This is me typing the word gremlin, without switching the keyboard layout. Wanted to read about the query language for graph databases, need it for work. Google surprises, it does surprise

This is me typing the word gremlin, without switching the keyboard layout. Wanted to read about the query language for graph databases, need it for work. Google surprises, it does surprise

When working with certain tasks, the difference between a CPU and a GPU is simply astounding. For example, I need to create many (millions) of embeddings, model BGE M3. Running this on my quite powerful 24-core Intel Core Ultra 9 285K processor takes 45.85 seconds to create 500 embeddings, while using an NVIDIA 5090 GPU, the same task is completed in just 0.36 seconds. It is so fast that I specifically wrote this benchmark to figure out whether my GPU is being utilized at all. The program that sends requests to TEI does it in test mode not actively enough (roughly a couple of times per second), and the GPU load graphs are practically zero.
— Testing http://localhost:8080/embed — <– CPU version
Requests completed: 500
Total time: 45.85 sec
Throughput: 10.90 req/sec
Average latency (Avg Latency): 4386.11 ms
P95 latency: 5021.88 ms
— Testing http://localhost:8090/embed — <– GPU version (NVIDIA 5090)
Requests completed: 500
Total time: 0.36 sec
Throughput: 1398.69 req/sec
Average latency (Avg Latency): 31.38 ms
P95 latency: 53.18 ms
========================================
RESULT: http://localhost:8090/embed is 99.22% faster
Mom sent it. This was given to me when I graduated from school. The education was quite good back then, at least. Part of the science classes were conducted at the institute.

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?

My first job as a programmer, with an office in Kolomna and for money. It was 1993, or maybe even a year earlier. 10th-11th grade of school. And this company still exists, and the guys I worked with are still there! Natalya Bakulina, Pavel Bunakov, Nikolai Kaskevich. Imagine that. Moreover, they started back in 1986, that is, 40 years ago already! I can hardly remember other commercial companies of such age in Russia. When I came to work there, there was MS DOS, they wrote in Turbo Pascal, but they had started many years before me on the SM-1420 computer, though back then, the company was not entirely commercial. At the time of my arrival, their system was a competitor of AutoCAD in the market, locally also competing with “Kompas”. I made an installer from 5.25″ and 3.5″ disks – to capture the spirit of the era. Later they switched to Delphi and Windows. After that, they narrowed down their focus, transitioning from CAD for engineering to CAD for furniture, where they still hold very strong positions.

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?
I whipped up this thing in just an hour. Do you think anyone besides me needs it?
Here’s the idea. Take any text – a Wikipedia article, for example. Highlight any segment, say something unclear. The LLM gives us an explanation, and instantly inserts a box right in the text which you can click to open the explanation. In this explanation, there might be something unclear too. We highlight it with the mouse from this explanation, and a box appears there too. This continues until everything is clear. All the boxes remain in the text, so you can always return to them. So, if the idea was unclear to me, maybe it will be to others, and then a ready link with explanations will come in very handy. The result can be shared with colleagues.
For explanations, not just the fragment is used, but also the context. For example, otherwise, the highlighted word Terrier would yield text about a dog breed, not about the search system.
Now that I have a plotter, I am fully experimenting with ways of algorithmic image stylization. To achieve what is attached, a Minimum Spanning Tree algorithm was used. Essentially, it converts an image into stochastic rasterization – that is, where it’s darker, there are more dots, and then connects the dots with lines so that all points are connected in a single network, the total length of all lines is minimal, and there are no closed loops (meaning it’s precisely a “tree” with branches, not a “web”).
And this is what I do with each of the CMYK channels, then combine the result into a color picture. On this picture, there seem to be no other colors except for these four CMYK ones, but in reality, there is a bit because some smoothing has crept in.
Printing such on a plotter, of course, is difficult, I will be waiting forever, but I am getting the hang of it, I have already printed the first color picture (it turned out so-so. Well, the first pancake is always lumpy. Comments below)

10 years at EPAM.
I would have never thought that I would enjoy working in the same place for an entire decade. What’s the secret? At EPAM, I am always evolving: projects change one after another, never letting me get bored.
I am currently on a project at a giant company: over 100 thousand employees and revenue of 30 billion dollars. Before this, it was the automotive industry — a behemoth with a staff of 175 thousand and a turnover of 150 billion. Somewhere around, there was a contract with a company of 80 thousand employees and 35 billion in revenue. True scale and genuinely serious challenges. And earlier, there were cosmetics brands, biotech, and the oil sector. In total, more than 20 projects of various calibers. Despite having over 100% workload every single day. And it seems that this year, I had more vacation than usual, yet still less than I could have taken. I traveled to Costa Rica, Mexico, Seattle, Antalya.
The point is, at each new place you learn something, sometimes from scratch. And that’s freaking awesome. It gives much more energy than if I had been “rooted” in any of these corporations for all 10 years. Perhaps, from a purely financial standpoint, people who stayed in one place at these companies earned more than me, but money isn’t the priority if it means sacrificing interest and enthusiasm. Living life at a job from which you are utterly exhausted is a questionable pleasure.
Last year at EPAM was maximally intense, and I sincerely hope that 2026 will not slow down.

Here is actually a quick screen capture of how my app for creating and visualizing volleyball schemes works.
Implementation details here: https://www.facebook.com/raufaliev/posts/pfbid0njrqH8oLcWGFsZcgE5o2pj3NcDNaYSQeCMY6twXNbZn6dc38m9kjhsBqA4YsMozcl