Telegram RegisterThe public register of Telegram

Channel

Product × Science

@product_science

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

5,901subscribers

-10 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001081286887
TypeChannel
Username@product_science
Created27 January 2017measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded6 August 2026
Last confirmed live12 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 12 August 2026
On Telegramt.me/product_science

Growth

5,9015,9115,9066 August 2026 — 5,911 subscribers6 August 2026 — 5,911 subscribers9 August 2026 — 5,908 subscribers12 August 2026 — 5,901 subscribers6 August 202612 August 2026
4 measurements spanning 7 days, net -10. 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 5,900–5,913 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 17:025,901-7
9 Aug 2026, 08:325,908-3
6 Aug 2026, 03:315,911no change
6 Aug 2026, 00:405,911first reading

Engagement

19 posts held, back to 9 May 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 9 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
9.86%
avg views ÷ 5,901 subscribers
Avg views / post
582
5 posts measured
Reaction rate
1.27%
reactions ÷ views · ER floor
Posts in window
5
of 19 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 9 August 2026
Posts held19 (9 May 20269 August 2026)
Views total2,909
Reactions total37
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken12 Aug 2026, 03:44 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 57s
Average length
3m 29s

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

224 reactions across 18 posts, in 9 distinct kinds. The most used accounts for 47.3% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥10647.3%
4921.9%
👍208.93%
🐳188.04%
🦄114.91%
🍾83.57%
🗿73.13%
🌚41.79%
🖕10.446%

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 18 of the 19 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 224reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 19 most recent posts we hold, published 9 May 2026 to 9 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.

Recent posts

9 Aug 2026, 12:02 UTC393 views4 reactionsread 12 August 2026
Photo

Добавляю коммерческую нотку к инфе выше в виде слайда от Дмитрия Долгова (CEO Waymo) из лекции для Y Combinator (это такой большой, популярный, богатый и престижный акселератор в Долине). Метрики, медологии верификации и валидация (V&V), интерпретируемость и объяснимость моделей – это ценнейший актив компаний, которые разрабатывают ИИ-системы, влияющие на критически-важные структуры повседневности. В самой лекции

4

Signed Anton Martsen

9 Aug 2026, 11:44 UTC407 views10 reactionsread 12 August 2026
Forwarded from @jdata_blog

Мы выпускаем образовательный ресурс open-xai. Привет, друзья! Я шла к этому очень долго и наконец-то готова выпустить образовательный ресурс open-xai! Ресурс построен как точка, где можно учиться просто всему, что есть в интерпретируемости. От классических методов, до геометрии и математики в моделях. Сейчас в нем: ⁃ 2 трека. XAI Practitioner — объяснять поведение моделей руками, от классики до LLM. Math of LLMs

🔥9👍1

Signed Anton Martsen

9 Aug 2026, 11:44 UTC412 views5 reactionsread 12 August 2026

На прострах интернета найден интересный курс по ExplainableAI (редкие материалы, а тут еще и на русском!) Вот емкая фраза из начального модуля, которую важно осознать всем, кто занимается аналитикой в ML-продуктах: Хорошая метрика не гарантирует, что модель работает правильно. Ниже про сам курс. Низкий поклон людям, которые приложили к этому руку.

5

Signed Anton Martsen

15 Jul 2026, 18:44 UTC729 views6 reactionsread 12 August 2026
Forwarded from @gulagdigital

Кто в ответе? Напряжённый союз автоматизации и человеческого поведения Выдержки из статьи Who’s in charge? The fraught union of automation and human behavior Автоматизация повсеместна — от автопилота в самолётах до циклов стиральной машины у вас дома. А с появлением современного искусственного интеллекта компьютеры и машины берут на себя всё больше задач. https://knowablemagazine.org/content/article/mind/2026/design

🦄3🔥2🖕1

Signed Anton Martsen

15 Jul 2026, 08:03 UTC968 views12 reactionsread 12 August 2026

После долгого перерыва мы возвращаемся с полностью новой версией Retentioneering 5.0. Мы переписали библиотеку с нуля и ускорили основные инструменты в примерно в 100 раз - теперь даже на обычном Mac можно за секунды исследовать датасеты из нескольких миллионов событий. Но скоростью дело не ограничивается: • все основные визуализации стали интерактивными; • появились сегменты, diff-режим в большинстве инструментов

🔥10👍2

Signed Anton Martsen

26 Jun 2026, 12:54 UTC≈1,070 views7 reactionsread 12 August 2026
Video

Video, posted without a caption

🍾7

Signed Anton Martsen

26 Jun 2026, 12:41 UTC≈1,040 views13 reactionsread 12 August 2026
Photo

Никогда не думал, что буду работать в автоиндустрии. Делаем машины на любой вкус: электро, автономные, большие, маленькие. Даже призовой фонд делаем из автомобилей.

🔥103

Signed Anton Martsen

25 Jun 2026, 11:37 UTC≈1,000 views9 reactionsread 12 August 2026

Сегодня идет конференция YoungCon – там всякое полезное. Включаем стрим на фоне и наблюдаем за трендами: https://yandex.ru/youngcon/#stream Ближайшее в программе: 〰️ 13:40 — Андрей Холодный, Руководитель подразделения HW‑разработки роботакси и грузовиков «Автономную машину нельзя купить — её нужно создать: настоящая история роботакси» 〰️ 14:00 — Олег Шипитько, CPO Яндекс Роботикс «Как превратить робота в универсал

🔥54

Signed Anton Martsen

24 Jun 2026, 08:52 UTC922 views29 reactionsread 12 August 2026
Video

Ваши идеи в коменты: как замерить качество технологии в такой ситуации?

🐳186🗿4🌚1

Signed Anton Martsen

23 Jun 2026, 08:00 UTC826 views4 reactionsread 12 August 2026

Едем потихоньку

🔥31

Signed Anton Martsen

23 Jun 2026, 08:00 UTC927 views17 reactionsread 12 August 2026
Forwarded from @autonomy_yandexVideo

🚛 700 километров без участия человека Роботрак Яндекса впервые в России прошёл маршрут Москва — Санкт-Петербург полностью в автономном режиме — около 700 км по трассе М-11 «Нева», без вмешательства человека. Водитель-испытатель был в кресле, но не принимал никакого участия. Грузовиком управляла ИИ-система: она самостоятельно обходила медленные машины, объезжала ремонтные зоны и проезжала пункты оплаты, точно рассчи

🔥143

Signed Anton Martsen

20 Jun 2026, 13:15 UTC≈1,050 views14 reactionsread 12 August 2026
Forwarded from @datarascalsPhoto

#ML Как и 60 лет назад, нейронки начали часто сравнивать с мозгом, а у мозга есть психологи (хотя и у нейронов уже появляются исследователи настроений). И как и в любой другой области знаний, у психологов есть свой собственный глоссарий, и я решил составить первый словарь по переводу с психологического на язык MLE. Меня осенило когда читал статью Kahneman-Tversky Optimisation — это же идея обесценивание в чистом

🔥95

Signed Anton Martsen

Showing the 12 most recent of 19 posts we hold for @product_science. 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.

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.

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

“Product × Science” (@product_science), 5,901 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/product_science.

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.