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 11 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
30 measurements spanning 37 days, net +248. 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 11,617–11,971 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 30
Measured (UTC)
Subscribers
Change
11 Sept 2026, 13:16
11,930
+8
8 Sept 2026, 21:39
11,922
-8
5 Sept 2026, 14:38
11,930
+7
3 Sept 2026, 13:41
11,923
+39
2 Sept 2026, 09:53
11,884
+23
1 Sept 2026, 08:48
11,861
+35
31 Aug 2026, 11:06
11,826
+84
30 Aug 2026, 07:57
11,742
-3
29 Aug 2026, 05:24
11,745
+69
28 Aug 2026, 06:17
11,676
+16
27 Aug 2026, 05:42
11,660
+2
26 Aug 2026, 02:54
11,658
-1
25 Aug 2026, 05:17
11,659
-6
24 Aug 2026, 03:08
11,665
+5
22 Aug 2026, 08:16
11,660
-1
21 Aug 2026, 02:05
11,661
-6
20 Aug 2026, 00:51
11,667
+5
19 Aug 2026, 01:15
11,662
-1
17 Aug 2026, 23:47
11,663
-6
16 Aug 2026, 21:14
11,669
first reading
Engagement
23 posts held, back to 28 December 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 47 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
23.4%
avg views ÷ 11,930 subscribers
Avg views / post
2,790
2 posts measured
Reaction rate
0.968%
reactions ÷ views · ER floor
Posts in window
2
of 23 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 31 August 2026
Posts held
23 (28 December 2025 – 31 August 2026)
Views total
5,580
Reactions total
54
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
2 Sept 2026, 23:33 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.
Reaction mix
1,596 reactions across 22 posts, in 28 distinct kinds. The most used accounts for 35.7% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
570
35.7%
❤
434
27.2%
🔥
301
18.9%
🤮
95
5.95%
💯
66
4.14%
😁
42
2.63%
👎
17
1.07%
🥰
13
0.815%
😱
9
0.564%
🤡
9
0.564%
👏
5
0.313%
😴
4
0.251%
🤔
4
0.251%
🌚
3
0.188%
👌
3
0.188%
🗿
3
0.188%
🤣
3
0.188%
🥱
3
0.188%
❤🔥
2
0.125%
🙏
2
0.125%
8 further kinds
8
0.501%
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 23 of the 23 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 1,682 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 23 most recent posts we hold, published 28 December 2025 to 31 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
10
across the posts below
Posts paid on
5
of 23 we hold a reading for · 22%
Most on one post
4
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @max_about_ai. 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 23 most recent posts we hold for this entry, published 28 December 2025 to 31 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.
Митап про личные финансы для айтишников и фаундеров
Меня позвали на онлайн-митап рассказать про мой опыт использования ИИ для личных финансов. Никаких секретных промптов и тем более финансовых советов не будет, но будет честный рассказ как я использую ChatGPT и Claude Code, чтобы экономить время и принимать более взвешенные личные финансовые решения.
Кто еще будет и о чем расскажут:
🔵 Владислав Носковец (CEO Caree…
AI Skills для агентов
Даже странно что за 9 месяцев с момента появления скиллов я ни разу про них не написал. Пора закрыть этот гэп, потому что скиллы стали де-факто стандартом для повторяемых инструкций агенту и основным способом расширения его возможностей.
Что такое скилл
Скилл - повторяемый способ решения целого класса задач: инструкции, скрипты, шаблоны, документацию и критерии проверки.
Скилл упаковывается …
Почему я пока не верю в универсальные темные фабрики софта (dark software factory)
Несколько месяцев назад появился тренд на построение полностью автоматизированных фабрик софта: систему, в которой AI-агенты получают цели, а дальше сами формируют задачи, пишут код, запускают тесты и выкатывают изменения в прод, без какого либо участия людей.
Эффективным менеджерам далеким от разработки эта идея кажется отличной. Мо…
Ликбез про RAG. ч.2. Фундаментальные ошибки.
За последние 3 года я пообщался с десятком+ команд из разных компаний, работающих над приложениями на основе RAG.
Две самые частые ошибки, которые я встречал:
- плохая подготовка данных - garbage-in > garbage-out,
- отсутствие evaluation (оценки качества) - не возможно улучшать то, что не измеряешь.
Разумеется, существует множество других проблем, но это фундамент для в…
Про Fable 5
Я успел протестировать Fable 5 до его блокировки, но не успел закончить тесты. Дождаться разблокировки не удалось, так что поделюсь тем, что есть:
1⃣ Fable гораздо лаконичнее других моделей. Ответы именно лаконичнее, а не короче, то есть плотность информации выше, чем у других моделей. Мне это очень нравится, не люблю воду в текстах.
2⃣ Ответы Fable на прямолинейные замечания могут иметь яркую эмоциона…
Мне интересно читать каналы, где автор не пересказывает позавчерашней Твиттер или презентации БигТехов, а делится своими мыслями по интересной мне теме. Когда 6 лет назад мне пришлось начать совмещать роль Technical Product Manager с моей основной ролью, я искал что бы почитать по новой для меня роли и нашел канал Ани Подображных. Сегодня хочу его порекомендовать, потому что до сих пор читаю.
Мне нравится, что посты…
AI ликбез: RAG (Retrieval Augmented Generation) ч.1
Решил, что RAG как концепция настолько важен для современных AI приложений, что несмотря на широкую общеизвестность, все равно заслуживает отдельной серии постов в канале.
1️⃣ RAG (Retrieval Augmented Generation) - это подход, когда перед генерацией LLM ответа, приложение ищет релевантую информацию в внешних данных и добавляет ее в контекст вопроса, делая генераци…
Про AI research
На этой недели моя AI лаба опубликовала первые научные статьи по AI engineering на arXiv (объяснимость ответов в graph RAG и синтетический эвал для LLM приложений). Команда - большие молодцы, потому что они справились, несмотря на огромное количество препятствий на нашем пути.
Не буду пересказывать статьи, поделюсь лишь нескольким практическими выводами, которые я сделал по ходу исследований:
1⃣ Gr…
Провел две недели отпуска в роад-трипе. Как писал выше, поиск use cases для применения AI не проще, чем освоение самих инструментов, поэтому решил поделиться парочкой сценариев использования, чем сам пользовался в путешествие.
1️⃣ Перевод фото меню, объяснение состава и выбор подходящих блюд из меню с помощью ChatGPT. Перевод меню в классических переводчиках обычно работает плохо и не объясняет что ожидать от блюда.…
Банально, но факт - сейчас открыто окно беспрецедентных возможностей. Как долго оно будет открыто и что наступит после - я не знаю, но точно знаю, что если делать что-то свое, то сейчас отличное время пробовать.
Поэтому моя жена решила открыть свой мини-стартап на международный рынок. Хочет бутсрапить небольшие SaaS приложения.
С вайб-кодингом это стало сильно проще, тем более, что у нее айтишный бэкграунд и образо…
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Последний месяц эксперементирую с Claude Cowork, OpenClaw и Manus. Мои краткие выводы:
0️⃣ Если у вас FOMO, что все вокруг в 10 раз эффективнее благодаря OpenClaw и другим AI агентам, то можете перестать нервничать - технологии все еще далеки от совершенства. Пока хайпа все еще больше чем пользы. Значит ли это, что не надо их пробовать или использовать? Нет! Потому что уже сейчас можно …
Поучаствовал в подкасте про Product Engineering. Вместе с Юрой Агеевым, автором подкаста Make Sense, поговорили про будущее ролей в разработке, кто такие Product Engineer, как ими стать если вы уже разработчик или продакт менеджер. Не обошли стороной вайб-кодинг и AI для личной эффективности.
Из разговора мне больше всего запомнилась история Юры про его опыт с AI агентами. У него кастомная команда агентов поверх Cla…
😁25❤20👍10🔥4🤩1🤷♂1
Showing the 12 most recent of 23 posts we hold for @max_about_ai. 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.
Polls
The poll we hold for this entry, as Telegram rendered it when we read the post. A poll’s figures keep moving after that, so each one is dated.
Shares as published. No per-option vote count is published by Telegram, so none is shown.
Percentages only — there are no per-option vote counts here, because Telegram publishes none. The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.
The shares need not add up to 100. Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.
Read from the 23 most recent posts we hold, published 28 December 2025 to 31 August 2026. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.
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 14 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 11 September 2026 — this
entry's latest reading, not the date you are reading this.
“Max: AI, Engineering and Startups” (@max_about_ai), 11,930 subscribers as measured 11 September 2026. Telegram Register, tgregister.com/channel/max_about_ai.
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.