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Channel

AI/ML Ready | Нейросети

@neuro_ready

On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Posts recorded as deleted · Citations · Cite this entry

14,104subscribers

+2,284 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-1002537147515
TypeChannel
Username@neuro_ready
DescriptionАвторский канал про искусственный интеллект и машинное обучение. Нейросети, гайды, советы, задачи, промпты и шпаргалки. Обновления каждый день! Cотрудничество: @energy_c РКН: https://clck.ru/3QREEC
CreatedBetween 1 March 2025 and 31 July 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live6 November 2026
Measurements held59
Confirmed unchanged1 time, most recently 6 November 2026
On Telegramt.me/neuro_ready

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

11,82014,10412,9627 August 2026 — 11,820 subscribers8 August 2026 — 11,820 subscribers10 August 2026 — 11,834 subscribers11 August 2026 — 12,111 subscribers12 August 2026 — 12,330 subscribers13 August 2026 — 12,372 subscribers14 August 2026 — 12,397 subscribers16 August 2026 — 12,420 subscribers17 August 2026 — 12,482 subscribers18 August 2026 — 12,582 subscribers19 August 2026 — 12,664 subscribers20 August 2026 — 12,715 subscribers21 August 2026 — 12,746 subscribers22 August 2026 — 12,772 subscribers24 August 2026 — 12,792 subscribers25 August 2026 — 12,825 subscribers26 August 2026 — 12,827 subscribers27 August 2026 — 12,844 subscribers28 August 2026 — 12,825 subscribers29 August 2026 — 12,838 subscribers30 August 2026 — 12,883 subscribers31 August 2026 — 12,888 subscribers1 September 2026 — 12,885 subscribers2 September 2026 — 12,881 subscribers4 September 2026 — 12,878 subscribers7 September 2026 — 12,879 subscribers10 September 2026 — 13,062 subscribers12 September 2026 — 13,087 subscribers14 September 2026 — 13,093 subscribers15 September 2026 — 13,105 subscribers18 September 2026 — 13,115 subscribers30 September 2026 — 13,416 subscribers5 October 2026 — 13,500 subscribers10 October 2026 — 13,508 subscribers13 October 2026 — 13,517 subscribers14 October 2026 — 13,503 subscribers15 October 2026 — 13,517 subscribers16 October 2026 — 13,650 subscribers17 October 2026 — 13,675 subscribers18 October 2026 — 13,716 subscribers19 October 2026 — 13,747 subscribers20 October 2026 — 13,772 subscribers22 October 2026 — 13,774 subscribers23 October 2026 — 13,795 subscribers24 October 2026 — 13,805 subscribers24 October 2026 — 13,794 subscribers26 October 2026 — 13,797 subscribers27 October 2026 — 13,791 subscribers27 October 2026 — 13,785 subscribers28 October 2026 — 13,887 subscribers29 October 2026 — 13,997 subscribers30 October 2026 — 14,046 subscribers31 October 2026 — 14,065 subscribers1 November 2026 — 14,076 subscribers2 November 2026 — 14,087 subscribers3 November 2026 — 14,099 subscribers4 November 2026 — 14,102 subscribers5 November 2026 — 14,099 subscribers6 November 2026 — 14,104 subscribers7 August 20266 November 2026
59 measurements spanning 91 days, net +2,284. 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 11,477–14,447 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 59
Measured (UTC)SubscribersChange
6 Nov 2026, 20:0814,104+5
5 Nov 2026, 17:3414,099-3
4 Nov 2026, 19:4714,102+3
3 Nov 2026, 22:3014,099+12
2 Nov 2026, 21:3614,087+11
1 Nov 2026, 21:4614,076+11
31 Oct 2026, 19:0014,065+19
30 Oct 2026, 21:1014,046+49
29 Oct 2026, 18:4713,997+110
28 Oct 2026, 21:2113,887+102
27 Oct 2026, 23:1913,785-6
27 Oct 2026, 00:4913,791-6
26 Oct 2026, 00:5913,797+3
24 Oct 2026, 22:1513,794-11
24 Oct 2026, 01:4813,805+10
23 Oct 2026, 03:5713,795+21
22 Oct 2026, 04:3013,774+2
20 Oct 2026, 22:5713,772+25
19 Oct 2026, 22:4513,747+31
18 Oct 2026, 23:4413,716first reading

Engagement

73 posts held, back to 25 July 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 64 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
11.6%
avg views ÷ 14,104 subscribers
Avg views / post
1,640
25 posts measured
Reaction rate
0.628%
reactions ÷ views · ER floor
Posts in window
25
of 73 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 6 November 2026
Posts held73 (25 July 2026 – 6 November 2026)
Views total40,916
Reactions total257
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken6 Nov 2026, 20:53 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
≈1,140
Videos
≈73
Links
≈660

Lifetime counters from Telegram’s own channel header, read 6 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
20s
Average length
10s

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.

Reaction mix

983 reactions across 73 posts, in 6 distinct kinds. The most used accounts for 36.5% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍35936.5%
❤31932.5%
🔥26126.6%
👎181.83%
😁131.32%
🤝131.32%

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 73 of the 73 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 983 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 73 most recent posts we hold, published 25 July 2026 to 6 November 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

Telegram’s own ‘edited’ marker: 0 of 25 posts held from the last 30 days (0%) 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).

6 Nov 2026, 07:10 UTC746 views7 reactionsread 6 November 2026

Извлекаем именованные сущности (NER) из текстов договоров с помощью spaCy. Ручной разбор юридических документов для поиска организаций, дат и сумм занимает слишком много времени. Библиотека spaCy позволяет автоматически находить и классифицировать именованные сущности в неструктурированном тексте. Мы напишем Python-скрипт, который загружает языковую модель и распознает ключевые сущности в фрагменте договора. Устано…

❤3👍2🔥2

5 Nov 2026, 06:10 UTC≈1,000 views10 reactionsread 6 November 2026
Photo

🐱 Полезная статья на Habr для тех, кто хочет улучшить навыки в: системной подготовке к ML-собеседованиям и фундаментальном проектировании карьеры в AI! В этой статье: • честный разбор структуры ML-инженерии в 2026 году — от математического фундамента (линейная алгебра, SVD, оптимизаторы) до промышленного кодинга; • практическая декомпозиция 36-недельного пайплайна: как правильно балансировать между классическим ML, …

🔥5👍3❤2

4 Nov 2026, 16:20 UTC≈1,060 views9 reactionsread 6 November 2026

Настраиваем EntityRuler в spaCy для извлечения сущностей под узкую предметную область. Стандартные языковые модели не знают специфические термины, артикулы и номенклатуру узких отраслей. Компонент EntityRuler позволяет задавать точные правила и шаблоны для распознавания кастомных сущностей без долгого дообучения нейросетей. Мы напишем Python-скрипт, который добавит пользовательские шаблоны в пайплайн spaCy и выделит…

❤3👍3🔥3

4 Nov 2026, 14:20 UTC≈1,110 views12 reactionsread 6 November 2026
Photo

Gemini Pro на 24 месяца за 800₽! Вместо 39.000₽ — всего 800₽ ($9.9) за 2 года. По акции сейчас это 33₽ в месяц, в 48 раз выгоднее! ➡Argon 4.0 + Claude Opus 5.5 — для кода ➡Nano Banana 2 Pro — для картинок ➡Генерация видео — ролики и анимации ➡AntiGravity + 5 ТБ облака ➡Opus 5.5 и Sonnet 5.5 ➡Youtube Premium Акция ограничена по времени, дальше будет официальная цена 480$! Приобрести можно в Магазине с 20.000+ поку…

👎8😁3👍1

4 Nov 2026, 08:10 UTC≈1,160 views9 reactionsread 6 November 2026
Photo

📂 Напоминалка по поиску конфликтующих требований с помощью нейросети! Противоречия в технических заданиях могут приводить к ошибкам разработки, проблемам с архитектурой и неожиданным ограничениям при внедрении новых функций. Нейросеть помогает быстрее находить несовместимые условия, выявлять неоднозначности и предлагать варианты решения. На картинке показан пошаговый подход: от подготовки требований и поиска противо…

👍4❤3🔥2

3 Nov 2026, 12:00 UTC≈1,160 views9 reactionsread 6 November 2026

Отслеживаем параметры и метрики обучения моделей с помощью MLflow. При регулярном обучении ML-моделей вручную записывать гиперпараметры и результаты экспериментов становится невозможно. Платформа MLflow позволяет автоматически логировать метрики, сохранять параметры запусков и наглядно сравнивать итерации. Мы напишем Python-скрипт, который фиксирует ход обучения модели в локальном реестре MLflow. Установим пакеты m…

❤4🔥3👍2

3 Nov 2026, 06:10 UTC≈1,180 views5 reactionsread 6 November 2026
Photo

📂 Напоминалка по прогнозированию влияния новых фич на инфраструктуру с помощью нейросети! Новые функции могут увеличивать нагрузку на CPU, память, базы данных и сетевые ресурсы, создавать узкие места и влиять на стабильность сервисов. ИИ помогает заранее оценивать потенциальные риски, прогнозировать рост нагрузки и планировать необходимые изменения. На картинке показано, как использовать нейросеть для анализа логов …

❤2👍2🔥1

2 Nov 2026, 17:03 UTC≈1,250 views8 reactionsread 6 November 2026

Оценка качества RAG-системы без размеченной выборки с помощью Ragas. Ручная проверка ответов RAG-пайплайна отнимает много времени и не масштабируется при изменениях. Библиотека Ragas позволяет автоматизировать оценку релевантности и точности генерации без золотого датасета. Мы напишем Python-скрипт для вычисления метрик качества ответов поисково-генеративной системы. Установим необходимые библиотеки ragas и datasets…

❤4👍3🔥1

2 Nov 2026, 08:10 UTC≈1,440 views12 reactionsread 6 November 2026
Photo

🐱 Феноменальная статья на Habr для тех, кто хочет улучшить навыки в: стандартизации взаимодействия ИИ-агентов и интеграции внешних инструментов! В этой статье: • разбор архитектуры Model Context Protocol (MCP) — нового открытого стандарта для связи LLM с базами данных, API и локальными сервисами; • практическая реализация MCP-серверов и клиентов: устранение необходимости писать кастомные N-to-N коннекторы под каждую…

🔥6👍4❤2

31 Oct 2026, 18:00 UTC≈1,690 views16 reactionsread 6 November 2026
Photo

Как адаптировать резюме под конкретную вакансию за минуту без ручного переписывания каждого пункта? Рекрутеры и автоматические ATS-системы отсеивают стандартные резюме, которые не содержат точных ключевых слов из описания вакансии. Тратить часы на ручную правку обязанностей утомляет, а языковая модель может за секунды переформулировать ваш опыт под требования конкретного работодателя. Создаем текстовый файл с систе…

❤7👍5🔥4

31 Oct 2026, 14:00 UTC≈1,460 views7 reactionsread 6 November 2026
Photo

📂 Напоминалка по поиску повторяющихся ошибок в проекте с помощью нейросети! Анализ логов, тикетов и сообщений об ошибках помогает находить общие причины сбоев, выявлять слабые места в коде и сокращать количество инцидентов. Нейросеть может группировать похожие ошибки, искать закономерности, предлагать варианты исправления и помогать автоматизировать контроль качества. На картинке показано, как использовать ИИ на все…

❤3👍3🔥1

30 Oct 2026, 07:10 UTC≈1,920 views12 reactionsread 6 November 2026

Прогнозируем продажи с помощью библиотеки Facebook Prophet. Прогнозирование будущих продаж помогает оптимизировать складские запасы и планировать бюджет компании. Библиотека Prophet от Meta позволяет быстро строить модели временных рядов с учетом сезонности и праздников. Мы напишем скрипт на Python, который генерирует исторические данные продаж и прогнозирует тренд на будущее. Для установки необходимых зависимостей…

❤5👍4🔥3

Showing the 12 most recent of 73 posts we hold for @neuro_ready. 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.

Posts recorded as deleted after publishing

We recorded 10 posts on @neuro_ready as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.

A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.

First seen missing
15 August 2026
Most recent seen missing
6 November 2026
Median time before seen missing
3 days
Seen missing in the last 30 days
3

Mentions

Named by 1 registered channel — 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.

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 6 November 2026 — this entry's latest reading, not the date you are reading this.

“AI/ML Ready | Нейросети” (@neuro_ready), 14,104 subscribers as measured 6 November 2026. Telegram Register, tgregister.com/channel/neuro_ready.

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.