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I took an amazing trip to SE Asia last month, including Angkor Wat. I had a hard time finding good reading or other resources to learn from before I went, in part because Amazon is awash in AI garbage. Here’s some books and podcasts I found useful about the Khmer empire in general and Angkor in particular: Ancient Angkor by Michael Freeman and Claude Jacques. The closest thing to a coffee-table book to preview what you will see. The practical information is outdated but the pictures and descriptions are good. Empire Podcast #185: The God Kings of Angkor Wat by William Dalrymple and Anita Anand. An entertaining and fully detailed account of the Khmer empire. It’s basically an excerpt from Dalrymple’s new book The Golden Road: How Ancient India Transformed the World. Fall of Civilizations Podcast #5: The Khmer Empire by Paul Cooper. Another history, not quite as magically well told as Dalrymple but full of good information. Angkor and the Khmer Civilization by Michael D. Coe. A highly recommended history of the Khmer region. Honestly I found this very dry and too detailed, but I did learn from it. Lonely Planet Pocket Guide: Siem Reap & the Temples of Angkor. We didn’t use this much but it seemed like a useful practical guide. OTOH it dates to 2018 so things have changed. My other advice for visiting Siem Reap and Angkor is: go. It is amazing. Plan for at least two full days of touristing there. Hire a private guide and driver if you can, it is absolutely worth it. (Email me for a recommendation.)
I’ve been doing Dry January this year. One thing I missed was something for apéro hour, a beverage to mark the start of the evening. Something complex and maybe bitter, not like a drink you’d have with lunch. I found some good options. Ghia sodas are my favorite. Ghia is an NA apéritif based on grape juice but with enough bitterness (gentian) and sourness (yuzu) to be interesting. You can buy a bottle and mix it with soda yourself but I like the little cans with extra flavoring. The Ginger and the Sumac & Chili are both great. Another thing I like are low-sugar fancy soda pops. Not diet drinks, they still have a little sugar, but typically 50 calories a can. De La Calle Tepache is my favorite. Fermented pineapple is delicious and they have some fun flavors. Culture Pop is also good. A friend gave me the Zero book, a drinks cookbook from the fancy restaurant Alinea. This book is a little aspirational but the recipes are doable, it’s just a lot of labor. Very fancy high end drink mixing, really beautiful flavor ideas. The only thing I made was their gin substitute (mostly junipers extracted in glycerin) and it was too sweet for me. Need to find the right use for it, a martini definitely ain’t it. An easier homemade drink is this Nonalcoholic Dirty Lemon Tonic. It’s basically a lemonade heavily flavored with salted preserved lemons, then mixed with tonic. I love the complexity and freshness of this drink and enjoy it on its own merits. Finally, non-alcoholic beer has gotten a lot better in the last few years thanks to manufacturing innovations. I’ve been enjoying NA Black Butte Porter, Stella Artois 0.0, Heineken 0.0. They basically all taste just like their alcoholic uncles, no compromise. One thing to note about non-alcoholic substitutes is they are not cheap. They’ve become a big high end business. Expect to pay the same for an NA drink as one with alcohol even though they aren’t taxed nearly as much.
The Trump administration has made aggressive threats against immigrants in the US. It’s not clear what’s coming, my biggest fear is a violent display of fascism. (Don’t call them camps!) But even if it’s a polite legal process it will be chaotic and disruptive to many neighbors. Back in 2018 I donated reactively to the Trump administration’s cruelty to immigrant families. This time I’m trying to get ahead of it. The need for the money is now, no matter what happens it is going to be a bad few years for immigrants in the US. To that end I asked on Metafilter about charities to donate to. I got back a remarkable reply listing 18 charities that all have some California focus. I donated to most of them. I want to highlight two groups in particular. One is RAICES. They work in Texas, not California, but they are well organized and effective. The other is KIND. They have a simple mission. They try to ensure every unaccompanied minor has legal representation in immigration court (something not guaranteed.) The other groups on the list are all also deserving of consideration.
LLMs are good search helpers. Here’s three search tools I use every day. All of these use an AI to synthesize answers but also provide an essential feature: specific web search results for you to verify and further research. I use these for conversational inquiries in addition to more traditional keyword searches. Phind is an excellent free LLM + search engine. The AI writes an answer to your query but is very careful to provide footnotes to a web-search-like list of links on the right. I use this mostly for directed search queries, things like “what’s an inexpensive TV streaming device?” where I might have used keyword search too. The Llama-70b LLM that powers the free version is quite good, sometimes I have general conversations with it or ask it to generate code. Bing CoPilot has a very similar output result to Phind. I find it a little less useful and the search result links are less prominent. But it’s a good second opinion. Bing has been a very good search engine for 10+ years, I’m grateful to Microsoft for continuing to invest in it. CoPilot results are sometimes volunteered on the main Bing page but you often have to click to get to the ChatGPT 4 Turbo enhanced pages. Kagi is what I use as my general search engine, my Google replacement. It mostly gives traditional keyword search results but sometimes it will volunteer a “Quick Answer” where Claude 3 Haiku synthesizes an answer with references. You can also request one. I think Phind and CoPilot do a better job but I appreciate when Kagi intercepts a keyword search I did and just gives me the right answer. Google has tried various versions of LLM-enhancement in search, I think the current version is called AI Overviews. It’s not bad but it’s also not as good as the others. Not mentioned here: ChatGPT or Claude. Those are general purpose LLMs but they don’t really give search results or specific references. In the old days they’d make up URLs if you asked but that’s improving.
8BitDo makes good game controllers. A wide variety of styles from retro to mainstream, with some unusual shapes. And wide compatibility with various systems: PC, Macs, Switch, Android. They’re well built, work right, and quite inexpensive. A far cry from the MadCatz-style junk we used to get. The new hotness is the Ultimate 2C, an Xbox-style wireless controller for the very low price of $30. But it works great, doesn’t feel cheap at all. The fancier mainstream choice is the Ultimate 2.4g at $50 which includes a charging stand and extra reprogrammability. But what’s really interesting to me are the odd layouts, often small or retro. The SN30 Pro is particularly interesting as a portable controller. SNES-styling but a full XBox style modern controller with two analog sticks, easy to throw in a suitcase. There’s a lot of fiddly details for this class of device. Controller type (XInput, DInput, switch, etc), wireless interface (Bluetooth or proprietary), etc. 8BitDo makes good choices and implementations for all that stuff I’ve tested. They seem to work well with Steam. They’re a popular brand so well tested. It helps that PC game controllers have mostly standardized around the Xbox layout and XInput. Steam can patch over any rough spots for older games.
More in programming
A Fediverse server for photographers, supports HDR. Like Pixelfed but newer?
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!
The resiliency startup has slashed the amount it spends on AI; down from $4,500 per dev, per month, in August, to $1,600 now.
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.
