Telegram RegisterThe public register of Telegram

Channel

Нескучный Data Science

@not_boring_ds

On this record: Growth · Engagement · Reactions · Stars · Advertising · Posts · Citations · Telegram's recommendations · Cite this entry

11,999subscribers

+52 since we began measuring on 7 August 2026

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

Register entry

Telegram ID-1001680781925
TypeChannel
Username@not_boring_ds
Created17 February 2022measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded7 August 2026
Last confirmed live12 August 2026
Measurements held7
Confirmed unchanged1 time, most recently 12 August 2026
On Telegramt.me/not_boring_ds

Growth

11,94611,99911,972.57 August 2026 — 11,947 subscribers7 August 2026 — 11,952 subscribers8 August 2026 — 11,950 subscribers9 August 2026 — 11,946 subscribers10 August 2026 — 11,954 subscribers11 August 2026 — 11,991 subscribers12 August 2026 — 11,999 subscribers7 August 202612 August 2026
7 measurements spanning 6 days, net +52. 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 11,938–12,007 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 18:5311,999+8
11 Aug 2026, 17:0311,991+37
10 Aug 2026, 14:3111,954+8
9 Aug 2026, 11:1111,946-4
8 Aug 2026, 13:1111,950-2
7 Aug 2026, 16:0111,952+5
7 Aug 2026, 04:1611,947first reading

Engagement

18 posts held, back to 13 June 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 15 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
29.8%
avg views ÷ 11,999 subscribers
Avg views / post
3,580
12 posts measured
Reaction rate
0.891%
reactions ÷ views · ER floor
Posts in window
12
of 18 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.

What these figures were computed from
WindowRolling 30 days · latest post in window 12 August 2026
Posts held18 (13 June 202612 August 2026)
Views total42,970
Reactions total383
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken12 Aug 2026, 12:49 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

Reaction mix

630 reactions across 18 posts, in 19 distinct kinds. The most used accounts for 24.9% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥15724.9%
14322.7%
👍9014.3%
👎7011.1%
🐳416.51%
🎉355.56%
345.40%
😁132.06%
💯121.90%
🙈91.43%
🤔71.11%
😱60.952%
🤣30.476%
🦄30.476%
🤯20.317%
🤷20.317%
👌10.159%
🗿10.159%
🙏10.159%

No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.

Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.

Coverage. Reactions were read on 18 of the 18 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 630reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 18 most recent posts we hold, published 13 June 2026 to 12 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.

Telegram Stars

Stars received
1
across the posts below
Posts paid on
1
of 18 we hold a reading for · 6%
Most on one post
1
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @not_boring_ds. 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 18 most recent posts we hold for this entry, published 13 June 2026 to 12 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
5.56%
1 of 18 posts carry an ad marker
Regulatory tokens
1
posts carrying an erid · 1 distinct token
Median views · ads
3,560
over 1 measured post
Median views · rest
4,120
over 17 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
2VtzqwwQoy412 August 20262 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 18 most recent posts we hold, published 13 June 2026 to 12 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

12 Aug 2026, 07:01 UTC≈1,130 views12 reactionsread 12 August 2026
Photo

Исследуйте сферу AI в T-Lab Т-Образование зовет участников в проект T-Lab. Это годовая программа научно-исследовательской работы в области AI. Станьте соавтором научных открытий, которые меняют индустрию! Вы сможете выбрать один из проектов в сфере AI и участвовать в исследовании на всех этапах. Итоги вашей работы могут лечь в основу научных публикаций и стать вектором новых продуктов. Кого ждут в T-Lab? Студенто

👍73🔥2

11 Aug 2026, 10:04 UTC≈1,790 views42 reactionsread 12 August 2026
Photo

Конференция Ozon Tech, уже успевшая заслужить высокоранговую славу, обещает и в этом году собрать много интересного для тех, кто работает с ML&DS. Программа E-CODE 2026 еще пополняется, но трек уже выглядит вполне серьезно: сложный индустриальный ML, генеративные подсказки, уровни дообучения и агентский Cotype. Такое нам определенно надо. Еще и участие бесплатное - осталось только зарегистрироваться: https://ecode.o

15🔥10🎉9👍2👎2🤣2🐳1🤷1

11 Aug 2026, 06:03 UTC≈1,660 views17 reactionsread 12 August 2026
Photo

ИИ крадёт у нас или учится у нас? 🧠 Недавно сидел на панельной дискуссии. Во время вопросов из зала прозвучало примерно следующее: — Как быть с тем, что ИИ будет обучаться на наших данных? Как защитить то уникальное, на создание чего человек потратил столько времени? Но тут возникает неудобный вопрос: а в чём именно заключается эта уникальность? Я люблю сравнивать работу ИИ с ЕИ — естественным интеллектом. Такое

12🔥3🐳1👎1

9 Aug 2026, 08:10 UTC≈2,180 views37 reactionsread 12 August 2026

Как понять по письму, что задача не выполнена? 📨 За годы работы в крупных компаниях мне пришлось прочитать очень много писем. Иногда их приходит до сотни в день. Поэтому навыки анализа данных приходится применять и здесь. Представьте: вы поставили кому-то задачу, подошёл срок — и вам присылают отчёт. Это может быть один абзац, а может быть целый лист А4. Вы читаете про процесс, коллег, сложности и то, как все герои

🔥21💯9🐳42🗿1

8 Aug 2026, 13:03 UTC≈1,900 views19 reactionsread 12 August 2026
Forwarded from @rads_ai

На конференциях только и разговоров, что об агентах. Вообще, я бы назвал это harness'ами. Они могут приносить кучу пользы, и их огромное количество: Claude Code, Codex, pi, Hermes, OpenClaw, Cursor, Muse Code... Хорошая новость в том, что под капотом +- одни и те же механизимы, и если в них разобраться, почти не важно, какой harness у вас под рукой. Поэтому проведу в понедельник, 10 августа в 18:30 открытый вебина

🔥104👍4🐳1

7 Aug 2026, 19:37 UTC≈2,460 views32 reactionsread 12 August 2026
Photo

❓ Научитесь отвечать на вопрос «зачем?», прежде чем ставить задачу ИИ Моё знакомство с Codex началось, когда появилась вполне конкретная задача: собрать сайт для @mirabistro. Сайты я делал и раньше — первый написал на PHP ещё в восьмом классе. Но никогда не вайбкодил и относился к этому довольно скептически. Несмотря на десять лет работы в анализе данных, мне казалось, что нормальный рабочий продукт всё равно прид

👍216🔥3🐳1🤷1

6 Aug 2026, 16:04 UTC≈2,470 views25 reactionsread 12 August 2026

🔥 ODS Moscow × Нескучный Data Science: бранч в эту субботу После поста про МИРА можно уже не делать вид, что выбор места — случайное совпадение и не очень нативная рекламная интеграция 😅 В эту субботу снова собираемся там на офлайн-бранч вместе с ODS Moscow. Будем знакомиться, есть и обсуждать ИИ, Data Science, карьеру и собственный бизнес. Заодно можно будет посмотреть на тот самый «очень дорогой pet-проект» из п

👍11🔥73👎21🐳1

4 Aug 2026, 10:19 UTC≈3,500 views51 reactions1 Starread 12 August 2026
Photo

🎉 Мы запустили МИРА бистро — это очень дорогой pet-проект 😅 👀 Внимательные читатели уже могли что-то заподозрить: последние бранчи ODS Moscow × Нескучный Data Science проходили именно там. Пора признаться — это было не случайное совпадение и не очень нативная рекламная интеграция. 🏦 Четыре с половиной года назад, когда мой банковский стаж уже перевалил за пять лет, в самом первом посте я признался, что никогда не

🎉26🔥136🙈41🐳1

3 Aug 2026, 07:14 UTC≈13,800 views66 reactionsread 12 August 2026
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От стажёра до Head of DS: что я узнал, изучив почти 700 карьер в Data Science 🧬 Наконец-то дождались 🗿 Чуть больше года назад вы заполнили мой зарплатный опрос. Я обещал проанализировать результаты, но 695 анкет превратились в технический долг, о котором мне регулярно напоминали в комментариях. Сегодня я этот долг возвращаю. Кажется, даже с процентами: вместо очередной таблицы «кто сколько получает» получилось бол

25🔥19🐳97👎5🦄1

2 Aug 2026, 06:01 UTC≈3,560 views16 reactionsread 12 August 2026
Advertisementerid 2VtzqwwQoy4

24 сентября скучно не будет – в этот день пройдёт Yandex Scale 2026, флагманская технологическая конференция Yandex Cloud. 🎨🎨🎨🎨🎨🎨🎨🎨🎨🎨 🎨🎨🎨🎨🎨🎨🎨🎨🎨🎨 🎨🎨🎨🎨🎨🎨🎨🎨🎨 Четыре офлайн-трека: Data – обновления сервисов управляемых баз данных, новые возможности аналитики и честный разбор одного из самых сложных кейсов миграции данных, плюс AI, Hybrid Infrastructure & DevOps и Security. А в онлайне — отдельный технологический трек

🔥9🐳42👎1

30 Jul 2026, 08:29 UTC≈4,120 views40 reactionsread 12 August 2026
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🥩 Как перестать быть жирным как поезд пассажирный? Сейчас я снова в «отпуске» и наконец возвращаюсь к серии постов про work-life balance. Год назад в предыдущем посте я рассказывал про свой первый подход к снаряду — борьбе с лишним весом: Я собрал дома спортзал, регулярно тренировался, купил велосипед и иногда проезжал на нём по 80 километров. Но желаемого результата не было. Тем не менее я продолжал пытаться. 🌍

19👍9😱4🔥2😁2👎1🤔1🤯1

17 Jul 2026, 12:58 UTC≈4,400 views26 reactionsread 12 August 2026
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🔥 Как прошёл офлайн-бранч ODS Moscow × Нескучный Data Science 🧠 Начали с доклада Альберта про то, как в Avito обучают собственную LLM. Это не история про «взяли открытую модель и немного дообучили», а полноценный процесс: выбор базовой модели, собственный токенизатор, адаптация на данных Avito, SFT и дальнейшее обучение с помощью RL. Для SFT собрали больше 800 тысяч примеров: открытые датасеты, синтетические данны

🔥157🐳4

Showing the 12 most recent of 18 posts we hold for @not_boring_ds. 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.

Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.

Citation-graph rank

Citation-graph rank — 23,379 of 1,151,006entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.

Forward network

Republishes

Channels on the register whose posts this channel has forwarded.

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 2 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.

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.

Machinelearning
@ai_machinelearning_big_data · 286,250
Telegram ranks this channel #19 of 95 here — alongside 94 others — read 10 August 2026
XOR
@xor_journal · 158,763
Telegram ranks this channel #30 of 93 here — alongside 92 others — read 12 August 2026

This channel appears in 2 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 12 August 2026 — this entry's latest reading, not the date you are reading this.

“Нескучный Data Science” (@not_boring_ds), 11,999 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/not_boring_ds.

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.