Троянский конь - это прошлый век! Теперь чтобы положить инфраструктуру жертвы, используй китайского кота 🐈⬛ #юмор
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Channel
@theatrum_llm
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
28subscribers
+0 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1003535292083 |
|---|---|
| Type | Channel |
| Username | @theatrum_llm |
| Description | Канал про ИИ как про новую алхимию. Перекос в сторону электроники, железа и странных DIY-идей. Тратим деньги, время и здравый смысл, веря, что в этот раз точно получится. Будут хорошие шутки, плохие шутки и всякая алхимическая дичь. |
| Created | Between 1 December 2025 and 31 May 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 11 August 2026 |
| Last confirmed live | 11 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/theatrum_llm |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 11 Aug 2026, 06:31 | 28 | no change |
| 7 Aug 2026, 22:19 | 28 | first reading |
16 posts held, back to 18 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 1 pageof Telegram’s post history, 20 posts per page.
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.
| Window | Rolling 30 days · latest post in window 10 August 2026 |
|---|---|
| Posts held | 16 (18 June 2026 – 10 August 2026) |
| Views total | 80 |
| Reactions total | 6 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 11 Aug 2026, 06:31 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.
Lifetime counters from Telegram’s own channel header, read 11 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.
26 reactions across 15 posts, in 4 distinct kinds. The most used accounts for 42.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 11 | 42.3% | |
| 😁 | 9 | 34.6% | |
| 👍 | 4 | 15.4% | |
| 🤣 | 2 | 7.69% |
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 16 of the 16 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 26reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 16 most recent posts we hold, published 18 June 2026 to 10 August 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.
Троянский конь - это прошлый век! Теперь чтобы положить инфраструктуру жертвы, используй китайского кота 🐈⬛ #юмор
❤1
ChatGPT увидел рваные джинсы в Омске 1970-х. Проблема в том, что их там не было. Часть 2 Чтобы раскрасить фотографию правильно, недостаточно понимать изображение. Нужно знать: 1970-е -> СССР -> Омск -> какие вещи тогда реально можно было достать -> насколько распространены были джинсы -> кто мог их носить. То есть внезапно задача "раскрась старую фотографию" становится проверкой не компьютерного зрения, а модели …
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ChatGPT увидел рваные джинсы в Омске 1970-х. Проблема в том, что их там не было. Часть 1. 📷 Недавно мне попалась ч/б фотография из Омска конца 70-х. Я загрузил её в ChatGPT и попросил раскрасить. Получилось красиво. Только очевидец тех событий посмотрела и сказала: Какие, нафиг, джинсы? Модель нарядила в джинсы половину процессии. У одного человека они были ещё и рваные на коленях. Для позднего СССР это уже какой-…
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Griaß eich, алхимики 👨🏻🔬 Хроники кибернетического томата #9 Физическая архитектура 🍅 Итак, проект работает уже 1.5 месяца в полевых условиях. За это время случилось 2 глобальных сбоя в работе raspberry pi (о чем расскажу в дальнейших постах)... что никак не отразилось на созревшей и благополучно съеденной дюжине помидоров. 🧩 Общий вид физической архитектуры системы Senior Pomidor выглядит так: балкон с горшками →…
#юмор
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#юмор
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#юмор
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Вот это прикол. Делаешь себе делаешь открытый проект про embodied AI для растения, собираешь его с помощью Codex, выкладываешь архитектуру, сенсоры, дневник, идею "растение говорит от первого лица" - и буквально позавчера OpenAI выкатывает Plant Talk. Пойду спрошу у своего томата, как он переживает конкуренцию с корпоративным фикусом...На самом деле, тот проект о другом. Мой проект: embodied AI для растения, Codex…
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Хроники Senior Pomidor 🍅 День 0. 🌱 Сегодня утром томат впервые проснулся как система. 📜 У него появились сенсоры, телеметрия, память, графики и первые логи. Он - как джун на проекте - пока ничего не "понимает" в человеческом смысле, но уже начинает оставлять следы: температура, влажность, свет, почва, время, состояние среды. 👨🔬 Это даже не первая, а ноль.первая версия Senior Pomidor - эксперимента про embodied AI…
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👾 Сотрудники Claude выложили курс по Claude Code и агентной разработке. 📜 В программе: * Claude Code * Skills, & Hooks * MCP * Plugins * Agents & Subagents * Agent SDK ⏳️ Курс не масштабный, около 2х часов. 🤌🏻 Как говорится: не смотрел, но одобряю. https://frontendmasters.com/courses/claude-code/ #рекомендасьон #schola
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🍅 Сегодня важная веха в проекте кибернетического томата! Пост об этом будет вечером, а пока картинка в тему. Old but gold.
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Наблюдают, как меня заменяет AI
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Showing the 12 most recent of 16 posts we hold for @theatrum_llm. 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.
Citation-graph rank — 824,904 of 1,345,403entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
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.
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 11 August 2026 — this entry's latest reading, not the date you are reading this.
“Theatrum LLMum” (@theatrum_llm), 28 subscribers as measured 11 August 2026. Telegram Register, tgregister.com/channel/theatrum_llm.
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.