I’m telling the manager now, why do we need to present our AI solution, it’s AI, let it present itself. I imagine that in the near future, bots will be knocking on doors to sell themselves (and maybe not just themselves), while the door will have built-in bot protection.
Yuki is like clockwork. Twice a year, he enters a mode of howling. Just like a wolf at the Moon. Last year, the autumn howl started on October 15-16, 2024, and ended on the 20th. Today, October 15, 2025, it began. Prior to this
* March 15, 2022,
* October 27, 2022,
* February 2, 2023,
* April 1, 2024, lasted four days.
* October 15-16, lasted 4 days
Update: October 19, 2025, it ended
Yuki, we have everything recorded!
Finally, I have released a book! It is called Recommender Algorithms — it contains more than 50 recommendation algorithms with mathematical explanations, detailed descriptions, and code examples.
It all started early in the spring in Germany, when I attended the ACM conference and made the first sketches of the book’s structure, analyzing reports on the RecSys stream. And now, six months later, the book has been published.
Why did it appear? Because there is no single, accessible source either online or in print where the recommendation algorithms of various types and purposes are thoroughly examined. There are articles focused on narrow aspects, but to collect and systematize the developments — from fundamental to the most recent — until now, it seems, no one has managed to do it for some reason. Maybe no one needed to. Suddenly, I found I needed to. I don’t know if I succeeded, but I am eager for your feedback.
Available on Amazon and Barnes and Noble. There is a Russian automatic translation (surprisingly, but very decent), but I do not know how to sell it yet.
https://www.testmysearch.com/books/recommender-algorithms.html?FB
(This is not my only book, but today — just about this one.)
Microsoft has one very nasty thing with Outlook for MacOS, which for some reason nobody tries to fix. If you have a meeting in 30 minutes, Outlook reminds you with a popup showing the upcoming meetings, where it “highlights” these meetings. Well, in my case, there’s no secret here, I could even share my screen during that time. But it would be nice if such notifications didn’t appear while screen sharing, especially while recording, because screen sharing goes through Teams, which is part of the same package as Outlook.
But what’s worse is something else. If you try to CLOSE this notification window while screen sharing on MacOS (especially if the recording is on), it causes the whole Outlook with all the emails there to pop up. And there might be things there that the viewers shouldn’t see. That is, by _closing_ the window, you suddenly reveal the titles of email messages. Which is completely unexpected (well, until you step on these rakes, then it’s not unexpected anymore).
Bought myself an AI microphone that listens to everything around and provides summaries. Decided to test it once. With it, you can’t even watch reels with the mic turned off on your computer, because it tries to merge and summarize everything it hears 😉
“..The team methodically moved through complex comparisons, but unexpected phrases like ‘Watch the video back if you didn’t notice’ and ‘Don’t be a sucker’ created a quiet, almost poetic dissonance—as if the universe whispered ‘Let it be’ amid spreadsheets and sprint tickets….”
While working on a book, I realized what kind of product I’m missing. It’s an AI diagram generator based on textual descriptions.
The idea is that the master document for the diagram is text. This textual description can be (and should be) quite detailed, so the generated diagram exactly matches the author’s vision. The diagram itself is not edited. That is, it can be edited – moving circles around, but ideally, after making changes, the system should update the text, generating from which will result in what the user adjusted.
The result — the diagram — should correspond as closely as possible to the description. If it does not match the description because, for example, it’s impossible to make a triangle with three obtuse angles, the system should do its best and provide a verbal response about what didn’t work. The user can then modify the task so that the system complies and produces the diagram correctly.
But then we understand that the author might have randomly achieved something that they liked with their flawed text. And if regenerated, it might turn out differently, and not necessarily better. Therefore —
You could ask the system to generate a diagram description from the diagram, which, if inputted back into the diagram generator, would result exactly in what the description was generated from. Yes, this description would be more verbose and complex, but it would more reliably describe the result.
So, from this point, you are no longer working with the diagram. You are working with text. If a diagram is needed — you simply compile the text into a diagram and it turns out as needed. But you don’t even work directly with the text. You work with this diagram-description text through an LLM, asking it to add some block, and the text changes, but changes in a way that everything doesn’t suddenly shift.
The final diagram should be in an object form, from which raster (PNG) or vector (SVG, EPS) images can be created.
It would also be great if such a system could take existing diagrams or diagram templates so that it could borrow styles and existing conventions on how to display what.
So, these are my fantasies. If anyone has ideas on how to implement this — let’s discuss 🙂
An interesting trick. To color the circle dark purple, you simply need to look at it and it will instantly change color. However, to revert it back, you just need to stop looking at it, and it will return to its original appearance (though you’re likely to look at another circle instead)
I decided a while ago to write a book on recommendation algorithms. With mathematics, code examples, a repository, etc. English, of course.
Accordingly, I am looking for volunteer reviewers who are knowledgeable in the field. Also those who have experience with print-on-demand on Amazon.
There’s already about 200 pages of content. About three months of work left. Working title Recommender Algorithms in 2026: A Practitioner’s Guide. Roughly half of it is still in draft form, with the first 80 pages about 80% complete.
I’ve built a mechanism to publish in HTML and PDF simultaneously. The HTML version is fully functional, with navigation. The navigation block reflects the current section, and as you scroll, it shifts to the one in front of the reader. Clicking on a section, of course, teleports you to what you clicked on. It’s all completely automatic.
So far, I have only one car model and brand that can deploy airbags not at the moment of impact, but a moment earlier, so that by the time of the impact, it’s not too late to do so. We’ll see what the news shows, but tests indicate that this thing works better than the traditional method. Reality might turn out to be harsher, but we’ll keep an eye on it.
It’s also interesting that the car started to receive new exciting features after purchase. I never had this experience before. What you bought it with, you lived with, and sometimes you could go to the dealership for something new, and it usually involved replacing something physical.
The previous update (not very useful to me, but maybe to someone) was about automatic detection of children and animals in the cabin. And if it turns out they were left inside while the owner left, the car does not turn off the climate control. And of course, it screams into the app that this is not a good thing to do.
It turns out USB-C cables are sometimes whole computers inside the odd form factor of a wire. Watching a video where guys from Adam Savage’s dissected an Apple Thunderbolt 4 cable ($130) using a CT scanner, explaining its internals, and comparing it to a similar cable for $12.
The cable connector contains a complex system that includes a full-fledged processor, two power supplies, and many other components. The processor splits data into multiple parallel streams and converts them into differential signals that travel through twisted, intertwined pairs of wires. The system sends two signals simultaneously, but in mirror-opposite directions. This helps protect the signal from interference (from vacuum cleaners, mobile phones, etc.). Indeed, the circuit board inside is nine-layered.
On the internal circuit board, there are interesting serpentine/wavy tracks with sizes in fractions of a millimeter. It turns out, Apple engineers intentionally made them longer to match the overall length with the neighboring longer tracks (because they include turns). This is necessary for the signals to reach the processor absolutely simultaneously, down to the nanosecond.
The cable itself inside is made up of many individually shielded smaller coaxial cables. There are more than a dozen of them.
The cheap cable lacks this smart electronics, no active components inside. It just has connectors and wires.
But the coolest thing – the guys post such scans in the video description as a link to a viewing program. There you can rotate and examine everything on your own. I’ll put it in the comments
Hi, I'm Rauf Aliev. 