the fact it's a logo and the link destination. The whole visible text should be in the accessible name, but extra information, like the meaning of the image and link destination can be added for context. That way the purpose of the link is explained. Why didn't you use an aria-label? Of course there are other and also valid solutions. By using plain simple HTML I want to honor the first rule of ARIA: If you can use a native HTML element or attribute with the semantics and behavior you require already built in, instead of re-purposing an element and adding an ARIA role, state or property to make it accessible, then do so. Happy Holidays!
Bad code <button type="button" onclick="window.open('https://example.com/other-page')">Link target description</button> Issues and how to fix them A button opening a link will be unexpected behavior for screen reader users. No matter how it is styled. Links disguised as buttons won’t show up in the link list of a site in assistive technologies. Use links for navigation to other pages or sections, and buttons for actions performed on the current page or within the application. Good code <a href="https://example.com/other-page">Link target description</a> Resources ` on MDN](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/a) - [`` on MDN](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/button) - [Buttons vs. Links by Eric Eggert](https://yatil.net/blog/buttons-vs-links)
The good intentions were there but in the HTML and Accessibility world, less is sometimes more. Bad code <label for="textinput">First name</label> <input type="text" id="textinput" aria-label="First name" placeholder="First name" title="First name"> Issues and how to fix them The aria-label, placeholder, and title attributes all provide the same informaton ("First name"), leading to the screenreader reading the same text multiple times. The aria-label is unnecessary since the input is already correctly labeled by the <label> element. <label> elements should be preferred to aria-label since they are also visible to sighted users. The title attribute is not needed here, as the label and placeholder already convey the necessary information. Using title in this context adds unnecessary complexity. The placeholder text should provide a hint / example to the user what kind of input is expected, it should not act as a label or contain the same content. Good code <label for="textinput">First name</label> <input type="text" id="textinput"> Resources <label> on MDN aria-label on MDN placeholder on MDN title on MDN
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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!
Internalize this graph, we post it on this blog a lot. If trends continue, China will have 10x the electricity of the US in 10 years. Whenever you think about geopolitical strategy, always keep it in mind, because whatever your plans are must consider this. Here’s a strategy that would make sense for America long term. Sell as many AI chips into China as possible, use the money to purchase solar panel and battery factories, and build out electricity capacity as quickly as possible. Fundamentally, compute is a function of electricity. That’s the sort of long term thinking that gives America a chance. With robotic labor, immigrants like Jensen and Elon, a business friendly culture, and an incredible chunk of land, it’s not so clear who is ahead in 50 years. Regardless, I do know this strategy would lead to massive prosperity for the citizens of both countries. However, that’s not close to what we see happening. We see a lot of short term zero sum thinking in action. It’s very scary what the US “AI race” mentality leads to. When exactly does the race end? Whatever capabilities you have today will eventually be matched, unless… …you believe that you can use ASI in a decapitating first strike. I’m worried that this is what these people see as the only way out. It probably doesn’t work, can you outthink steel and drone production? I don’t see how, see my critiques here. But I’m not totally sure, I’m not a superintelligence. And if it does work, that may be the end. My likelihood of imminent WWIII went up a lot with the nationalization of NVIDIA.
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.
