27 measurements spanning 32 days, net +288. 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 22,691–23,066 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 27
Measured (UTC)
Subscribers
Change
6 Sept 2026, 17:01
23,023
+25
4 Sept 2026, 03:41
22,998
+25
2 Sept 2026, 18:28
22,973
+6
1 Sept 2026, 18:55
22,967
+19
31 Aug 2026, 15:28
22,948
+14
30 Aug 2026, 12:54
22,934
+14
29 Aug 2026, 14:13
22,920
+24
28 Aug 2026, 14:38
22,896
+5
27 Aug 2026, 14:04
22,891
+27
26 Aug 2026, 15:18
22,864
+29
25 Aug 2026, 14:24
22,835
+14
24 Aug 2026, 13:28
22,821
-2
21 Aug 2026, 11:43
22,823
+10
20 Aug 2026, 13:07
22,813
+25
19 Aug 2026, 12:07
22,788
-5
18 Aug 2026, 10:23
22,793
+11
17 Aug 2026, 13:28
22,782
+4
16 Aug 2026, 05:15
22,778
+16
14 Aug 2026, 15:47
22,762
-7
13 Aug 2026, 08:26
22,769
first reading
Engagement
223 posts held, back to 3 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 58 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
8.53%
avg views ÷ 23,023 subscribers
Avg views / post
1,960
176 posts measured
Reaction rate
0.408%
reactions ÷ views · ER floor
Posts in window
176
of 223 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. It is computed over the 165 of 176 measured posts that carry a reaction reading, and over those same posts' views.
What these figures were computed from
Window
Rolling 30 days · latest post in window 3 September 2026
Posts held
223 (3 August 2026 – 3 September 2026)
Views total
345,559
Reactions total
1,325
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 11:56 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
1,631 reactions across 191 posts, in 14 distinct kinds. The most used accounts for 32.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
530
32.5%
🔥
447
27.4%
👍
199
12.2%
custom 5276311376892010436
134
8.22%
😢
74
4.54%
😁
63
3.86%
custom 5323257658654860293
59
3.62%
🤯
50
3.07%
💯
23
1.41%
🤔
20
1.23%
🌭
19
1.16%
😐
10
0.613%
😱
2
0.123%
😨
1
0.061%
Custom emoji. 2 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 210 of the 223 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 1,765 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 223 most recent posts we hold, published 3 August 2026 to 3 September 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
133
across the posts below
Posts paid on
12
of 223 we hold a reading for · 5%
Most on one post
114
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @it_mentors. 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 223 most recent posts we hold for this entry, published 3 August 2026 to 3 September 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.
🔥 Срок: 85 дней
💵 Оффер: 378000 руб. gross
Отзыв на ментора: @grisha_devops #grisha_devops
Специальность: DevOps / SRE #devops
Услуга: Смена стэка / специальности #смена_стэка_и_специальности
Трудоустройство на 378к в DevOps
Пришел к Грише в начале мая, до этого успел поработать 3 месяца фронтом на проекте. Про девопс мало что знал и мало что понимал
Придя к Грише получил материал для изучения, также мне помогли…
🔥 Срок: 75 дней
💵 Оффер: $3000 gross
Отзыв на ментора: @offgleb #offgleb
Специальность: HR / Рекрутинг #hr
Услуга: С коммерческим опытом #c_коммерческим_опытом
#валютные_удалёнки
Порядка трех месяцев работал с Глебом, достаточно информативно, очень глубокие первые звонки по разбору карьерного трека, по формированию карьерного листа. Так как ситуация у меня достаточно непростая, Глеб смог помочь, смог направить по …
🎓 Срок: 475 дней
💵 Оффер: 255000 руб. gross
Отзыв на ментора: @nizov_as #nizov_as
Специальность: Machine Learning / Data Science #data_science
Услуга: Без коммерческого опыта #без_коммерческого_опыта
Оффер на 255.000₽ gross окладом в NLP/LLM в 18 лет
Пришел к Саше, будучи в 11 классе. До этого опыта в IT не было - только желание найти работу уже на начальных курсах вуза и не быть бедным студентом. Мы обсудили стр…
👍 Срок: 191 день
💵 Оффер: 184000 руб. gross
Отзыв на ментора: @m1122ax #m1122ax
Специальность: QA / Тестирование #qa
Услуга: Без коммерческого опыта #без_коммерческого_опыта
Оффер прилетел 24 ноября, и вот я уже в деле.
По образованию я бизнес-аналитик, закончил вуз, но честно в профессию так и не пошёл. Вместо этого несколько лет работал кондитером, и со временем понял, что физическая работа просто выматывает: у…
👍 Срок: 153 дня
💵 Оффер: €5800 gross
Отзыв на ментора: @haskelmuse #haskelmuse
Специальность: Java #java
Услуга: С коммерческим опытом #c_коммерческим_опытом
#валютные_удалёнки
Пришел на обучение к Владу с трехлетним бэкграундом работы в РФ бигтехах, мой опыт при этом был довольно слабый, так как большую часть времени на работе писал тесты и делал совсем простые задачи. Пришел с запросом на поиск ВУ, так как давно…
🎓 Срок: 312 дней
💵 Оффер: 305000 руб. gross
Отзыв на ментора: @nizov_as #nizov_as
Специальность: Machine Learning / Data Science #data_science
Услуга: Без коммерческого опыта #без_коммерческого_опыта
#ai_вайбкодинг
Оффер на 305.000₽ gross в NLP/LLM с нуля опыта в IT
Точка А
0 комерческого опыта в IT, профильная вышка + курсы по ml
Перед приходом на менторство собесился на стажерские позиции в классическом ml, зв…
🔥 Срок: 74 дня
💵 Оффер: $5420 gross
Отзыв на ментора: @kirasamsonova #kirasamsonova
Специальность: AQA / Автотестирование #aqa
Услуга: С коммерческим опытом #c_коммерческим_опытом
#валютные_удалёнки
Хочу сказать огромное спасибо Кире и всей команде за работу со мной. В итоге я нашел русскоязычную валютную удаленку на 5000 долларов в месяц на Senior QA Automation Java, причем это не B2B контракт, а полноценное офор…
🎓 Срок: 245 дней
💵 Оффер: $2230 gross
Отзыв на ментора: @zhukovsd #zhukovsd
Специальность: Java #java
Услуга: Без коммерческого опыта #без_коммерческого_опыта
Хочу поделиться своим опытом менторства у Сергея и рассказать, как в итоге получилось прийти к своему первому офферу.
В IT я хотел работать ещё со школы. Пробовал разные языки программирования и разные направления разработки, а после поступил в вуз на IT-спе…
👍 Срок: 189 дней
💵 Оффер: 250000 руб. gross
Отзыв на ментора: @iliarty #iliarty
Специальность: Системный аналитик #системный_аналитик
Услуга: Без коммерческого опыта #без_коммерческого_опыта
Пришел на обучение с уже небольшим it контекстом, но именно с аналитикой общих дел не имел.
Доверился конкретно Илье потому что увидел как 2 моих друга реально находят работу после обучения.
Учился с января 2026, закончил в ко…
🎓 Срок: 285 дней
💵 Оффер: 196000 руб. gross
Отзыв на ментора: @castello #castello
Специальность: QA / Тестирование #qa
Услуга: Без коммерческого опыта #без_коммерческого_опыта
К Мише я пришел в октябре прошлого года. Из знаний на тот момент были только те, что я получил на одном из практикумов аутсорс-компании. Плюс, какое-то время я думал о необходимости ментора. Но, попытавшись самому что-то найти, надолго меня …
🔥 Срок: 80 дней
💵 Оффер: 340000 руб. gross
Отзыв на ментора: @analystdement0r #analystdement0r
Специальность: Аналитик данных / Продуктовая аналитика #аналитик_данных
Услуга: С коммерческим опытом #c_коммерческим_опытом
Пришел к Ване после стажировки в одном бигтехе. Ситуации была такая: прошел стажировку - не взяли в штат - что делать дальше?
Взял у него бесплатный созвон, где мы разобрали векторы моего дальнейше…
👍 Срок: 168 дней
💵 Оффер: 300000 руб. gross
Отзыв на ментора: @art185 #art185
Специальность: Системный аналитик #системный_аналитик
Услуга: Без коммерческого опыта #без_коммерческого_опыта
300к в 21 год, устроился совсем без опыта, только закончил учебу. В направлении ИТ я изначально и двигался, выходил на поиски стажировок, получать первый опыт, но столкнулся с огромным переполнением на рынке труда таких новичков…
❤2👍2
Showing the 12 most recent of 223 posts we hold for @it_mentors. 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
@it_mentors edited 3 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
12 August 2026
Most recent edit
27 August 2026
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 22 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.
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 6 September 2026 — this
entry's latest reading, not the date you are reading this.
“IT менторы - отзывы на офферы” (@it_mentors), 23,023 subscribers as measured 6 September 2026. Telegram Register, tgregister.com/channel/it_mentors.
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.