2 measurements taken within a single day. 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 868–870 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
7 Aug 2026, 12:14
869
no change
7 Aug 2026, 06:32
869
first reading
Engagement
20 posts held, back to 16 March 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 1 pageof Telegram’s post history, 20 posts per page.
ERR · 30 days
68.8%
avg views ÷ 869 subscribers
Avg views / post
598
2 posts measured
Reaction rate
1.59%
reactions ÷ views · ER floor
Posts in window
2
of 20 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 July 2026
Posts held
20 (16 March 2025 – 31 July 2026)
Views total
1,195
Reactions total
19
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Aug 2026, 06:32 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.
Reaction mix
357 reactions across 17 posts, in 11 distinct kinds. The most used accounts for 39.2% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
140
39.2%
❤
100
28.0%
🎉
28
7.84%
😎
19
5.32%
custom 5386399931378440814
17
4.76%
🗿
16
4.48%
👍
12
3.36%
custom 5456258317477230911
10
2.80%
🆒
8
2.24%
👏
6
1.68%
🤯
1
0.28%
Custom emoji. 2 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 areTelegram’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 17 of the 20 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 357reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 16 March 2025 to 31 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
18
across the posts below
Posts paid on
4
of 18 we hold a reading for · 22%
Most on one post
10
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @awesome_dl. 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 20 most recent posts we hold for this entry, published 16 March 2025 to 31 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.
Age of Autoresearch
Был обычный вечер, и мне было откровенно лень гонять гипотезы руками. Я описал pipeline текстом, попросил Gemini судить результат каждой попытки и лёг спать. Утром в логе было 40 проверенных гипотез.
Потом выяснилось, что у этого уже есть имя — autoresearch — и пара громких прецедентов до меня.
написание кода с ассистентом → делегирование подзадач → цикл, который перебирает сам
Написал эссе о …
Новая эра интерфейсов
Мне давно хочется создавать системы, которые живут и адаптируются как человек — прям как живое существо рядом с тобой. Чтобы сайт подстраивался лично под тебя, а не был статичной менюшкой для всех сразу. Меня всегда напрягало, что сайты вообще не адаптируются под человека: ну есть a/b по ui/ux, но концептуально сайт один и тот же для всех.
И тут в апреле вижу в твиттере демку flipbook.page — м…
Krea-2: taste is what matters
4 месяца назад я перешёл в Krea и вчера мы выкатили модель Krea-2 и я мега рад этим поделиться.
Меня давно бесило что большинство image-моделей, тот же ChatGPT Image или Nano Banana, оптимизированы под среднюю красивую картинку — генеришь 4 варианта одного промпта и по факту получаешь один и тот же концепт просто под разными углами. Это убивает разнообразие стилей и прям не даёт вырази…
История о том, как штука для рисования треугольников стала самым важным чипом на планете
Мы все каждый день используем GPU, но мало кто копался в том, какая история за ними стоит. Я решил разобраться с нуля — и больше всего меня зацепила красота инженерных решений: каждое следующее — прямое следствие предыдущего.
отрисовка треугольников → параллелизм → SIMD → unified cores → CUDA → ML → Tensor Cores
Ни одно звено …
Когда мы получим realtime видео? Считаю от first principles (Лонгрид)
Мне всегда было интересно предсказывать будущее — на знании будущего можно заработать деньги, лучше определить путь своего развития. А как это делать?
Ответ у меня начал зарождаться в 2023, во время прослушивания интервью Хинтона о будущем искусственного интеллекта. Он упомянул, что скоро LLM будут инференситься на отдельных устройствах (смотря …
How to build your search engine?
Понимание бэкенда + архитектуры помогает делать ваш вайбкод one-shot готовым решением. Поэтому я люблю читать блоги про о том, как создается архитектура разных приложений - так нарабатывается насмотренность для решения архитектурых задач. А тут бац, и человек собрал свой поисковик с 0 нуля и рассказал все нюансы создания:
- как парсить данные
- как их готовить
- как их хранить
- как…
Всем привет, live update: перехожу в стартап Krea.ai!
Год в роли тимлида был продуктивным. Выстроил систему генерации контента (горжусь почти реалтайм видео генерацией), пару раз спидранил идеи до прода меньше чем за 3 дня и чудо - начал писать код, который бэкендеры не хотят переписать сразу.
Сейчас понял: пора в эпицентр хайпа. Хочу в SF, поближе к умным ребятам и направлению World Models (мне очень нравится идея…
Привет! Меня зовут Олег, я исследую, как оптимально скейлить языковые модели в Jülich Supercomputing Centre. Пока Андрей подзаряжается энергией для будущих постов, с его позволения поделюсь тут нашей новой работой — “Optimal Scaling Needs Optimal Norm”. Всем, кто задумывался о правильном тюнинге гиперпараметров — будет интересно!
Главная проблема в скейлинге — как подбирать гиперпараметры (learning rate, batch size …
Пока я путешествую по разным странам и набираюсь опыта, у меня не всегда остаётся время писать в канал. Хотя идей накопилось немало — год назад я о многих из них даже не думал. Чтобы заполнить паузу и заодно набраться мотивации через новых подписчиков, решил поучаствовать в папке.
Я честно рекомендую ребят, которых приведу ниже — уделил время просмотру их контента:
- Тимлид и работа руками — про то, как совмещать …
Awesome DL pinned «Пост знакомство Я Андрей Филатов – Team Lead Gen AI CV в стартапе, занимаюсь применением генеративных моделей для создания визуального контента: создание изображений/видео, редактирования изображений/видео, создание персонализированных генераций. В ML/DL…»
Showing the 12 most recent of 20 posts we hold for @awesome_dl. 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 — 933,977 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.
Mentions
Names
Channels on the register whose handles appear in this channel's posts.
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.
Handles this channel named that no longer answer
Dead references
1
handles named in this channel’s posts, vacant today
Evidenced gone
0
we ourselves saw one of these resolve, at some point
Never seen alive
1
vacant every time we have ever looked
@awesome_dl named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all.A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
@impressivenamebot named in 1 post, 8 August 2026 – 8 August 2026
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 7 August 2026 — this
entry's latest reading, not the date you are reading this.
“Awesome DL” (@awesome_dl), 869 subscribers as measured 7 August 2026. Telegram Register, tgregister.com/channel/awesome_dl.
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.
