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.
Update: figured it out, looks like the lock needs 6v + 6v for different purposes. Maybe the power part and electronics.
Anyone who knows electronics, help me understand. Red wires are connected to contacts that respond to the tester. A total of 8 batteries. I can’t see a classic snake configuration here. Can’t understand why the lower right ones are responding. I want to connect an external adapter
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
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.
I assembled a plotter from a kit. It’s practically a Lego set – you spill out the parts from the box and then read the manual. It worked right away. I have some ideas about what to do with this thing, I’ll tell you sometime.
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 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.
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.
Today I sold a refrigerator. It has a story. The essence of it is that it’s not a refrigerator, although it looks like one. It’s a freezer. And it freezes on average to minus 18 degrees. I bought it second-hand, thinking it was a refrigerator. The buyer also came today thinking it was a refrigerator.
And here I realize that minus 18 degrees is not at all what I need.
Well, I am a Solution Architect. I didn’t want to dig into it, I just drove to Lowe’s and bought a simple blinker. It turns on and off according to schedule whatever is plugged into it. I stuck a radio thermometer inside (I had one) and adjusted the blinking frequency (20 minutes) so that the internal temperature was on average +4 degrees Celsius. The radio thermometer showed that the temperature fluctuations were very small – nominally plus or minus 0.5 degrees from +4, even less. And so it worked for me for some months until I realized that I just didn’t need it.
Sold it today with the adapter. It’s gone to the people.
Just for laughs. I asked Gemini how to export the entire AWS configuration for local analysis, and they recommended using the aws-nuke command for permanently deleting everything, but if you add a dry-run flag, you’ll get the configuration… and someone actually follows such advice 🙂 and then we wonder
Hi, I'm Rauf Aliev. 