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От обезьяны к LLM💡

@decent_researcher

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

125subscribers

+2 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1002359800654
TypeChannel
Username@decent_researcher
CreatedBetween 1 September 2024 and 31 March 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded9 August 2026
Last confirmed live14 August 2026
Measurements held3
Confirmed unchanged1 time, most recently 14 August 2026
On Telegramt.me/decent_researcher

Growth

1231251246 August 2026 — 123 subscribers9 August 2026 — 123 subscribers14 August 2026 — 125 subscribers6 August 202614 August 2026
3 measurements spanning 8 days, net +2. 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 123–125 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
14 Aug 2026, 07:24125+2
9 Aug 2026, 03:15123no change
6 Aug 2026, 13:51123first reading

Engagement

12 posts held, back to 31 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 1 pageof Telegram’s post history, 20 posts per page.

ERR · 30 days
66.6%
avg views ÷ 125 subscribers
Avg views / post
83.2
5 posts measured
Reaction rate
4.09%
reactions ÷ views · ER floor
Posts in window
5
of 12 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 8 August 2026
Posts held12 (31 May 20268 August 2026)
Views total416
Reactions total17
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken9 Aug 2026, 03:15 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

42 reactions across 10 posts, in 3 distinct kinds. The most used accounts for 54.8% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥2354.8%
1842.9%
👍12.38%

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 10 of the 12 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 42reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 12 most recent posts we hold, published 31 May 2026 to 8 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
2
across the posts below
Posts paid on
1
of 12 we hold a reading for · 8%
Most on one post
2
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @decent_researcher. 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 12 most recent posts we hold for this entry, published 31 May 2026 to 8 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

8 Aug 2026, 13:13 UTC37 views1 reactionsread 9 August 2026
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Посмотрел доклад с датафеста: Всеволод Викулин | Реальная автоматизация: в каких процессах AI-агенты приносят пользу. Вообще задумался об использовании агентов. Получается агенты хорошо умеют в Intelligence, а за Judgement должны пока отвечать люди. Но как люди научаться Judgement, если они не будут делать Intelligence ? Или я что-то не понимаю ? Т.е. получается, что теперь работать нужно в 100 раз больше, чтобы пр

1

27 Jul 2026, 08:01 UTC123 views4 reactionsread 9 August 2026

Мне недавно показали скилл grill-me и это, один из самых полезных скилов. Смысл простой: агент не бросается сразу что-то делать, а сначала закидывает вопросами: уточняет цель, ограничения, спорные места, риски, критерии успеха. Потом сводит всё в понятный план, согласовывает его с тобой. И только после этого начинает выполнять задачу.

🔥4

26 Jul 2026, 14:43 UTC108 views4 reactionsread 9 August 2026
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#БылоБольно #НоПонравилось Два года закрыты. Прошёл выпускной самое время поделиться впечатлениями. Если честно — ШАД я прошёл, но выжал из него далеко не всё. Послевкусие: 1. Вести заметки по мере прохождения материала. Обязательно. Это единственный совет, который я готов давать без оговорок, потому что проверил его с обеих сторон. 2. Курсы и оценки — не самое важное. Мне показалось, что важнее то, как именно ты

4

26 Jul 2026, 14:41 UTC70 views4 reactionsread 9 August 2026
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#БылоБольно #НоПонравилось ШАД. Курсы 2-го года. Третий семестр. Осенние даются проще: больше концентрации. Считаю его самым продуктивным за все два года — все курсы попали в меня. А в конце получилось съездить на Новый год в ШАД, что тоже было очень круто. Natural Language Processing Наверное, самый полезный курс в контексте текущего развития машинного обучения. Спасибо Ежу за классные лекции и семинары — его оче

4

26 Jul 2026, 14:40 UTC78 views4 reactionsread 9 August 2026
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#БылоБольно #НоПонравилось ШАД. Курсы 1-го года. Первый семестр. Всё было в новинку: не было понимания, как выстраивать планирование времени, что нужно делать, что не нужно, когда и на что обращать внимание. В конце семестра на пробных собеседованиях обнаружил, что курс вроде бы закрыт, а в голове осталось мало, знания разрозненные и не структурированные. Собственно, после этого и решил начать куда-то писать и стр

4

17 Jul 2026, 10:33 UTC266 views9 reactionsread 9 August 2026

🎉 Подготовили с Ильёй Шигабеевым статью "Dialogs: a studio-quality expressive conversational Russian speech corpus for dialog assistants", которую приняли на Interspeech 2026. Это был мой первый подобный опыт, и теперь я могу с уверенностью сказать: если кажется, что всё, что может пойти не по плану, обязательно пойдёт не по плану — скорее всего, так и будет 😄 Переносы записей, сорванные дедлайны, организационные сл

🔥54

12 Jul 2026, 14:36 UTC228 views3 reactionsread 9 August 2026

🎙 audiogear обновился Обкатал инструмент на 27 датасетах и собрал все грабли в новую версию. 🛡 Один битый файл больше не роняет прогон. Реальная история: один кривой mp3 убивал набор на 549k клипов, а клип длиной 2 мс валил миллионный golos. Теперь любой сбой на клипе (не только OOM) на любом пути исполнения — серийном, CPU-параллельном, батчевом, prefetch — превращается в sentinel-значение (NaN/−1), и прогон едет

🔥2👍1

9 Jul 2026, 19:05 UTC194 viewsread 9 August 2026
Forwarded from @claudedevolper

Claude сбросили лимиты, продолжаем работать.

1 Jul 2026, 06:14 UTC≈1,380 views5 reactions2 Starsread 9 August 2026

🎙 audiogear — как разметить миллионы аудиозаписей для TTS Выложил инструмент, которым готовлю датасеты под синтез речи. Указываешь папку с аудио → получаешь таблицу, где у каждой записи дописаны фичи: MOS, SQUIM (STOI/PESQ/SI-SDR), SNR, pitch, темп речи, выразительность, bandwidth, WER/CER, пол, эмоция. Плюс две вещи, на которых обычно спотыкаются: 🔹 Консенсус-ASR для аудио без текста — гоняет несколько русских мод

🔥5

21 Jun 2026, 11:16 UTC210 views3 reactionsread 9 August 2026
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Как то я уже делал агента для игры в blackjack. Решил провалидировать мои результаты с помощью Claude Code. В прошлой версии у меня вышло: Q-learning + подсчёт карт + сплит давали плюс. С помощью Claude Code перепроверил код — и плюс исчез. «Выигрыш» держался на двух тихих багах: — бутстрап в терминальных состояниях (агент тянул «будущую награду» из уже закончившихся раздач); — сломанный график epsilon (исследован

🔥3

14 Jun 2026, 11:52 UTC182 viewsread 9 August 2026
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Посмотрел доклад Ежа про LLMы ≠ Мы. Нечеловеческие приёмы в рассуждениях языковых моделей с attention. Если коротко, то пока что не понятно как думают LLM и думают ли они вообще. Но несколько основных мыслей из доклада напишу ниже: Часть LLM, которая позволяет LLM-мам думать это Attention. Attention — это не просто «механизм внимания», а способ несколько раз переосмыслить вход (подсветить важные токены для ответа),

31 May 2026, 17:16 UTC187 views5 reactionsread 9 August 2026
File

29 мая выступал на конференции «Иванниковские чтения» с докладом «Обучение доменно-инвариантных представлений для дерматоскопических изображений». В работе исследовал переносимость моделей между различными доменами. Под доменом в данном случае подразумеваются разные дерматоскопы, условия съёмки и популяции пациентов. Для решения задач доменной адаптации и выравнивания распределений использовал BYOL в сочетании с tr

🔥41

Showing the 12 most recent of 12 posts we hold for @decent_researcher. 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 — 304,463 of 1,481,306entries 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

Republishes

Channels on the register whose posts this channel has forwarded.

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 5 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.

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.

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.

“От обезьяны к LLM💡” (@decent_researcher), 125 subscribers as measured 14 August 2026. Telegram Register, tgregister.com/channel/decent_researcher.

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.