AI – Page 10 – Hi, I'm Rauf Aliev.

Unlocking Smartwatches with Unique Heart Rhythms: A Missed Opportunity? | August 06 2025, 16:43

Why has no one made it so that smartwatches only unlock on the wrist of their owner, reading their unique heartbeat or other biometric data? This is in addition to having the owner’s phone nearby.

Officially, you can’t disable this in the settings of an Apple Watch — Apple intentionally made it such that when you put on the watch for the first time each day, it always requires a code, even if the iPhone is nearby. This is due to security policy: the watch may end up on someone else’s wrist, and the phone may just be nearby.

Moreover, every person has unique heart rhythm patterns, which include, for example, slight variations in the intervals between heartbeats, characteristics of the heart signal shape, and how the heart responds to different stresses. These microscopic differences create a unique picture” of heart rhythm that is difficult to fake or replicate. Watches have quite a lot of time, after being worn and before they are needed unlocked, to collect, process, and decide whether to unlock or not.

Navigating Code Generation with AI: Essential Skills for Programmers | August 04 2025, 14:28

I am currently using Gemini extensively for code generation, and I see a skill that programmers need to have to be successful in this field. It’s the ability to quickly read and understand someone else’s code, as well as explain why AI generation needs to be redone and how. For the former, you simply need to know the language very well and read “from the sheet,” because there will be little time to ponder. For the latter, you need to know patterns well and understand where they apply and where they do not. AI will still mess up using patterns inappropriately for a long time.

Moreover, a person will still need to understand “as a whole” 90% of the code generated by AI, and also manage to find time to comprehend each generated line of code. If you relax and miss it, the system may produce even working, but very poorly maintainable code. For instance, there is an unwritten rule that individual files should not contain so much code, and if it grows, you need to refactor, breaking one large into two or three. Sometimes this requires rewriting logic, but this rewriting is always aimed at one task – to simplify maintenance. And AI, while rewriting, also “improves” the code at the same time. And this is quite difficult to prohibit.

In addition, the very concept of LLM implies the limitation of the contextual window. Which gets filled with code very quickly. To create an illusion for the user that everything is working even with a large volume of code, LLMs are able to do preliminary processing, extracting only relevant pieces for processing and setting aside irrelevant ones, so that the relevant ones fit into the actual contextual window. But this process is very unreliable, and once it works, and the second time it turns out that something important was set aside, and as a result, the system did not see the whole picture and generated code, which includes a function very similar to the function set aside, and now we have two almost identical ones.

Besides, currently logic is distributed between the DB and the code. That is, data often controls the code. And data in LLMs simply often do not fit. There is too much of it. In the end, without programmers, current LLM architectures cannot cope. But the requirements for programmers’ qualifications will only increase with LLMs, not decrease. So yes, juniors should be worried, but leads not so much 🙂

Seattle Airport Chaos: IT Glitches and Alaska Airlines Grounded | July 21 2025, 07:07

Seattle Airport is at a standstill – some nonsense with IT systems, Alaska Airlines planes are not taking off (grounded).

UPDATE: remember the door that fell off Boeing mid-flight? It was Alaska Airlines and Boeing 737 Max, which I am currently sitting in.

Why Aren’t Smart Systems Widely Used in Commercial Vehicles? | July 18 2025, 20:33

I wonder why smart systems, cameras, driver assistance systems in driving are not used on commercial transport such as trucks and buses? It’s one thing to integrate such statuses into a $35K car, and another into a truck or bus, whose prices start at least at $100-150K, and in some cases more. Buses are often purchased by organizations for whom an extra $5-10K on a price of $100-150 may not make much of a “difference” in deciding what to buy. Although of course understanding that there, with a tender for the minimum price, every thousand could be decisive. On the other hand, insurance might be lower, and it can be nicely sold to people (passengers). Also, it seems that truck drivers falling asleep are simply more dangerous and costly than personal car drivers falling asleep.

Why Don’t We Have Self-Sustaining Solar-Powered Drones Yet? | July 16 2025, 01:33

I wonder why we still don’t see autonomous drones that could lead an “eternal” life: landing on roofs, deploying solar panels, charging from the sun, and taking off once a day for whatever their mission might be? When you consider the energy aspects, it seems like a feasible scheme. For instance, a heavy drone weighing about 8 kg could carry foldable solar panels with an area of 1.5 m² and a battery with a capacity of 2 kWh. In one sunny day, such panels could collect about 1.2 kWh of energy — enough for it to fly for 20 minutes at a speed of 40–50 km/h, take photos, and transmit them via the mobile network. And there would still be a reserve of energy for several cloudy days.

Even a light drone weighing 2 kg with small panels (0.5 m²) could rise into the air for 10–15 minutes every day if it managed to find good weather and a sunny roof. The power required for hovering for such devices is about 150–200 W, and solar panels with 20% efficiency at mid-latitudes can produce up to 350–400 Wh per day. The balance comfortably adds up, especially if not chasing speed and if there’s no rush on the roof.

Such a “solar nomad” could live for weeks and months, flying from roof to roof and charging in anticipation of missions. At first glance, the technology of batteries and panels already allows this to be done. Or am I missing something?

AI-Powered Smart Glasses: Revolutionizing Real-Time Discussion and Information Access | July 15 2025, 20:19

Here’s what would be great to do with AI – a system that reads the screen, listens to what’s being discussed on the call, including what you say, and what is said to you, and _on the screen_, and better yet, directly on smart-glasses, gives pop-up tips and hints that help you timely ask a counter-question or request a clarification, or respond to a question directed at you. Not just for passing interviews, although that would also be nice, but for more effectively conducting discussions — from technical to commercial ones.

In the case of smart-glasses, you could enjoy this without a computer in front of your eyes. I’m just afraid of having to send absolutely everything that happens around you to the cloud, analyze it, and return it in real time, which is technologically challenging (=expensive).

Such a system would be no less useful for conducting interviews than for passing them. For example, you ask someone a question, they start to respond, and then the system suggests — aha, it seems they are struggling with this topic. Let’s ask this question. Then you decide whether to ask this or something else. Why not? It’s convenient. Of course, the interviewee could employ the same system, and then it would not be simple.

Right now, I’m flipping through a book by Johannes Itten on color, and I think about how I miss dynamic illustrations and commentary. I’ve reached Piero della Francesca and for the life of me, I can’t recall what his paintings are like. This is where smart-glasses would come in handy. You look at a word, snap your fingers, and around it appear pop-up windows with contextual illustrations, comments, and links to detailed information, which you can visit now, or save to read later. It would be possible to ask any question verbally while looking at the text segment it pertains to and get an answer not verbally, but in a pop-up window that you can quickly close if you didn’t find anything new, or perhaps add a clarification by voice, after which the content in the window updates.

If I had smart-glasses, I would experiment with this. It seems straightforward.

North Korea’s Tech Control: Red Star OS and Surveillance Smartphones | July 13 2025, 00:58

In the latest video about North Korea from Lankov, I heard something interesting: a device owner cannot open someone else’s file, whether on a computer or on a phone, unless it is signed with a special digital signature from the government. Intrigued, I researched the details for myself and for you.

On their phones, they use a modified old “KitKat” Android (2013), and on computers—a modified Fedora Linux, Red Star OS 3, with a shell that mimics the macOS interface from Apple (the previous one mimicked Windows XP). It is said that this design choice may have been influenced by the fact that leader Kim Jong Un was seen with an iMac on his desk, and apparently, he said make it the same.

North Korean smartphones are equipped with hidden surveillance features that automatically take screenshots every five minutes, storing them in a secret folder accessible only to authorities, not the user. According to other sources, screenshots are taken when applications start, apparently pseudo-randomly. There is also censorship: if you type “South Korea” (남조선) in any app, the system automatically replaces it with “puppet state” (괴뢰국가). One hundred percent of the phones are obviously Chinese, modified by China for Korea. By the way, the collected screenshots are accessible to users, but they cannot be deleted. This application, Trace Viewer, is clearly created to remind users: everything that they do on the tablet or phone can be known to the government.

All media content in Red Star OS, including documents, images, audio and video files, is automatically marked with a watermark containing a unique serial number of the hard drive, which allows authorities to track its origin and distribution. That is, you cannot take a photo and send it to someone, because it will either just not open on that phone, or, apparently, in rare cases, if sharing is allowed, in the new place there will be traces of both who is the author of the photo and who is the next owner. But this is underdeveloped, and direct file sharing is still limited. You can only use it yourself. Of course, nothing can be deleted from the phone without a trace. It is not allowed to have more than one device per person (seems to apply separately to a tablet and a phone).

North Korean mobile devices use a strict system of digital signatures (NATISIGN for government-approved content and SELFSIGN for content created on the device), which means that any file without these signatures cannot be opened at all. The system of signatures and signature verification is at the level of the operating system, not applications. This applies to all files that people create, both on phones and on computers. I see a huge number of edge cases here, but there is little information and no one to ask.

The penalties for accessing unauthorized foreign media, such as K-pop or South Korean dramas, are extremely harsh. If an “undesirable file” is found on a CD inserted into a computer with Red Star OS, the system will eject the CD, record the path to the file, display a graphical warning, take screenshots, and then forcefully reboot the system after 1000 seconds.

North Korea manages a national intranet network called Kwangmyong, “walled garden,” which is completely isolated from the global internet and is available to most citizens only for government-approved websites and email systems.

When you first launch the browser Naenara (based on Firefox 3.5), the default homepage is the IP address “10.76.1.11.” That is, their internet is essentially an intranet.

Exploring the Technological Marvels of Tesla’s Full Self-Driving Capabilities | July 11 2025, 03:59

I read various engineering blogs about Tesla’s autopilot (FSD) — simply because for the last month and a half I’ve been almost constantly riding as if in a taxi — you set the destination and hardly ever need to intervene, the car travels from point A to point B completely independently. This is certainly the future.

Such systems exist not only at Tesla. For example, Mercedes has one (Drive Pilot). Others only help in traffic jams at best. Though Tesla seems to be the only one that works on all roads.

So, returning to engineering curiosities. Tesla has an AI model production on its “farm” called Dojo — an exaFLOP supercomputer on Tesla chips. Videos from cameras are fed into it, and it trains models that are then sent out for autonomous operation across the entire fleet of Tesla cars.

The FSD architecture comprises about 48 specialized neural networks, trained on Dojo, which together form about 1,000 different prediction tensors. Tesla is gradually moving from modular networks (object recognition + planning) to end-to-end training — directly converting video frames into steering trajectory/action. This is akin to a “black box” — the neural network learns directly from human behavior, without manual tuning of knobs; an extremely cool engineering solution, but, I suspect, complex to debug.

By the way, it is claimed that Tesla has switched from C++ to Python. And that this shift to end-to-end training has made 300,000 lines of C++ code unnecessary, where various corner cases and rules for resolving different scenarios were accounted for — now it’s at the model level.

Tesla has abandoned radar and ultrasonics, switching to purely camera solutions (Vision Only) with “Hardware 4” (HW4, FSD Computer 2): 16 GB RAM, 256 GB flash memory, performance 3–8× higher than HW3.

Assess the performance: 22 milliseconds to create a 3D scene with cars, pedestrians, cyclists around — information is collected from 8 cameras 36 times per second.

85 ms for the entire cycle from receiving the image to changing the plan and commands to the wheels. Fantastic!

More than 4 million Teslas on the roads collect data daily, and in the FSD Beta version, more than a billion miles of autonomous driving have been recorded. This “live” dataset is used to train networks on the most real-world scenarios, including rare “edge-case” incidents (strange accidents, road conditions, etc.).

In June 2025, Tesla for the first time delivered a Model Y from the factory in Austin to a customer’s home without a driver or remote operator — fully autonomously. This is very cool.

The Vision network not only analyzes the current frame but also stores features from previous ones (at a distance of ≈1 m). This allows it to remember recently crossed markings/signs, even if they have already left the field of view – very similar to human memory.

Awaiting the Next Big Thing from Boston Dynamics | July 10 2025, 20:09

I’ve been thinking, it’s been a while since there was an exciting video from Boston Dynamics. Remember, each one used to create quite a stir online. I checked their channel. And it looks good. Hundreds of millions in investments from the Pentagon/US Department of Defense suggest that it won’t stop at drones.

https://youtu.be/I44_zbEwz_w?si=51szmPYzdYtBGs6X

Advancing Full-Text Search: Testing and Refining with Multi-User Platforms | July 06 2025, 04:35

I have developed expertise in full-text search testing. Essentially, it’s a turnkey multi-user platform that, given roughly 1000 queries and several search engine configurations, can produce reports with graphs, metrics, and conclusions by morning, showing why configuration A performs better than B, and here’s why. It calculates all those NDCG@k, MAP, precision, recall, and about a dozen other metrics. It uses LLM, but only at the final stage, after all the math is done.

So, here’s my question. I’m looking for someone who has faced the same issue in their project, to understand the demand and the ask.

The problem the system solves is defined as follows: there is a functional search for goods, documents – Solr, Coveo, Elasticsearch, Algolia – it doesn’t matter, and there are hypotheses on how to improve it, but there is also the fear that improving one aspect might break another. Well, my thing helps to see this in numbers and graphs, providing a conclusion with justification, including statistical significance and other metrics.

It also acts as a virtual search assessor. For each search result, it can give a rating, assessing how well each document matches the query. This is a very non-trivial task (especially for large documents), involving chunking, embeddings, LLM evaluation of relevant chunks, etc. Non-trivial, but it works.

It also can analyze search queries and break them into groups based on similarity. For instance, such segmentation might show that users sometimes separate the words forming a brand name with a space, and sometimes not. These different variants would be grouped together.

I would like to discuss this with someone who knows more about this topic than I do, someone who has/had such problems and has somehow solved them.

I currently feel like my product is unique in the market. Actually, it’s not even on the market yet. But I really don’t see anything similar out there. Maybe nobody needs it?

I won’t publically post screenshots yet. The picture is merely for attracting attention.

Please share if there might be relevant people in your network.