Telegram RegisterThe public register of Telegram
Telegram profile photo for Neural Networks | Нейронные сети

Channel

Neural Networks | Нейронные сети

@neural

On this record: Topic · Growth · Engagement · What this channel posts · Posts · Citations · Telegram's recommendations · Cite this entry

11,595subscribers

+129 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1001083894110
TypeChannel
Username@neural
Created28 November 2016 — measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded7 August 2026
Last confirmed live25 September 2026
Measurements held26
Confirmed unchanged1 time, most recently 25 September 2026
On Telegramt.me/neural

Topic

Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 11 September 2026 and assigned it the closest of 31 fixed categories, at 100% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.

Growth

11,46611,59511,530.57 August 2026 — 11,466 subscribers7 August 2026 — 11,466 subscribers7 August 2026 — 11,467 subscribers11 August 2026 — 11,494 subscribers15 August 2026 — 11,491 subscribers17 August 2026 — 11,492 subscribers19 August 2026 — 11,496 subscribers20 August 2026 — 11,501 subscribers21 August 2026 — 11,509 subscribers22 August 2026 — 11,517 subscribers24 August 2026 — 11,527 subscribers25 August 2026 — 11,528 subscribers26 August 2026 — 11,525 subscribers28 August 2026 — 11,522 subscribers29 August 2026 — 11,529 subscribers31 August 2026 — 11,542 subscribers1 September 2026 — 11,547 subscribers2 September 2026 — 11,551 subscribers3 September 2026 — 11,553 subscribers5 September 2026 — 11,548 subscribers8 September 2026 — 11,555 subscribers11 September 2026 — 11,564 subscribers13 September 2026 — 11,565 subscribers14 September 2026 — 11,581 subscribers16 September 2026 — 11,590 subscribers25 September 2026 — 11,595 subscribers7 August 202625 September 2026
26 measurements spanning 50 days, net +129. 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 11,447–11,614 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 26
Measured (UTC)SubscribersChange
25 Sept 2026, 17:2111,595+5
16 Sept 2026, 17:5911,590+9
14 Sept 2026, 17:3711,581+16
13 Sept 2026, 02:1511,565+1
11 Sept 2026, 05:2111,564+9
8 Sept 2026, 10:3611,555+7
5 Sept 2026, 06:2211,548-5
3 Sept 2026, 09:1411,553+2
2 Sept 2026, 02:2711,551+4
1 Sept 2026, 03:3711,547+5
31 Aug 2026, 03:2511,542+13
29 Aug 2026, 04:2411,529+7
28 Aug 2026, 02:4611,522-3
26 Aug 2026, 05:2711,525-3
25 Aug 2026, 07:4211,528+1
24 Aug 2026, 05:3711,527+10
22 Aug 2026, 13:3511,517+8
21 Aug 2026, 07:1611,509+8
20 Aug 2026, 05:5211,501+5
19 Aug 2026, 06:2411,496first reading

Engagement

23 posts held, back to 11 June 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 44 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
5.65%
avg views ÷ 11,595 subscribers
Avg views / post
655
1 post measured
Reaction rate
—
this channel exposes no reaction counts
Posts in window
1
of 23 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 31 August 2026
Posts held23 (11 June 2026 – 31 August 2026)
Views total655
Reactions total—
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken2 Sept 2026, 23:42 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
3m 56s
Average length
1m 58s

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.

Recent posts

25 Aug 2026, 11:45 UTC≈1,150 viewsread 2 September 2026
Photo

Бесплатный доступ к ChatGPT и Claude без аккаунта? 👀 DuckDuckGo добавил в Duck.ai несколько популярных AI-моделей: Claude Haiku 4.5, GPT-5.4 mini, Mistral Small 4, gpt-oss-120b и Gemma 4 31B. Можно переключаться между разными моделями прямо в одном диалоге: если одна AI застряла на задаче, передать разговор другой. DuckDuckGo делает упор на приватность: запросы проходят через анонимизирующий прокси, а провайдеры м…

15 Aug 2026, 11:13 UTC≈1,380 viewsread 2 September 2026
Photo

⚡️ Harness Engineering - полный курс на русском Как заставить AI-агента писать код надёжно. Не «какую модель выбрать», а как спроектировать вокруг неё рабочую систему: инструкции, инструменты, среду, состояние и верификацию. Курс - от нуля до продвинутых тем: теоретическая база из 11 блоков, 14 модулей, 8 практических проектов, 14 лабораторных работ, диагностический протокол «как починить агента», библиотека готовы…

9 Aug 2026, 10:37 UTC≈1,550 viewsread 2 September 2026
Photo

Видеокарта RTX 5090 стоит уже дороже трёшки в Воркуте — цена доходит до 500–620 тысяч рублей. За эти же деньги на севере можно купить полноценную трёхкомнатную квартиру площадью до 70 квадратных метров.

5 Aug 2026, 10:00 UTC≈1,560 viewsread 2 September 2026
Photo

🚨 Белый дом подготовил новые правила проверки передовых ИИ-моделей Разработчики закрытых моделей смогут добровольно передавать их правительству США за 30 дней до публичного релиза. Проверка будет проходить в защищённой среде с жёстким контролем доступа и журналированием каждого пользователя. Главное исключение — модели с открытыми весами. Правила распространяются только на закрытые продукты и прямо не должны ограни…

3 Aug 2026, 11:26 UTC≈1,360 viewsread 2 September 2026
Photo

Новую версию легенды подвезли

27 Jul 2026, 21:36 UTC≈1,700 viewsread 2 September 2026
Photo

🔥 Илья Суцкевер готов масштабировать SSI в 10 раз - NVIDIA вложит около $5 млрд Safe Superintelligence и NVIDIA объявили о долгосрочном партнёрстве. SSI получит доступ к новейшим системам Vera Rubin, что должно увеличить её вычислительные мощности на порядок. NVIDIA также инвестирует в компанию: официально сумму не раскрывают, но Financial Times и Reuters называют около $5 млрд. До этого SSI в основном опиралась на…

22 Jul 2026, 14:50 UTC≈1,530 viewsread 2 September 2026
Photo

Как алгоритмы и ИИ за несколько лет превратили Месси из героя в «злодея» После ЧМ-2022 Месси был национальным героем. К 2026 году соцсети заполнили ролики, где его называют симулянтом, провокатором и любимчиком судей. На поле изменилось немного. Изменилась машина распространения контента. Схема работает просто: берётся реальный эпизод, из него убирается контекст, затем создаются сотни похожих нарезок. Алгоритмы про…

21 Jul 2026, 13:39 UTC≈1,340 viewsread 2 September 2026
Photo

Как LLM генерирует ответ: вся магия - в одном цикле Вышло понятное введение в инференс - процесс, который происходит, когда уже обученная модель отвечает на запрос. Упрощённо работу LLM можно представить так: nextToken(input, frozenWeights) → token Модель получает текст и неизменяемые веса, предсказывает следующий токен, добавляет его к контексту и повторяет вычисление: prompt → token → token → token → ... …

16 Jul 2026, 20:43 UTC≈1,240 viewsread 2 September 2026
Forwarded from @ai_machinelearning_big_data

Kimi K3 только что появилась в Kimi Code CLI В документации Kimi Code уже есть новая модель Kimi K3 - её называют самым сильным flagship-моделем Kimi на сегодня. Упор: кодинг, игры/3D и knowledge-задачи. Что интересно по спекам: * model ID: k3 * контекст: до 1M токенов * reasoning сейчас только на max * low и high обещают добавить позже * на Moderato доступно до 256K * до 1M открывается на Allegretto и выше Перек…

15 Jul 2026, 10:34 UTC≈1,270 viewsread 2 September 2026
Photo

Исследователи дали coding agent задачу собрать training environment и научить vision-модель считать цветные звёзды. С ограничением по времени. Агент работал через autoresearch-пайплайн на NeMo RL, NeMo Gym и reusable skills. Он сам поднимал окружение, запускал обучение, проверял результаты и двигал эксперимент дальше, пока исследователь только направлял процесс. Результат: Qwen3-VL-2B поднялась с 25% до 96.9% accu…

14 Jul 2026, 11:16 UTC≈1,100 viewsread 2 September 2026
Forwarded from @machinelearning_booksPhoto

Spatially Speculative Decoding ускоряет авторегрессионные image-модели до 13.3×. Идея простая: перестать делать вид, что картинка - это просто длинная строка токенов. Обычно AR image-модель разворачивает 2D-изображение в последовательность и генерирует её токен за токеном. Это работает, но убивает скорость: каждый следующий шаг ждёт предыдущий. SSD добавляет маленькие draft-heads, которые используют пространственн…

Showing the 12 most recent of 23 posts we hold for @neural. 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

Republished by

Channels on the register that have forwarded this channel's posts into their own feed.

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 4 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.

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.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

Искусственный интеллект. Высокие технологии
@vistehno · 71,491
Telegram ranks this channel #5 of 94 here — alongside 93 others — read 20 August 2026
Data Science
@datascienceiot · 42,634
Telegram ranks this channel #16 of 76 here — alongside 75 others — read 29 August 2026
Python вопросы с собеседований
@python_job_interview · 24,881
Telegram ranks this channel #23 of 90 here — alongside 89 others — read 15 September 2026
Data Science. SQL hub
@sqlhub · 35,960
Telegram ranks this channel #27 of 87 here — alongside 86 others — read 2 September 2026
Анализ данных (Data analysis)
@data_analysis_ml · 50,656
Telegram ranks this channel #27 of 93 here — alongside 92 others — read 25 August 2026
Python/ django
@pythonl · 58,866
Telegram ranks this channel #36 of 84 here — alongside 83 others — read 22 August 2026
Нейроскептик
@neuroskep · 29,484
Telegram ranks this channel #42 of 90 here — alongside 89 others — read 8 September 2026
Machine learning Interview
@machinelearning_interview · 30,308
Telegram ranks this channel #68 of 98 here — alongside 97 others — read 7 September 2026
DevOps
@DevOPSitsec · 23,684
Telegram ranks this channel #77 of 90 here — alongside 89 others — read 18 September 2026
Artificial Intelligence && Deep Learning
@DeepLearning_ai · 57,432
Telegram ranks this channel #85 of 87 here — alongside 86 others — read 23 August 2026
Data Science Jobs
@datascienceml_jobs · 21,227
Telegram ranks this channel #86 of 97 here — alongside 96 others — read 24 September 2026

This channel appears in 11 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

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 25 September 2026 — this entry's latest reading, not the date you are reading this.

“Neural Networks | Нейронные сети” (@neural), 11,595 subscribers as measured 25 September 2026. Telegram Register, tgregister.com/channel/neural.

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.