Channel
@todual_education
On this record: Topic · Growth · Engagement · Reactions · Posts · Citations · Cite this entry
770subscribers
-12 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002311982731 |
|---|---|
| Type | Channel |
| Username | @todual_education |
| Created | Between 1 September 2024 and 31 March 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 16 September 2026 |
| Measurements held | 5 |
| Confirmed unchanged | 1 time, most recently 16 September 2026 |
| On Telegram | t.me/todual_education |
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 why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 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 |
6 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 1 page 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 6 posts for this entry, the most recent from 3 August 2026. An engagement rate over an empty window would be a number about nothing.
110 reactions across 6 posts, in 8 distinct kinds. The most used accounts for 24.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| custom 5357294278043264482 | 27 | 24.5% | |
| 🔥 | 26 | 23.6% | |
| 💅 | 16 | 14.5% | |
| 💯 | 12 | 10.9% | |
| 🥰 | 11 | 10.0% | |
| 👏 | 8 | 7.27% | |
| 👨💻 | 5 | 4.55% | |
| 🤩 | 5 | 4.55% |
Custom emoji. One row above is a 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 count beside it is 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 6 of the 6 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 110 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 6 most recent posts we hold, published 17 July 2026 to 3 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.
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…
🔥6💅5🥰4
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 —…
👨💻5🤩5💅4
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 ➡️ …
👏8💅7custom 53572942780432644825
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…
custom 53572942780432644828🔥7🥰7
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…
🔥7💯6custom 53572942780432644826
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 6 most recent of 6 posts we hold for @todual_education. 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.
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.
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.
Named by
Channels on the register whose posts name this channel's handle.
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.
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 16 September 2026 — this entry's latest reading, not the date you are reading this.
“TO DUAL” (@todual_education), 770 subscribers as measured 16 September 2026. Telegram Register, tgregister.com/channel/todual_education.
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.