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Channel

QA❤️4Life | Testing | Тестирование ПО

@QA4Life

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Citations · Telegram's recommendations · Cite this entry

7,864subscribers

-63 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001809206919
TypeChannel
Username@QA4Life
CreatedBetween 1 October 2022 and 30 September 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live6 September 2026
Measurements held12
Confirmed unchanged1 time, most recently 6 September 2026
On Telegramt.me/QA4Life

Growth

7,8637,9277,8957 August 2026 — 7,927 subscribers7 August 2026 — 7,927 subscribers7 August 2026 — 7,926 subscribers10 August 2026 — 7,907 subscribers14 August 2026 — 7,889 subscribers17 August 2026 — 7,879 subscribers20 August 2026 — 7,884 subscribers24 August 2026 — 7,878 subscribers27 August 2026 — 7,876 subscribers30 August 2026 — 7,870 subscribers2 September 2026 — 7,863 subscribers6 September 2026 — 7,864 subscribers7,8647 August 20266 September 2026
12 measurements spanning 30 days, net -63. 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 7,853–7,937 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
6 Sept 2026, 18:417,864+1
2 Sept 2026, 05:347,863-7
30 Aug 2026, 02:057,870-6
27 Aug 2026, 12:067,876-2
24 Aug 2026, 04:467,878-6
20 Aug 2026, 15:517,884+5
17 Aug 2026, 16:177,879-10
14 Aug 2026, 16:067,889-18
10 Aug 2026, 21:217,907-19
7 Aug 2026, 18:357,926-1
7 Aug 2026, 14:317,927no change
7 Aug 2026, 14:277,927first reading

Engagement

62 posts held, back to 30 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 20 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
7.06%
avg views ÷ 7,864 subscribers
Avg views / post
556
41 posts measured
Reaction rate
0.766%
reactions ÷ views · ER floor
Posts in window
41
of 62 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 27 of 41 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 28 August 2026
Posts held62 (30 July 202628 August 2026)
Views total22,778
Reactions total123
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken28 Aug 2026, 21:13 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

Video runtime
21m 35s
Average length
2m 42s

Measured directly from 8 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no mark.

Reaction mix

148 reactions across 35 posts, in 12 distinct kinds. The most used accounts for 30.4% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
4530.4%
👍3322.3%
🔥3221.6%
🤣138.78%
😁117.43%
💯32.03%
🙏32.03%
🤡32.03%
😢21.35%
👏10.676%
😍10.676%
😭10.676%

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 39 of the 62 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 148 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 62 most recent posts we hold, published 30 July 2026 to 28 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

28 Aug 2026, 09:34 UTC340 viewsread 28 August 2026
Photo

🔥 Alibaba запустила QwenWork - универсального ИИ-сотрудника Платформа, где задачу описывают обычным языком, а агент сам доводит её до готового результата. Ответ конкуренту в лице ChatGPT Work от Alibaba вышел сразу в публичной бете по всему миру. • 📝 Задача текстом: описываешь, что нужно, агент выполняет работу и отдаёт готовый артефакт • 🧠 Память о пользователе: со временем агент запоминает, как вы привыкли работа

28 Aug 2026, 09:34 UTC258 viewsread 28 August 2026
Video

🔥 Claude Cowork получил собственный браузер Больше не нужно расширение: когда задача требует открыть сайт, браузер запускается прямо внутри приложения Claude. Агент сам переходит по страницам, читает их, нажимает кнопки и заполняет формы. • 🧭 Работа без setup: ни расширений, ни настроек - веб-часть задачи просто отдаётся Claude • 🖥 Изолированный браузер: это браузер Claude, а не ваш. Он не видит ваши вкладки, закла

28 Aug 2026, 07:32 UTC401 views4 reactionsread 28 August 2026
Photo

🗞 Моя новая статья на Хабре: регрессионное тестирование в Scrum 🖐Всем привет! Сегодня залил на Хабр статью-исследование, над которой работал несколько недель. Главная мысль: регрессия в Scrum - это управление риском, а не прогон перед релизом. Разбираю проблему от анализа влияния до критериев релиза. Пять идей из статьи: • 🔍 Анализ влияния - делается до разработки, а не после • 🎯 Приоритизация - набор проверок соб

🔥4

27 Aug 2026, 06:46 UTC517 viewsread 28 August 2026
Photo

🔥 SQL Noir - учим SQL, расследуя преступления SQL Noir - браузерная игра, где ты детектив в нуарном агентстве 80-х, а улики спрятаны в базе данных. Чтобы раскрыть дело, приходится писать SQL-запросы. • 🕵️ Каждый кейс - это бриф, схема базы и набор данных. Ищешь подозрительные паттерны, связываешь таблицы, проверяешь алиби • 🛠 Встроенный SQL-редактор на SQL.js - ничего устанавливать не нужно, открыл и поехал • 📈 Сло

26 Aug 2026, 21:11 UTC449 views1 reactionsread 28 August 2026
Forwarded from @AI_4lifePhoto

🔥 Alibaba показала архитектуру будущего Qwen4 Вышла Qwen3.8-Flash-Next - открытая мультимодальная MoE-модель, которая служит ранним показом архитектуры для будущего семейства Qwen4. Веса открыты, поэтому разобрать новинку можно уже сейчас. • 🧠 Параметры: 125 млрд в основном блоке, но на каждый токен активируются только 6 млрд. Ещё 51 млрд вынесены в отдельную N-gram-память, 4 млрд - в модуль предсказания нескольких

1

26 Aug 2026, 21:11 UTC317 viewsread 28 August 2026
Forwarded from @AI_4lifePhoto

🔥 Загадочная Ox Alpha оказалась GLM-5.3-Flash Несколько дней модель тестировали анонимно под именем ox-alpha на OpenRouter и OpenCode, и она стала самой популярной за неделю. Теперь Z.ai раскрыла карты: это GLM-5.3-Flash, первая мультимодальная модель в линейке GLM-5. • 🧠 Мультимодальность: работает с текстом, изображениями и видео • ⚡️ Эффективность: 320 млрд параметров всего, но при каждом запросе задействуется т

26 Aug 2026, 20:28 UTC404 views2 reactionsread 28 August 2026
Photo

Почему баги бессмертны, а интеграция — это «золотая жила» для тестировщика 🔔 Специфика проектов сильно различается: на одних «хромает» стабильность визуального интерфейса и логика клиентской части (фронтенд), на других — архитектура и стабильность обработки данных на стороне сервера (бэкенд). Однако фундаментальное правило разработки неизменно: до тех пор, пока программный код пишется людьми (или генерируется нейрос

👍1🔥1

26 Aug 2026, 13:57 UTC429 viewsread 28 August 2026
Forwarded from @AI_4lifeVideo

🔥 OpenAI показала первые результаты чипа Jalapeño, спроектированного при помощи ИИ OpenAI опубликовала результаты тестов собственного ускорителя для инференса. Компания проверила Jalapeño на GPT-OSS 120B, DeepSeek R1 670B и Kimi K2.5 1T через открытый бенчмарк InferenceX от SemiAnalysis. • ⚡️ Производительность на ватт: в пиковом режиме Jalapeño выполнил в 1,5-1,9 раза больше полезной AI-работы на каждый ватт • ⏱️

26 Aug 2026, 13:56 UTC415 views0 reactionsread 28 August 2026
Forwarded from @AI_4lifePhoto

🔥 Apple обновила Mac mini: M6 для обычных задач и M5 Pro для тяжёлых Новый Mac mini вышел сразу в двух версиях. Apple делает упор на локальный AI, постоянную работу агентов и профессиональные нагрузки в компактном корпусе. • 🧠 Mac mini с M6: до 4 раз быстрее в AI-задачах и до 2 раз быстрее в графике и работе с накопителем по сравнению с прошлым поколением • ⚙️ M6: 12-ядерный CPU, 12-ядерный GPU с Neural Accelerator

25 Aug 2026, 22:03 UTC589 views1 reactionsread 28 August 2026
Video

Это космос!!! Друзья вы не поверите, но мне это удалось !!! 🤝 Я связал Hermes и Notebook LM со всеми его инструментами и фишками. Теперь я могу управлять его содержанием через своего агента. Давать ему задачи на глубокие исследования и собирать базу знаний, строить крутейшие отчёты, получать презентации и многое другое. Короче теперь мой Hermes - просто мега монстр!!! ☝️ Говорю очень тихо на видео, потому что уже

1

25 Aug 2026, 20:07 UTC562 views1 reactionsread 28 August 2026
File

Claude Code. Полное руководство по AI-ассистенту для разработчиков. (2026) 📚 Claude Code: полное руководство для разработчиков Вышла русскоязычная книга Ранаса Мукминова о Claude Code - агентном инструменте Anthropic, который работает с кодовой базой, файлами и терминалом. • 💻 База: установка, первые шаги, архитектура Claude Code и работа с запросами • 🧩 Разработка: написание кода, рефакторинг, отладка, тестирован

1

Showing the 12 most recent of 62 posts we hold for @QA4Life. 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

@QA4Life edited 2 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
10 August 2026
Most recent edit
19 August 2026

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 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.

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.

Серьезный тестировщик 🐞
@serious_tester · 30,213
Telegram ranks this channel #26 of 92 here — alongside 91 others — read 7 September 2026
Artsiom Rusau QA Life - Тестировщик с нуля
@qachanell · 30,260
Telegram ranks this channel #35 of 92 here — alongside 91 others — read 7 September 2026
Тестировщик от бога
@godoftesting · 29,604
Telegram ranks this channel #36 of 90 here — alongside 89 others — read 8 September 2026

This channel appears in 3 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 6 September 2026 — this entry's latest reading, not the date you are reading this.

“QA❤️4Life | Testing | Тестирование ПО” (@QA4Life), 7,864 subscribers as measured 6 September 2026. Telegram Register, tgregister.com/channel/QA4Life.

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.