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Near-Miss at Leningrad: The Unknown Predecessor to Chernobyl | April 26 2026, 17:32

On the anniversary of Chernobyl. Those interested in this topic may not know that a similar accident could have occurred ten years earlier on the main (very first RBMK-1000 unit) Leningrad Nuclear Power Plant.

There was a nearly identical situation: one turbine in operation, reactor shutdown by emergency protection, and subsequent power escalation.

Back then, the situation was saved by the Chief Reactor Operator Mikhail

Karrask, who, acting intuitively and relying on his experience with industrial reactors, introduced into the reactor in portions

12 manual control rods

BEFORE pressing the emergency shutdown button.

A couple of years ago, Karrask passed away. This story is almost unknown outside the industry. For proof, google his obituary on Rosatom.

The technical part. The main danger of the RBMK reactors at that time was in the design of the control rods. At the bottom, they were equipped with graphite “displacers”. When the emergency shutdown button was pressed, the rods began moving down, and in the first seconds, the graphite tips did not dampen the reactor, but on the contrary, displaced the water and increased the power in the lower part of the active zone. But precisely, the instructions in case of trouble suggested pressing the emergency shutdown button. If you followed the instructions, “Chernobyl” would have happened earlier.

After the incident at the Leningrad NPP, a commission was formed. Experts (including those from the Kurchatov Institute of Atomic Energy) pointed out the dangerous design flaws of the RBMK – the positive void coefficient of reactivity and incorrect design of the rods. Unfortunately, extensive changes to the design of all RBMK reactors were not made at that time. Only operational regulations recommendations were given, which, as history showed, were insufficient to prevent the tragedy in Chernobyl.

Silence for Cents: Exploring the SwissBrand 120-Pack Mouth Tape | April 24 2026, 18:25

If your other half needs to be silenced temporarily, you can buy a kit for 120 silencings from us, averaging just under six cents per silence

The Art of the Unresolved Finale: Viewer Frustration as a Narrative Tool | April 20 2026, 13:27

We finished watching the series “Pete”. It seems like TV directors do everything to ensure that the last episode offers no answers, resembling just a regular mid-season episode. In many TV shows, the second-to-last or third-to-last episodes answer the questions, while the final one rarely satisfies, always adding a multitude of hooks and new questions, probably serving as an invitation to a next season that may never come. Or it might, but for now the director doesn’t know what it will entail and leaves much unsaid. However, the likely goal is to irritate viewers so that they flock to Reddits and Facebooks to discuss what they’ve seen. A logical end was only seen in the series Chernobyl, it seems.

Crabs in Love: Monogamous Parasites of Sea Turtles | April 15 2026, 21:56

WOW, it turns out that under the shell near the anus of sea turtles, the parasitic crabs Planes minutus make themselves at home, and there’s only enough space for a cozy duo, so they form a monogamous pair and live happily ever after inside the turtle’s butt (had no idea what to do with this information, so I brought it here). In relation to the turtle, this is commensalism. It’s when it’s good for one (or in our case, two), and the third doesn’t give a damn. I see a scientific paper claiming that they sometimes mistake the turtle for ocean debris, where there’s room for more than one wife, and then, goodbye monogamy. But, at least, no butts involved.

Navigating the Depths of High-Dimensional Spaces | April 13 2026, 23:17

I am now working a lot with high-dimensional vectors, and some things that I hadn’t fully realized before are really starting to tickle my brain. Our 3D intuition doesn’t just not work there—it lies.

It turns out that any two random vectors in high-dimensional space are almost certainly nearly perpendicular to each other. Almost all the space is one continuous “equator”.

Much of machine learning is built on exactly this. If your embeddings suddenly show high cosine similarity (for example, 0.8 — this is not a statistical error, but a powerful signal. It’s almost impossible to randomly converge like this in a 1000-dimensional world.

In such spaces, almost all the mass of data is concentrated in an extremely thin surface layer. The “insides” of objects are mathematically empty.

This can be easily verified with such an imaginary example. Take the “skin” of a multidimensional sphere with a thickness of just 1% of the radius. The volume of the sphere is proportional to the radius raised to the power of its dimensionality.

• In three-dimensional space, the pulp (0.99 of the radius) occupies 97% of the volume, you raise 0.99 to the third power.

• In 1000D, the pulp occupies just 0.000043%.

You can understand it differently. For a point to be closer to the origin, it requires that along all axes the coordinates need to be close to the origin. If one axis has a high value, that’s it, the point has gone. If you take points randomly, the mere probability that they all at once will be below any value decreases with the growth of dimensionality, and decreases quickly.

All the “meat” of the data always ends up in the skin. Any sample in High-D is essentially a set of boundary values.

For white noise in high dimensions, the distance between the closest and the farthest neighbor becomes almost the same. The concept of “closeness” simply degrades.

Sky-High Prices at the CIA-Adjacent Gas Station | April 11 2026, 21:16

We have one gas station near the CIA that simply sets gas prices 40 percent higher than anywhere else. Just an ordinary shabby station that follows the principle of “if it works, don’t fix it.”

CPU vs GPU: A Speed Challenge in Embedding Creation | April 11 2026, 18:08

When working with certain tasks, the difference between a CPU and a GPU is simply astounding. For example, I need to create many (millions) of embeddings, model BGE M3. Running this on my quite powerful 24-core Intel Core Ultra 9 285K processor takes 45.85 seconds to create 500 embeddings, while using an NVIDIA 5090 GPU, the same task is completed in just 0.36 seconds. It is so fast that I specifically wrote this benchmark to figure out whether my GPU is being utilized at all. The program that sends requests to TEI does it in test mode not actively enough (roughly a couple of times per second), and the GPU load graphs are practically zero.

— Testing http://localhost:8080/embed — <– CPU version

Requests completed: 500

Total time: 45.85 sec

Throughput: 10.90 req/sec

Average latency (Avg Latency): 4386.11 ms

P95 latency: 5021.88 ms

— Testing http://localhost:8090/embed — <– GPU version (NVIDIA 5090)

Requests completed: 500

Total time: 0.36 sec

Throughput: 1398.69 req/sec

Average latency (Avg Latency): 31.38 ms

P95 latency: 53.18 ms

========================================

RESULT: http://localhost:8090/embed is 99.22% faster