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Channel

Neural Networks | Нейронные сети

@neural

On this record: Topic · Growth · Engagement · What this channel posts · Posts · Citations · Handles named that no longer answer · Telegram's recommendations · Cite this entry

11,724subscribers

+258 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
DescriptionВсе о машинном обучении По всем вопросам - @notxxx1 № 4959169263
Created28 November 2016 — measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded7 August 2026
Last confirmed live10 November 2026
Measurements held52
Confirmed unchanged2 times, most recently 10 November 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 the other topics.

Growth

11,46611,74411,6057 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 subscribers4 October 2026 — 11,582 subscribers8 October 2026 — 11,586 subscribers13 October 2026 — 11,595 subscribers14 October 2026 — 11,592 subscribers15 October 2026 — 11,588 subscribers16 October 2026 — 11,587 subscribers18 October 2026 — 11,610 subscribers19 October 2026 — 11,614 subscribers20 October 2026 — 11,618 subscribers21 October 2026 — 11,624 subscribers22 October 2026 — 11,733 subscribers23 October 2026 — 11,734 subscribers24 October 2026 — 11,736 subscribers25 October 2026 — 11,738 subscribers26 October 2026 — 11,741 subscribers27 October 2026 — 11,743 subscribers28 October 2026 — 11,744 subscribers29 October 2026 — 11,739 subscribers30 October 2026 — 11,742 subscribers31 October 2026 — 11,744 subscribers1 November 2026 — 11,743 subscribers2 November 2026 — 11,741 subscribers3 November 2026 — 11,734 subscribers4 November 2026 — 11,732 subscribers6 November 2026 — 11,729 subscribers8 November 2026 — 11,724 subscribers11,7247 August 20268 November 2026
52 measurements spanning 94 days, net +258. 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 11,424–11,786 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 52
Measured (UTC)SubscribersChange
8 Nov 2026, 16:5911,724-5
6 Nov 2026, 14:0111,729-3
4 Nov 2026, 15:5811,732-2
3 Nov 2026, 13:1911,734-7
2 Nov 2026, 10:4611,741-2
1 Nov 2026, 09:0711,743-1
31 Oct 2026, 11:0011,744+2
30 Oct 2026, 14:2611,742+3
29 Oct 2026, 13:0011,739-5
28 Oct 2026, 12:5011,744+1
27 Oct 2026, 10:4011,743+2
26 Oct 2026, 09:4511,741+3
25 Oct 2026, 10:5811,738+2
24 Oct 2026, 11:0011,736+2
23 Oct 2026, 14:0611,734+1
22 Oct 2026, 16:4611,733+109
21 Oct 2026, 09:3011,624+6
20 Oct 2026, 07:2811,618+4
19 Oct 2026, 08:0711,614+4
18 Oct 2026, 11:0011,610first reading

Engagement

47 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 61 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
8.54%
avg views ÷ 11,724 subscribers
Avg views / post
1,000
16 posts measured
Reaction rate
—
this channel exposes no reaction counts
Posts in window
16
of 47 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 9 November 2026
Posts held47 (11 June 2026 – 9 November 2026)
Views total16,019
Reactions total—
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken11 Nov 2026, 09:07 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

Photos
≈943
Videos
≈207
Links
≈9,560

Lifetime counters from Telegram’s own channel header, read 11 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.

Video runtime
13m 39s
Average length
2m 44s

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.

Recent posts

Telegram’s own ‘edited’ marker: 1 of 16 posts held from the last 30 days (6.25%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).

8 Nov 2026, 10:06 UTC404 viewsread 11 November 2026
Forwarded from @ai_machinelearning_big_dataPhoto

📌 Тест моделей OpenAI и Anthropic на социальную дедукцию Подкастер Дваркеш Патель потратил 20 000 долларов на API, организовав турнир среди 11 моделей по игре Blood on the Clocktower, усложненного аналога Мафии. 🟡Правила игры Весь смысл - в борьбе за информацию. Участвуют несколько игроков и ведущий, который раздаёт тайные роли и сам ни за кого не играет. Меньшинство - злые: демон и двое помощников, они с самого …

3 Nov 2026, 09:48 UTC≈1,050 viewsread 11 November 2026
Video

ИИ-агент удалил базу за 9 секунд. И бэкапы тоже? Агента в Cursor попросили починить ключи доступа, а он одним API-запросом снёс продовый том стартапа PocketOS вместе с бэкапами. Разбираем, почему агенту нельзя давать ключи от прода.**ИИ-агент удалил продовую базу за 9 секунд. И бэкапы тоже** Разработчик стартапа PocketOS попросил агента в Cursor исправить проблему с ключами доступа в тестовом окружении. Но агент на…

31 Oct 2026, 14:57 UTC≈1,100 viewsread 11 November 2026
Photo

ИИ опроверг гипотезу, на которой 27 лет строилась теоретическая информатика OpenAI и Anthropic независимо заявили, что их AI-модели нашли способ опровергнуть Exponential Time Hypothesis (ETH) - гипотезу 1999 года, на которой основаны тысячи исследований сложности алгоритмов. В чём суть? Есть задача 3-SAT: подобрать значения true и false для переменных так, чтобы логическое выражение стало истинным. ETH предполага…

29 Oct 2026, 09:52 UTC970 viewsread 11 November 2026

Qwenputer - редакция AI-модели Qwen3.5, способная симулировать RISC-V CPU для выполнения кода на C и MicroPython В рамках проекта Qwenputer исследователи подготовили расширенный вариант AI-модели Qwen3.5-4B, в котором реализован встроенный симулятор полноценного 32-разрядного CPU на базе архитектуры RISC-V (RV32IM), поддерживающего расширение Zba и оснащённого сопроцессором bignum для арифметических операций с больш…

28 Oct 2026, 19:58 UTC≈1,030 viewsread 11 November 2026

Отдельный Git worktree защищает ваши изменения от ИИ-агента. Но не изолирует от него компьютер. Агент по-прежнему может выйти за пределы каталога, прочитать другие файлы или обратиться к сети – если клиент не ограничивает это отдельно. JetBrains рассказала, как решает эту задачу в облачных агентах Air: - На каждую задачу создаётся новая виртуальная машина, которая не используется повторно. - Агент работает в Docke…

28 Oct 2026, 15:46 UTC≈1,190 viewsread 11 November 2026
Photo

Opus 5.5 вскрывает любую программу: реверс-инжиниринг с ИИ Как с помощью Claude Opus 5.5, REA и Ghidra разобрать закрытый бинарник, восстановить логику и собрать свою чистую реализацию. https://uproger.com/opus-5-5-reverse-engineering-rea/

24 Oct 2026, 20:59 UTC≈1,330 viewsread 11 November 2026
Photo

OpenAI переманивает крупных клиентов Anthropic скидками до 40%, пишет The Information. По данным издания, Replit получила скидку более 30% по контракту стоимостью свыше $100 млн в год и перевела основную часть расходов на OpenAI. Изменение кода заняло несколько часов. В Rippling доля OpenAI в задачах программирования с мая выросла с 16% до 52%, а Anthropic — упала с 57% до 18%. DataCamp предложили 40% скидки при и…

23 Oct 2026, 13:15 UTC967 viewsread 11 November 2026
Forwarded from @ai_machinelearning_big_dataVideo

✔️ OpenAI обязала шлюзы и роутеры моделей проходить проверку С 20 ноября сторонние сервисы, дающие доступ к API, то есть шлюзы, роутеры и реселлеры, смогут работать только после одобрения компании. Базовых правил уже недостаточно, от них требуют фильтры безопасности, согласование блокировок и разбор случаев вредоносного использования API. Вместе с этим введена и система SafetyID, через которую партнёр будет изолир…

19 Oct 2026, 10:12 UTC≈1,160 viewsread 11 November 2026
Photo

Модели решений: Drex 2.0 против Jev Nace AI выпустила Drex 2.0, MoE на 35B параметров для решений внутри ИИ-систем. Сравниваем с Jev, разбираем бенчмарки и сценарии: фильтр кода, экономия на вызовах LLM, договоры. В архитектуре ИИ-систем набирает силу отдельный класс моделей: модели решений. Они не пишут текст и не рассуждают вслух, а получают контекст и выбирают один вариант из заранее заданного набора. Главным ор…

18 Oct 2026, 10:47 UTC≈1,050 viewsread 11 November 2026
Forwarded from @ai_machinelearning_big_dataVideo

🪰 «Матрица» для мухи: симуляцию мозга подключили к миру, созданному ИИ Odyssey заявила, что подключила программную модель мозга плодовой мушки к Odyssey-3. По словам команды, теперь она самостоятельно перемещается по сгенерированным виртуальным мирам. Odyssey-3 создаёт интерактивную среду, которая меняется в ответ на действия внутри неё. Это позволяет исследовать поведение агента в окружении, которого никогда не су…

Showing the 12 most recent of 47 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 · 70,139
Telegram ranks this channel #5 of 94 here — alongside 93 others — read 20 August 2026
Data Science
@datascienceiot · 42,496
Telegram ranks this channel #16 of 76 here — alongside 75 others — read 29 August 2026
Python вопросы с собеседований
@python_job_interview · 24,906
Telegram ranks this channel #23 of 90 here — alongside 89 others — read 15 September 2026
Data Science. SQL hub
@sqlhub · 36,025
Telegram ranks this channel #27 of 87 here — alongside 86 others — read 2 September 2026
Анализ данных (Data analysis)
@data_analysis_ml · 51,923
Telegram ranks this channel #27 of 93 here — alongside 92 others — read 25 August 2026
Python/ django
@pythonl · 58,287
Telegram ranks this channel #36 of 84 here — alongside 83 others — read 22 August 2026
Data Science | Machinelearning [ru]
@devsp · 19,659
Telegram ranks this channel #38 of 91 here — alongside 90 others — read 30 September 2026
Нейроскептик
@neuroskep · 29,744
Telegram ranks this channel #42 of 90 here — alongside 89 others — read 8 September 2026
Machine learning Interview
@machinelearning_interview · 30,560
Telegram ranks this channel #68 of 98 here — alongside 97 others — read 7 September 2026
DevOps
@DevOPSitsec · 23,892
Telegram ranks this channel #77 of 90 here — alongside 89 others — read 18 September 2026
DataFlair
@dataflair · 18,577
Telegram ranks this channel #78 of 83 here — alongside 82 others — read 4 October 2026
Artificial Intelligence && Deep Learning
@DeepLearning_ai · 56,955
Telegram ranks this channel #85 of 87 here — alongside 86 others — read 23 August 2026
Data Science Jobs
@datascienceml_jobs · 21,642
Telegram ranks this channel #86 of 97 here — alongside 96 others — read 24 September 2026

This channel appears in 13 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 8 November 2026 — this entry's latest reading, not the date you are reading this.

“Neural Networks | Нейронные сети” (@neural), 11,724 subscribers as measured 8 November 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.