Учиться — это естественная привычка, а не очередная задача в календаре.
Спринты — https://to-dual.education/
B2B — https://to-dual.education/business
Чат — https://t.me/+KrDFPGLkISIwYzM6
Карьера: @cliquejobs
Вопросы: @dual_support
Created
Between 1 September 2024 and 31 March 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
Technology — 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 20 September 2026 and assigned it the closest of 31 fixed categories, at 59% 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 the other topics.
Previous handles
@todualru was previously reachable at this handle, before switching to its current one. This says nothing about who or what answers at the old handle today — only that this channel no longer does.
@todual_education held 6 August 2026 – 6 August 2026
Growth
10 measurements spanning 96 days. 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 764–784 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
10 Nov 2026, 16:22
782
no change
10 Nov 2026, 15:47
782
+15
18 Oct 2026, 06:36
767
+1
10 Oct 2026, 17:00
766
-1
1 Oct 2026, 13:37
767
-3
16 Sept 2026, 01:37
770
-2
7 Sept 2026, 10:20
772
-8
14 Aug 2026, 08:36
780
-2
7 Aug 2026, 05:30
782
no change
6 Aug 2026, 22:02
782
first reading
Engagement
12 posts held, back to 17 July 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 2 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
26.7%
avg views ÷ 782 subscribers
Avg views / post
209
4 posts measured
Reaction rate
7.07%
reactions ÷ views · ER floor
Posts in window
4
of 12 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
Window
Rolling 30 days · latest post in window 9 November 2026
Posts held
12 (17 July 2026 – 9 November 2026)
Views total
834
Reactions total
59
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
10 Nov 2026, 16:22 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.
What this channel posts
Photos
944
Videos
3
Links
233
Lifetime counters from Telegram’s own channel header, read 10 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Reaction mix
222 reactions across 12 posts, in 11 distinct kinds. The most used accounts for 23.4% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
custom 5357294278043264482
52
23.4%
🔥
45
20.3%
custom 5395808289168584601
36
16.2%
💅
18
8.11%
💯
18
8.11%
custom 5395823227064841506
13
5.86%
👨💻
12
5.41%
🥰
11
4.95%
👏
8
3.60%
🤩
5
2.25%
😍
4
1.80%
Custom emoji. 3 of the rows above are Telegram custom emoji, which the public preview renders as an element carrying only a numeric id — no character, and no image we can reach. The id is printed as-is rather than substituted with a look-alike glyph, because a stand-in would be our invention showing where a measurement should be. The counts beside them are Telegram’s.
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 12 of the 12 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 222 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 12 most recent posts we hold, published 17 July 2026 to 9 November 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 12 we hold a reading for · 8%
Most on one post
1
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @todualru. 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 12 most recent posts we hold for this entry, published 17 July 2026 to 9 November 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.
Recent posts
Telegram’s own ‘edited’ marker: 0 of 4 posts held from the last 30 days (0%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
Разыгрываем билет на ProductCamp и другие призы
Какое обучение запомнилось вам надолго? Курс, после которого вы сменили профессию. Наставник, чью фразу часто повторяете. Или воркшоп, где всё пошло не по плану, но именно там вы натренировали важный скилл.
Рассказывайте в комментариях, среди авторов историй разыграем три приза. А ещё лучшие кейсы помогут нам собрать подборку сильных практик 🤓
*️⃣ Что можно выиграть:…
Пока мы собираем ответы для нашей диагностики «Драйверы освоения ИИ в России», поговорим о том, с чего начинается любое исследование, — о вопросах.
Зададите вопрос некорректно, и получите среднюю оценку без ценных данных.
Чтобы такого избежать, мы выпустили гайд о том, как правильно формулировать вопросы. Кстати, по нему мы сами собирали опрос для исследования.
А для вас собрали выжимку в карточках: как проверять …
Как на самом деле работают с ИИ в крупных российских компаниях?
О том, как ИИ меняет работу, говорят многие. Но цифры часто берутся из зарубежных отчётов, хотя наш контекст устроен иначе.
Поэтому мы запустили своё исследование. Хотим понять, насколько компании освоили ИИ, что на это влияет и как ускорить процесс.
Сразу после прохождения опроса-диагностики вы получите: свой индекс освоения ИИ, три задания для перех…
Обычно от нейросети ждут помощи по работе: текст, таблица, код. Но она все увереннее проникает в повседневную жизнь, в саморазвитие и личностный рост.
Может выслушать, как психолог, поспорить с вашим решением перед важными переговорами, помочь не бросить привычки.
В карточках разобрали 5 таких ролей и показали, где нейросеть правда помогает, а где ей верить нельзя.
А в какой роли нейросеть уже выручала вас?
Даша, комьюнити-менеджер TO DUAL, отвечает за сопровождение участников в наших продуктах и клиентских проектах.
Как это устроено изнутри, она разобрала в статье для блога, а мы вынесли из нее главное в карточки.
Если коротко, сопровождение это поддержка рядом со сложным курсом. С ней проще не потеряться в большой программе, вернуться после паузы и удержать мотивацию, когда хочется все бросить.
📎 Полная версия на с…
Не бойтесь учиться новому и браться за задачи, на которые нет готового ответа. Трус не работает в ML!
Это Мариам Каприелова — Senior ML-разработчица в СберЗдоровье и кандидат технических наук — отвечает на вопрос, какой навык сильнее всего помог в первый год карьеры. Спойлер: она назвала смелость и готовность разбираться в неизвестном.
Продолжаем нашу совместную с @cliquejobs рубрику карьерных интервью. Мы уже пого…
Последнюю неделю мы много рассказывали об исследованиях:
• шаги перед стартом,
• план обработки результатов,
• методы работы с данными.
Теперь ваш черёд делиться исследовательскими инсайтами и разбирать сложные кейсы *️⃣
В комьюнити TO DUAL запустили асинхронную Q&A-сессию про исследования. Вы можете задать вопрос или обратиться за советом к Валерии Швецовой — руководителю обучения и исследований TO DUAL.
А ещё —…
Когда после интервью, опросов и сессий накапливается много данных, возникает желание сразу разложить их по привычным категориям…и пропустить то, что не вписывается в исходную логику😟
Руководитель обучения и исследований TO DUAL Валерия Швецова рассказывает, как аффинити-диаграмма помогает собрать разрозненные факты в темы и найти важные закономерности внутри исследования.
Листайте карточки и осваивайте методику ➡️
…
Как вы знаете, у части наших проектов исследования встроены в процесс с самого начала. А также мы проводим исследования как отдельный продукт для наших заказчиков. Поэтому вопрос, что делать с собранными данными, для нас не теоретический.
Собрать данные проще, чем превратить их в решение. Между сбором данных и следующим продуктовым шагом выстраивается целый путь: нужно разобрать наблюдения, найти закономерности, сфо…
В TO DUAL мы много работаем с исследованиями — и в L&D, и в диджитал-проектах это один из ключевых этапов. Исследования помогают не опираться только на интуицию: понять реальную потребность, увидеть контекст, ограничения и не слить ресурсы на решение не той проблемы.
Поэтому, прежде чем собирать спринт или запускать продукт, мы изучаем рынок, аудиторию и конкурентов, а главное — определяем, на какие вопросы вообще х…
Эффект Даннинга-Крюгера все знают: чем меньше умеешь, тем ты увереннее.
Но с ИИ он работает наоборот — и это меняет то, как стоит относиться к своей работе с нейросетями 😟
Разобрали в карточках ⬆️
custom 53572942780432644828💯6🔥6
Showing the 12 most recent of 12 posts we hold for @todualru. 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.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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 1 registered channel — 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.
Named by
Channels on the register whose posts name this channel's handle.
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.
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 10 November 2026 — this
entry's latest reading, not the date you are reading this.
“TO DUAL” (@todualru), 782 subscribers as measured 10 November 2026. Telegram Register, tgregister.com/channel/todualru.
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.
