Tag: Engineering

Insights from a Visit to the Civil War Medical Museum | March 10 2026, 15:59

Today a big interesting historical post.

Where I was: Historical Museum of Military (Civil War) Medicine in Frederick, MD. Entry is $9, $15 with a guide. For an hour and a half, we got a very smart guy who gave an interesting lecture, making the provincial museum really very interesting. We even tipped the guy afterward.

A few interesting facts that I didn’t know before. During the Civil War in the USA (1861-1865), there was a monstrous scale of losses – over 600,000 people. One in every ten was mobilized for the war. That is, excluding women, children, and the disabled – yes, someone from almost every family fought.

Apparently, Americans were not very experienced in wars back then, and organizing large groups of people was based on the “fend for yourself” principle. From gastrointestinal diseases alone, nearly four times more people died than from wounds. Soldiers cooked everything themselves – there was no cook or porridge for the platoon. They split into micro-groups of a few people, pooled whatever they had, and fried it on a fire. For some reason they mainly fried, not boiled (which also contributed to diseases). Their main rations were salt pork and hard-tack — crackers as hard as a stone. Fried hard-tacks were called Skillygalee.

Remember, it was not like that in European wars. And all because there were many of them, and they quickly figured out how to make them more effective. Plus, there was also a civil war, poorly organized and spontaneous.

Initially, soldiers were handed money in the field and they sent it to their families as best they could (not all reached its destination). For Southerners, money devalued faster than they could carry it to the tent. Back then, each state issued its own money. They write about 8000 different banknotes at that time. I didn’t quite believe it, started researching, and it turned out that this is still a very conservative estimate. Yes, anyone (state, city, private bank, railway, factory, and even a pharmacy) could print their own paper money. Each bank issued banknotes of its own design for different denominations ($1, $2, $3, $5, etc.). In 1860, there were about 1600 private banks in the USA, and almost each issued its own range of notes. But in the end, greenbacks – federal money prevailed.

They also told us about Dorothea Dix, the head of army nurses for the Union. She introduced an interesting age standard for the nurses. No “young and beautiful.” Only women over 30 years of age, “plain-looking,” no jewelry, fashionable dresses, or crinolines – only strict brown or black dresses. At that time, the appearance of a woman in a male military camp was considered almost indecent. Dorothea wanted the soldiers to see in the nurses strict mothers or aunts, not objects of flirtation.

To join the army, a volunteer was required to have at least two teeth opposing each other. Why? A soldier needed to quickly bite off the tip of a paper cartridge to pour the powder into the barrel. No teeth — you’re useless in battle.

Back then, they shot with Minié balls – made of soft lead. It was huge caliber (thumb-sized) and when it hit the body, it didn’t just pass through, it “burst” and literally turned bone into fine crumble. Repairing such a bone was impossible, so amputation became the only way to save a person from gangrene. At least there was some form of anesthesia (chloroform/ether).

Before the Civil War in the USA, people were buried where they died. But the war generated a demand: affluent Northern families wanted to bring their sons’ bodies home. That’s when embalming flourished. Right behind the front line were tents of “embalming surgeons” who for a decent sum (about $50–$100 for an officer) extracted blood and injected chemicals (arsenic and zinc) into the body. Actually, the museum building included such a place. Lincoln’s body after his assassination was transported across the entire country on a train, and it looked “alive” thanks to this new technology, which became the best advertisement for the new industry.

Overall, Frederick is a very nice city, full of art and nonconformists 🙂 Like our Leesburg, but 20 times bigger.

P.S. It was interesting to study what drove people to go die. Of course, our guide said “patriotism”.

But if you google, it turns out not quite so. Of course, in 1861 no one knew that the war would last 4 years and take 600,000 lives.

Reason #1 – boredom. Life on a farm in the mid-19th century was incredibly monotonous. War seemed like the greatest adventure in life. Guys thought: “I’ll go, see the world, shoot, become a hero, and then return to harvest.”

Reason #2 – naivety. The first volunteers went to the front as if on a picnic. In the first major battle (Bull Run), civilians from Washington even came with picnic baskets to watch the “spectacle,” until they were swept away by the retreating crowd of bloodied soldiers.

Reason #3 – “honor.” In the 19th century, “honor” was not an empty word. If you were a healthy guy and didn’t go to the army, you became an outcast in your own town. It’s written that girls often refused to go out with those who didn’t wear a uniform.

Reason #4 – “regimental solidarity.” As I already said, regiments were formed from neighbors. Not going to war meant betraying your friends, brothers, and father. Shame before neighbors was stronger than the fear of death.

What did they fight for?

Here the goals of the North and South radically differed:

For the North, the main idea was “Integrity of the Union.” For them, the USA was a great experiment in democracy that could not be allowed to fail. The slogan “Save the Union” was more powerful than “Free the Slaves.” At first, not everyone was ready to die for abolition of slavery.

For the South (Confederacy), the main motivation was “Defending their homes.” Most Southern soldiers did not own slaves (slavery was too expensive a luxury for ordinary farmers). But they were convinced that the “Yankee northerners” were coming to seize their land, burn their homes, and impose their rules. They saw themselves as heirs of Washington, fighting against “tyrant” Lincoln.

Reason #5 – bounties

When initial enthusiasm faded (by 1863), pure calculation played its part. States and the federal government started paying huge “enlistment bounties.” A sum of $500–$1000 was equivalent to a few years’ earnings for a laborer. For a poor immigrant (Irish or German) just off the boat in New York, it was a chance to provide for a family or buy a farm after the war.

In 1862-63, both sides introduced the draft, as volunteers were running out. This exposed social injustice.

In the North, you could officially avoid the army by paying $300 (huge money for a poor man, but manageable for the middle class) or find a “substitute” (a person who would fight in your place for money).

In the South, there was the “Twenty Negro Law.” Owning 20 or more slaves exempted you from service, as you were “needed in the rear for production control.”

This caused fierce resentment among ordinary soldiers. The famous “Draft Riots” in New York (1863) were sparked precisely by this sense of injustice.

So there you have it 🙂

From MS-DOS to Modern CAD: My Journey with Bazis Soft | March 06 2026, 17:43

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.

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

Exploring the Mystical Connection Between π² and g in Defining a Meter | March 01 2026, 17:11

It turns out that π² ≈ g is not some mystical coincidence. When the first scientists contemplated the definition of the meter, there was one elegant proposal: to make the meter equal to the length of a pendulum that takes exactly one second to swing from one side to the other.

For a mathematical pendulum, the period of oscillation is calculated by the formula: T = 2π √(L / g). If we take the length L = 1 meter and set the full period T = 2 seconds (so that it takes exactly one second for each half swing), the equation implies: g = π² (m/s²).

The definition of the meter was later changed: it was tied to one ten-millionth of the distance from the equator to the North Pole along the meridian passing through Paris. However, this geodetic definition was inspired by the earlier idea with the pendulum. Notably, both approaches match up with an accuracy of 1%. Essentially, since the old “pendulum” definition was the main candidate for a long time, values were adjusted so that the new meter was convenient and close to the measurements customary at that time.

It is also interesting that the number of seconds in a year roughly corresponds to the number of pi * 10^7. Earth’s orbital speed is about v = 30 km/s. The distance from the Sun to Earth is approximately r = 150,000,000 km. Thus, over a year, Earth travels a path of about d = 2 * π * r. Then, the orbital period equals T = d/v = π * 2 * r/v = π * 10⁷ seconds.

Exploring LLMs and AI: Connecting Neural Processors to Natural Language Learning | February 15 2026, 15:41

Some thoughts on LLMs and artificial intelligence in general. And in the end about neuromorphic processors and Intel Loihi.

As you all know, fundamentally LLMs operate on the principle of “propose the likely next word using the context from the previous N words,” and then the word enters the context, and the process repeats all over again for the next word. Well, and the context is also processed considering the importance of words.

Now let’s think about how children were taught languages in primitive societies. There were no alphabets, nor grammar. But the grammar itself, according to estimates, was quite complex—based on observations of the small languages of small peoples. Simple grammar is modern when the language has spread to millions and billions.

That is, a child’s brain had to reconstruct grammar in its neurons simply from the flow of speech from those around and through testing the understanding of what was said. It’s likely that the child was corrected if they spoke incorrectly, but somehow this grammar and sound extraction had to settle in the brain—and here the same mechanism as in LLMs is used: which words/sounds go next in what context is determined by latent and uninterpretable rules, which each person in childhood creates in their brain in their own way. That is, roughly speaking, it trains the ML model every time from scratch on the flow of speech from those around. A child does not know what a “case” is, but feels what ending is statistically more likely in a given context.

Actually, modern cognitive science (Karl Friston’s theory) asserts that the brain is literally a “prediction machine.” We constantly generate hypotheses about the next sound or word and correct them when they don’t match (prediction error).

The peculiarity of LLMs is that for them, teachers are texts and images, but for a child’s brain, it’s the living world around, and if all the texts they hear were digitized, their volume wouldn’t even be enough to train a very weak model. LLM sees the word “apple” next to the word “red.” A child sees an apple, feels its smell, taste, weight, and simultaneously hears the sound. This “stitching” of different sensory channels allows building neural connections thousands of times faster than on plain text. That is, modern LLMs take a brute force approach—simply observing the speech of billions, not just their immediate environment. A good question is how the human brain manages to learn from a relatively small dataset. However, it’s a big question whether this dataset is small—for example, lip movements, facial expressions, context provide a lot for building this neural network in the biological brain.

About the context: unlike LLMs, a child understands the speaker’s intention. If mom looks at a cup and says “hot,” the child’s brain limits the search space of meanings to one cup. And if he didn’t understand, he’ll get burned and remember.

One might assume, of course, that the brain already has a ready network at birth. It’s true, but science can’t yet explain it properly. Our entire genetic program has about 20,000 genes encoding proteins, and these 20,000 are responsible for everything—where and how the lungs, heart, bones, blood should be built, and they themselves are of mind-boggling complexity, and somewhere among 3 billion nucleotides and 20,000 genes this information must be recorded.

Apparently, genes encode not a map but an algorithm of self-assembly. Essentially, the architecture of the neural network is built dynamically, and this process begins long before birth. Then it is calibrated by all the signals received by the unborn child, and by the time of birth, there is already a somewhat tuned network in the brain.

It’s likely that the child’s brain is millions of neural networks of different “architectures” that evolve and merge in the learning process. Unlike LLMs, here learning and usage are strictly separated in time. But most importantly—the brain, although the most energy-consuming in the body, consumes very little energy in absolute terms, especially compared to the current “candidates for replacements in hardware.”

In the last few years, there has been active development in the field of neuromorphic systems (for example, the old IBM TrueNorth processor and the actively developing Intel Loihi). In conventional AI, neurons transmit numbers (0.15, 0.88…). In neuromorphic systems, they transmit “spikes” (impulses)—as in the living brain (and the architecture is called Spiking Neural Network – SNN). A few years ago, Intel released Loihi 2. Fully programmable. Neurons on Loihi can change their connections (synapses) right during operation. Supports plasticity—the very biological mechanism when the connection between neurons is strengthened if they often “fire” together. But the main thing—it consumes very little.

In this architecture, the model can continue learning “on the fly” right during operation, without forgetting old data (Continual Learning). Besides that—extreme energy efficiency.

Loihi 2 cannot multiply matrices as modern GPUs do, so completely new software has to be written for them (and this is moving very slowly). No PyTorch or TensorFlow—for Loihi there is only the Lava framework available today. And 1 million neurons from Loihi 2 is very little for LLMs. Therefore, Intel creates systems like Hala Point—it’s an array of 1152 Loihi 2 processors. It contains up to 1.15 billion neurons. Theoretically, in terms of performance per watt, such a system can surpass traditional GPUs by 10–50 times when working with AI models.

Experimental LLMs are already being launched on Loihi 2 (for example, models with 370 million parameters). They are not yet going to replace ChatGPT in the cloud, but theoretically, they are the future for “smart” robots and gadgets that need to understand human speech while running off a small battery.

We’ll observe. It might turn out to be a dud, or it could be another major revolution.

Exploring Algorithmic Stylization in Plotter Art: A CMYK Fractal Journey | February 01 2026, 04:18

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)

Navigating the Future: Embracing Earth’s Magnetic Field as a GPS Alternative | January 10 2026, 17:41

I learned today that there is and is actively used a technology for navigation using the Earth’s magnetic field. It is used as a replacement or an extension of GPS.

For example, there is the Scandinavian ferry Express 5 of Bornholmslinjen, which insures against GPS problems (which do happen) by using MagNav navigation. Unlike GPS, the Earth’s magnetic field cannot be jammed or spoofed—it simply exists. The ferry follows the same route, and generally, navigation could even be achieved through household fishing sonars.

But there are a few startups that use this technology for indoor navigation, where GPS signals cannot reach. It’s claimed that the navigation accuracy is within 1 meter. That’s more interesting.

GiPStech, Oriient, Mapsted.

The basis of this technology is a process called magnetic fingerprinting. Engineers or mapping robots walk through a building with a smartphone, recording unique distortions of the magnetic field at every point. These distortions are created by the steel frame of the building, rebar in the walls, and large electrical equipment. A database is formed where each coordinate (x, y, z) corresponds to its unique magnetic field vector (intensity, inclination, deviation).

The collected data is uploaded to the cloud platform of the provider company. There, they undergo noise cleaning and are “stitched” together with the digital floor plan. When a user walks through a shopping center, their smartphone reads data from the built-in magnetometer in real-time. Special software (SDK) compares the current readings with those stored in the database. For accuracy to be within 1–2 meters, the system relies not only on magnets. It uses sensor fusion—combining data from the magnetic field with inertial sensors (accelerometer counts steps, gyroscope determines turns) and sometimes Wi-Fi/Bluetooth signals for rough localization.

This technology is certainly being actively implemented for drones. The main technical difficulty there is dealing with their own interference and considering that the magnetic field changes, requiring constant map updates. Electrics, engines create strong magnetic fields, which “drown out” the natural background of the Earth. However, various filtering algorithms (including neural networks) are used, which in real-time “subtract” motor interference from the overall sensor readings. From what I understand, at high altitudes (kilometers), the magnetic field is more “smooth”, therefore the accuracy is lower (about 1–5 km). But if several drones fly together and exchange signals, overall they can provide very good accuracy each. Additionally, a group of drones can measure the gradient (rate of change) of the magnetic field in space, tying location not to absolute values, but to relative ones. Essentially, using a group of drones turns the navigation system from a set of individual receivers into a distributed phased array antenna, capable of filtering global interferences and working with much weaker useful signals. Considering that small drones capable of staying airborne for long periods can be released into the air by the hundreds (and cost pennies), this is a quite promising area for military.

There’s an interesting startup, Zerokey. They release QUANTUM RTLS 2.0. This device provides spatial accuracy to 1.5mm. It’s used in production, for example. Their video shows a “watch” on a worker’s hand that monitors the correctness of assembling something on a table. Here, the principle is ultrasonic, and it’s understandable that these “watches” are paired with stationary sensors and further multilateration.

A Decade at EPAM: Thriving Through Change and Challenge | January 05 2026, 13:43

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.

The Unintended Consequences of Misguided Incentives | January 04 2026, 13:30

About KPIs. In English, there’s a concept called perverse incentive, “a harmful stimulus.” It occurs when you try to quash evil, but the methods become the perfect fertilizer for it. There’s a saying, “When a measure becomes a target, it ceases to be a good measure” (Marilyn Strathern based on Goodhart’s Law).

A classic example is the “Cobra Effect.” In colonial India, the British decided to reduce the snake population and offered a reward for every head. The plan seemed as reliable as a Swiss watch until Indians began breeding cobras on farms for the “harvest.” When the authorities realized they were being duped and cancelled the payments, the farmers simply released the now-useless snakes into the wild. As a result, there were many more cobras than before the program started 🙂

In a similar way, the French in Hanoi battled rats by paying money for severed tails. The city became overrun with lively yet tailless rats: the Vietnamese cut off the “currency” and released the creatures to breed further, to not lose a stable income.

In the 19th century, archaeologists searching for dinosaur bones and ancient fossils paid locals for every piece found. As a result, resourceful diggers intentionally shattered whole, priceless skeletons into small pieces to earn more by submitting them separately. Science wept, but the KPI for “number of finds” soared. A similar tragedy occurred with the Dead Sea Scrolls: Bedouins cut the found scrolls into small pieces to sell each fragment separately.

In the USA, this malady struck infrastructure. When building the Transcontinental Railroad, the government paid Union Pacific subsidies for every mile laid. In Nebraska, engineers, in a single corrupt impulse, drew a huge loop—the Oxbow Route. The extra 9 miles of detour made no sense for logistics but brought the builders hundreds of thousands of dollars “out of thin air.”

But if the “loop” in Nebraska was just theft, then the mistakes of U.S. Secretary of Defense Robert McNamara were a tragedy. An aficionado of numbers and mathematical models, he tried to manage the Vietnam War like a Ford assembly line.

When General Edward Lansdale timidly noted that McNamara’s formulas lacked the variable “the spirit and will of the Vietnamese people,” the secretary noted it in pencil in his notebook. And then erased it. He said that if something cannot be measured, it’s unimportant. The main metric became the body count. Officers onsite, eager to curry favor, began labeling everyone indiscriminately as “enemies,” painting an illusion of imminent victory in Washington, while the actual situation spiraled into the abyss.

In science, there’s a radical principle similar to Occam’s Razor— “Newton’s Flaming Laser Sword” (also known as “Alder’s Razor”). Its essence: if something cannot be tested by experiment (or measurement), it’s not even worthy of discussion.

It sounds reasonable for physics, but in life, it’s a direct path to what sociologist Daniel Yankelovich called the degradation of perception. He described this as a descent through four steps:

1. First, we measure only what is easy to measure.

2. Then we ignore what is difficult to measure or requires qualitative assessment.

3. The third step—we decide that what cannot be measured is not so important.

4. And the final step—we declare that what cannot be measured actually does not exist.

And at that moment, we become blind. We view the world through the keyhole of metrics, while in the room behind the door, cobras are bred, dinosaur bones are broken, and wars are lost.

Exploring ASML’s Advanced Chip-Making Equipment with Veritasium | January 02 2026, 00:47

Veritasium released a very cool report yesterday from ASML about the equipment used to print chips for your little phones, cameras, and laptops.

For those who aren’t familiar with the process. First, a monocrystal is grown from ultra-pure silicon and cut into thin wafers, then multiple layers of thin dielectrics, conductors, and semiconductors are repeatedly applied to the wafer surface, each time shaping the necessary areas using photolithography, etching, and ion doping, eventually creating billions of transistors and connecting metallic paths; finally, the wafer is tested, cut into individual crystals, and packaged into casings, making them into finished microchips.

This process had a limitation – the width of the paths and the distance to the next one are limited by the wavelength of the light used, and reducing it is difficult because there’s nothing to focus such a beam with – lenses simply absorb/reflect everything. In EUV lithography (extreme ultraviolet), the wavelength is 13.5 nm. This is virtually soft X-ray radiation.

The video explains details about the ASML machine costing 400 million dollars. Instead of refracting lenses, highly complex systems of reflecting mirrors are used. These mirrors are the smoothest surfaces ever created by humanity. If the mirror of this machine were enlarged to the size of the Earth, the largest bump on it would not be thicker than a playing card. To enable the mirrors to reflect X-rays, up to 76 alternating layers of tungsten and carbon, each less than a nanometer thick, are applied. All this is done by Zeiss. In addition, this mirror has a controlled curvature—it is constantly adjusted by robots with precision up to picoradians. The precision of the mirror control is so high that if a laser were mounted on it, directed at the Moon, the system could choose on which exact side of a 10-cent coin lying on the moon’s surface to hit with the beam.

But. We don’t have a “light bulb” that emits light in the EUV range.

To generate this light, a laser “shoots” at a droplet of molten tin the size of a white blood cell, traveling at 250 km/h. The first pulse flattens the droplet into a disc, the second and third turn this “disc” into plasma – and all this occurs within just 20 microseconds. When hit by the laser, the droplet heats up to 220,000 Kelvin — approximately 40 times hotter than the surface of the Sun. This plasma emits that very necessary light. And it does so 50,000 times a second. They say it’s been brought up to 100,000. Imagine, at a hundred thousand laser shots per second, it never misses a single one. All this happens in a deep vacuum. To clean the mirrors from tin particles, the chamber is constantly blown with hydrogen at a speed of 360 km/h — faster than a Category 5 hurricane. This process is described by the same formula (Taylor-von Neumann) that describes a nuclear explosion or supernova explosion.

The machine layers the chip with an error margin of no more than five atoms, while the matrix swings back and forth with an overload of 20G.

A single High-NA machine is transported in 250 containers on 25 trucks and seven Boeing 747 aircraft.

Link to the video – in the comments. Or search on YouTube on the channel veritasium.