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Канал для разработчиков про AI. Модели, ИИ-агенты, практические кейсы и новости из мира AI. Всё, что можно применить в работе.
Created
Between 1 August 2025 and 31 October 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 100% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and the other topics.
Channels linked by matching posts
This channel is in a group of 3 registered channels linked by matching posts, directly or through other channels in the group. This channel may share no posts with some of the channels listed below, and listed channels may share none with each other. Links are found by comparing text fingerprints across every channel on the register.
Which channel, if either, published first is deliberately not shown in this list. “First” could only mean first in our own crawl, which depends on how deep we happened to reach into each channel’s history, not on when the content was actually first posted. Our one hand-check read 45 of the highest-confidence flagged pairs against the live Telegram pages. In 18 of them the live page showed a “Forwarded from” credit that our database had no record of, so reading that pair as an unattributed copy was wrong; the check did not measure who published first. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Near-verbatim matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.
65 measurements spanning 98 days, net +3,097. 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 15,103–19,130 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 65
Measured (UTC)
Subscribers
Change
12 Nov 2026, 01:07
18,665
+47
10 Nov 2026, 02:58
18,618
+67
8 Nov 2026, 17:50
18,551
+39
7 Nov 2026, 11:26
18,512
+5
6 Nov 2026, 11:05
18,507
+31
5 Nov 2026, 12:19
18,476
+18
4 Nov 2026, 10:01
18,458
+15
3 Nov 2026, 09:49
18,443
+11
2 Nov 2026, 07:55
18,432
+17
1 Nov 2026, 10:39
18,415
+17
31 Oct 2026, 10:47
18,398
+23
30 Oct 2026, 11:05
18,375
+39
29 Oct 2026, 13:21
18,336
+29
28 Oct 2026, 14:58
18,307
+29
27 Oct 2026, 17:40
18,278
+56
26 Oct 2026, 15:50
18,222
+52
25 Oct 2026, 12:15
18,170
+49
24 Oct 2026, 08:48
18,121
+87
23 Oct 2026, 09:16
18,034
+35
22 Oct 2026, 05:49
17,999
first reading
Engagement
80 posts held, back to 23 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 73 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
60.2%
avg views ÷ 18,665 subscribers
Avg views / post
11,200
30 posts measured
Reaction rate
0.893%
reactions ÷ views · ER floor
Posts in window
30
of 80 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 November 2026
Posts held
80 (23 July 2026 – 11 November 2026)
Views total
337,300
Reactions total
3,012
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
12 Nov 2026, 04:38 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
463
Videos
145
Links
420
Lifetime counters from Telegram’s own channel header, read 12 November 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.
Video runtime
10m 43s
Average length
36s
Measured directly from 18 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
8,028 reactions across 78 posts, in 16 distinct kinds. The most used accounts for 36.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
2,933
36.5%
👍
2,596
32.3%
🤯
1,025
12.8%
❤
578
7.20%
😁
392
4.88%
⚡
232
2.89%
😢
77
0.959%
🤩
46
0.573%
💯
35
0.436%
👏
31
0.386%
🤣
26
0.324%
custom 5307751405082130995
18
0.224%
😱
17
0.212%
👌
12
0.149%
custom 6325473957755488220
7
0.087%
custom 5361813743279821319
3
0.037%
Custom emoji. 3 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 80 of the 80 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 8,193 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 80 most recent posts we hold, published 23 July 2026 to 11 November 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
102
across the posts below
Posts paid on
77
of 80 we hold a reading for · 96%
Most on one post
4
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @ai_for_devs. 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 80 most recent posts we hold for this entry, published 23 July 2026 to 11 November 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.
Recent posts
Telegram’s own ‘edited’ marker: 3 of 30 posts held from the last 30 days (10.0%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
⚡️ Telegram встроил HTML-виджеты прямо в сообщения
Мессенджер всё ближе к ИИ-агентам. Уже писали про общение ботов и экспериментальный AI Assistant с Teleharness. Теперь в Bot API появились интерактивные HTML-виджеты прямо в чате.
Бот может прислать интерфейс на HTML/CSS/JS: с кнопками, графиками и обновляемыми данными.
Всё отображается прямо в чате, нативно, без открытия отдельного Mini App. Нажатия уходят боту, …
⚡️ SpaceXAI переписали стек Grok 4.8 на C/C++
Команда отказалась от JAX, библиотеки для быстрых вычислений при обучении моделей, и SGLang, движка, который запускает модель и обрабатывает запросы.
Замену написали с нуля, опираясь на наработки Starlink по передаче больших объёмов данных. Код в основном на C, местами есть C++ и даже ассемблер.
Основное обучение уже закончили. Сейчас Grok 4.8 решает задачи и учится на…
В честь релиза Anthropic раздали дополнительные сбросы лимитов.
На подписке за $100 и $200 в месяц число ресетов зависит от того, как давно вы подписаны:
Меньше 6 месяцев » 1
От 6 до 12 месяцев » 2
Больше года » 3
Использовать их можно до 8 марта.
$20 в месяц — один ресет и неделя на пробу Fable (можно потратить до половины недельного лимита).
@ai_for_devs
⚡️ Anthropic выпустили Claude Fable 5.2
Если верить таблице Anthropic, Fable и Mythos обошли всех конкурентов в 12 из 14 популярных бенчмарках.
https://www.anthropic.com/claude-fable-and-mythos-5-2
У Mythos 5.2 всё ещё закрытый доступ. Anthropic дают его проверенным организациям. У Fable больше ограничений на запросы по кибербезопасности и биологии, зато модель доступна всем.
По сравнению с Fable 5.1 токены подеш…
⚡️ Уже послезавтра — AI Dev Live
11–12 ноября пройдёт AI Dev Live — онлайн-конференция про разработку с AI-агентами. В программе 18 докладов от 18 топовых спикеров, которые сами работают с агентами и строят инструменты для разработчиков.
Что будут разбирать:
– Агентная разработка: длинные задачи, контекст, базы знаний и работа с легаси
– Качество и безопасность: AI-ревью, проверка сгенерированного кода и защита да…
⚡️ Telegram готовит экспериментальную сборку с ИИ-агентом
В сети появились подробности экспериментальной сборки Telegram со встроенным AI Assistant и доступом к перепискам, файлам и приложениям.
Вместе с ним работает Teleharness — отдельное приложение для настройки моделей, инструментов, разрешений и памяти агентов.
Судя по опубликованному материалу, агент умеет:
— Искать вопросы в переписках, на которые забыли от…
⚡️ OpenRouter: потребление токенов выросло в 25 раз, а цена продолжает падать
Самый популярный и крупный API-провайдер OpenRouter собрал свежий отчет по использованию моделей за январь–октябрь 2026 года. Только за октябрь через платформу прошло 790 трлн токенов.
Что интересного в отчете:
» Потребление выросло почти в 25 раз с января. И на 37% относительно сентября. При этом на агентов приходится 76% всех токенов —…
С вас идея, а Т-Банк за нее заплатит
В Т-Банке ищут трейдеров, разработчиков и тех, кто просто увлекается инвестициями. Зарегистрируйтесь на программу «Инвест. Премаркет» до 11 ноября.
Ваши AI-сценарии для бизнеса смогут побороться за призовой фонд программы — 2 500 000 ₽. А сами идеи Т-Банк поможет масштабировать и сделать частью бизнеса.
Успейте подать заявку до 11 ноября
⚡️ OpenAI представили GPT‑6 Cyber
Это специализированная Astra, которую дообучали на поиске багов и создании рабочих эксплойтов.
Именно эта модель нашла почти тысячу уязвимостей в Linux. На тесте разработки эксплойтов ExploitGym она решила на 51% больше задач, чем GPT‑5.6 Cyber.
https://openai.com/index/introducing-gpt-6-cyber/
Широкого доступа к новому флагману пока нет. Mozilla, Palo Alto Networks, Calif и Cisc…
Кстати, обнаружился самый абсурдный баг за последнее время.
В определённых конфигурациях Alpine Linux несколько сотен букв "A" вместо пароля позволяли войти в учётку)
Исследователь раскопал это с помощью роя AI-агентов: они проверили около 17 600 необычных вариантов входа.
https://sshhole.com
На странице он разбирает целую цепочку старых изменений, которые по отдельности должны были повышать безопасность)
@ai_fo…
⚡️ Будущая модель OpenAI нашла 948 способов получить root в Linux
OpenAI снова показывают, что умеет их ещё не выпущенная кибермодель. Возможно, это та самая модель, которая месяц назад выдала сотни математических работ.
Связь между ними компания пока не подтвердила, но масштаб задач впечатляет)
Цель выбрали отличную, на Linux сейчас работают все 500 суперкомпьютеров, а у популярных облачных провайдеров на него пр…
⚡️ В Claude Code обновление статусов сабагентов стало на 99% дешевле
Свою Decision Model вроде Jev Anthropic пока не представили, зато нашли очевидное применение своей маленькой модели в работе сабагентов.
Раньше Claude Code каждые 30 секунд перечитывал весь диалог фонового сабагента, чтобы обновить строчку его статуса.
Причём той же моделью! Если сабагент работал на Opus, то и несколько слов о том, чем он занят, …
🔥85😁51👍26❤9🤯7
Showing the 12 most recent of 80 posts we hold for @ai_for_devs. 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.
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 17 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 12 November 2026 — this
entry's latest reading, not the date you are reading this.
“AI for Devs” (@ai_for_devs), 18,665 subscribers as measured 12 November 2026. Telegram Register, tgregister.com/channel/ai_for_devs.
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.