Facebook keeps showing me ads (in this case – a vest) and occasionally chooses very “successful” spots for a freeze frame that serves as a video preview in the feed. But, I must say, it achieves its goal and I click to see what kind of madness this is.
What a delight, Windows 95 in the browser copy.sh/v86.
I’ve launched an electronic open source application for my book Recommender Algorithms! It’s a “sandbox” where you can “run” various recommendation algorithms with different settings, and view specific visualizations for each algorithm that help understand how it works. For instance, for algorithms like ItemKNN, SLIM, or EASE, a key visualization is a heatmap of the learned similarity matrix (item-item similarity matrix). This allows you to see which pairs of items the model considers “similar” (or “influencing” each other). For SLIM, for example, a useful “Sparsity Plot” shows that the similarity matrix indeed turned out to be sparse. For associative rule algorithms (Apriori, FP-Growth, Eclat) the visualization is not a graph, but interactive tables with found “Frequent Itemsets” and generated “Association Rules,” which can be filtered and sorted.
Additionally, there is a parametric mechanism for creating a “game dataset” — Dataset Wizard. It works like this – there are template datasets that describe items through characteristics. For example, recipes through flavors. Or movies through genres. The system generates random users with a random set of characteristics from the same set — and there are many sliders to make this distribution more contrasted or complex. Next, a matrix of user ratings of items is created – conditionally, if the characteristics of the user and the item match, then the rating will be higher because “tastes match”; conversely, if they differ, then the rating will be lower. Here too, sliders add noise and scarcity – randomly removing part of the matrix. The characteristics of products and users are not fed into the recommendation algorithm; they are hidden, but they are used to visualize the results.
The third component of the application is the tuning of hyperparameters. Essentially, it’s an auto-configurator for a specific dataset. An iterative approach is used, which is much more efficient than a full search (Grid Search) or random search (Random Search). In short, the system analyzes the history of past runs (trials) and builds a probability “map” (surrogate model) of which parameters will likely yield the best result. Then, it uses this map to smartly choose the next combination to test. This method is called Sequential Model-Based Optimization (SMBO).
The code is open source and will be further supplemented with new algorithms and new visualizations.
Link to the code in the comments.
Link to the site where the code is deployed and where you can check out the application is also in the comments.
I found a useful Chrome extension – SingleFile. It solves a problem like this – you need to share a browser page that is not public, for example, via iMessage or Telegram. This is not so trivial to do. For example, you can save a .mhtml file from the browser on your laptop, and send it, but only recipients on an iPhone cannot open it. Saving as a standard .html is also not an option, as images and styles are not preserved. Taking a screenshot only captures a small fragment. Installing an extension that creates a long, large PNG of the entire page – this PNG cannot be opened on an iPhone from Telegram at least, only the top renders. Printing to PDF is also not a solution – the result is very poor and highly dependent on the developers’ desire to make a print-friendly version.
SingleFile allows you to create a snapshot of a page from the browser, a regular .html, which can be opened anywhere, with embedded styles and images. But what is especially convenient, before exporting, you can remove anything you don’t want to share through the WebInspector, and it won’t appear in the final .html. The extension is open source on GitHub, and it doesn’t send anything anywhere. Apparently, if there was dynamic loading through JS on the page, it saves not the JS, but the result of the loading, and the JS is cut out.
In general, it’s convenient, a good thing, use it.
(I had an interview released on the internal portal today, and I needed to share it with my family in our family chat)
Incidentally, in my Tesla, there’s a very clever system for identifying the driver. If I enter the car first but sit in the passenger seat, placing my phone immediately in the central console for charging, and then Nadia enters second but sits in the driver’s seat and also places her phone there, her profile is selected automatically because she’s the driver, even though both phones are on charge under the central console.
So, there are two possibilities: either there is an antenna which can precisely detect that a phone has crossed the driver’s door rather than entering the car in any other way, or there is a camera focused on the driver. In any case, it’s very reassuring that it “just works”.
Wow, wine
Rajesh Exports states on their website that they process 35% of the gold mined on the planet. Of course, they are exaggerating, but overall, India and Rajesh do shape the market. It turns out that 11% of all the gold on the planet is adorned on Indian women. Additionally, it was found that in 1947, 70% of all mined gold was in the USA. From 1934 to 1970, it was legally prohibited for private individuals to own gold in the USA. Approximately 22% of all the gold ever accounted for on the surface of the Earth has been mined from a plateau in South Africa called the Witwatersrand. And if you consider all the gold mined throughout history, it would amount to less than an Olympic swimming pool.
China buys up silver, with India not far behind. Interestingly, platinum is significantly used in the production of catalytic converters for vehicles – almost 40% of the global production goes there. China, of course, holds much of this production.
Practically every smartphone, tablet, or touchscreen monitor that we use is coated with a thin layer of indium tin oxide (ITO). This material has a unique combination of properties: it is almost completely transparent while also conducting electricity excellently. This allows the screen to register your touches.
Although lithium is now strongly associated with batteries, historically and still today, a significant portion of it is used in the production of glass and ceramics.
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 🙂
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.
Hi, I'm Rauf Aliev. 