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
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.
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
Remember the American cult that had 39 members simultaneously self-extinguish in a mansion near San Diego, believing that they would be picked up by aliens? Well, their website is still up and running. The earliest version of this site from 1999 is virtually indistinguishable from what’s on the site now. The only difference is the ® symbol, which was after the name of the cult in 1999, but not now.
I Googled what’s up with their trademark registration. Just recently, in 2020, the company “The Evolutionary Level Above Human Foundation” registered (or renewed) rights to this trademark. The category is indeed listed as Lace, Ribbons, Embroidery, Fancy Goods, but the name of the company leaves no doubt that they are thinking about aliens.
I Googled some more. Turns out, this foundation, The Evolutionary Level Above Human (TELAH), acts as the “guardian of the legacy of the group ‘Heaven’s Gate'”, and has sued Stephen Havel and other defendants for copyright and trademark infringements, accusing them of illegally distributing archival materials and selling themed merchandise. The last update shows the parties are obligated to hold a meeting by the end of March 2024 to try to negotiate confidentiality and authentication of evidence without further judicial intervention.
Specifically, the foundation consists of real people from Arizona, Mark and Sara King, and the organization is registered as a corporation. They respond to emails and send out books and cassettes if you transfer them money.
Other former members are trying to challenge their “right” to use cult materials, such as recordings on tapes in court.
In short, some kind of life goes on there.
That is, the next time you think of Flat Earthers as “some pranksters pretending to be weirdos”, remember these folks, maintaining their website and selling books by their “prophets”.
We bought a Tesla in mid-2025 – comparing gasoline costs to electricity costs.
Looking just at charging the Tesla, the stats are separate. Since buying, we’ve used 5000 kWh costing $738 – covering 13,550 miles. Meaning, traveling 18 miles (28 km) costs one dollar. On a Toyota RAV4, one dollar spent at the gas station gets me 10 miles (16 km).
Funny, over here an entertainment center is a cabinet
Here we have the ultimate tricksters. If you accidentally choose an answer for “would you like to donate?”, getting to “oh, I don’t want to yet” takes about 10 minutes and is fraught with the risk of losing your seats. Because 1) there is no option for ‘don’t want to’ 2) any selection ranges from $5 to $9.60 3) refreshing the page results in an error, forcing you to reselect seats and try not to hit those radio buttons again. And by the way, these were the last two seats in the auditorium. They weren’t available yesterday, but showed up today.
I’ve further developed a new tool for myself for working with information and organizing it. The main idea is a web-based notebook for research, studying subjects, working on them, integrated with AI and PDF support.
The main problem with typical PDF readers and notes is that the context is lost as soon as you switch to a new tab. In my tool, each text fragment or PDF becomes a node in a “live” hypertext tree, which I can access from multiple computers at any time.
Work process:
– Contextual AI. I can ask the AI to clarify complex passages right within the document. The explanation stays right where the question was asked. Moreover, it is a separate document, linked to the specific spot in the source. When clicked, you see both the original and the explanation on the screen at the same time.
– Panels instead of windows. If the explanation itself requires clarification, a new panel opens to the right. This allows for an endless chain of queries, never losing the place in the original text. That is, you see several panels at once, and unnecessary ones can be closed.
– PDF support. I can upload a PDF, select an area on the page (e.g., a complex diagram or a list of authors), and the LLM instantly extracts data, supplements, or explains them. The explanation is attached to the spot where it was requested, just like with non-PDFs.
– Nested annotations. My comments are not just static text. They can contain their own PDFs, links, and further sub-tasks for AI, maintaining a depth of nesting that reflects how we actually think.
This is not just a file storage system, but an “engine” for building knowledge.
The tool suits me personally very well, but perhaps it only solves my specific tasks. What do you think, would something like this be useful to others? Would it be useful to you? Should I develop the project into a fully-fledged product and give it to other users for testing?
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 whipped up this thing in just an hour. Do you think anyone besides me needs it?
Here’s the idea. Take any text – a Wikipedia article, for example. Highlight any segment, say something unclear. The LLM gives us an explanation, and instantly inserts a box right in the text which you can click to open the explanation. In this explanation, there might be something unclear too. We highlight it with the mouse from this explanation, and a box appears there too. This continues until everything is clear. All the boxes remain in the text, so you can always return to them. So, if the idea was unclear to me, maybe it will be to others, and then a ready link with explanations will come in very handy. The result can be shared with colleagues.
For explanations, not just the fragment is used, but also the context. For example, otherwise, the highlighted word Terrier would yield text about a dog breed, not about the search system.
Hi, I'm Rauf Aliev. 