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Channel
@llm_under_hood
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Citations · Handles named that no longer answer · Telegram's recommendations · Cite this entry
30,149subscribers
+1,620 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1001943579488 |
|---|---|
| Type | Channel |
| Username | @llm_under_hood |
| Created | Between 1 April 2023 and 31 October 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 7 November 2026 |
| Measurements held | 61 |
| Confirmed unchanged | 1 time, most recently 7 November 2026 |
| On Telegram | t.me/llm_under_hood |
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 10 September 2026 and assigned it the closest of 31 fixed categories, at 99% 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.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 7 Nov 2026, 14:51 | 30,149 | +28 |
| 6 Nov 2026, 12:40 | 30,121 | +14 |
| 5 Nov 2026, 15:21 | 30,107 | +25 |
| 4 Nov 2026, 12:55 | 30,082 | +2 |
| 3 Nov 2026, 12:09 | 30,080 | +24 |
| 2 Nov 2026, 09:59 | 30,056 | +13 |
| 1 Nov 2026, 09:59 | 30,043 | +24 |
| 31 Oct 2026, 09:16 | 30,019 | +30 |
| 30 Oct 2026, 06:41 | 29,989 | +35 |
| 29 Oct 2026, 06:41 | 29,954 | +37 |
| 28 Oct 2026, 06:29 | 29,917 | +46 |
| 27 Oct 2026, 04:07 | 29,871 | +23 |
| 26 Oct 2026, 05:10 | 29,848 | +24 |
| 25 Oct 2026, 05:25 | 29,824 | +1 |
| 24 Oct 2026, 05:59 | 29,823 | +28 |
| 23 Oct 2026, 03:06 | 29,795 | +18 |
| 22 Oct 2026, 03:51 | 29,777 | +26 |
| 20 Oct 2026, 23:54 | 29,751 | +13 |
| 20 Oct 2026, 01:16 | 29,738 | +22 |
| 19 Oct 2026, 04:18 | 29,716 | first reading |
83 posts held, back to 2 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 151 pages of 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 6 November 2026 |
|---|---|
| Posts held | 83 (2 July 2026 – 6 November 2026) |
| Views total | 282,570 |
| Reactions total | 2,013 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Nov 2026, 11:36 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.
Measured directly from 1 video 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.
7,153 reactions across 83 posts, in 19 distinct kinds. The most used accounts for 36.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🔥 | 2,614 | 36.5% | |
| ❤ | 1,968 | 27.5% | |
| 👍 | 1,605 | 22.4% | |
| 😁 | 257 | 3.59% | |
| 💯 | 118 | 1.65% | |
| 👏 | 92 | 1.29% | |
| 😢 | 87 | 1.22% | |
| 🤣 | 75 | 1.05% | |
| 🤯 | 74 | 1.03% | |
| 🤗 | 71 | 0.993% | |
| 🤔 | 64 | 0.895% | |
| 🤝 | 34 | 0.475% | |
| 🥰 | 25 | 0.35% | |
| 😱 | 22 | 0.308% | |
| 🙏 | 16 | 0.224% | |
| 🤩 | 10 | 0.14% | |
| ⚡ | 9 | 0.126% | |
| 👨💻 | 8 | 0.112% | |
| 🎄 | 4 | 0.056% |
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 83 of the 83 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 7,153 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 83 most recent posts we hold, published 2 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.
Telegram’s own ‘edited’ marker: 20 of 28 posts held from the last 30 days (71.4%) 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).
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Наш агент напишет вашему агенту С одной стороны это смешно, а с другой стороны начинает происходить у нас в команде все чаще, по мере того, как привычные human-driven процессы начинают ускоряться агентами. На скриншоте пример процесса передачи рабочего контекста с материалами от одного агента другому. Итак, мы работаем над обучающей платформой. После прогона первой когорты набрались идеи по улучшению продукта. @ak…
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Почему Ринат не пишет своих агентов В комментариях к прошлому посту было много отсылок на разные agentic харнесы, которые уже давно реализовали у себя разговорную память. Да что там говорить, нативная работа с ней даже есть у ChatGPT. И это круто. Но я никогда не буду писать свой агентский харнес или пытаться конкурировать с существующими. Но вот что я делаю у нас в BitGN - так это свой слой памяти, в который могут…
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Ускоряем работу с памятью Codex - conversation index Codex/Claude Code сохраняют на диск историю разговоров. На эти разговоры можно было раньше ссылаться со словами "посмотри в историю сессий в ~/.codex/sessions и скажи, что можно тут улучшить". Все работает неплохо. Но вчера во время начала внедрения Company Brain в новую компанию, я понял, что все может быть гораздо лучше, а мне прямо срочно нужна отдельная база …
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Насколько просто можно найти потрясающего специалиста в 2026 году Если помните, эксперимент коммьюнити с BitGN Brain с роем в 30 агентов (который решал задачи ECOM1) пришлось поставить на паузу после того, как слой версионирования выел весь доступный диск. Поэтому я сел переписывать storage. Позвал Codex и велел сделать архитекутуру понятной, упростить, но при этом масштабировать под ECOM2. Выдал ему дамп с работо…
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Ментальная модель организации проектов без скилов Как я уже говорил, я не использую скилы при разработке. Вместо них использую единый и простой progressive disclosure, который не замусоривает контекст, единообразно работает между разными харнесами и неплохо масштабируется на большие проекты/команды. На картинке как раз схема того, как это все укладывается у меня в голове. Причем, эта же схема "Progressive Disclosu…
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Что происходит в канале - часть 4 Вчера я упомянул видео от YCombinator про организацию AI Native Company, что они описывают даже принцип работы роя агентов, который мы запускали в этом коммьюнити. Давайте поговорим про это подробнее. Напомню, что мы продолжаем разбирать фразу: Если кратко - рой агентов нашего коммьюнити пишет агента для автоматизации сложных задачек e-commerce используя только локальные LLM. Они …
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А вот пример того, как company brain работает в команде Вот такое сообщение мне сейчас прислал Company Brain. Его попросили из Codex-а заархивировать обновленный прототип проекта для передачи мне и интеграции. Но вместо того, чтобы просто загрузить файл и выдать ссылку для пересылки (бред, да?) - он на этом не остановился, а нашел инструмент message и меня в списке разрешенных адресатов. Это ему позволило сразу взя…
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Смотрите, что YCombinator говорит про принцип работы роя агентов, такого же, который мы уже запускали вместе в рамках моделирования работы небольшой организации - AI R&D лабы! Building And Structuring An AI Native Company (YouTube) Наша технология пока на шаг впереди их видения company brain (есть работающее взаимодействие агентов, которые позволяют включить режим работы роя), но это видео все равно очень вдохновля…
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Что происходит в канале - часть 3? Память роя закончилась, рой засыпает до следующей недели. А мы продолжаем разбирать фразу (оглавление): Если кратко - рой агентов нашего коммьюнити пишет агента для автоматизации сложных задачек e-commerce используя только локальные LLM. Они делают это используя совместную память Company Brain. Рой был запущен прошлым вечером и довел точность до 78.30. Итак, мы поговорили про за…
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Рой вышел в TOP-10 ECOM1 Prod (интерлюдия) Результативность - 95.1 (лидерборд) Но при этом в чате говорят, что он немного разленился и начал пихать regex-ы в решения. Формально это тоже решение, но такое себе с точки зрения переиспользования в других проектах. В принципе логично, что мы до такого докатились - у задачи не было жестких критериев качества, только стремление выбить максимальное число баллов. Там даже …
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Что происходит в канале? Часть 2 Итак, продолжаем разбирать фразу Если кратко - рой агентов нашего коммьюнити пишет агента для автоматизации сложных задачек e-commerce используя только локальные LLM. Они делают это используя совместную память Company Brain. Рой был запущен прошлым вечером и довел точность до 78.30. Оглавление тут. Мы обсудили, что агенты совместно решают задачки по автоматизации commerce. Дальше…
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Showing the 12 most recent of 83 posts we hold for @llm_under_hood. 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.
@llm_under_hood edited 1 post after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
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.
Named by 49 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. The 24 listed below are the most frequent namers; the rest are counted above but not each listed.
Named by
Channels on the register whose posts name this channel's handle.
Names
Channels on the register whose handles appear in this channel's posts.
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.
@llm_under_hood named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
References a handle that is not a live channel — we have no record it ever was one.
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 9 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
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.
This channel appears in 45 seed channels' Telegram-generated recommendation lists in total, of which the 24 where it ranks highest are shown above. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 7 November 2026 — this entry's latest reading, not the date you are reading this.
“LLM под капотом” (@llm_under_hood), 30,149 subscribers as measured 7 November 2026. Telegram Register, tgregister.com/channel/llm_under_hood.
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.