Telegram RegisterThe public register of Telegram

Channel

Ilyas Salikhov

@dev_salikhov

On this record: Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Citations · Cite this entry

1,630subscribers

+122 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 1,000–3,162.

Register entry

Telegram ID-1003349678181
TypeChannel
Username@dev_salikhov
CreatedBetween 1 November 2025 and 11 March 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live13 August 2026
Measurements held5
Confirmed unchanged1 time, most recently 13 August 2026
On Telegramt.me/dev_salikhov

Growth

1,5041,6301,5676 August 2026 — 1,508 subscribers6 August 2026 — 1,508 subscribers7 August 2026 — 1,506 subscribers9 August 2026 — 1,504 subscribers13 August 2026 — 1,630 subscribers6 August 202613 August 2026
5 measurements spanning 7 days, net +122. 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 1,485–1,649 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
13 Aug 2026, 04:151,630+126
9 Aug 2026, 23:341,504-2
7 Aug 2026, 03:441,506-2
6 Aug 2026, 07:361,508no change
6 Aug 2026, 07:041,508first reading

Engagement

17 posts held, back to 11 March 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
62.0%
avg views ÷ 1,630 subscribers
Avg views / post
1,010
2 posts measured
Reaction rate
3.96%
reactions ÷ views · ER floor
Posts in window
2
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
WindowRolling 30 days · latest post in window 22 July 2026
Posts held17 (11 March 202622 July 2026)
Views total2,020
Reactions total80
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken7 Aug 2026, 18:12 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
47s
Average length
24s

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

615 reactions across 17 posts, in 12 distinct kinds. The most used accounts for 43.1% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥26543.1%
👍17628.6%
609.76%
💯518.29%
👾325.20%
152.44%
😁50.813%
30.488%
🤔30.488%
🆒20.325%
🎉20.325%
🐳10.163%

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 615reactions 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 11 March 2026 to 22 July 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
15
across the posts below
Posts paid on
3
of 17 we hold a reading for · 18%
Most on one post
12
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @dev_salikhov. 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 11 March 2026 to 22 July 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

22 Jul 2026, 08:18 UTC≈1,000 views42 reactionsread 7 August 2026

Это база Убежден, что каждый разработчик, считающий себя экспертом, должен понимать устройство СУБД (системы управления базами данных) не хуже, чем языков, на которых он пишет. Ошибки по части данных обходятся очень дорого, иногда просто необратимы. Не продумали индексы: запросы в проде стали грузить сервер в полку. Ошиблись в миграции: затерли часть данных при переносе в новые таблицы. Банально полезли в прод и вып

🔥22👍115💯31

20 Jul 2026, 10:08 UTC≈1,020 views38 reactionsread 7 August 2026
Video

Погода лучше не придумаешь, не забываем про тело, разгружаем мозг. Недавно в Forbes прочитал про MB Barbell, тренажеры которых встречаю в Москве. Оказалась компанией из Петрозаводска, основана аж в 1986 года. Основатель, Вадим Маркелов, запатентовал уличный тренажер с изменяемой нагрузкой, сейчас выпускают 12к уличных тренажеров в год. Ребята еще крутыши, вложились в городскую среду родного города. По своей инициати

🔥29👍531

2 Jul 2026, 11:47 UTC≈1,750 views39 reactionsread 7 August 2026
Photo

Как отдельный трек, мы развиваем сквозного агента компании Orpheus, который помогает нам во всё большем количестве бизнес-процессов компании, не только в разработке. И получаются интересные прецеденты, когда Orpheus делает задачи по улучшению самого себя 👾 🔗 Инженерия и AI | Ilyas Salikhov

🔥21👍10👾71

2 Jul 2026, 06:22 UTC≈1,690 views51 reactionsread 7 August 2026
Photo

Как уже говорил ранее, с февраля разработчики в RetailCRM массово переходят на агентскую разработку. Прошло 5 месяцев этого пути, хотел бы подбить промежуточные выводы. Ключевые цифры 1. Число смерженных Merge Request-ов +26%. Доля MR, разработанных с агентами 50-55% 2. Объем изменений, заезжающих с MR +250%. Доля строк с агентами 75-80% 3. Медиана Time to market задач держится плюс-минус на том же уровне 4. Средний

23👍16🔥7👾4🎉1

24 Jun 2026, 14:20 UTC≈1,570 views38 reactionsread 7 August 2026

Завтра выступаю на Ecom Expo 26, крупнейшей выставке для интернет-торговли. Расскажу про опыт внедрения AI на операционном уровне в RetailCRM и лестницу автономии AI-агентов. Если будете на выставке, буду рад пообщаться, у нас большой стенд там. Записей доклада, насколько знаю не делают, но буду еще с этой темой на других конференциях, следите за каналом 🙂 🔗 Инженерия и AI | Ilyas Salikhov

🔥17👍1262🤔1

19 Jun 2026, 22:01 UTC≈2,270 views66 reactions2 Starsread 7 August 2026
Photo

Можно ли заменить gpt-5.4-mini открытой моделью. Бенч-тест на агенте Exoskeleton После истории с Fable снова все начали думать об альтернативах, которые не обрубят «с той стороны». С подачи Рината я тут упоролся и провел большое исследование: взял Exoskeleton-агента и прогнал его на 10 открытых семействах моделей без какого-либо изменения кода. В посте самое ключевое, а в конце найдёте ссылки на полную версию. Мет

🔥39👍169👾2

18 Jun 2026, 11:30 UTC≈1,300 views61 reactionsread 7 August 2026

Регулярно участвую в собеседованиях, и в последнее время все чаще попадаются кандидаты, которые проходят его с AI-суфлерами. Просто нет цензурных слов. Нет, я не против использования AI, и понятно, что в работе мы активно его используем. Но кандидаты совершенно отключают мозг и просто читают то, что им нагенерила LLM. Уже не счесть, сколько собеседований я просто останавливал, когда видел подобное. Не понимаю, чем д

💯32👍179🔥2😁1

3 Jun 2026, 06:53 UTC≈1,960 views41 reactions1 Starread 7 August 2026

У Валеры важный пост, из которого хочу выделить два ценных тезиса, дополнив от себя. 1️⃣ Агенты не заменяют опыт Клод, курсор и любой другой агент не застрахует вас от проблем, особенно в проде. Нужно пройти через ошибки, увидеть, что падает, понять, как мониторить и отлаживать, а потом страховаться от таких случаев. В том числе на уровне harness в агентской разработке. Это приходит только с опытом. НО. Агенты пом

👍28🔥7💯6

1 Jun 2026, 22:09 UTC≈1,740 views34 reactions12 Starsread 7 August 2026

Экзоскелет — архитектура агента для E-commerce AI Agent Challenge / May 2026 Обещал про архитектуру агента. Тут кратко, по ссылкам в конце полная версия 🗒 Название архитектуры отражает суть: модель gpt-5.4-mini — это не очень сильное «тело», на которое надет экзоскелет, дающий ему силу и точность. Экзоскелет подстраховывает и усиливает модель на всех этапах решения задачи. Причем экзоскелет тоже гибридный: в каких

🔥22👍7👾5

31 May 2026, 20:15 UTC≈1,270 views44 reactionsread 7 August 2026

E-commerce AI Agent Challenge / May 2026 30 мая участвовал в челендже по разработке AI-агентов для E-commerce. Участвовал первый раз. Тематика челенджей меняется, но в этот раз, подумал: уж в какой теме участвовать, если не в родной по екому. Агенты должны уметь работать с поиском товаров, корзинами, чекаутом, сбоями оплаты, мошенничеством и многое другое. Задачи разные и динамические, от прогона к прогону вводные

🔥28👍10👾2🆒21🎉1

21 May 2026, 15:52 UTC≈1,160 views32 reactionsread 7 August 2026

AI-first разработка. Главный барьер оказался не там, где ждали В этом году в отделе разработки RetailCRM системно перешли на работу в паре с AI-агентами. До этого агентов использовали отдельные энтузиасты, теперь это рабочая модель для всей команды. Начали с базы: массовое локальное использование агентов на задачах + harness под них. Уже понятно, что трансформация должна затронуть не только реализацию задач и не то

🔥15👍10👾5💯2

13 May 2026, 07:34 UTC990 views35 reactionsread 7 August 2026
Video

Спорт в ежедневной рутине Честно сказать, я не особо спортсмен. В детстве не занимался целенаправленно каким-то спортом. Ходить в спортивный зал я тоже не любитель. Важнее для меня, чтобы спорт присутствовал в ежедневной рутине. Чтобы спортивная активность вплеталась в день, а не была слотом в календаре. Отжаться между созвонами. Зайти на турники, пока гуляешь с ребенком в коляске. Поприсядать, когда захотелось. Э

🔥18👍10💯51🐳1

Showing the 12 most recent of 17 posts we hold for @dev_salikhov. 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 — 799,260 of 1,160,990entries 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 1 registered channel — 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 13 August 2026 — this entry's latest reading, not the date you are reading this.

“Ilyas Salikhov” (@dev_salikhov), 1,630 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/dev_salikhov.

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.