Category: Learning

Mastering Cross-Posting: From Facebook Frustrations to Dual Blogging Excellence | May 23 2026, 14:28

I have perfected the cross-posting from Facebook to my two blog sites [which almost no one visits] – beinginamerica.com and raufaliev.com. When a new post is published on Facebook, a mechanism is triggered to translate the post into English, process attached images, generate descriptions for them, create a title based on the text of the post and descriptions of the images, generate tags from the same basis, record the post in turso db – this is a cloud database, free up to certain limits, create embeddings via openai, record in qdrant cloud – this is also a cloud database, but vector-based, and finally, upload images to wordpress via API, and publish the post in English and Russian via API.

All would be well, but of all the APIs, the silliest one is Facebook’s. Firstly, for pages like mine, transitioned to New Experience, it’s almost impossible to use most of this API. Well, it’s possible, but you have to spend a long time proving to Facebook that you really need it, by showing startup documents, demonstrating the application, etc. Obviously, they are reluctant to deal with something that takes content out of their system. In addition, the token that gives access to the latest messages is relatively short-lived (possibly a few weeks), and it needs to be obtained anew through a browser only. So, any automation requires regular attention, otherwise it breaks.

If you mess up and don’t offload the latest posts through this Facebook Graph API in time, they just disappear from the list of recent ones and that’s it, no more API access to them. The only way is to request an archive download from Facebook. This download is also rather silly – it requires a lot of transformations and removing unnecessary stuff. For example, in the file containing posts, which I process, for some reason there are links that I sent in comments without accompanying text. And the comments are in a separate file!

To assign tags, I had to solve a separate challenge. Here’s the thing: there are about 10,000 posts over all time. That’s a big chunk, and you can’t build tags from it because it doesn’t fit into the contextual window of the LLM. But you need to. So, I did this: a script takes random posts from the 10,000 in such a volume that their total size is just below the specified limit in tokens, and at the end of this block, it adds the prompt “generate the most common tags for me, 30 pieces” (I simplify the prompt used). In the end, I ran this 10 times and got 10 sets of tags with 30 pieces each, generated for different slices of the database. That made 300 tags, some of which are complete duplicates, while others are synonyms and closely related in meaning. All this is fed into the LLM, and we get a list of tags and a hierarchy of tags. Now we have a limited set of tags that reflect the 10,000 posts as closely as possible. Turns out, that in almost 20 years on Facebook, my breakdown is as follows:

Tag Posts

==================================================

#Russia 3412

#Thoughts 3146

#Tech 3105

#Culture 2765

#Hobbies 2726

#AI 1603

#Science 1367

#Software 1358

#Travel 1298

#Learning 1138

#Society 1050

#Nature 958

#Education 915

#Business 902

#Art 894

#Programming 889

#Humor 840

#History 807

#Gadgets 750

#Moscow 713

#USA 614

#Cinema 567

#Webdev 493

#Music 476

#Sports 473

#Mindset 443

#Auto 400

#Books 386

…

and so on. This list includes both tags from the limited list and tags that the LLM appointed to content simply because it didn’t find anything suitable in the limited one.

Tags from the limited list became categories on the site. The rest of the tags + these just became regular wordpress tags.

As for image search. I had two ideas on how to do it. The first – OpenCLIP. It’s pretty straightforward but requires hosting the model somewhere. Easy on my machine, but inconvenient to start it each time, plus I planned to move the migrator to a cheap server on Amazon. It’s also okay to calculate in cloud models, but you have to pay a bit, which is yet another dependency. But the main thing – it works quite well without it. I generate descriptions for images using OpenAI, which is used for translating into English anyway, and then create embeddings using a large model. So far, all search tests are a great success. Especially when there’s text on the image, and it’s a big question whether OpenCLIP would have interpreted it successfully.

In the end:

1) wordpress raufaliev.com – free

2) wordpress beinginamerica.com – free

3) turso db where all posts are stored – free

4) qdrant cloud where embeddings are stored – free

5) openai for translation and image descriptions – not free, but inexpensive (cost $30 for post processing over a year).

I attach two screenshots – how the search by images works, and by texts, as well as the migrator dashboard.

Navigating Nabokov: A Companion Glossary for “Lolita” | April 08 2026, 11:24

I have finally finished the book The Reader’s Glossary – essentially a 5200-word dictionary for “Lolita” by Nabokov, but organized not alphabetically, like regular dictionaries, but in order of the occurrence of complex words, divided by chapters and indicating the context of the word or phrase. The website – readersglossary dot com (see the first comment). It is expected to be used, among other things, as a companion book while reading the original. Yes, it’s twice as thick 🙂

The dictionary turned out quite thick – 600-700 pages. It is available in four languages – Russian, English, French, and German. Moreover, the translations (RU, FR, DE) or clarifications (in ENG) are not abstract but contextual, taking into account how Nabokov himself translated the fragment from English (“Lolita” was first written in English, then translated into Russian).

On my website, there are huge fragments of these dictionaries RU, FR, DE, EN available for review (each about 1/3 of the total volume).

There is also a full-fledged interactive dictionary on the site, where you can enter a word and see its translation or explanation. The dictionary mainly contains complex words, but we know that complexity has its own definition for everyone, so all words are divided into three categories and highlighted with different frames. Probably for a well-read Anglophone, the first category (dotted) is completely useless (about 50% of the dictionary), for the less-read, maybe 20% are useless. But I decided not to cut it further, because the book is not only for Anglophones but also for those for whom English is a second language, and there those dotted frames are very handy.

Overall, I did this “for myself and friends,” just for fun, not as a commercial project. Therefore, I am quite sober in understanding that it has a super niche audience, and if even once a week someone finds it useful, it’s already nice.

Although it was something like a hobby, the book took a lot of time. To achieve what I did, I developed a dozen applications/scripts, a couple of which have their own interactive UI, in which I spent many hours over two months of work. And of course, I learned a lot in the process, which is actually the main fun of it.

So, come to the website – readersglossary dot com. Link in the comments

P.S. In Russian – only as a PDF for now. Amazon doesn’t allow selling books in Russian, only in a small number of European languages in addition to English. The French and German versions of the dictionary will be released on Amazon about a week from now.

Navigating the Lexical Complexity of Nabokov’s “Lolita” | April 02 2026, 15:56

I’ve finished the first version of a dictionary-style book on Nabokov’s “Lolita”. The chart shows how the complexity of vocabulary is distributed across the pages of the book. The lower chart averages 25 sentences, displaying the number of complex words on the vertical axis, with colors indicating their complexity/rarity (purple – the most complex, red – less complex, yellow – even less so). But I have already removed two levels, and overall, for a foreigner, all five levels are challenging. In the book, level 3 is marked with a dashed line, level 4 with a simple frame, and level 5 with a double frame. Currently, there are 5794 words, of which 541 are fifth level, 1070 are fourth, 1883 are third, 1393 are second, and 54 are first (the simplest ones). Considering that the first version ended up being 1148 pages, the dictionary will need to be significantly streamlined by removing what can be dispensed with. This mainly pertains to the first and second levels, and some from the third and fourth. The rarity of words is calculated in three ways: through LLM, and through two lists of word frequencies in the English language corpus (300K words).

Not all words are complex. For instance, in the sentence “With the ebb of lust, an ashen sense of awfulness, abetted by the realistic drabness of a gray neuralgic day, crept over me and hummed within my temples.” someone well-acquainted with English might not know the words ebb, abet, drabness, while everything else is familiar, but lower the requirements for the reader, and the dictionary might not be very useful for such cases.

Or consider the sentence:

Homo pollex of science, with all its many sub-species and forms; the modest soldier, spic and span, quietly waiting, quietly conscious of khaki’s viatric appeal; the schoolboy wishing to go two blocks; the killer wishing to go two thousand miles; the mysterious, nervous, elderly gent, with brand-new suitcase and clipped mustache; a trio of optimistic Mexicans; the college student displaying the grime of vacational outdoor work as proudly as the name of the famous college arching across the front of his sweatshirt; the desperate lady whose battery has just died on her; the clean-cut, glossy-haired, shifty-eyed, white-faced young beasts in loud shirts and coats, vigorously, almost priapically thrusting out tense thumbs to tempt lone women or sadsack salesmen with fancy cravings.

My browser even highlights four words here.

I have definitions of words in English, German, French, and Russian. I’ve encountered the issue that different words from the text are considered complex in different languages, yet they are unified for me. So, I’ll have to mark, for example, French words in the English text separately, so they are not included in the French version, since there, the reader knows, for instance, what quel mot means.

Overall, this weekend I’ll be manually removing about half, and then I can make the cover and list it on Amazon.

Smartfolio.me: Revolutionizing Knowledge Organization with Advanced Features | March 19 2026, 04:01

My creation – the knowledge organization tool Smartfolio.me – has gained new features. I’m attaching a five-minute video overview.

It’s like Google Docs, but you can embed documents within each other, creating a network of connected knowledge, and these documents can be PDFs and regular texts.

Upload a PDF, the program converts it into images, and you can highlight any sections right on the pages to leave a comment or ask a question.

If something in the text is unclear, you highlight the area and press “elaborate” — the LLM will detail everything thoroughly, taking into account the context of the entire document, and the explanation will stay linked to the highlighted fragment.

You can simply cut out a piece from a PDF, and the LLM extracts clean text or a ready-made formula from it.

In the PDF window, there is now a small panel — all comments and explanations are immediately visible there, so you can quickly jump to the necessary parts.

You can cut out a diagram or graph from a PDF, copy it as a picture, and paste it into your text. It will automatically crop “on the fly” and save in the database, not as a copy but as a link to the page with crop parameters.

If you delete the page link in the text, it won’t disappear completely but will go into a special list, from where you can reattach it somewhere else or delete it finally. The same document can be inserted in several places. If you add a comment to it, it updates everywhere where this document is linked.

Mathematics is fully supported — LaTeX formulas can be not only viewed but also clicked to adjust them in the editor.

You can generate formulas by description. Just write in words what formula you need (for example, “binomial distribution”), and the system itself outputs the ready formula code.

Now there is a system of plugins – essentially isolated experimental functions separate from the main program. For instance, there is a plugin that recursively collects all subpages into one long document — convenient if you need to read or print everything at once.

Or consider the “YouTube Transcript Cleaning” plugin. If there is a dirty lecture text from YouTube, the plugin will punctuate, paragraph, and create neat headers.

If you insert a link to a website, it opens in a column next to it — you can read the source and simultaneously take your notes. However, some websites do not allow embedding on foreign pages. The system recognizes such sites, and they open in a new tab.

The left panel with the list of pages can be hidden or resized with the mouse, so it doesn’t take up space on the screen.

You can simply copy and paste an image or screenshot, and it will not just insert, but also upload to the database.

It supports working from a mobile phone. On the phone, the interface switches to a single-column mode for convenient reading and commenting on the go.

Multiple databases are supported – you can switch between them. You can connect different databases and different LLMs and switch between them.

Exploring the Multifaceted Uses of “Oblong” in English and Russian | March 17 2026, 13:50

Sometimes in English, there are very unusual words that are very difficult to translate into Russian. Here, for example, is the word oblong. As an adjective, it translates as “elongated, oblong,” but in the book, both uses are nouns. Often oblong refers to a face – that is, close to an oval, but oblong is a broader concept that describes any figure having an elongated appearance. My mom bought an oblong tablecloth for her new table.

As a noun, it is also used, and quite frequently (though less so than as an adjective). As a noun, oblong means “a rectangular object or flat figure with unequal adjacent sides.” Rulers are considered elongated items (oblongs). Laptops, tablets, and flat-screen TVs are oblongs of different sizes. A rectangle can be defined as oblong; however, not all elongated figures are rectangles. The same face, for example. Additionally, in mathematics, an oblong number is what in Russian is called a rectangular number (the product of two consecutive numbers. For example, 12). In general, it’s utterly baffling.

The word has been alive since the 15th century, by the way. So, in my book, it appears twice, and both times as nouns. In the first case, Nabokov translated it as “corner,” and in the second – “a small oblong of smooth silver” as “a little piece.”

Exploring Multilingual Vocabulary in Nabokov’s Works with Apple Books | March 15 2026, 23:20

Man, it’s really convenient. Just sitting here reading.

The usage pattern is as follows: I hold the phone in my hands. There, in apple books, this and that book. You see an unfamiliar word – it will likely be in the word list of the chapter. The definition takes into account the translation by Nabokov himself. Then you look a couple words ahead, put the phone down, continue reading. You encounter those words, and they are still in your short-term memory, and hooray, you understand. During a break, you load the next couple of words into your brain. You have to hold the phone and flip through, each page contains 4-5 definitions.

Now, every word has definitions in English (interpretation), French, and German. Consequently, I can publish four books.

Overall, my level of English matches what my app predicts about which words will be challenging. But someday I’ll need the same for French, and it will require an assessment of the difficulty level for each word because even some basic words will be unclear to me. I’m not sure that a book with basic words will be handy. With rare ones – definitely handy.

Crafting Nabokov’s Dictionary: A Multilingual Lexical Journey | March 15 2026, 18:30

I’m reading Nabokov and decided to take a break to create a convenient app “Nabokov’s Dictionary” and am considering selling it on Amazon as a book. Essentially, it looks like this (see screenshot) – definitions of complex words in English, Russian, German, and French, in the same order they appear in the original book.

Would you buy such a book?

To accurately make their definitions, I also wrote an aligner – a program that matches sentences and paragraphs in English with their translations (Nabokovian) into Russian. And when a word’s definition is created, it uses not only the knowledge of LLM but also the Russian translation by the author. It’s worth separately discussing how the algorithm works (I invented it myself because everything I found online did not work as I needed). It first finds long sentences and matches the longest sentences with their pair through cosine similarity of embedding vectors created through the multilingual e5 model. These sentences become anchors. Then, assuming that for long sentences the error is almost excluded, the longest sentence between anchors is found, and everything repeats recursively. There are many situations where a sentence in Russian has no equivalent in English and vice versa, where a sentence is split into two, or conversely two are merged into one. The algorithm handles this as best as it can. The result is quite a good quality of alignment. To such an extent, that errors in alignment can hardly be found (but they are likely still there). Either way, it is only needed for the context for translating words, even if there are rare errors, it’s not a big deal.

Would you buy such a book?

Exploring English: Verbs, Misunderstandings, and Learning Through Contrast | March 06 2026, 23:57

About the English language. When Yuki sees another dog, he adorably places his chin on the ground and presses his paws to his face, but I have to tell him every time not to approach because once he lets them get closer, he suddenly starts growling and instigating a fight. And what verb would you choose for that?

Well, from school I knew that roar meant growl. And I even told everyone “roar” for the first week until I googled it and realized that in roar, it’s tigers, lions, and motorcycles, but for dogs, it’s growl or even snarl (with teeth showing).

Or take the phrase “cook food.” To cook comes to mind, but actually, to cook implies thermal processing (fire, stove). If you’re “cooking” a salad, tea, or a sandwich, a native speaker would say make. Saying “I’m cooking salad” is like you decided to boil it.

Or suppose you decided to watch a movie. In English, the choice of verb depends on where you are and how large the screen is. When you go to the cinema, you use the verb see. “Let’s go see the new Dune movie at the cinema.” If you say “I watched a movie at the cinema,” they’ll understand, but it sounds a bit technical, as if you were sitting there closely studying the screen like a security guard monitoring it.

But. When you turn on your television, laptop, or projector in your living room, watch comes into play. The verb watch implies extended attention to something on a smaller (relative to theater) screen. By the way, if the screen is off, you look at it (as an item). Once you turn it on and a picture appears, you start to watch it.

Generally, for an advanced level, it makes sense to attach each concept to a scale, to remember the words in shades of intensity. For example,

Cry -> Weep -> Sob.

Annoyed -> Irritated -> Angry -> Furious -> Livid.

Smile -> Chuckle -> Laugh -> Giggle -> Guffaw

Spitting -> Drizzling -> Raining -> Pouring

and so on.

And then further distinguish them by paired opposites, like the smile-cry from the example above.

It’s very easy to remember when put together.

But it’s necessary to try to apply them, otherwise it’s no good. Some words may be bookish, and here it’s important in what context it is said. If you told a friend in a pub: “I cannot comprehend this beer” – it would sound as if you’re writing a dissertation on that beer

Seeking Alpha Testers for a Revolutionary Text and PDF Management Tool | March 03 2026, 03:02

Looking for alpha-testers. As part of R&D and for my own tasks, I wrote a productivity tool (I actually wrote about this in my last post, but Facebook said that because I put a link in the post, only 12% saw it). Now I want to check if it will be useful to anyone else. If the idea resonates with you — let me know, and I will share access.

Website smartfolio dot me. What’s the main idea?

It’s an online notebook for working with text and PDFs, organized as a graph. It looks like Google Docs, but there’s an important difference: you can attach “child” documents to specific parts of the main text to expand on details or clarify concepts. These “comments” themselves are full documents and can have their own nested branches.

If there’s a fragment in the text that is unclear, you can ask the system to explain it (this will require your Google Gemini API key).

The system uses the full context of the document to generate a response.

Explanations are permanently attached to a specific place in the text.

This is super convenient when reading complex scientific articles. For instance, you can highlight the authors’ surnames in a PDF and instantly get a background on them — the information will be attached right to that fragment on the page.

Typical workflow

Upload a complex text and read it right in the app from either a mobile or a computer. As you go, add manual or AI-generated notes to important or unclear sections for future reference.

I do not store your documents, PDFs, images, or API keys on my servers. All data is stored in Turso DB (SaaS, free up to 5 GB).

Screenshots on the website’s main page best describe the project.

How to try?

To register in the app, you need an invite code. Just write me in the comments or in a private message, and I will send it.

Website smartfolio-dot-me

Revolutionizing Research: Introducing a Web-Based Notebook Integrated with AI and PDF Support | February 19 2026, 16:19

I’ve further developed a new tool for myself for working with information and organizing it. The main idea is a web-based notebook for research, studying subjects, working on them, integrated with AI and PDF support.

The main problem with typical PDF readers and notes is that the context is lost as soon as you switch to a new tab. In my tool, each text fragment or PDF becomes a node in a “live” hypertext tree, which I can access from multiple computers at any time.

Work process:

– Contextual AI. I can ask the AI to clarify complex passages right within the document. The explanation stays right where the question was asked. Moreover, it is a separate document, linked to the specific spot in the source. When clicked, you see both the original and the explanation on the screen at the same time.

– Panels instead of windows. If the explanation itself requires clarification, a new panel opens to the right. This allows for an endless chain of queries, never losing the place in the original text. That is, you see several panels at once, and unnecessary ones can be closed.

– PDF support. I can upload a PDF, select an area on the page (e.g., a complex diagram or a list of authors), and the LLM instantly extracts data, supplements, or explains them. The explanation is attached to the spot where it was requested, just like with non-PDFs.

– Nested annotations. My comments are not just static text. They can contain their own PDFs, links, and further sub-tasks for AI, maintaining a depth of nesting that reflects how we actually think.

This is not just a file storage system, but an “engine” for building knowledge.

The tool suits me personally very well, but perhaps it only solves my specific tasks. What do you think, would something like this be useful to others? Would it be useful to you? Should I develop the project into a fully-fledged product and give it to other users for testing?