Everyone knows what an RNG is—an algorithm that generates a random number.
However, all algorithms are susceptible to attacks and vulnerabilities, so services with high security requirements tend to avoid relying on them—cryptography, encrypted communication, digital signatures, and so on. Some banks or systems ask users to move their mouse or press keys a certain number of times to generate random events.
Physical RNGs can be hardware-based or rely on physical phenomena, but in any case, they use a real source of entropy.
Some unusual sources of entropy used or studied:
1. Lava lamps – the chaotic movement of wax in dozens of lava lamps.
2. Geiger counter – intervals between events of radioactive decay.
3. Goldfish in an aquarium – random movement of fish in water.
4. A laundromat with a microphone – noise from washing machines.
5. A dance floor with pressure sensors – chaotic movements of people.
So your security might depend not only on software and algorithms, but also on fish in an aquarium.
However, all algorithms are susceptible to attacks and vulnerabilities, so services with high security requirements tend to avoid relying on them—cryptography, encrypted communication, digital signatures, and so on. Some banks or systems ask users to move their mouse or press keys a certain number of times to generate random events.
Physical RNGs can be hardware-based or rely on physical phenomena, but in any case, they use a real source of entropy.
Some unusual sources of entropy used or studied:
1. Lava lamps – the chaotic movement of wax in dozens of lava lamps.
2. Geiger counter – intervals between events of radioactive decay.
3. Goldfish in an aquarium – random movement of fish in water.
4. A laundromat with a microphone – noise from washing machines.
5. A dance floor with pressure sensors – chaotic movements of people.
So your security might depend not only on software and algorithms, but also on fish in an aquarium.
Today, YouTube surprised me with something wonderful – a CAD competition.
For a while, my information bubble was filled with Tetris competitions, then Japanese game shows and GeoGuessr (that’s the one where you're given a spot in Google Street View and have to guess the exact coordinates on the planet – totally addictive, highly recommend).
And now, CAD competitions:
1. Participants are given a technical drawing of a model.
2. They must recreate the model in CAD.
3. The winner is the one who sends the correct model weight to the chat the fastest.
Unlike other types of competitions, this one is more relevant to my interests – it’s fascinating to see the approaches participants take in modeling, and even if it’s not exactly about best practices, you can still pick up some useful techniques.
For a while, my information bubble was filled with Tetris competitions, then Japanese game shows and GeoGuessr (that’s the one where you're given a spot in Google Street View and have to guess the exact coordinates on the planet – totally addictive, highly recommend).
And now, CAD competitions:
1. Participants are given a technical drawing of a model.
2. They must recreate the model in CAD.
3. The winner is the one who sends the correct model weight to the chat the fastest.
Unlike other types of competitions, this one is more relevant to my interests – it’s fascinating to see the approaches participants take in modeling, and even if it’s not exactly about best practices, you can still pick up some useful techniques.
I came across a study on the topic of progress in various fields with and without AI.
From personal experience, I agree with the conclusions. Using AI provides a previously impossible boost in the early stages, which helps reduce the overall time needed for learning, improvement, etc.
At the same time, if you rely solely on AI, your maximum efficiency will be limited by the capabilities of the model you're using. The quality of models is improving, but I’m convinced that combining AI with your own thinking will yield better results in the foreseeable future.
The article contains plenty of additional information and charts - worth reading if you're interested.
From personal experience, I agree with the conclusions. Using AI provides a previously impossible boost in the early stages, which helps reduce the overall time needed for learning, improvement, etc.
At the same time, if you rely solely on AI, your maximum efficiency will be limited by the capabilities of the model you're using. The quality of models is improving, but I’m convinced that combining AI with your own thinking will yield better results in the foreseeable future.
The article contains plenty of additional information and charts - worth reading if you're interested.
All publication schedules were thrown off due to the vacation, so I’ll be writing about whatever is currently relevant, without sticking to specific dates.
Today’s post is about my recent development — a Telegram emulator. Why build such a "reinvention of the wheel," what it can do, and how long it took to develop — read about it on the blog: https://positroid.tech/en/post/local-telegram-emulator.
Today’s post is about my recent development — a Telegram emulator. Why build such a "reinvention of the wheel," what it can do, and how long it took to develop — read about it on the blog: https://positroid.tech/en/post/local-telegram-emulator.
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You can watch fire, water, and Cursor writing tests for three projects at once — forever.
I suddenly remembered touch typing—I mastered it about five years ago and haven’t looked at the keyboard since.
Before that, I could already type quickly and had developed some muscle memory, but I still had to glance at the keyboard from time to time, especially for special characters and function keys.
There were several attempts to learn it, starting back in childhood with Solo on the Keyboard, and the most successful one was five years ago using typingclub.com.
The service is as simple as it gets, fairly gamified—I recommend it.
I dug up some old stats: Russian—39 words per minute, English—33 words per minute.
I retested just now: Russian—61 words per minute, English—42 words per minute.
I won’t say whether that’s a lot or a little—it’s enough for me. Still, I’m surprised by people who still type with just two fingers, even though most of their job is writing. And during coding sections of interviews or design interviews (about architecture), it’s a secondary but still noticeable skill.
Before that, I could already type quickly and had developed some muscle memory, but I still had to glance at the keyboard from time to time, especially for special characters and function keys.
There were several attempts to learn it, starting back in childhood with Solo on the Keyboard, and the most successful one was five years ago using typingclub.com.
The service is as simple as it gets, fairly gamified—I recommend it.
I dug up some old stats: Russian—39 words per minute, English—33 words per minute.
I retested just now: Russian—61 words per minute, English—42 words per minute.
I won’t say whether that’s a lot or a little—it’s enough for me. Still, I’m surprised by people who still type with just two fingers, even though most of their job is writing. And during coding sections of interviews or design interviews (about architecture), it’s a secondary but still noticeable skill.
Today’s blog post is about image2model — generating a 3D model from an image: https://positroid.tech/en/post/image2model
I had done something similar with the Caretaker before, but in the article series, it was easy to overlook that part, even though the tools are very easy to use and quite helpful.
I had done something similar with the Caretaker before, but in the article series, it was easy to overlook that part, even though the tools are very easy to use and quite helpful.
I've been into 3D printing for over 7 years now.
And I've never dried filament. Occasionally, there was some stringing, but nothing out of the ordinary. Until last month.
I ordered two spools of filament from a no-name manufacturer for $8/1kg each. The packaging was poor, but that wasn’t the main issue.
The filament just wouldn’t print properly. I mean, it did print, but with a lot of stringing and defects. No matter the temperature setting, the results were awful. I even tried drying it in the oven at 50°C for 4 hours — no improvement.
Eventually, I gave up and bought a filament dryer. I got the cheapest one that has both heating and airflow — the Creality Dry Box 2.0.
Threw the spool in at 65°C for 8 hours and... it was a miracle — all the problems vanished. Flawless print quality, perfect temperature tower, and great Benchy.
It seems that all these years, many of my printing and retraction issues — especially with Bowden setups — could have been drastically reduced...
And I've never dried filament. Occasionally, there was some stringing, but nothing out of the ordinary. Until last month.
I ordered two spools of filament from a no-name manufacturer for $8/1kg each. The packaging was poor, but that wasn’t the main issue.
The filament just wouldn’t print properly. I mean, it did print, but with a lot of stringing and defects. No matter the temperature setting, the results were awful. I even tried drying it in the oven at 50°C for 4 hours — no improvement.
Eventually, I gave up and bought a filament dryer. I got the cheapest one that has both heating and airflow — the Creality Dry Box 2.0.
Threw the spool in at 65°C for 8 hours and... it was a miracle — all the problems vanished. Flawless print quality, perfect temperature tower, and great Benchy.
It seems that all these years, many of my printing and retraction issues — especially with Bowden setups — could have been drastically reduced...
Image generators have long since reached production-level quality.
The recent "nano-banana," which turned out to be Gemini 2.5 Flash Image Preview, marks a new level in image editing. Before it, the top models were GPT-4 ImageGen by OpenAI and Flux Kontext, but when it comes to consistency in preserving characters, they fall far behind Google’s model.
As an illustration — there’s a character named The Pupa from Solar Opposites — there’s barely any fan art of him online, mostly just screenshots from the show.
Now, with a single prompt, you can get a consistent sticker pack. I’ve got an idea for a Telegram sticker pack generator — could be pretty solid.
P.S. The model is available for free in Google AI Studio (with a US VPN) or via OpenRouter.
The recent "nano-banana," which turned out to be Gemini 2.5 Flash Image Preview, marks a new level in image editing. Before it, the top models were GPT-4 ImageGen by OpenAI and Flux Kontext, but when it comes to consistency in preserving characters, they fall far behind Google’s model.
As an illustration — there’s a character named The Pupa from Solar Opposites — there’s barely any fan art of him online, mostly just screenshots from the show.
Now, with a single prompt, you can get a consistent sticker pack. I’ve got an idea for a Telegram sticker pack generator — could be pretty solid.
P.S. The model is available for free in Google AI Studio (with a US VPN) or via OpenRouter.
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Section: “How It’s Made”
An engineer’s self-awareness occasionally needs nourishment in the form of knowledge about some object.
A large supermarket chain recently started giving out toys with purchases. On their own, they’re nothing special—just cubes with strange prints on them.
But they stick to each other magnetically, regardless of which side or orientation. Anyone who’s ever played with magnets or remembers basic physics knows that magnets have poles. You can’t just attach two magnets together from any side. Unless you’re very strong.
So, I took the whole thing apart—and it turned out that the corners of the cubes contained loose little magnets that simply flip to the correct side when brought close to another cube. Genius lies in simplicity.
P.S. I appropriated the magnets, even though I’ve got stronger ones lying around for crafts, and they’re dirt cheap anyway. But in the world of making, everything can come in handy.
An engineer’s self-awareness occasionally needs nourishment in the form of knowledge about some object.
A large supermarket chain recently started giving out toys with purchases. On their own, they’re nothing special—just cubes with strange prints on them.
But they stick to each other magnetically, regardless of which side or orientation. Anyone who’s ever played with magnets or remembers basic physics knows that magnets have poles. You can’t just attach two magnets together from any side. Unless you’re very strong.
So, I took the whole thing apart—and it turned out that the corners of the cubes contained loose little magnets that simply flip to the correct side when brought close to another cube. Genius lies in simplicity.
P.S. I appropriated the magnets, even though I’ve got stronger ones lying around for crafts, and they’re dirt cheap anyway. But in the world of making, everything can come in handy.