😈 Челлендж по запуску 12 простых IT-проектов за 12 месяцев Уже 3 года как ребята из комьюнити инди-хакеров запускают 1 простой продукт в онлайне каждый месяц. И в реальном времени показывать: как разрабатывают, продвигают и сколько получилось заработать на запусках таких микро-проектов. Например, вот 👉 Telegram-бот для ИИ-фотосессий, который заработал $280К за полгода. Он не пытается заменить большие ИИ-сервисы, а…

Channel
Generative Ai
@deeplearning_ru
On this record: Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Citations · Cite this entry
3,717subscribers
+52 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 | -1001089529223 |
|---|---|
| Type | Channel |
| Username | @deeplearning_ru |
| Created | 16 August 2016 — measured — cross-checked against a third-party dataset (ext.tg_channel) |
| First recorded | 6 August 2026 |
| Last confirmed live | 12 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 12 August 2026 |
| On Telegram | t.me/deeplearning_ru |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Aug 2026, 12:58 | 3,717 | +6 |
| 9 Aug 2026, 05:02 | 3,711 | +46 |
| 6 Aug 2026, 04:33 | 3,665 | no change |
| 6 Aug 2026, 00:39 | 3,665 | first reading |
Engagement
19 posts held, back to 19 March 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 3 pagesof Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 13.7%
- avg views ÷ 3,717 subscribers
- Avg views / post
- 510
- 3 posts measured
- Reaction rate
- —
- this channel exposes no reaction counts
- Posts in window
- 3
- 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.
| Window | Rolling 30 days · latest post in window 3 August 2026 |
|---|---|
| Posts held | 19 (19 March 2026 – 3 August 2026) |
| Views total | 1,530 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Aug 2026, 22:57 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
- 4m 01s
- Average length
- 48s
Measured directly from 5 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
101 reactions across 14 posts, in 7 distinct kinds. The most used accounts for 42.6% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 43 | 42.6% | |
| 🤯 | 22 | 21.8% | |
| 🔥 | 14 | 13.9% | |
| 👍 | 12 | 11.9% | |
| 😁 | 7 | 6.93% | |
| 🤔 | 2 | 1.98% | |
| 🥰 | 1 | 0.99% |
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 15 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 101reactions 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 19 March 2026 to 3 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
- 1
- across the posts below
- Posts paid on
- 1
- of 19 we hold a reading for · 5%
- Most on one post
- 1
- single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @deeplearning_ru. 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 19 most recent posts we hold for this entry, published 19 March 2026 to 3 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
Вчера Anthropic поделились историей: после взлома HuggingFace одной из моделей OpenAI они начали масштабную ретроспективную проверку своих запусков Claude. В ходе проверки тестов обнаружили три инцидента, в которых модель Claude вышла в интернет, а затем получила несанкционированный доступ к реальным системам трех разных организаций. Во всех трех случаях перед Claude была поставлена задача в формате «Захват флага» —…
Moonshot, как и обещали, открыли веса Kimi K3 😏 Модель уже доступна на Hugging Face. Kimi K3 — их самая мощная модель: 2,8 трлн параметров в MoE-архитектуре, нативное понимание изображений и контекстное окно на 1 млн токенов. Причём открыли не только веса. Moonshot также выложили часть стека вокруг K3: высокопроизводительные attention-ядра, библиотеку коммуникации для MoE и инфраструктуру для масштабного запуска а…
Anthropic описала четыре типа циклов, которые можно строить с помощью Claude. Каждый из них передаёт агенту разную часть процесса принятия решений. Термин loop engineering сейчас используется настолько широко, что стоит чётко понимать, чем именно отличаются эти четыре варианта: что запускает цикл и что определяет момент его завершения. > Цикл на основе запросов. Вы отправляете запрос, Claude выполняет задачу, самос…
❤2🔥2🥰1
Нашёл отличную книгу — The Hitchhiker’s Guide to Agentic AI, которая охватывает практически весь стек Agentic AI. Главная ценность книги - это широкий обзор всего направления: архитектура LLM, обучение моделей, методы обучения с подкреплением, системы инференса, оценка моделей, агентные системы и многое другое. Лучше всего использовать её как карту знаний. Сначала просмотреть оглавление, найти темы, в которых есть п…
Знакомьтесь: Clips. Бесплатная открытая замена Loom, заточенная под агентов. 😋 В отличие от Loom, агент понимает Clips просто по ссылке. Каждый клип содержит API и метаданные, благодаря которым агент может изучить его содержимое. Агенты видят и слышат не только транскрипт, а вообще всё, что происходит на видео в любой момент времени. Делишься баг-репортом, фидбеком, анализом — и передаёшь это агенту, чтобы он улучш…
Вышел Harness-1 — поисковый агент на 20B параметров с довольно необычной идеей. Вместо того чтобы заставлять модель хранить всю историю поиска в контексте, авторы решили вынести состояние наружу и обучить модель работать через специальный harness. Получился агент на 20B параметров, который на длинных поисковых задачах конкурирует с гораздо более крупными моделями. Обычно поисковые агенты работают по схеме: поиск →…
❤7👍2
Если у вас есть видеокарта с 8 ГБ VRAM, то у меня для вас хорошие новости. Вчера чувак тестировал Unsloth Gemma 4 12B Q4_K_XL на карте с 8 ГБ VRAM. Народ был в шоке и сразу спросил: А 25B+ модель на бюджетной карте вообще реально запустить? Оказалось — да. Чувак запускает локально огромную MoE-модель на 26 миллиардов параметров на обычном ноутбуке с RTX 4060 8 ГБ и 16 ГБ оперативки. Что по скорости: - стабильные…
🤯6❤5😁2
Посмотрел выступление Anthropic про то, как они собирают агентов, которые могут работать часами. Схема такая: planner → agent → evaluator Это маленькая продуктовая команда из агентов. У каждого своя роль, свой контекст и своя зона ответственности. Но есть важные нюансы. 1. Planner: верхний план и спринты Planner получает короткий запрос и превращает его в структуру работы: — что собираем — какие большие части н…
🔥3❤2🤔1
Антропики выкатили Claude Opus 4.8. Доступна уже сегодня по той же цене 🎉 Из самого интересного – заметно подтянули честность модели. По словам Anthropic, Opus 4.8 примерно в 4 раза реже пропускает баги в коде, который написал сам, и не пытается выдать сломанное решение за рабочее. Также в Claude Code появилась новая фича: dynamic workflows (research preview) – для самых сложных задач Claude строит план, запускает …
🤯4❤2
Microsoft открыла исходный код Webright. Это скилл для ИИ-агентов, позволяющий управлять браузерами. Фишка в том, что внутри он использует Playwright, генерирует код на лету, а производительность просто бешеная. 🧑🍳
🤯4🔥2
🔥 AlphaProof Nexus: формальные доказательства начинают превращаться в инженерный пайплайн Google DeepMind показали AlphaProof Nexus - систему, которая автономно закрыла 9 открытых задач Эрдёша, часть из которых висела десятилетиями. По оценке авторов, стоимость решения одной задачи составила всего несколько сотен долларов. Кроме этого, система доказала 44 открытые гипотезы из OEIS, закрыла 15-летний вопрос в алгебр…
❤4👍3🔥2
Showing the 12 most recent of 19 posts we hold for @deeplearning_ru. 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 — 280,970 of 1,151,006entries 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 2 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.
Named by
Channels on the register whose posts name this channel's handle.
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 12 August 2026 — this entry's latest reading, not the date you are reading this.
“Generative Ai” (@deeplearning_ru), 3,717 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/deeplearning_ru.
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.