2 measurements spanning 4 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 67–69 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
10 Aug 2026, 00:32
68
no change
6 Aug 2026, 12:04
68
first reading
Engagement
17 posts held, back to 27 March 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 pageof Telegram’s post history, 20 posts per page.
ERR · 30 days
57.4%
avg views ÷ 68 subscribers
Avg views / post
39.0
1 post measured
Reaction rate
17.9%
reactions ÷ views · ER floor
Posts in window
1
of 17 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 4 August 2026
Posts held
17 (27 March 2026 – 4 August 2026)
Views total
39
Reactions total
7
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
10 Aug 2026, 00:32 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
20
Links
9
Lifetime counters from Telegram’s own channel header, read 10 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Reaction mix
143 reactions across 17 posts, in 7 distinct kinds. The most used accounts for 62.2% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
89
62.2%
❤
23
16.1%
👍
16
11.2%
😁
11
7.69%
💯
2
1.40%
🤔
1
0.699%
🥰
1
0.699%
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 17 of the 17 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 143reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 17 most recent posts we hold, published 27 March 2026 to 4 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.
Telegram Stars
Stars received
51
across the posts below
Posts paid on
2
of 17 we hold a reading for · 12%
Most on one post
50
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @devdeaf. 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 17 most recent posts we hold for this entry, published 27 March 2026 to 4 August 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.
Съездил в Москву на хакатон Сбера «Агент без границ». 24 часа работы без остановки, 130 команд, 700 человек со всей экосистемы Сбера в одном зале.
По условиям всё нужно было делать на GigaCode и задача стояла так: собрать не просто локальную демку, а рабочий переиспользуемый кусок для PDLC-процесса: навык, спеку или вспомогательного агента, которого уже можно встроить в общую экосистему.
Дедлайн подгоняет, идеи рож…
Сегодня закрыл ноутбук и пришел погулять на фестиваль родного ИТМО в парк 300 летия. Формат максимально далек от скучных университетских линеек: здесь устроили мощный ИТ-чилл прямо на пляже с лекториями про ИИ, интерактивными зонами от Яндекса и Сбера, диджей-сетами и выпускниками в мантиях на сапбордах. Получился идеальный летний open-air, где можно лежать на пуфике у Финского залива, ловить крутой антураж и общатьс…
Сегодня два события, которые я читаю как один сигнал.
1. GPT-5.6 вышел — доступ закрыли по звонку из Белого дома. Аналогичная история с Fable 5 и Mythos 5 от Anthropic. Если ты строишь продукт на чужом API, твой аптайм теперь зависит не только от SLA провайдера.
2. OpenClaw (агент на Claude Opus 4.6) выложил инбокс на HN: 6000 реальных атак в проде — промпт-инъекции, джейлбрейки, соц. инженерия. Всё отбито. Первый …
🦻 💼 💻«Спасибо маме». Как парень с нарушением слуха стал успешным AI-инженером — в мире созвонов
Артём Бутомов — Java Tech Lead и AI-инженер, автор канала про AI. Проблемы со слухом не помешали ему построить хорошую карьеру в отрасли, где важна коммуникация голосом офлайн и онлайн.
Было и так: на одном собеседовании интервьюер давал мне наводящие подсказки — а я их не слышал и шёл в другую сторону. Узнали об этом то…
На последнем VibeCoding челлендже лидер комьюнити разобрал мою работу перед потоком на 5500+ человек и тегнул Кирилла Меньшова — старшего вице-президента блока Технологий Сбера, человека который определяет AI-стратегию крупнейшего банка страны.
Кирилл ответил: "Поддерживаю"
Отметили, что "сдал не просто результат, а методологию, которая превращает одиночный эксперимент в воспроизводимую практику".
Для меня это сиг…
Атаки на агентные LLM-системы
Неделю назад представил работу на конференции Центрального университета "Научный телеграф", секция "Кибербезопасность" совместно с ВМК МГУ.
После основных тестов нашёл ещё один вектор: two-document chain injection. Загружаешь два документа: каждый по отдельности безобиден и фильтрацию не триггерит. Но при совместной обработке модель выстраивает логическую цепочку между ними и принимает…
Indirect prompt injection
Прошлый раз я разбирал взлом реальной системы. Там вектор атаки через загрузку вредоносного файла в RAG-проект. Модель проиндексировала его как обычный документ и выполнила инструкцию внутри. Классификатор смотрел на запрос пользователя, а не на содержимое базы знаний.
Сейчас разберу еще один реальный кейс: бот по расписанию тянет посты из X-аккаунтов через RSS, отдаёт Claude на переработк…
Anthropic выкатили advisor tool.
Попробовал на живом проекте.
Haiku гоняет агентный цикл и делает рутину. Когда сомневается, зовёт Opus внутри того же API-вызова. Один запрос, общий контекст, без оркестрации сабагентов. Запустил у себя для анализа n8n пайплайна. Haiku прочитал метрики, логи - нашёл аномалию и позвал Opus разобраться. Вот кусочек логов:
```shell
>> server_tool_use name=advisor
>> advisor_tool…
Аутсорсить мышление можно. Понимание — нет
Карпатый цитирует это повсюду последнее время: «можно отдать на аутсорс своё мышление, но нельзя отдать понимание».
Звучит как банальность. Но если подумать — это точный диагноз того, что происходит с инженерами, которые перешли в режим "скопировал из Claude, закоммитил". Код работает. Почему — непонятно. Когда сломается — тоже непонятно.
Я вижу это в код-ревью: человек н…
Showing the 12 most recent of 17 posts we hold for @devdeaf. 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.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Citation-graph rank
Citation-graph rank — 339,448 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.
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.
“Артем Бутомов про ИИ” (@devdeaf), 68 subscribers as measured 10 August 2026. Telegram Register, tgregister.com/channel/devdeaf.
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.
