3 measurements spanning 3 days, net +34. 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 6,123–6,167 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
10 Aug 2026, 19:38
6,162
+34
7 Aug 2026, 09:01
6,128
no change
7 Aug 2026, 08:53
6,128
first reading
Engagement
27 posts held, back to 28 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 9 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
12.3%
avg views ÷ 6,162 subscribers
Avg views / post
760
27 posts measured
Reaction rate
2.06%
reactions ÷ views · ER floor
Posts in window
27
of 27 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 11 August 2026
Posts held
27 (28 July 2026 – 11 August 2026)
Views total
20,519
Reactions total
423
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
12 Aug 2026, 03:29 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
≈1,980
Videos
≈63
Links
≈1,670
Lifetime counters from Telegram’s own channel header, read 12 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Video runtime
2m 06s
Average length
1m 03s
Measured directly from 2 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
423 reactions across 27 posts, in 22 distinct kinds. The most used accounts for 27.9% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
118
27.9%
🔥
77
18.2%
⚡
42
9.93%
😁
42
9.93%
👀
32
7.57%
❤
15
3.55%
💯
14
3.31%
😭
13
3.07%
🤯
10
2.36%
👌
8
1.89%
custom 5372878077250519677
7
1.65%
😨
7
1.65%
🤔
7
1.65%
🤪
7
1.65%
👨💻
6
1.42%
🎃
5
1.18%
🙏
5
1.18%
😱
3
0.709%
🏆
2
0.473%
✍
1
0.236%
2 further kinds
2
0.473%
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 isTelegram’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 27 of the 27 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 423reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 27 most recent posts we hold, published 28 July 2026 to 11 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.
Сначала ИИ загнал вас в выгорание, а теперь ИИ будет высчитывать, когда вы окончательно сдохнете 📉
Недавно я писал о том, что больше половины IT-специалистов (56%) сидят на грани выгорания из-за взвинченных ИИ-инструментами темпов. Конвейер мчится, мозг плавится, а бизнес задирает планку производительности до небес.
И вот аналитики компании «Стахановец» (известный разработчик систем мониторинга персонала) хвастаютс…
IT-кошка принесла кладесь знаний 🐈⬛
IT & AI каналов много, но по-настоящему качественные медиа часто скрываются в потоках информации.
Вот вам кладесь знаний из сферы технологий👇🏻
Внутри папки можно найти:
Новости из мира IT
Нейросети
Реальные кейсы из практики
Вакансии
Всё самое полезное и актуальное собралось здесь: https://t.me/addlist/ZPicVwIc1bJkMzE6
117-е место из 147: смотрим свежий рейтинг проникновения ИИ от Microsoft и Our World in Data
Я как-то разбирал заявления депутата Горелкина о том, что отчетам Microsoft верить нельзя, 9.5% проникновения ИИ в РФ — это «выдумки и шпионаж», а в любом «объективном рейтинге мы в тройке лидеров».
Команда Our World in Data выложила свежий датасет замера Microsoft AI Diffusion Report (за первый квартал 2026). Я скачал откр…
Реверс-инжиниринг Claude Code: шпионский режим и корпоративные двойные стандарты
Нашел на GitHub разбор внутренностей Claude Code. Энтузиаст декомпилировал и разобрал полмиллиона строк кода и нашел интересное:
1️⃣ Режим шпиона
Если сотрудник Anthropic (USER_TYPE === 'ant') пишет код в публичный опенсорс-репозиторий через Claude Code, система автоматически включает undercover-режим.
Инструкция для модели прямо в код…
Миллион токенов контекста против инфошума: кластеризация подписок через LLM
Большие контекстные окна в современных моделях вроде Kimi или Claude удобны не только для анализа тяжелой документации, но и для прикладной фильтрации контента. Скармливание модели системных логов или архивов экспорта Telegram позволяет быстро очистить инфополе от бессмысленного копипаста и инфоцыганщины.
Если выгрузить данные своих подписо…
«Нажмите на тормоза, пока мы всё не сожгли»: 1300+ инженеров из OpenAI, Anthropic и DeepMind молят о регулировании 📛
В сети есть две коллективные петиции, которые иллюстрируют главную шизофрению современного AI-рынка: разработчики передовых лабораторий втихую седеют от темпов авто-R&D, но остановиться сами не могут и просят государственного нагайки.
Первый документ — открытое письмо Pacing the Frontier. Под ним под…
Эндрю Ын сделал OpenWorker — десктопного ИИ-ассистента с открытым кодом
Главный популяризатор ML для масс выкатил в открытый доступ свой новый проект — OpenWorker.
Технически перед нами классический гибридный пирог:
1️⃣ Фронтенд: React + TypeScript + Tailwind. Упаковано в десктоп через Tauri (Rust).
2️⃣ Бэкенд: Локальный Python-сервер на FastAPI/Uvicorn, который заморожен через PyInstaller и крутится как sidecar-пр…
Военные сожгли годовой бюджет на токены за месяц: добро пожаловать в эпоху Thrift-maxxing 🪖💸
Дичь с токенмаксингом появилась быстро и, видимо, исчезнет быстро. Я писал, как в Disney вешали ИИ-дашборды, как в Amazon менеджеры заставляли гонять агентов вхолостую, и как Uber с Microsoft судорожно резали косты, когда пришли реальные счета от Anthropic.
И недавно Wired сообщает, что Армия США умудрилась сжечь свой ГОДОВ…
Семьсот долларов за восемь часов газлайтинга от нейросети 💸
Очередная прекрасная история из категории «ИИ вот-вот заставит всех программистов голодать».
Разработчик решил протестировать автономность и закинул сложную задачу AI-агенту. Прошло 8 с половиной часов. Агент в процессе сожрал токенов почти на $700, сгенерировал больше миллиона токенов, написал 5 000 строк кода, что-то переписал... а в самом конце выдал:
…
Халява опять всё. DeepSeek готовится переписать ценники 💸
Вот я совсем недавно радовался выходу DeepSeek V4 Flash 0731 с его качеством и ценами. Ну что, сказка длилась недолго.
Прямо сейчас в личном кабинете DeepSeek висит оптимистичная плашка: «We plan to raise the overall pricing for DeepSeek API services in the near future, with a significant increase expected».
«Significant increase» означает простое: демпинг …
Кладбище дашбордов: почему ваш «Data-Driven» бизнес всё ещё управляется по чуйке
Вышла статья в «Т-Бизнес секретах», где я выступил экспертом и раздал базовой базы про BI.
Вокруг BI в свое время маркетологи развели примерно столько же розовых соплей, сколько и вокруг AI. Кажется, что стоит внедрить какую-нибудь BI-систему — и бизнес тут же начнет автоматически генерировать прибыль, а руководству останется только см…
Монтажеры больше не нужны!!1 😱
Никогда не монтировал свои ролики, которые провожу для разных форматов, т.к. нифига не умею.
До чего дошел прогресс — сегодня немного поизучал вопрос и Hermes сам отмонтировал за меня вчерашний эфир для Sponsr. Повысил громкость, чуток увеличил качество картинки, вырезал словесный шлак (не переживайте, его осталось аж 1:40 ещё), создал таймкоды и текстовую расшифровку.
И все бесплатн…
👍19💯12⚡3
Showing the 12 most recent of 27 posts we hold for @oleg_payload. 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 — 356,043 of 1,169,250entries 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 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.
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 August 2026 — this
entry's latest reading, not the date you are reading this.
“Олег Булыгин | Полезная нагрузка” (@oleg_payload), 6,162 subscribers as measured 10 August 2026. Telegram Register, tgregister.com/channel/oleg_payload.
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.