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Channel

Data Portal | DS & ML

@LLMScience

Not marked by Telegram, checked 24 September 2026

On this record: Topic · Growth · Engagement · What this channel posts · Stars · Advertising · Posts · Posts edited after publishing · Citations · Telegram's recommendations · Cite this entry

10,236subscribers

+1,880 since we began measuring on 9 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1002177572398
TypeChannel
Username@LLMScience
CreatedBetween 1 June 2024 and 30 September 2024 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded9 August 2026
Last confirmed live27 September 2026
Measurements held13
Confirmed unchanged1 time, most recently 27 September 2026
On Telegramt.me/LLMScience

Topic

Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 11 September 2026 and assigned it the closest of 31 fixed categories, at 99% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.

Growth

8,35510,2369,295.59 August 2026 — 8,356 subscribers10 August 2026 — 8,355 subscribers12 August 2026 — 8,376 subscribers16 August 2026 — 8,368 subscribers19 August 2026 — 8,366 subscribers22 August 2026 — 8,406 subscribers25 August 2026 — 8,431 subscribers28 August 2026 — 8,418 subscribers31 August 2026 — 8,405 subscribers3 September 2026 — 8,385 subscribers9 September 2026 — 8,382 subscribers17 September 2026 — 8,674 subscribers27 September 2026 — 10,236 subscribers9 August 202627 September 2026
13 measurements spanning 48 days, net +1,880. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 8,073–10,518 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
27 Sept 2026, 00:4110,236+1,562
17 Sept 2026, 23:218,674+292
9 Sept 2026, 09:428,382-3
3 Sept 2026, 17:408,385-20
31 Aug 2026, 15:078,405-13
28 Aug 2026, 07:268,418-13
25 Aug 2026, 11:378,431+25
22 Aug 2026, 19:578,406+40
19 Aug 2026, 03:358,366-2
16 Aug 2026, 07:078,368-8
12 Aug 2026, 17:168,376+21
10 Aug 2026, 01:218,355-1
9 Aug 2026, 18:168,356first reading

Engagement

88 posts held, back to 4 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 17 pages of Telegram’s post history, 20 posts per page.

Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 88 posts for this entry, the most recent from 28 August 2026. An engagement rate over an empty window would be a number about nothing.

What this channel posts

Video runtime
1m 52s
Average length
37s

Measured directly from 3 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.

Telegram Stars

Stars received
1
across the posts below
Posts paid on
1
of 88 we hold a reading for · 1%
Most on one post
1
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @LLMScience. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.

Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.

This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.

Counted over the 88 most recent posts we hold for this entry, published 4 August 2026 to 28 August 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.

Advertising

Ad load
2.27%
2 of 88 posts carry an ad marker
Regulatory tokens
2
posts carrying an erid · 2 distinct tokens
Median views · ads
528
over 2 measured posts
Median views · rest
747
over 86 measured posts

An ad marker, not a judgement about a post. A post is counted here because it carries one of two explicit markings: an erid token, which Russian law has required on paid placements since 2022 and which is issued against a specific advertising contract, or a #реклама / #ad hashtag in the body, which is the channel declaring it itself. The first is documentary; the second is a self-declaration and is weaker. No classifier reads the text and decides — nothing on this site guesses that a post is an advertisement.

This is a floor, and it can only ever be a floor. A channel that runs paid placements without marking them produces no marker for us to count, and an unmarked ad is indistinguishable from an ordinary post on the public surface. The ad load above therefore means “the share of posts that declared themselves”, never “the share of posts that were paid for”. A low figure is not evidence of a channel that runs few ads.

Both figures are medians, and no ratio between them is published. Each is a view reading that actually occurred on a post, picked by percentile_disc rather than averaged, so one viral post cannot move it and no interpolated value is invented between two readings. The sample on one side is under five posts, which is too thin to compare. The two figures are shown side by side with the count behind each, and deliberately not divided into a headline like “ads get x% fewer views” — an arithmetic that is easy to print and, at this sample size, means nothing.

Advertising tokens recorded on this entry
eridPostsFirst seenLast seen
2VtzqvfcpBD111 August 202611 August 2026
2VtzqxUz24T119 August 202619 August 2026

A token repeated across several posts is one advertising contract placed more than once, which is what the identifier is for. The strings are reproduced exactly as they appeared in the post or in its click-through URL and are not validated against any registry — we record the marker a channel published, and whether it resolves to a real contract is a question for the register that issued it.

Measured over the 88 most recent posts we hold, published 4 August 2026 to 28 August 2026. Views are the latest single reading held for each post, and any reading at or above 1,000 is rounded by Telegram to three significant figures.

Recent posts

28 Aug 2026, 11:13 UTC371 viewsread 28 August 2026
Photo

NVIDIA AI Infrastructure & Operations (NCA-AIIO) — полный курс для подготовки к сертификационному экзамену. Что вы изучите • Получите практическое понимание этого направления глубокого обучения • Разберётесь, как модели обучаются, оптимизируются и улучшаются на данных • Научитесь подготавливать данные и признаки для построения моделей • Поймёте, как объединять поиск, эмбеддинги и генерацию в практические ИИ-системы…

27 Aug 2026, 15:17 UTC594 viewsread 28 August 2026
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«Математические методы в науке о данных с Python» Себастьяна Роша. https://mmids-textbook.github.io

27 Aug 2026, 06:32 UTC674 viewsread 28 August 2026
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Нашёл ещё один хороший бесплатный учебник — Classical Mechanics. Это больше 300 страниц классической механики уровня первого курса университета. Внутри скорость и ускорение, импульс, кинетическая энергия, силы, работа и мощность, движение в двух измерениях, вращательная динамика, гравитация, гармонические колебания, волны и многое другое. Что особенно хорошо — авторы постоянно связывают физику с математикой. Напр…

27 Aug 2026, 03:59 UTC643 viewsread 28 August 2026
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Представляю вам сильно обновлённую открытую книгу Machine Learning Engineering за август 2026 года. https://github.com/stas00/ml-engineering Если удобнее читать в PDF, теперь в ней уже 498 страниц. https://github.com/stas00/ml-engineering#ebook-versions-of-the-book Книга получила огромное обновление. Характеристики современного высокопроизводительного железа привели в актуальное состояние, а многие примеры и разб…

26 Aug 2026, 07:10 UTC671 viewsread 28 August 2026
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Использование искусственного интеллекта как полноценного участника команды при анализе данных. Курс Габора Бекеша «Data Analysis with AI», обновлённый летом 2026 года, больше ориентирован на практику, чем на теорию. И при этом он полностью бесплатный и с открытым исходным кодом. https://gabors-data-analysis.com/ai-course/

26 Aug 2026, 05:39 UTC705 viewsread 28 August 2026
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Бесплатный курс по современной робототехнике. Учебник Modern Robotics Кевина Линча и Фрэнка Парка теперь доступен в формате полноценного курса на YouTube. В программе шесть частей. Основы движения роботов, кинематика, динамика, планирование и управление движением, манипуляция и колёсные мобильные роботы, а в конце итоговый проект по мобильной манипуляции. Разбираются пространство конфигураций, движения твёрдых тел…

25 Aug 2026, 15:10 UTC710 viewsread 28 August 2026

Нашёл отличный открытый курс от Zhejiang University — Large Model Basics. У проекта уже больше 16 тысяч звёзд на GitHub, и это не просто один учебник. Внутри последовательно разобраны Transformer, архитектуры больших моделей, промпты, цепочки рассуждений, LoRA, редактирование моделей, RAG и другие базовые темы. Но самое полезное здесь — структура. К каждой главе приложен список оригинальных научных работ. Отдельн…

25 Aug 2026, 14:11 UTC698 viewsread 28 August 2026
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❗️ Распространите ❗️ Найден ИИ-ассистент, который реально экономит рабочее время и не сливает корпоративные данные Сотрудникам не придется тратить время на поиск информации по разрозненным документам, регламентам и ссылкам. С корпоративным ИИ-ассистентом от Selectel и KTS все собрано в единой базе знаний с умным поиском. С вас — запросы на естественном языке, с ассистента — мгновенные структурированные ответы, кр…

25 Aug 2026, 09:01 UTC707 viewsread 28 August 2026

«Learning Mathematics» — самая читаемая публикация за всю историю алгебры. Автор очень лично рассказывает о том, что математика — не что-то, доступное только людям с особым талантом или врождённым даром. Ей может научиться каждый — постепенно, шаг за шагом, с практикой, временем и упорством. Если вы хотя бы раз думали «математика просто не для меня», возможно, эту статью стоит прочитать. https://algebrica.org/lea…

25 Aug 2026, 07:53 UTC725 viewsread 28 August 2026
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Полный курс по Hugging Face Transformers. Что вы изучите: — Поймёте устройство трансформеров и LLM без отношения к ним как к «чёрному ящику» — Разберётесь, как модели обучаются, оптимизируются и улучшаются на данных — Научитесь работать с последовательностями, текстом и задачами языкового моделирования — Сформируете практическое понимание этой части LLM и генеративного ИИ — Разберётесь с последовательностными модел…

25 Aug 2026, 04:50 UTC693 viewsread 28 August 2026
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Если хотите разобраться, как обучить большую модель с нуля, рекомендую посмотреть вот это ↓ Сооснователь ARC Prize и создатель Keras Франсуа Шолле недавно посоветовал путь для изучения LLM с нуля. Он сказал, что если вам 17 лет или вообще сколько угодно и вы хотите с нуля научиться создавать LLM, просто прочитайте главы 15 и 16 его книги *Deep Learning with Python*. Я бегло их просмотрел, и эти две главы точно сто…

24 Aug 2026, 12:14 UTC749 viewsread 28 August 2026
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Лекции курса «Machine Learning & Causal Inference: A Short Course», подготовленного профессором Стэнфордского университета Сьюзан Эйти, можно посмотреть по ссылке ниже. https://youtube.com/playlist?list=PLxq_lXOUlvQAoWZEqhRqHNezS30lI49G-&si=uwNV2GcR4yOBQA_d

Showing the 12 most recent of 88 posts we hold for @LLMScience. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.

Posts edited after publishing

@LLMScience edited 2 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.

An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.

First edit seen
18 August 2026
Most recent edit
19 August 2026

Forward network

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Mentions

Named by 12 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.

Names

Channels on the register whose handles appear in this channel's posts.

A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

Python Portal
@PythonPortal · 50,417
Telegram ranks this channel #15 of 80 here — alongside 79 others — read 25 August 2026
Montana.exe
@dejavu041 · 49,886
Telegram ranks this channel #60 of 80 here — alongside 79 others — read 25 August 2026
Python Developer
@python_tg · 20,963
Telegram ranks this channel #68 of 85 here — alongside 84 others — read 25 September 2026

This channel appears in 3 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

Cite this entry

A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 27 September 2026 — this entry's latest reading, not the date you are reading this.

“Data Portal | DS & ML” (@LLMScience), 10,236 subscribers as measured 27 September 2026. Telegram Register, tgregister.com/channel/LLMScience.

Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.