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Channel

MLOps notes

@ai_watchtower

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

181subscribers

+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-1002080266318
TypeChannel
Username@ai_watchtower
DescriptionPipelines, Infrastructure & Coffee
CreatedBetween 1 November 2023 and 31 May 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded8 August 2026
Last confirmed live8 August 2026
Measurements held2
On Telegramt.me/ai_watchtower

Growth

1817 Aug 2026, 19:42 — 181 subscribers8 Aug 2026, 13:01 — 181 subscribers7 Aug 2026, 19:428 Aug 2026, 13:01
2 measurements taken within a single day. 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 180–182 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
8 Aug 2026, 13:01181no change
7 Aug 2026, 19:42181first reading

Engagement

19 posts held, back to 2 October 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 19 posts for this entry, the most recent from 13 July 2026. An engagement rate over an empty window would be a number about nothing.

What this channel posts

Photos
47
Videos
2
Links
94

Lifetime counters from Telegram’s own channel header, read 8 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.

Reaction mix

37 reactions across 13 posts, in 5 distinct kinds. The most used accounts for 48.6% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
1848.6%
🔥1643.2%
👀12.70%
💯12.70%
🕊12.70%

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 14 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 37reactions 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 2 October 2025 to 13 July 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

13 Jul 2026, 16:13 UTC209 viewsread 8 August 2026

Любопытная статья про монополию пайтона в ml https://hackernoon.com/why-the-ai-industry-still-pays-a-python-tax

18 Jun 2026, 06:31 UTC335 views3 reactionsread 8 August 2026
Forwarded from @DevOPSitsecPhoto

4 MCP-сервера, о которых стоит знать каждому DevOps-инженеру 1. Kubernetes MCP - расследовать pod’ы в CrashLoopBackOff; - дебажить неудачные деплои; - анализировать состояние кластера. Repo: https://github.com/Flux159/mcp-server-kubernetes 2. AWS MCP - разбирать резкие скачки расходов в AWS; - находить неиспользуемые ресурсы; - troubleshooting облачной инфраструктуры. Repo: https://github.com/awslabs/mcp 3. Te

🔥3

17 Jun 2026, 07:32 UTC232 viewsread 8 August 2026
Forwarded from @machinelearning_interviewPhoto

Tensordyne анонсировала прорывную систему для inference. Компания заявляет о логарифмических AI-чипах, которые дают в 17 раз больше токенов на ватт и в 13 раз более высокую пропускную способность, чем NVIDIA Blackwell. Главное математическое улучшение, по их словам, в том, что они реализовали эффективные логарифмические вычисления прямо на уровне железа. В логарифмическом пространстве умножение превращается в сложе

16 Jun 2026, 17:18 UTC196 views4 reactionsread 8 August 2026

Сегодня наткнулась на интересную статью о планах Китая инвестировать около 295 миллиардов долларов в создание национальной сети AI-датацентров, которая будет работать преимущественно на китайских технологиях. На первый взгляд это выглядит как очередная новость про инвестиции в AI. Но чем больше я об этом думаю, тем больше мне кажется, что речь идёт о гораздо более масштабной теме. Последние годы мы привыкли восприн

🔥31

19 May 2026, 05:18 UTC320 views2 reactionsread 8 August 2026
Forwarded from @k8securityPhoto

RBAC в Kubernetes часто становится последней линией защиты между скомпрометированным workload и полным захватом кластера. В докладе “RBAC Atlas: Mapping Real-World Kubernetes Permissions and Exposing Risky Projects” разбирается, насколько опасными могут быть избыточные permissions в популярных Kubernetes проектах. Автор представил RBAC Atlas — каталог identities и overly permissive RBAC политик, найденных в реальных

1🕊1

24 Apr 2026, 09:16 UTC399 views2 reactionsread 8 August 2026
Photo

https://www.youtube.com/watch?v=kWBpQZIGmik

👀1💯1

22 Apr 2026, 15:05 UTC341 views3 reactionsread 8 August 2026
Forwarded from @mlops_infraFile

Собрал ссылки по темам из моего доклада, чтобы можно было не только посмотреть презентацию, но и пойти дальше в детали. 1. Три подхода к построению MLOps-платформы Когда мы проектировали курс по MLOps для Яндекс Практикума, отдельно разобрали три варианта архитектуры MLOps-платформ на примерах известных западных компаний. Если хочется посмотреть на разные платформенные подходы не абстрактно, а через реальные архитек

🔥21

14 Apr 2026, 08:09 UTCviews —

MLOps notes pinned «Мы с Антоном и другими ребятами выпустили курс по MLOps. Скидка в 15% по промокоду в посте ниже 🫶»

14 Apr 2026, 08:08 UTC210 views6 reactionsread 8 August 2026

Мы с Антоном и другими ребятами выпустили курс по MLOps. Скидка в 15% по промокоду в посте ниже 🫶

3🔥3

14 Apr 2026, 08:08 UTC293 views3 reactionsread 8 August 2026
Forwarded from @mlops_infra

Мы написали курс по MLOps! Хочу поделиться проектом, в который я вложил реально много сил за последний год - курсом «MLOps для разработки и мониторинга моделей» в Яндекс Практикуме.🚀🚀🚀 Для меня это была не просто работа "поучаствовать в курсе" - Я составлял ядро программы в соответствии с текущим рынком (и моим опытом), формировал ТЗ для авторов, проектировал инфраструктуру курса. Это были сотни часов работы, перес

🔥3

3 Apr 2026, 17:21 UTC313 views4 reactionsread 8 August 2026
Forwarded from @data_secrets_careerPhoto

Что почитать на выходных? Рекомендуем книгу CUDA for Deep Learning. Она показывает, что происходит между PyTorch и железом: как работают ядра, память и параллелизм, и почему именно они определяют скорость обучения и инференса. В книге последовательно разбирается путь от базовых CUDA-ядер до оптимизаций уровня современных LLM, включая profiling и поиск узких мест. Книга дает не просто техники, а объяснение, где тер

4

Showing the 12 most recent of 19 posts we hold for @ai_watchtower. 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

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

“MLOps notes” (@ai_watchtower), 181 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/ai_watchtower.

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.