273
My head is about to break because of the School of Data Analysis course. Diagrams of interactions between MCP components give me nightmares. Let's talk about constants in physics.
Everyone knows that absolute zero is approximately -273 degrees Celsius. But what is the source of this constant? Is it experimental or theoretical?
A piece of totally impractical, but dear to me, knowledge is the following. If we take the melting point of water and its boiling point as reference points, divide the whole range into 100 equal parts using an expanding liquid like mercury as a measure, then a decrease in temperature by 1 degree Celsius leads to the gas shrinking by approximately 1/273 of its volume at 0 degrees Celsius. So absolute zero is the point at which the gas shrinks to nothing.
Phew. So much easier than two stage-embedding retrieval.
My head is about to break because of the School of Data Analysis course. Diagrams of interactions between MCP components give me nightmares. Let's talk about constants in physics.
Everyone knows that absolute zero is approximately -273 degrees Celsius. But what is the source of this constant? Is it experimental or theoretical?
A piece of totally impractical, but dear to me, knowledge is the following. If we take the melting point of water and its boiling point as reference points, divide the whole range into 100 equal parts using an expanding liquid like mercury as a measure, then a decrease in temperature by 1 degree Celsius leads to the gas shrinking by approximately 1/273 of its volume at 0 degrees Celsius. So absolute zero is the point at which the gas shrinks to nothing.
Phew. So much easier than two stage-embedding retrieval.
👍2
Shad Intensive. Memory. Guardrails.
Just want to share that I'm listening to this course. With a delay, but I'm trying to eat this mammoth piece by piece.
Today, in the "Memory and Guardrails" lecture, I didn't hear anything that gave me an insight I would like to share. Just common words about context length and context compaction in the memory part. In the guardrails part, they mentioned sources of danger like every user input, RAG, and API. I believe this is the usual computer security paranoia: you can't trust anyone. And it's much better to turn your computer off, drop it in liquid cement, and let it set.
The only thing that really interests me is not what I understood, but what I didn't. Surprisingly, I don't quite get this "context window" concept. Probably it's just my hallucination. If not, and if there is something interesting here, I'll share it.
Just want to share that I'm listening to this course. With a delay, but I'm trying to eat this mammoth piece by piece.
Today, in the "Memory and Guardrails" lecture, I didn't hear anything that gave me an insight I would like to share. Just common words about context length and context compaction in the memory part. In the guardrails part, they mentioned sources of danger like every user input, RAG, and API. I believe this is the usual computer security paranoia: you can't trust anyone. And it's much better to turn your computer off, drop it in liquid cement, and let it set.
The only thing that really interests me is not what I understood, but what I didn't. Surprisingly, I don't quite get this "context window" concept. Probably it's just my hallucination. If not, and if there is something interesting here, I'll share it.
Shad Intensive. Evaluation.
It seems I’ll be eating this mammoth in small pieces for ages. So for now, here’s a link to a nice post on agent evaluation
It seems I’ll be eating this mammoth in small pieces for ages. So for now, here’s a link to a nice post on agent evaluation
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Поляков считает: AI, код и кейсы
Как тестировать AI-агентов: на полях лекций в ШАД
Продолжаю Agents Week от ШАД. Четвёртая лекция — как проверять качество агентов. Тема, которую все откладывают и которая больше всего бьёт по репутации ИИ в проде или интегратора.
📋 Что советует лекция
…
Продолжаю Agents Week от ШАД. Четвёртая лекция — как проверять качество агентов. Тема, которую все откладывают и которая больше всего бьёт по репутации ИИ в проде или интегратора.
📋 Что советует лекция
…
Are we doing meme shitposting today?
Anonymous Poll
81%
Memes! Yeah!
6%
I'll unsubscribe immediately
0%
I've already unsubscribed
25%
TGIF!!!
TGIF. Meme
First of all, let's check the results of the poll. One subscriber promised to unsubscribe, and the other 9 voted for memes and shitposting. A naive approach would be to think that if I publish the meme, I'll lose 1 subscriber. But you can solve the proportion, and it gives -27.6 subscribers. The result is stunning, so, let's see. I expect to drop to 248.4 subscribers.
The topic of today's Friday meme is The Soup.
First of all. I stole these memes from The Wizard . I think that it is a magnificent channel and I ask you to promote it as widely as you can.
And now to the chase.
Dad's soup
My dad cooks absolutely hellish food.
It’s a sort of averaged recipe, because there are lots of variations.
He takes soup — but reheating it is not my dad’s style.
He pours the soup into a frying pan and starts frying it.
He adds a huge amount of onion, garlic, tomato paste, flour for thickness, and mayonnaise on top.
The whole thing fries until smoke starts coming out.
Then he takes it off the heat, lets it cool on the balcony, brings it back, pours on even more mayonnaise, and starts eating.
He eats straight from the pan, scraping it with a spoon, muttering under his breath, “oh, damn.”
Sweat is standing on his forehead.
Sometimes he politely offers me some, but I refuse.
Needless to say, the aftermath is monstrous.
The stench is so intense that the wallpaper peels off the walls.
P.S. To be honest, all this subscribe/unsubscribe stuff is starting to get to me. I’d really appreciate some support — even a couple of emojis wouldn’t hurt.
P.P.S. Tomorrow I’ll try to pull myself together and write something clever. Probably continue the “Titanic” line.
First of all, let's check the results of the poll. One subscriber promised to unsubscribe, and the other 9 voted for memes and shitposting. A naive approach would be to think that if I publish the meme, I'll lose 1 subscriber. But you can solve the proportion, and it gives -27.6 subscribers. The result is stunning, so, let's see. I expect to drop to 248.4 subscribers.
The topic of today's Friday meme is The Soup.
First of all. I stole these memes from The Wizard . I think that it is a magnificent channel and I ask you to promote it as widely as you can.
And now to the chase.
Dad's soup
My dad cooks absolutely hellish food.
It’s a sort of averaged recipe, because there are lots of variations.
He takes soup — but reheating it is not my dad’s style.
He pours the soup into a frying pan and starts frying it.
He adds a huge amount of onion, garlic, tomato paste, flour for thickness, and mayonnaise on top.
The whole thing fries until smoke starts coming out.
Then he takes it off the heat, lets it cool on the balcony, brings it back, pours on even more mayonnaise, and starts eating.
He eats straight from the pan, scraping it with a spoon, muttering under his breath, “oh, damn.”
Sweat is standing on his forehead.
Sometimes he politely offers me some, but I refuse.
Needless to say, the aftermath is monstrous.
The stench is so intense that the wallpaper peels off the walls.
P.S. To be honest, all this subscribe/unsubscribe stuff is starting to get to me. I’d really appreciate some support — even a couple of emojis wouldn’t hurt.
P.P.S. Tomorrow I’ll try to pull myself together and write something clever. Probably continue the “Titanic” line.
❤4😁3🔥1
Titanic: family bonds
Nowadays, it is hard to imagine an ML person without a Jupyter notebook. So let’s think a little outside the box and consider other options.
Quite recently, I discovered an interesting option for CSV table analysis. It consists of two instruments: DBeaver and DuckDB. The former is a Swiss army knife for database access, and the latter is a plugin that is quite powerful, or so I was told, but can also be used to open CSV files.
So, you can create a new DuckDB connection in DBeaver (ducks and beavers, yeah...), select the titanic.csv file you got from Kaggle, and start running queries.
SibSp is a factor, so to speak, horizontal in the ancestry tree. It is the sum of siblings and spouses. Parch is vertical in these coordinates: parents and children.
The first two queries show us the strength of these factors separately. The third uses the nested query technique to produce a derived factor, family size. One can see that these factors are useful, but the derived factor gives a stronger depletion/enrichment effect. This time, we were lucky enough to engineer a new feature.
I would say that family sizes 2, 3, and 4 vote for survival; the others vote against it.
Nowadays, it is hard to imagine an ML person without a Jupyter notebook. So let’s think a little outside the box and consider other options.
Quite recently, I discovered an interesting option for CSV table analysis. It consists of two instruments: DBeaver and DuckDB. The former is a Swiss army knife for database access, and the latter is a plugin that is quite powerful, or so I was told, but can also be used to open CSV files.
So, you can create a new DuckDB connection in DBeaver (ducks and beavers, yeah...), select the titanic.csv file you got from Kaggle, and start running queries.
SibSp is a factor, so to speak, horizontal in the ancestry tree. It is the sum of siblings and spouses. Parch is vertical in these coordinates: parents and children.
The first two queries show us the strength of these factors separately. The third uses the nested query technique to produce a derived factor, family size. One can see that these factors are useful, but the derived factor gives a stronger depletion/enrichment effect. This time, we were lucky enough to engineer a new feature.
I would say that family sizes 2, 3, and 4 vote for survival; the others vote against it.
👍1
Let’s rock ROC
All this time, it bothered me that the ROC in the post contains only three points. I’ve been thinking about this situation for a while, and I think I’ve found a small but genuinely new idea. I’m going to explain it slowly in a series of upcoming posts. But first, I want to check whether this thought is actually trivial. Please vote in the poll below, and feel free to leave comments.
Quick recap:
We have a model that gives scores of -1, 0, and 1. When we plot the ROC curve, we get a graph with 4 points and an AUROC of 56%.
The question is: can we calculate the dispersion of AUROC in this case?
All this time, it bothered me that the ROC in the post contains only three points. I’ve been thinking about this situation for a while, and I think I’ve found a small but genuinely new idea. I’m going to explain it slowly in a series of upcoming posts. But first, I want to check whether this thought is actually trivial. Please vote in the poll below, and feel free to leave comments.
Quick recap:
We have a model that gives scores of -1, 0, and 1. When we plot the ROC curve, we get a graph with 4 points and an AUROC of 56%.
The question is: can we calculate the dispersion of AUROC in this case?
Telegram
Algorithms. Physics. Mathematics. Machine Learning.
Titanic. Age.
We started talking about the Titanic dataset. Let's discuss the Age factor. We saw that Sex, Pclass and Embarked are strong features and to study them we used that these features are categorical with low cardinality. Age is different. It's…
We started talking about the Titanic dataset. Let's discuss the Age factor. We saw that Sex, Pclass and Embarked are strong features and to study them we used that these features are categorical with low cardinality. Age is different. It's…
Can we calculate the dispersion of AUROC in this case?
Anonymous Poll
18%
Of course, it's 0. Obviously.
55%
AUROC is a metrics, it has no dispersion
36%
Your question is both stupid and offending
27%
Ouch. I think I have an idea. (And will share in comments)
27%
Shut up and post more memes.
Plan: from school-level math to a March 2026 arXiv
The stakes turned out to be higher than I expected. Our quiet little discussion of the Titanic dataset may have suddenly wandered into publication-level territory. You can check the comments under the poll.
So now I have a difficult choice to make: either post more TFWR and beginner-level programming, or start a short series about the picture above. Let’s postpone TFWR for about a week and focus on ROC with ties.
LLMs make this kind of thing harder to understand. They help you solve the immediate problem quickly, but they often blur the core idea. So let’s try to chase the idea itself.
The plan:
👉 Digitization of objects
👉 What a model is in ML
👉 ROC and AUROC
👉 Stochastic wandering
👉 Binomial distribution
👉 Variance of AUROC for low-cardinality model outputs
The stakes turned out to be higher than I expected. Our quiet little discussion of the Titanic dataset may have suddenly wandered into publication-level territory. You can check the comments under the poll.
So now I have a difficult choice to make: either post more TFWR and beginner-level programming, or start a short series about the picture above. Let’s postpone TFWR for about a week and focus on ROC with ties.
LLMs make this kind of thing harder to understand. They help you solve the immediate problem quickly, but they often blur the core idea. So let’s try to chase the idea itself.
The plan:
👉 Digitization of objects
👉 What a model is in ML
👉 ROC and AUROC
👉 Stochastic wandering
👉 Binomial distribution
👉 Variance of AUROC for low-cardinality model outputs