Telegram RegisterThe public register of Telegram

Channel

Эрдэни GenAI финтех

@erdeni_ai

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

634subscribers

-1 since we began measuring on 7 August 2026

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

Register entry

Telegram ID-1001523779304
TypeChannel
Username@erdeni_ai
CreatedBetween 1 August 2021 and 28 February 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded8 August 2026
Last confirmed live15 August 2026
Measurements held3
Confirmed unchanged1 time, most recently 15 August 2026
On Telegramt.me/erdeni_ai

Growth

634635634.57 August 2026 — 635 subscribers8 August 2026 — 635 subscribers15 August 2026 — 634 subscribers7 August 202615 August 2026
3 measurements spanning 7 days, net -1. 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 634–635 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
15 Aug 2026, 02:58634-1
8 Aug 2026, 02:49635no change
7 Aug 2026, 16:24635first reading

Engagement

20 posts held, back to 28 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 1 pageof Telegram’s post history, 20 posts per page.

ERR · 30 days
17.2%
avg views ÷ 634 subscribers
Avg views / post
109
20 posts measured
Reaction rate
2.48%
reactions ÷ views · ER floor
Posts in window
20
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. It is computed over the 12 of 20 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 7 August 2026
Posts held20 (28 July 20267 August 2026)
Views total2,185
Reactions total35
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken8 Aug 2026, 02:49 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
15s
Average length
15s

Measured directly from 1 video 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

35 reactions across 12 posts, in 7 distinct kinds. The most used accounts for 25.7% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍925.7%
😁925.7%
720.0%
🤣411.4%
😱38.57%
🔥25.71%
😨12.86%

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 12 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 35reactions 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 28 July 2026 to 7 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

7 Aug 2026, 04:44 UTC58 viewsread 8 August 2026

В Российских брокерах с плечом торговать могут только квалифицированные инвесторы, чтобы маргин-Коля не пришел каждому. В крипте есть есть аналоги с плечом — фьючерсы. Трейдинг vs Инвестиции. ИМХО Трейдер (лудоман) — торгует с плечом на разнице цен в краткосроке обычно. Инвестор — торгует без плеч на долгий срок. На языке криптанов инвесторы называются HODLer Spot (держатели спотов). А трейдеры торгуют фьючерсам

7 Aug 2026, 04:43 UTC55 views3 reactionsread 8 August 2026
Forwarded from @findullaPhoto

Как инвестиции в кредит разрушили будущее миллионов молодых корейцев. В первой половине 2026 года южнокорейский фондовый рынок стал одним из самых горячих в мире. Огромное количество частных инвесторов покупало акции, связанные с ИИ и полупроводниками — прежде всего Samsung, SK Hynix и ETF с кредитным плечом. Поэтому у нас оперативка стоит как крыло самолета. А да, ссд тоже. Спасибо ИИ-буму. Причем многие покупали

👍3

6 Aug 2026, 03:54 UTC74 viewsread 8 August 2026
Forwarded from @ai_tabletPhoto

Новая библиотека SIRIN для оценки и мониторинга contextual hallucinations в RAG и memory-based LLM системах (статья arxiv + github ) Идейно внутри три инструмента: - probing по hidden states - легкий классификатор по hidden states генератора но нужно немного разметки - LLM-as-a-judge. Есть response-level и span-level варианты, где модель пытается локализовать конкретные галлюцинированные фрагменты. - uncertainty (e

5 Aug 2026, 16:45 UTC69 viewsread 8 August 2026
Forwarded from @nokiddingpartnersFile

🤖 Агент или чат-бот? Определяем агента по 6 критериям Вышло неплохое исследование ИИ-агентов от "Яков и Партнёры" (ex. McKinsey), которое даёт четкое определение тому, что же всё-таки считать ИИ-агентом, а что - дорогостоящим чат-ботом с красивым названием. 🔥 Главный парадокс: внедрение ≠ прибыль • 88% компаний используют ИИ хотя бы в одной из функций, но положительный эффект на прибыль заявляют лишь 6%. • В России

5 Aug 2026, 15:45 UTC65 viewsread 8 August 2026
Forwarded from @mashkka_dsPhoto

📌#шпаргалка GraphRAG за 2 минуты Классический RAG — это ctrl+F на стероидах: вопрос → ищем похожие куски текста → LLM отвечает по ним. Отлично работает для точечных вопросов («что такое LoRA?»), но ломается на глобальных («какие основные темы всего корпуса?») — ответ размазан по документам, и ни один чанк его не содержит. GraphRAG решает это так: 1️⃣ LLM заранее проходит по всему корпусу и извлекает сущности и связ

5 Aug 2026, 02:17 UTC96 views3 reactionsread 8 August 2026

Инсайт с фин консультации: "Инвестиции не имеют смысла, если есть кредиты от 20%. Никакие бумаги не покажут такую доходность."

😱3

3 Aug 2026, 10:57 UTC112 views2 reactionsread 8 August 2026
Forwarded from @DSTGSUM

🦾💰📊 Qwen 3.8-Max: новая мультимодальная модель Вышла модель Qwen 3.8-Max с 2,4 трлн параметров (95B активных) и контекстом 1M токенов. Цена: $2 вход / $6 выход. Сильные стороны — мультимодальность (PaperBench 93,0, MathVision 95,2, CharXiv 88,4, OmniDocBench 92,1). Слабее в чистом кодинге (DeepSWE 56,6, FrontierSWE 73,5). Веса обещаны через неделю. [1] [NEU1][2] [CGE] 🍏🖥️🤖 DeepSeek V4 Flash локально на Mac Вышла GGU

2

2 Aug 2026, 12:14 UTC127 views1 reactionsread 8 August 2026
Forwarded from @Ai_bolno_mlPhoto

Мои полномочия все. Эскалирую таску наверх😐😐😐 Гадина кодексовая🤖 сожгла мне все токены, теперь свечки жжем, пока переезжали с React на next.js (никуда не переехали, конечне: он ниче не может сделать, бездарность) Если вы вайбкодите свой сайт и у вас фронт на реакте, а с вероятностью 99% нейронка вам его сразу на реакте и делала, то с 🟠🟠🟠 большие проблемы и сайт не будет продвигаться в поисковой выдаче (что для меня

😁1

2 Aug 2026, 12:14 UTC103 views4 reactionsread 8 August 2026
Forwarded from @Ai_bolno_ml

В чем самый мощный профит смотреть записи докладов с конференций 🟠На днях друг проходил NLP Сис Диз в Авито💚. 🟠Друг НЕ пошел со мной в субботу на Тиньков Turbo ML conf и УГАДАЙТЕ какой продукт показывала на демо стенде команда Авито - даааа тот же про который был системный дизайн - вопрос с Сис Диза раскрывать не буду пока потом будет отдельный ютуб разбор На конфе они приводили там метрики, что система на 13% уско

👍4

Showing the 12 most recent of 20 posts we hold for @erdeni_ai. 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.

Mentions

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

“Эрдэни GenAI финтех” (@erdeni_ai), 634 subscribers as measured 15 August 2026. Telegram Register, tgregister.com/channel/erdeni_ai.

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.