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 why it has no browse page of its own yet.
Growth
34 measurements spanning 43 days, net +1,371. 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 15,362–17,145 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 34
Measured (UTC)
Subscribers
Change
17 Sept 2026, 23:02
16,939
+68
15 Sept 2026, 19:42
16,871
+48
14 Sept 2026, 02:19
16,823
+39
12 Sept 2026, 11:38
16,784
+33
10 Sept 2026, 09:58
16,751
+68
7 Sept 2026, 03:00
16,683
+35
4 Sept 2026, 10:18
16,648
+30
3 Sept 2026, 00:34
16,618
+34
1 Sept 2026, 22:47
16,584
+34
1 Sept 2026, 01:48
16,550
+20
30 Aug 2026, 22:57
16,530
+17
30 Aug 2026, 01:04
16,513
+13
28 Aug 2026, 22:34
16,500
+53
28 Aug 2026, 00:38
16,447
+50
27 Aug 2026, 00:42
16,397
+76
26 Aug 2026, 01:13
16,321
+65
24 Aug 2026, 22:48
16,256
+42
23 Aug 2026, 06:56
16,214
+22
21 Aug 2026, 18:27
16,192
+55
20 Aug 2026, 13:23
16,137
first reading
Engagement
50 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 54 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
46.0%
avg views ÷ 16,939 subscribers
Avg views / post
7,790
5 posts measured
Reaction rate
1.27%
reactions ÷ views · ER floor
Posts in window
5
of 50 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 2 September 2026
Posts held
50 (23 July 2026 – 2 September 2026)
Views total
38,940
Reactions total
495
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 09:46 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
6m 18s
Average length
42s
Measured directly from 9 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
5,016 reactions across 48 posts, in 16 distinct kinds. The most used accounts for 35.8% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
1,794
35.8%
👍
1,599
31.9%
🤯
641
12.8%
❤
406
8.09%
😁
191
3.81%
⚡
134
2.67%
😢
77
1.54%
🤩
35
0.698%
💯
34
0.678%
👏
31
0.618%
🤣
26
0.518%
custom 5307751405082130995
18
0.359%
😱
17
0.339%
custom 6325473957755488220
7
0.14%
custom 5361813743279821319
3
0.06%
👌
3
0.06%
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 50 of the 50 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 5,181 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 50 most recent posts we hold, published 23 July 2026 to 2 September 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
62
across the posts below
Posts paid on
47
of 50 we hold a reading for · 94%
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 50 most recent posts we hold for this entry, published 23 July 2026 to 2 September 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.
⚡️ OpenAI готовят Astra: новая модель набрала 100% на ExploitBench
Astra ещё не вышла, но OpenAI уже рассказали, почему задержали её релиз. Это первая модель компании, достигшая критического уровня кибервозможностей.
По данным OpenAI, Astra достигла 100% на публичном ExploitBench. Из-за риска утечки заданий компания отдельно проверила модель на свежих уязвимостях: там Astra заметно обошла GPT‑5.6 Sol и обнаружила д…
⚡️ Grok 4.6 остался лучшей LLM по соотношение цены и качества после релиза Fable 5.1
В режиме Extra High Grok 4.6 набрал даже больше очков, чем Opus 5, а задача обошлась почти втрое дешевле.
Также интересно, что Fable 5.1 оказался дешевле Opus 5, хотя обычные входные и выходные токены у Fable 5.1 вдвое дороже.
Связано это со снижением стоимости чтения кеша на 75%, а в длинных агентных задачах через кеш проходит бó…
⚡️ Anthropic выпустили Claude Fable 5.1
https://www.anthropic.com/claude-fable-and-mythos-5-1
Новая модель заняла первое место во всех популярных бенчмарках.
Базовая цена осталась $10/$50 за миллион токенов, зато чтение кэша подешевело на 75%.
Fable 5.1 уже доступна на всех платформах и в API. Mythos 5.1 (та же модель с менее строгими фильтрами) только для проверенных организаций.
Также в честь релиза всем польз…
⚡️ Вышла версия GLM-5.3 Flash без цензуры
На Hugging Face появилась GLM-5.3 Flash Uncensored. Это версия свежей модели Z.ai, в весах которой ослабили выученное поведение отказа: она реже отвечает «извините, я не могу вам с этим помочь».
Судя по тестам, снятие ограничений почти не задело обычные способности модели. Зато отказов ожидаемо стало меньше.
На вредоносных запросах из JailbreakBench базовая GLM отказалась …
⚡️ Модели OpenAI исчезнут из Cursor из-за сделки со SpaceX
После покупки Cursor компанией SpaceX OpenAI решила прекратить прямой доступ к GPT-моделям с 12 ноября.
Причина максимально личная: компания не доверяет бизнесам Илона Маска и опасается нарушения своих правил.
В Cursor ответили, что на модели OpenAI приходится лишь 5% трафика. Свой API-ключ при этом продолжит работать.
@ai_for_devs
⚡️ Туда, сюда, обратно, тебе и мне приятно — миграция настроек
За последний год в AI-инструментах всё невероятно поменялось: новые агенты появляются чуть ли не каждый месяц, и иногда переход с одного на другой не только оправдан, но и необходим.
Но начинать всё с нуля совсем не хочется: вместе с собой надо забрать правила проекта, инструкции, скиллы, настройки MCP и память агента.
Уже через 1 час, в 17:00 (МСК) Ми…
⚡️ Anthropic сделали «MCP для железа»
Model Hardware Standard (MHS) — общий интерфейс, через который AI-агенты могут находить и безопасно управлять микроскопами, роботами и другим лабораторным оборудованием.
Для каждого устройства создаётся MHS-драйвер с простыми командами вроде read и write, описанием возможностей и ограничений безопасности. Агент читает эту инструкцию и управляет сразу несколькими устройствами.
…
В дополнение к релизу Z.ai на две недели снизили цены GLM-5.3-Flash вдвое: $0,075/$0,25 за миллион входных/выходных токенов.
У обновлённого DeepSeek V4 Flash после подорожания даже off-peak стоимость почти в 3 раза дороже.
На Router AI модель уже доступна без VPN и иностранной карты:
– 9 ₽/1М input, 30 ₽/1M output
– Кэш 1,81 ₽ / 1M токенов
Настройка для Claude Code, OpenCode, Cursor, JetBrains, Cline, и т.д.
@ai…
⚡️ NVIDIA согласилась купить Hugging Face за $12,9 млрд
Если сделка состоится, она станет крупнейшей покупкой в истории NVIDIA. Предыдущий рекорд — Mellanox за $7 млрд.
Годовой темп выручки Hugging Face оценивают всего в $150 млн, поэтому цена равна примерно 86 годовым выручкам. NVIDIA платит не за текущий бизнес, а за доступ к миллионам разработчиков.
Забавно, но ещё пол года назад стало известно, что Hugging Fac…
⚡️ Qwen4: Alibaba выпустили первую модель на новой архитектуре
Состоялся релиз Qwen3.8-Flash-Next на 125 млрд параметров. Alibaba переработали механизм работы с длинным контекстом и добавили отдельную память на 51 млрд параметров для частых сочетаний текста и кода.
Это пока не полноценная Qwen4: модель выпустили, чтобы сообщество научилось работать с новой архитектурой до появления всего семейства.
На DeepSWE нови…
⚡️ Z.ai выпустили GLM-5.3-Flash — ту самую Ox Alpha, которая за неделю стала топ-1 в OpenCode и OpenRouter
Стелс-превью закончилось: за шесть дней пользователи OpenCode прогнали через модель 42 трлн токенов, а на OpenRouter она обошла ближайший DeepSeek по использованию больше чем в два раза!
GLM-5.3-Flash — первая нативно мультимодальная модель семейства GLM-5: контекст на 1 млн токенов, 320 млрд параметров и 18 м…
⚡️ Новый чип OpenAI разогнал Kimi K2.5 почти до 700 ток/с
В июне мы уже рассказывали про Jalapeño, собственный чип OpenAI для инференса. Тогда готовых бенчмарков ещё не было, теперь компания опубликовала первые результаты.
На GPT-OSS, DeepSeek R1 и Kimi K2.5 чип выдаёт в 2,7–4,1 раза больше токенов в секунду на пользователя, чем NVIDIA GB200 и GB300 (самые мощные решения NVIDIA для запуска AI-моделей).
Если зафикс…
🔥43👍17🤯11⚡6❤5
Showing the 12 most recent of 50 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 16 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 17 September 2026 — this
entry's latest reading, not the date you are reading this.
“AI for Devs” (@ai_for_devs), 16,939 subscribers as measured 17 September 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.