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Channel

Становимся продвинутым QA

@be_pro_qa

On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry

939subscribers

+0 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1001709123855
TypeChannel
Username@be_pro_qa
CreatedBetween 1 December 2021 and 30 April 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live13 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 13 August 2026
On Telegramt.me/be_pro_qa

Growth

939940939.56 August 2026 — 939 subscribers6 August 2026 — 939 subscribers7 August 2026 — 940 subscribers13 August 2026 — 939 subscribers6 August 202613 August 2026
4 measurements spanning 7 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 939–940 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
13 Aug 2026, 15:35939-1
7 Aug 2026, 05:00940+1
6 Aug 2026, 13:51939no change
6 Aug 2026, 06:36939first reading

Engagement

20 posts held, back to 18 March 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.

ERR · 30 days
30.7%
avg views ÷ 939 subscribers
Avg views / post
288
3 posts measured
Reaction rate
1.33%
reactions ÷ views · ER floor
Posts in window
3
of 20 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 2 of 3 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 5 August 2026
Posts held20 (18 March 20265 August 2026)
Views total865
Reactions total8
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken6 Aug 2026, 13:51 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

123 reactions across 16 posts, in 6 distinct kinds. The most used accounts for 45.5% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥5645.5%
2419.5%
👍2117.1%
1713.8%
😢43.25%
🤝10.813%

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 16 of the 20 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 123reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 20 most recent posts we hold, published 18 March 2026 to 5 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.

Recent posts

5 Aug 2026, 06:40 UTC168 views4 reactionsread 6 August 2026

Каждый запуск пайплайна стоит денег API Anthropic не блокирует доступ мгновенно, как только баланс достигает нуля. В итоге одна незамеченная за ночь ошибка может привести к тому, что баланс AI-токенов не просто обнулится, а уйдет в глубокий минус. Когда тестовый скрипт начинает вести себя как оставленный без присмотра банкомат, реактивного мониторинга уже недостаточно. Нам нужна архитектурная дисциплина. Вот страте

🔥3👍1

29 Jul 2026, 06:50 UTC264 viewsread 6 August 2026

Почему ваш LLM-as-a-Judge "слишком вежливый" (и как с этим бороться) Я часто вижу, как команды внедряют LLM-as-a-Judge и радуются стабильным метрикам 1.0. На деле это не показатель качества системы, а показатель того, что «судья» слишком добрый и апрувит всё подряд, включая грубые нарушения бизнес-логики. Простой пример: клиент требует возврат за кроссовки не того цвета (ошибка комплектации), а бот вежливо отказыва

22 Jul 2026, 05:00 UTC433 views4 reactionsread 6 August 2026

Почему мне нравится работать ML Evaluation инженером после 20 лет опыта в QA Многие спрашивают, как мне живется в моей новой роли. Этот пост - ответ на вопрос. ПОЧЕМУ МНЕ НРАВИТСЯ В ML EVALUATION: • Сложная архитектура вместо стандартной автоматизации. Когда "вспомогательные таски" включают кастомные методы клонирования удаленных репозиториев с датасетами в зависимости от условий и содержимого или работу с системн

👍31

15 Jul 2026, 11:24 UTC474 views3 reactionsread 6 August 2026
Photo

⚡️Mentorpiece Vacy Index июль 2026: IT-найм продолжает падать, но сокращение Automation замедлилось 🟠 Появились признаки улучшения ситуации по Automation QA и Senior QA – число нанимающих компаний по этим ролям за месяц уменьшилось всего на 0.2%. Это минимальное падение за последние три месяца, и оно в рамках погрешности измерений. По сравнению с Manual QA, где меньше чем за полгода процент нанимающих компаний упа

3

9 Jul 2026, 08:58 UTC431 views4 reactionsread 6 August 2026

Пройдет ли AI код-ревью? ИИ может за час сделать то, на что раньше уходили недели. Но готовы ли вы выкатить этот результат в прод без код-ревью от сеньора? 🚩 Скорее всего, нет. «Работающий код» больше не критерий качества. Критерий - поддерживаемый код. По мере того как мы перекладываем задачи на ИИ-агентов, главный инженерный вопрос смещается с «Как мне это написать?» на «Насколько чисто это написано?» Почему код

👍31

30 Jun 2026, 06:00 UTC482 views10 reactionsread 6 August 2026

Как короткое слово может превратить ваш AI-продукт в юридический кошмар В сфере ML Evaluation, особенно при использовании подхода LLM-судьи, мы часто попадаемся в ловушку «гало-эффекта» (Halo Effect). Если ответ тестируемой AI-модели звучит авторитетно и профессионально, LLM-судья автоматически ставит высокий балл, напрочь упуская из виду смысл. ЛОВУШКА «ЛЕНИВОГО СУДЬИ» Представьте инструмент для краткого изложени

🔥91

22 Jun 2026, 06:35 UTC496 viewsread 6 August 2026

Теперь ежемесячно публикуем такую инфографику по индексу IT-найма:

22 Jun 2026, 06:35 UTC550 views4 reactionsread 6 August 2026
Photo

⚡️Mentorpiece Vacy Index июнь 2026: IT-найм продолжает падать 🟠 Процент нанимающих IT-компаний по ролям Automation QA и Senior QA за месяц снизился, продолжая небольшое, но постоянное падение последние месяцы. • Что в июне с наймом по другим IT-ролям/странам (спойлер: автоматизация плохо себя чувствует и в 🇺🇸, а AI-роли растут, но пока слабо) • Вакансии AutomationQA/SeniorQA и AI QA/ML Evaluation - превью ежедневно

😢4

18 Jun 2026, 10:34 UTC631 views10 reactionsread 6 August 2026

🔻 Российское IT падает уже семь месяцев С декабря получаю многочисленные сообщения о сокращениях во всё большем и большем числе российских IT-компаний. Но любой кризис не вечен. Вопрос состоит только в том, как определить наступление этого самого момента. Обычно ориентируются на число открытых сейчас IT-вакансий. Но это относительный параметр (1000 открытых вакансий для конкретной IT-роли – это много или мало?), к

91

16 Jun 2026, 07:01 UTC457 views9 reactionsread 6 August 2026

Почему поздно учить автоматизацию Идея этого поста пришла мне в голову, когда неделю назад мы с менторами, SDET крупных международных компаний, на регулярной встрече обсуждали перспективы рынка автоматизаторов и пришли к довольно интересным выводам. Ещё несколько лет назад путь из manual QA в автоматизацию был очевидным апгрейдом: более сложные задачи, соотношение вакансий 70/30 в пользу автоматизаторов, выше зарпл

7👍2

Showing the 12 most recent of 20 posts we hold for @be_pro_qa. 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.

Citation-graph rank

Citation-graph rank — 88,931 of 1,480,688entries 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

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 13 August 2026 — this entry's latest reading, not the date you are reading this.

“Становимся продвинутым QA” (@be_pro_qa), 939 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/be_pro_qa.

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.