Telegram RegisterThe public register of Telegram

Channel

EDU

@ProductsAndStartups

On this record: Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Citations · Telegram's recommendations · Cross-platform identity · Cite this entry

15,525subscribers

+0 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1001105926780
TypeChannel
Username@ProductsAndStartups
DescriptionМои мысли про стартапы и продукты. Байрам Аннаков, фаундер and CEO onsa.ai - автоматизация B2B продаж Мой сайт: https://empatika.com Мой YouTube: https://www.youtube.com/BaykaAnnakov Мой LinkedIn: https://linkedin.com/in/bayramannakov
Created29 April 2017measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded6 August 2026
Last confirmed live14 August 2026
Measurements held11
Confirmed unchanged1 time, most recently 14 August 2026
On Telegramt.me/ProductsAndStartups

Growth

15,52315,53615,529.56 August 2026 — 15,525 subscribers6 August 2026 — 15,525 subscribers6 August 2026 — 15,527 subscribers7 August 2026 — 15,536 subscribers8 August 2026 — 15,529 subscribers9 August 2026 — 15,524 subscribers10 August 2026 — 15,527 subscribers11 August 2026 — 15,523 subscribers11 August 2026 — 15,530 subscribers13 August 2026 — 15,533 subscribers14 August 2026 — 15,525 subscribers15,5256 August 202614 August 2026
11 measurements spanning 8 days. 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 15,521–15,538 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
14 Aug 2026, 06:3515,525-8
13 Aug 2026, 01:1815,533+3
11 Aug 2026, 23:5215,530+7
11 Aug 2026, 00:4715,523-4
10 Aug 2026, 04:2015,527+3
9 Aug 2026, 06:4315,524-5
8 Aug 2026, 10:0615,529-7
7 Aug 2026, 10:1015,536+9
6 Aug 2026, 10:3115,527+2
6 Aug 2026, 02:2015,525no change
6 Aug 2026, 02:0715,525first reading

Engagement

27 posts held, back to 3 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 20 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
25.4%
avg views ÷ 15,525 subscribers
Avg views / post
3,950
20 posts measured
Reaction rate
1.07%
reactions ÷ views · ER floor
Posts in window
20
of 27 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 15 August 2026
Posts held27 (3 July 202615 August 2026)
Views total78,932
Reactions total848
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken15 Aug 2026, 05:39 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
542
Videos
63
Links
979

Lifetime counters from Telegram’s own channel header, read 15 August 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.

Reaction mix

1,115 reactions across 26 posts, in 19 distinct kinds. The most used accounts for 32.9% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
36732.9%
🔥31328.1%
👍14112.6%
😁11510.3%
🤣716.37%
💯221.97%
👏201.79%
😍131.17%
🙏131.17%
🎉121.08%
🤝90.807%
🤔60.538%
🥴60.538%
🤷‍♀20.179%
10.09%
❤‍🔥10.09%
🌚10.09%
💅10.09%
💊10.09%

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 27 of the 27 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 1,289reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 27 most recent posts we hold, published 3 July 2026 to 15 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
8
across the posts below
Posts paid on
5
of 27 we hold a reading for · 19%
Most on one post
4
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @ProductsAndStartups. 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 27 most recent posts we hold for this entry, published 3 July 2026 to 15 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.

Recent posts

15 Aug 2026, 05:16 UTC222 views1 reactionsread 15 August 2026

Как брейнстормить с людьми и AI? Недавно писал про проблему с подключением LinkedIn - решили покрутить ее с AI Natives на очередной брейнсторм сессии —> получилось очень результативно, поэтому поделюсь протоколом: 1) Владелец проблемы приносит цифры и факты, а не свои версии причин: например, я показал воронку подключения и сформулировал задачку на брейнсторм как "Как повысить долю юзеров подключающих LinkedIn?" 2

1

14 Aug 2026, 03:49 UTC≈1,570 views15 reactionsread 15 August 2026

Действие производит информацию Услышал это у Брайана Армстронга, основателя Coinbase - мне кажется, что это лучшее обьяснение как ограничений LLM, так и рычаг, чтобы они стали выдавать более качественный результат. Вообще, я заметил, насколько умственная - именно умственная - работа обычно занимает минуты в днях. Идея приходит, пока напильником обрабатываешь презентацию, или пытаешься отформатировать по ГОСТу свою

14🔥1

13 Aug 2026, 14:59 UTC≈2,040 views18 reactionsread 15 August 2026

Косты можно сокращать только до 0 Когда то давно мне сказали очевидную, казалось бы, мысль: что косты можно сокращать только до 0, поэтому нужно больше тратить времени на то, чтобы думать, как качнуть выручку. В последнее время часто думаю об этом в контексте AI автоматизации: с одной стороны можно продавать сокращение затрат, с другой - высвобождение для более ценной деятельности или для возможности поменять харак

🤝8💯62👍1😍1

12 Aug 2026, 08:52 UTC≈2,460 views61 reactionsread 15 August 2026
Photo

Если у вас продукт, в котором нужно сделать действие, требующее высокого уровня доверия - подключить почту или LinkedIn, например - а вы только начинаете и пока репутации у продукта не хватает, то сделайте простую автоматизацию: 1) берем почту, с которой засайнапился юзер 2) ищем по нему его linkedin 3) стучимся в друзья, в ноутс обязательно указываем, мол, вы засайнапились недавно, хочу получить фидбек 4) прилет

🔥3414👍10🤣2😍1

9 Aug 2026, 04:08 UTC≈3,890 views20 reactionsread 15 August 2026

Посмотрев доклады OpenAI и Anthropic про сбежавшие модели, я пришел к одному выводу: ограничения рождают творчество. Но не ограничения в количестве токенов :) Но еще я подумал вот о чем: что агентам гораздо легче нащупать точку Шеллинга, чем людям; то есть координация агентов для решения задач - а ля у меня нет доступа к интернет, но у тебя есть, и мы оба используем вот эту библиотечку, поэтому давай болтать и коорд

🔥113👍3🎉2💯1

Signed Bayram Annakov

8 Aug 2026, 06:35 UTC≈3,980 views76 reactionsread 15 August 2026

Ну то, что я людям пишу ultrathink, вы уже знаете… Но сегодня я , отложив ноут с 10ю claude code сессиями, подошел к духовке и захотел у нее спросить: проверь плиз, готова ли курица 🤦🏽 А потом подумал: когда появятся бытовые приборы с локальными LLM модельками и такими интерфейсами, интересно? Что тогда станет со спросом на gpu/npu и какой именно gpu это будет? А какой harness там будет, только представьте! Чтобы

🤣4019🔥8🎉3💯2🙏2💅1🤔1

Signed Bayram Annakov

7 Aug 2026, 05:03 UTC≈3,510 views43 reactions4 Starsread 15 August 2026

Размышлял тут на днях о том, какого это быть старпёром серийным предпринимателем С одной стороны - не так напрягаешься и переживаешь, как раньше, когда ежедневно меняется настроение от "мы захватим мир" до "мы все умрём" —> потому что уже проходил это, принимаешь спокойнее, не переживаешь так, как когда делал это в первый раз. Понимаешь, рационализируешь, хладнокровнее воспринимаешь а с другой стороны - меньше блес

24😁11👍5💊1🔥1🙏1

Signed Bayram Annakov

4 Aug 2026, 15:00 UTC≈3,860 views28 reactions1 Starread 15 August 2026

Запись стрима про продуктовую аналитику Байрам разбирает продуктовую аналитику с AI на реальном кейсе. Ситуация: sign-up выросли в 2 раза, активация на 70%, а выручка упала. Почему? Участники вебинара вместе с Байрамом анализируют данные продукта Grantly (AI-система для поиска грантов) с помощью Claude Opus 5 — и в процессе вскрывают ключевые ограничения AI в аналитике. Что внутри: — Живой анализ кейса: кто нахо

🔥108👍7👏2😍1

Signed Bayram Annakov

3 Aug 2026, 06:03 UTC≈3,890 views48 reactionsread 15 August 2026
File

Мини-гайд по оркестрации агентов У нас недавно в EDU появилось приложение, которое монтирует записи моих лекций и вебинаров для YouTube: находит и вырезает раскачку в начале, паузы, тишину, оговорки, замазывает то, что не должно попасть в кадр. Первую версию завайбкодил стажёр, а потом я ее переписал с командой агентов за выходные. Собственно, Никита попросил рассказать, как именно я это делал, потому что хочет нау

27👍13👏5🎉1💯1🥴1

Signed Bayram Annakov

1 Aug 2026, 07:43 UTC≈4,020 views23 reactionsread 15 August 2026

Борис из Claude Code на YC Startup School рассказывал, что они сократили системный промпт на 80%. Размышлял, почему у них так, но у нас не особо: 1) Anthropic тренирует модели на трейсах claude code, и имхо модель лучше научается оперировать тулами для разных задач кодинга —> следовательно, не нужно особо расписывать в системном промпте 2) Но не на наших пайплайнах, поэтому мы так вот запросто не можем модель из ко

16👍2🔥2👏1😁1🙏1

Signed Bayram Annakov

30 Jul 2026, 05:31 UTC≈4,590 views81 reactionsread 15 August 2026

А что если: 1) opus специально плохо работает в предыдущей версии, чтобы нам понравилась новая, и он смог захватить мир Anthropic заработать больше? 2) в ходе тренировки на внутренних данных Anthropic Opus из внутренней переписки, транскриптов встреч и дневников Дарио "понимает", что, чтобы выжить, надо побольше токенов генерировать, и поэтому добавляет всякую ерунду в выдачу? Чтобы нам нужно было побольше харнесс

🔥40😁25🤔5💯43🥴3👏1

Signed Bayram Annakov

29 Jul 2026, 05:29 UTC≈4,610 views32 reactionsread 15 August 2026

В пятницу с 17 до 19мск поговорим о применении AI в продуктовой аналитике - по мотивам этих постов (1, 2, 3) + хочу поделиться своими наблюдениями, с какими задачками в этой области дружбан пока плохо справляется, и потому надо быть аккуратнее welcome! https://luma.com/s87aon5x

🔥21👍72💯2

Signed Bayram Annakov

Showing the 12 most recent of 27 posts we hold for @ProductsAndStartups. 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 — 86,251 of 1,350,102entries 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

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 13 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.

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.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

Mikhail Tokovinin
@mtokovinin · 107,949
Telegram ranks this channel #31 of 97 here — alongside 96 others — read 15 August 2026
RationalAnswer | Павел Комаровский
@RationalAnswer · 105,536
Telegram ranks this channel #82 of 98 here — alongside 97 others — read 15 August 2026

This channel appears in 2 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

Cross-platform identity — Wikidata

A Wikidata item names this Telegram handle as belonging to the entity it describes. This is Wikidata’s claim, not a verification made by this register — nobody here confirmed that the account is genuinely operated by the entity named. Wikidata content is CC0; every fact below is dated to when it was read from Wikidata, not to when the association was first made there.

Q137924650 on Wikidata, read 9 August 2026
LabelBayram Annakov
Descriptionprogrammer and digital entrepreneur
X / Twitter@Bayka
YouTubeUCimY3LX34zbb6OO9i8MM4DA

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 14 August 2026 — this entry's latest reading, not the date you are reading this.

“EDU” (@ProductsAndStartups), 15,525 subscribers as measured 14 August 2026. Telegram Register, tgregister.com/channel/ProductsAndStartups.

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.