Telegram RegisterThe public register of Telegram

Channel

Машинное обучение / ИИ Бибилиотека

@machinelearning_library

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

125subscribers

+0 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-1002366958083
TypeChannel
Username@machinelearning_library
DescriptionКниги по машинному обучению
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 live9 August 2026
Measurements held2
On Telegramt.me/machinelearning_library

Growth

1257 August 2026 — 125 subscribers9 August 2026 — 125 subscribers7 August 20269 August 2026
2 measurements spanning 2 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 124–126 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
9 Aug 2026, 02:46125no change
7 Aug 2026, 14:08125first reading

Engagement

8 posts held, back to 4 April 2025the 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.

Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 8 posts for this entry, the most recent from 16 May 2026. An engagement rate over an empty window would be a number about nothing.

What this channel posts

Photos
77
Videos
3
Links
62

Lifetime counters from Telegram’s own channel header, read 9 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.

Video runtime
27m 38s
Average length
9m 13s

Measured directly from 3 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

12 reactions across 5 posts, in 3 distinct kinds. The most used accounts for 58.3% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
758.3%
🔥433.3%
👍18.33%

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 5 of the 8 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 12reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 8 most recent posts we hold, published 4 April 2025 to 16 May 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

16 May 2026, 09:16 UTC47 views1 reactionsread 9 August 2026
Forwarded from @rust_codePhoto

👣 Я заставил LLM писать Rust полгода. Вот что они стабильно ломают Полгода я использовал Claude, GPT и Cursor как основной инструмент для написания Rust-кода в проде. Не как «помощник для бойлерплейта», а как полноценного второго разработчика на монолите примерно в 80 тысяч строк (бэкенд обработки потоковых данных, tokio, sqlx, немного unsafe в hot path). Доля сгенерированного кода в коммитах последних шести месяце

1

31 Jan 2026, 08:06 UTC84 viewsread 9 August 2026
Photo

💡 Новое исследование предупреждает о преступном использовании тысяч развертываний открытых моделей ИИ. В ходе 293-дневного наблюдения выяснилось, что 7,5 % системных промптов пропускают вредоносную активность, а хосты сосредоточены в основном в Китае и США. Многие установили Ollama для локального запуска ИИ и оставили его доступным из сети. Сканирование в течение 293 дней выявило 175 108 публичных серверов Ollama

9 Dec 2025, 05:16 UTC86 views1 reactionsread 9 August 2026
Forwarded from @ai_machinelearning_big_dataVideo

✔️ Релиз GLM-4.6V с нативной поддержкой вызова функций. В линейку вошли флагманская GLM-4.6V на 106 млрд. параметров и облегченная GLM-4.6V-Flash (9B). Обе получили контекстное окно в 128k токенов и генерацию смешанного контента, где текст комбинируется с изображениями. Модель может передавать изображения и скриншоты во внешние инструменты без предварительной конвертации в текст, а также встраивать визуальные резуль

👍1

25 Oct 2025, 11:27 UTC90 viewsread 9 August 2026
Forwarded from @ai_machinelearning_big_dataVideo

✔️ IBM совершила прорыв в квантовых вычислениях: на обычных FPGA-чипах Всего через два дня после новости от Google - ещё один крупный квантовый прорыв. IBM заявила, что один из её ключевых алгоритмов квантовой коррекции ошибок теперь способен работать в реальном времени на FPGA-чипах AMD, без использования экзотического оборудования. Это делает квантовые вычисления быстрее, дешевле и ближе к практическому примене

6 Aug 2025, 10:14 UTC108 viewsread 9 August 2026
Forwarded from @ai_machinelearning_big_dataPhoto

🖥 gpt-oss работает на специальном формате промптов — Harmony, и без него модель просто не будет выдавать корректные ответы. Зачем нужен Harmony? Этот формат нужен для: — 🧠 генерации chain of thought рассуждений — 🔧 корректного вызова функций и использования инструментов — 📦 вывода в разные каналы: обычный ответ, reasoning, tool call — 🗂️ поддержки tool namespaces и иерархических инструкций 💡 Harmony имитирует OpenA

3 Jul 2025, 09:06 UTC132 views5 reactionsread 9 August 2026
Forwarded from @ai_machinelearning_big_dataPhoto

🌟 LLM Speedrunning Benchmark: ИИ-ассистенты пока не способны улучшить код, написанный человеком. Пока одни восхищаются способностью ИИ писать код по текстовому описанию, в компании Марка Цукерберга решили устроить ему настоящее испытание на профессионализм и создали «The Automated LLM Speedrunning Benchmark» — полигон, где нейросетям предлагается не просто написать что-то с нуля, а воспроизвести и улучшить уже сущес

3🔥2

28 Apr 2025, 10:50 UTC147 views2 reactionsread 9 August 2026
Forwarded from @ai_machinelearning_big_dataPhoto

🦾 Berkeley Humanoid Lite — открытый человекоподобный робот Калифорнийский университет Беркли представил проект Humanoid Lite — результат многолетних исследований и экспериментов по созданию простых в производстве человекоподобных роботов. Платформа полностью придерживается принципов Open Hardware: в ней используются свободно распространяемое ПО, серийные комплектующие, доступные в розничной продаже, а также детали,

🔥2

4 Apr 2025, 09:52 UTC240 views3 reactionsread 9 August 2026
Photo

🔥 «Упражнения по машинному обучению» В этой книге более 75 упражнений. И она абсолютно БЕСПЛАТНА. 🔗 Книга 🔗 GitHub @machinelearning_books

3

Showing the 8 most recent of 8 posts we hold for @machinelearning_library. 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

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

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

“Машинное обучение / ИИ Бибилиотека” (@machinelearning_library), 125 subscribers as measured 9 August 2026. Telegram Register, tgregister.com/channel/machinelearning_library.

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.