Tag: AI

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.

How Gemini Transformed Low-Resolution Previews into High-Quality PDFs | January 03 2026, 14:18

How unexpectedly useful Gemini turned out to be in a simple task – to create a high-quality PDF from a low-resolution preview. Nano Banana Pro was used, meaning, the output was raster, not vector. Look at the difference. Very often it is impossible to even make out the text, so from time out it turned into time dute;-). But overall, not bad.

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.

Visualizing Volleyball Plays: A Glimpse into My App’s Functionality | January 01 2026, 21:37

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

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

New Year’s Eve in an Empty IMAX: A 3D Avatar Experience | January 01 2026, 17:24

How did we ring in the New Year? In an empty movie theater watching Avatar IMAX 3D.

The CGI is simply stunning. Seriously, it might be the most photorealistic film in history from a computer graphics perspective. The detail in individual faces—there were times I could swear I was looking at human faces painted (and that’s a compliment). And there’s a lot going on in the background too. You really need IMAX and ideally 3D because it’s one of the few movies where IMAX 3D technology isn’t just used in certain scenes, but everywhere.

The main villain Varang is absolutely amazing. Every time she appeared on screen, she stole the spotlight. Despite the CGI, they perfectly conveyed all the complex emotions of her character. They made her truly merciless, sexy, and dangerous. It turned out cool.

The three-hour runtime is densely packed with action, with practically no scenes that make you want to yawn.

Crafting a Custom Volleyball Play Editor | December 23 2025, 21:39

Tomorrow is the flight to Costa Rica, and here I am creating (or created) a volleyball playbook editor for Nadya. As a coach, she prepares for her sessions and leaves behind hundreds of pages of text with diagrams on each page. The text is handwritten, and theoretically, it’s simple to convert to a digital format, but converting the diagrams into high-quality vector format is exhaustive—there are so many. So, I decided to make the software yesterday. And today, the first version is ready to use. This is a diagram editor, somewhat remotely similar to a diagram editor. Also got to dig into the fabric framework.

The process looks like this. Gemini/ChatGPT through an API can convert hand-drawn diagrams into a structure that my program understands. Then we open this file in the program, and tweak a bit if necessary. Or maybe even redraw from scratch – for simple diagrams, it’s even easier. There are four types of objects – player, cone, target, text. Any can be connected with arrows, solid or dashed, labeled with text or numbers or not, in any chosen color, straight or curved. If you touch an object with the mouse, all connected arrows will follow.

The result can be saved in a file. You can open a template and based on it create something new. You can generate a Python script – yesterday it was still relevant, today generally not needed anymore – high-resolution SVG/PNGs are made directly from this app (yesterday they were made separately in Python).

It’s clear why you wouldn’t just ask Gemini/ChatGPT to do something for ready-made vector editors: firstly, they are too flexible and limiting LLM’s imagination is quite difficult. As a result, you get stylized, unusable images. Here, instead, there is a framework consisting of four objects and that’s all, LLM knows about it and only generates what can be represented with them. Secondly, this framework operates with objects, not elementary vector primitives.

Overall, this is the first step towards my idea of an automatic diagramming system based on descriptions. Where you give an LLM a diagram description, and it consistently generates what is written in the description, and if you make any corrections, they will be taken into account during regeneration.

The Uncertain Future of Automation and Employment Disparities | December 21 2025, 15:27

Everyone is waiting for a cyberpunk future where each cafe table is served by an android. But it seems that it will never happen. The automation of the service sector is stagnating and will continue to do so for one simple reason: maintaining a human is becoming cheaper than servicing an industrial robot.

Food and clothing are rapidly depreciating. Production volumes are such that feeding and clothing a “bag of skin” today costs pennies. Now compare this with the cost of developing, software, and maintenance of a complex robot waiter or cleaner. A human is a self-regulating system that fuels and updates itself. And if worn out, easily replaced. Pure economy!

In the “First World,” the motivation to labor hard will disappear. Why go to a hard, boring job if basic needs are met with minimal effort, and everything else is done by others who really need to? People in developed countries will work only where there is thrill and pleasure. Eventually, we will face a shortage of hands where it is “not cool,” but there won’t be robots there either – too expensive.

Poor countries will be stuck in the past. Their populations are growing like yeast. Choosing a job there is a luxury available only to a few. An excess of labor makes work almost free.

I think the world is facing a harsh imbalance. Developed countries will likely permanently close their borders to avoid diluting their comfort, and all industries that are still difficult or expensive to automate will simply move to poor regions. Perhaps, developed countries will become less likely to conflict with one another, as there will be too many resources to make every resident happy.

But it will be harder with poor countries. Why invent a complex robot if you can relocate a factory where thousands are ready to work for food, which becomes cheaper every year? This has long been happening and will most likely continue for a long time.

Conventional programmers in the USA won’t be replaced by AI, but by programmers from Southeast Asia and South America. Several layers of AI for quality control and one manager approving AI conclusions and automatic layoffs and hiring will oversee them. And those programmers who remain in developed countries will focus more on orchestration than on coding. This role requires even more intelligence, and only one in ten current individuals will be capable. Only, the reason for such a crisis will not be AI.

Also, I think that the borders of the future world may close in one direction. It will become increasingly difficult to enter developed countries from developing and poor ones, but the opposite will be facilitated by authorities. Africa is growing so fast that it will surely become a problem if people there are not already prepared for life beyond their villages.

The future is not about the uprising of machines. It’s when some work for pleasure, and others because they are cheaper than electricity and gears.

Do you agree, or am I exaggerating too much?