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.
An interesting photo from Iran. An American Lockheed Martin KC-130 Hercules tanker and Sikorsky HH-60 Pave Hawk helicopters connected to it. If you think about it, it’s incredibly complex. Look, the plane has to fly at a super low speed for it – close to the stalling speed – while the helicopters, in contrast, must push to their limits to keep up. To avoid entering into a spin, the plane is forced to rapidly lose altitude, consequently, the helicopters must also purposely drop altitude. The helicopters are positioned lower than the plane, so if the pilot slows down even more (though how much more can he slow down?), and the helicopters don’t slow down, the hose could hit the rotor blades and that’s it. The helicopters also gain several tons during refueling, which adds to the complexity. Why refuel two at once? It’s more complicated. Actually, it’s both more complicated and simpler, because the load on the wings is distributed symmetrically, making it easier for the plane to maintain a stable course. It’s also interesting how the issue of static electricity is handled in the dry air.
A good addition from the comments (Sergey Snegirev):
1. It is noted that the Hercules stalls at 100-110 knots (depending on the air temperature and altitude, which is important in Iran), and the photo shows the flaps deployed, allowing it to stay up to even 90 knots. Meanwhile, the Pave Hawk can accelerate up to 190 knots (but obviously, nobody performs AAR at max speed), with a cruising speed of 150 knots, so there’s quite a sizable overlap. It’s assumed that AAR takes place around 120 knots on the video.
3. It is noted that AAR happens at exactly the same altitude, so there’s no need to lose altitude
4. It is noted that the tanker – like any other aircraft – can dissipate static electricity using an electrostatic discharger. The refueling hose has a contact that equalizes the potentials of the two aircraft before fuel delivery.
5. It is noted that the fuel used to refuel external aircraft is stored in tanks in the aircraft’s cargo section, separate from the tanker’s own fuel system. Even if the pumping were from the tanker’s own fuel tanks, it would come from the central tank, which is set up to transfer fuel from the side tanks.
It is noted that on the underside of the plane’s wings, several metal “antennas” are installed on the flaperons to discharge static electricity into the air. It is noted that when the hose is connected, a wire passes through and the potentials on the bodies are equalized.
Everything is normal, I’m working, suddenly there’s a knock from the backyard and the power goes out in the whole house. I look over my right shoulder – and there through the window IN MY LOCKED BACKYARD is some guy.
I go out, say hello. He’s like, “I’ve done everything.” I say, I noticed, but who are you exactly? He’s like—you submitted an application to switch to the EV tariff. Well, I had to press a button here. I say and you’ve pressed everything already?
He says – yes. I say then close the door behind you, because my dog got freaked out here.
Then the dog comes up. Sleepy. Stunned. Who’s this, he asks.
But generally, it would be nice at least to be warned about visits and the sudden power cut. I could have shut down my computer or something.
But now it will be $0.0732 per kWh at night, and $0.12694 during the day.
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.
We had a thunderstorm last night. The whole county is buzzing because everyone thinks that something exploded just before midnight. Several posts in a row on social media. In short, it was thunder. But a bit more rare than usual. Caused by a 401 kA lightning, dubbed the Wild House Shaker. A typical lightning strike is 30 kA. If the numbers are to be believed, 401 kA is really damn a lot. They will likely say we haven’t had such lightning here for decades.
Attaching an interesting map.
The points on the map show superbolts — lightning strikes with an energy of no less than 1M J. Red points — particularly powerful superbolts with an energy of more than 2M J. That is, superbolts mostly occur in the northeastern part of the Atlantic and in the Mediterranean Sea, and less frequently — in the Andes, off the coast of Japan, and near South Africa.
this is what the page from which I took the map says (translation):
“New work shows that superbolts most often occur over the Mediterranean Sea, the northeastern Atlantic, and over the Andes, as well as in smaller amounts to the east of Japan, in tropical oceans, and near the southern tip of Africa. Unlike regular lightning, superbolts often strike over water.
“Ninety percent of lightning occurs over land,” said Holzworth (that’s the main guy on lightning at the University of Washington).
“But superbolts mostly arise over water, right up to the coastline. For example, in the northeastern Atlantic, the distribution maps of superbolts clearly show the outlines of the coasts of Spain and England.”
“The average energy of a discharge over water is higher than over land—that we knew,” he said. “But we did not expect such a stark difference.”
The season for superbolts also does not match the usual patterns of lightning. Regular lightning most often occurs in the summer—the three main so-called “lightning chimneys” coincide with summer thunderstorms over America, Africa south of the Sahara, and Southeast Asia. However, superbolts, which are more common in the Northern Hemisphere, occur in both hemispheres from November to February.
The reason for such a distribution remains a mystery. In some years, there are significantly more superbolts than in others: the end of 2013 was record-breaking, and the end of 2014 was the second largest, while in other years such events were much less frequent.
“We speculate that this may be related to sunspots or cosmic rays, but we will leave that for future research,” said Holzworth.
“For now, we are just demonstrating that there is a previously unknown pattern.”
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.
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.
At the beginning of the year, I bought an NFC smart lock for the front door for 170 bucks. Recently, I wrote a review on Amazon stating that the batteries lasted only a month and a half, and if it continues like this, I will end up paying almost the same amount annually. The manufacturer has responded saying they will refund the money. They didn’t ask to remove the review, and I don’t even know if that’s possible.
For fun, I decided to make a matrix of who is friends with whom and who is enemies with whom. For each country-country pair, I asked Gemini which of the five categories the relations fall into: “at daggers drawn” (purple), “predominantly unfriendly” (red), “neutral” (yellow), “predominantly friendly” (blue), “friends” (green). Lisa said that “neutral” should be purple. Overall, the quality of Gemini’s assessments is quite good.
Among all countries, three red lines stand out. These are countries that are on very bad terms with many others. Well, you guessed Russia right. And what is the second country? Israel? No, it’s Belarus and Venezuela.
In the top five countries that everyone is friends with and who have many friends themselves, LLM included the USA, United Kingdom, Canada, France, and Germany. There is an anti-rating – these are countries that have very bad relations (“at daggers drawn”) with many others. In this rating, Russia is in first place with 21 countries, and Israel is in second place with 18 enemies. Following them, with a significant gap, are Syria and the USA with 9 enemies each. There is also a separate Conflict Zone rating – this is the sum of red and purple. Russia, Venezuela, Belarus, Israel, USA, Iran, Ukraine.
There is a “pacifists’ club”. These are the ones who have no enemies at all, sorted by the number of friends. Rating: Bahamas, Vatican, Luxembourg, Angola, Singapore, Iceland, Jamaica, Tanzania, Zambia.
I was curious, what if I apply the formula: the enemy of my enemy is my friend? What would change? This led to new colors on the matrix – logic friends.
The most unexpected leader of the Master Pragmatists ranking was Taiwan (25 logical connections). Why so? In the logic of LLM, Taiwan is a country that is officially recognized by few, but because of its global opposition to China, it automatically becomes a “logical friend” for everyone who has strained relations with Beijing. This is confirmed in the Shadow Bridges section: Taiwan has 23 connections beyond its region. It literally “stitches” different parts of the world together through a common problem.
The report “Secret Partners” – a list of geopolitical oxymorons. These are pairs that are “at daggers drawn” in official news but are forced to be friends by Gemini’s calculation. For example, Afghanistan – USA/United Kingdom. Despite the status “rather bad relations”, Gemini’s logic sees them as “logical friends”. Possibly due to common regional threats (like ISIS) or dependence on humanitarian and back channels. Or here’s a strange alliance “Belarus — Hungary”. Nominal — different camps, factually — similar style of rhetoric and common “enemies” in Brussels. Eritrea — Ethiopia: Status “at daggers drawn”, but at the same time, they became logical friends.
In the report “Most Controversial,” the first places are taken by the USA, and then with a significant gap, Russia, and even larger – United Kingdom, Canada, Ukraine. These are countries with the highest Love x Hate product value. That is, countries that have many friends and enemies at the same time.
Another report – the indifferent ones. About them, LLM couldn’t say much, apparently because they bother no one (both literally and figuratively). There are, for example, Madagascar and Haiti.
I also tried to cluster by the strength of friends and got four groups of countries.
The largest cluster. Core: China, Russia, Iran, India, and BRICS+ countries, as well as almost the entire African continent (from Egypt to South Africa) and a significant part of the Middle East (UAE, Saudi Arabia, Qatar).
The second cluster mainly included European countries. Core: France, Germany, United Kingdom. The algorithm determined Ukraine and Israel to be here. Logically: their survival depends on “predominantly friendly relations” with the European core. In this same club are Armenia, Georgia, and Serbia. Apparently, despite all the political swings, Gemini considers their ties to Europe more fundamental than any others.
The third cluster included the USA, Canada, Brazil, Mexico, and, for example, Taiwan. Officially, it can be a “logical friend” to all of China’s enemies, but by “strength of friends,” it is permanently sewn to the American block. The Vatican also ended up here, which makes this club not only economic but also somewhat “values-based.”
The fourth cluster, the most compact and specialized, included countries of Oceania and Southeast Asia. Leaders: Australia, Japan, New Zealand, Singapore. This turned out to be a club of countries trying to balance in the most complex region of the planet. Here are almost all island states (Fiji, Samoa, Tonga).
Hi, I'm Rauf Aliev. 