Tag: science

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.

Designing 3D Volleyball Training Tools on the Fly | January 01 2026, 21:21

What I did on the plane to/from vacation and sometimes in between: 3D visualization and editing volleyball schemes for Nadya (she’s a coach). This court in the attached image freely rotates, players can be placed on it, and the ball and player paths are shown – all in 3D.

The ball’s trajectory is calculated so that it does not cross the net when moving from A to B (Bezier formula). Players can take several poses – right now there are hastily made poses for serve, attack, block, pass/receive. Interestingly, in the code: I had to write a bit of “volleyball brains”. The system itself calculates the ball’s trajectory through Bezier curves so that it always passes over the net. Moreover, the height of the launch depends on the type of action: for an attack, the ball “launches” from a higher point than for a pass. I also added auto-rotation: the 3D model itself turns its face to where, according to the scheme, it needs to pass or run.

The longest and most difficult task was creating the 3D model of a female volleyball player. To generate a realistic volleyball player, I used the tripo3D service. It gave me a model in a neutral pose (for free). Theoretically, you can then use Blender and the Rigify plugin to attach an armature to it and move its arms and legs, which would recalculate the model.

However, in reality, this approach does not work well: the AI-generated model contains a large number of geometric errors, which the renderer forgives but Rigify does not. They can be roughly divided into two types — incorrect polygon normals and issues with non-manifold geometry, which are significantly more challenging to fix. Inside the body, there may be “floating” clusters of polygons or intersecting surfaces. When Rigify tries to calculate weights (which bone affects which part of the skin), this internal noise confuses the algorithm, and as a result, the weights are distributed chaotically (for example, moving the arm might start pulling the mesh on the stomach). Plus, the model is slightly asymmetrical.

Non-manifold is a geometry error where the topology of an object ceases to be correct in terms of a three-dimensional body: edges may belong to more than two polygons, polygons may only touch at vertices or edges without a common volume, and “hanging” surfaces or zero thickness may appear inside the model. Such geometry formally does not describe a closed volume, causing problems with rigging and deformations. Moreover, the model needs to be simplified because millions of polygons are not needed for rendering in real-time in a browser.

I fixed these using MashLab, additionally refining by hand (“with a file”). In the end, the model turns out slightly different from the original almost everywhere. The original model had “skin” in the form of textures – the face, shirt, and shorts had to be colored. How to transfer all this to a simplified model? For that, there’s a special operation in Blender called Baking. This also involves some tricks. In the end, it didn’t transfer perfectly, but perfection isn’t necessary yet.

Next, we attach the armature to the “joints”, and after about three hours of figuring out why everything does not work as it should, it finally worked. I made four poses, and now each circle (player) can be told which pose it is in.

I’ll also need to make dynamic changes to the uniform colors – that shouldn’t be difficult. There’s also an idea to transfer poses from photographs – this is more complicated, but generally feasible. Using MediaPipe/AlphaPose, you can detect key points in 2D, then some models like HMR/HybrIK can “lift” flat coordinates into 3D space, outputting relative joint rotation angles. The resulting data can be attempted to be projected onto a Rigify skeleton. Since the proportions of the generated volleyball player and the person in the photo may not match, that’s exactly why Inverse Kinematics (IK) is used. This part is quite complex, but overall it’s not strictly necessary – just interesting to figure out and make something functional.

Video in the comments

Exploring the Golden Carpenter Ants of Costa Rica | December 31 2025, 14:28

Golden Carpenter ants from my last trip to Costa Rica. They are huge, about 1.5cm. They build nests in wood. Unlike termites, they don’t eat the wood, they just make a home for themselves there. They don’t have stingers, but if disturbed too much, they can bite with their jaws (as seen in the photo on the left) and inject formic acid. Their distinctive feature is a golden abdomen. I’ll write about other notable leaf-cutter ants next time, half of my phone is full of them.

Night Encounter with the Fer De Lance: Survival Against the Jungle’s Deadliest Snake | December 29 2025, 18:06

Last night, I encountered the most dangerous, most venomous snake in the jungle – Fer De Lance (Bothrops asper) on a path. Generally, local clinics have antivenom and with it, the mortality rate drops to zero, but it’s better not to test it – the venom causes rapid necrosis and disrupts blood clotting. It also has a built-in night vision camera in its head. The system is so sensitive that the snake can detect a temperature difference of just 0.003 °C. This helps it hunt warm-blooded rodents and birds at night, when visual sight is limited.

It’s also super fast. Four and a half meters per second. That’s faster than you can blink. Indeed, if you’re not sitting right in front of its nose, there’s enough time for it to switch to a combat position, allowing you to jump away – so I managed to get by from behind.

Exploding Trees in the Jungle: Nature’s Deadly Defense Mechanism | December 29 2025, 14:08

You can find anything in the jungle at night. A tree exploding with poisonous spikes suddenly.

It’s not the tree that explodes, of course, but its fruits. They resemble little pumpkin-capsules. When ripe and dry, the fruit’s walls gradually stretch, accumulating internal tension. At some point, this “biological device” bursts with a loud pop, throwing seeds at tremendous speeds — up to ~70 m/s covering dozens of meters, the sound resembling a gunshot.

All parts of the tree are extremely toxic. The sap contains irritants that cause severe burns and rashes on skin contact, and temporary or even permanent blindness in the eyes.

There are simply more trees around with spikes (non-poisonous), but long and sharp. Just like the one shown in the series pluribus

Exploring the “Christmas Tree” in Oil & Gas | December 18 2025, 18:34

Oh, how many wonderful discoveries the spirit of enlightenment brings…

it turns out, Christmas tree in the oil & gas industry is a wellhead equipment. I am testing this search for work

Decoding Complex Queries: A Transformative Approach to Search Functionality | December 17 2025, 03:25

Oh, I just solved a really cool problem. It’s tricky to explain though. But I’ll try.

So, the client has 10 search websites. They all use one index but throw different queries at it. To what the user enters, a very long and complex query is added, generated by a module on Sitecore. It includes template and page IDs that need to be included or excluded. Ultimately, it’s impossible to understand what’s going on there. There could be ten opening brackets and some randomly closing ones, but it worked with Coveo. Reformatting helped, but not much.

And each site has its own version of this. Meanwhile, the same IDs appear periodically. I first tried to manually figure this out, but it was a nightmare. Nothing helped. There are also nested conditions. For example, “exclude this template” not globally, but only if that field equals one.

Here’s what I did:

I wrote a script that parses this textual “mess” into an abstract syntax tree (AST). This allowed to turn an unreadable string into a structured JSON object, where it’s clear: here’s AND, there’s OR, and here — a specific condition.

Then I turned these conditions into Boolean algebra formulas. Using the SymPy library, I “fed” these formulas to simplification algorithms. Mathematics itself eliminated duplicates, collapsed excessive nesting, and removed conditions that are logically absorbed by others. As a result, the “trees” became flat and understandable.

In the attachment — the original tree and the simplified one.

To be sure that I didn’t break anything during simplification, I wrote a test generator. It takes the simplified logic, puts it back into a working curl, and checks whether the number of found documents (totalCount) matches the original request. The numbers matched — meaning, the logic is preserved 100%.

Having simplified and standardized structures for each site in hand, I built a comparison matrix. The script analyzed them and highlighted Common Core — conditions that are guaranteed to be required (or prohibited) on all sites without exception, and Specifics — unique “tails” that distinguish one site from another.

In the attached screenshot: REQ means that the condition is guaranteed to be met for any document that goes through this request. NOT — definitely not met. OPT — the condition is present in the request, but it’s not strict by itself. It only works in conjunction with something else. “.” — the condition is not mentioned in the request at all.

For 3 sites it responds instantly, for 10 it takes about 30 minutes.

And of course, all data in all screenshots are thoroughly obfuscated.

Decoding Dog Signals: What Does the Ironing Board Mean? | December 17 2025, 01:55

Help decode the signal being sent into the universe by a dog. The same gesture towards the refrigerator means wants treats, to the door – open it (outside or inside), to the knee – pet me, to the cat – a complex indecipherable set of emotions. Question – what could it mean towards an ironing board?

I have tried everything. Gave food. Poured water. Took for walks. Opened the backyard. Played with a ball with him. Definitely petted him. Only thing that worked was leaving the room. But then when you come back – he returns to playing at the foot of the ironing board. You turn around – he looks and waits for something.

Apparently, he concluded that to get everything at once, he needs to do it with an ironing board

Exploring Open Data: A Deep Dive into Loudoun County’s 1.5 Million Trees | December 15 2025, 15:40

I’m checking out what open data we have in our county to play with data analysis over the weekend, and discovered, for instance, an open database of all 1.5 million trees in the county. The screenshot shows just a tiny part around my house.

From Idea to Chess AI: Building a Neural Network to Predict Moves | December 15 2025, 04:33

While figuring out neural networks, I decided to come up with a game-related task for myself. What if I find some ready-made games, and train a neural net to predict moves based on the board situation. Said and done. Of course, generating code is faster with LLM, but I wrote the detailed assignment myself and designed the architecture on my own. In 40 minutes (!) from the idea to the result, I already had a working solution that, at least in the first half of the game, does not mess up too much.

In the screenshot is CuteChess – it works with any chess engine, and in my case, it’s a simple Python script. The script takes the board situation and feeds it to the model. It selects the top 5 moves, and only these top 5 are analyzed deeply for several moves ahead and assesses the position. That is, the neural network suggests possible moves based on the analysis of 20,000 games (534,453 positions). From the results, the best is chosen. It uses the minimax algorithm for this, if that means anything to anyone (it didn’t to me, so Gemini here helped me)

How the model is trained. On the lichess website, you can download games, there are hundreds of gigabytes. I took a file with 800,000 played games from the year 2014. From these 800,000, I select 20,000, specifically looking with a script for games where the result is not a draw (1-0 or 0-1). Next, I calculate the difference (Winner_Rating minus Loser_Rating). It’s not the best metric, but it’s better than nothing. The bigger this difference, the more “confident” the win should be (the strong punish the weak). Thus, I get 20,000 such games.

“Ignoring the moves of the weak” (to avoid teaching the model bad play) is implemented during the training stage of the model. Essentially, the logic is: “If it’s White’s turn now, and White won this game — we learn. If it’s Black’s turn now, and Black lost — we skip and don’t teach the net this move.”.

The neural network is trained in batches of 128 positions at a time. The network receives a board position as input and outputs 4096 — the probability assessment for each possible move.

Selecting games takes about 5 minutes. Training the model on my computer takes about 10 minutes for 20,000 games. You could leave it to train on 100K or a million, and it would definitely be better. No need anymore – I figured it out 🙂

You can view the game here:

https://lichess.org/JWeaIrVW