Channel
@wearefired
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
340subscribers
+0 since we began measuring on 8 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1003670196777 |
|---|---|
| Type | Channel |
| Username | @wearefired |
| Description | Инженеры учат нейронки работать за себя. Кейсы из жизни. https://ai.ovc.me/ Для связи @ovcme |
| Created | Between 1 December 2025 and 31 May 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 8 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/wearefired |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 8 Aug 2026, 07:02 | 340 | no change |
| 8 Aug 2026, 01:32 | 340 | first reading |
20 posts held, back to 9 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 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 7 August 2026 |
|---|---|
| Posts held | 20 (9 July 2026 – 7 August 2026) |
| Views total | 1,797 |
| Reactions total | 71 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 8 Aug 2026, 07:02 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 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.
130 reactions across 20 posts, in 11 distinct kinds. The most used accounts for 40.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🔥 | 52 | 40.0% | |
| 👍 | 28 | 21.5% | |
| ❤ | 20 | 15.4% | |
| 🤣 | 9 | 6.92% | |
| 😁 | 7 | 5.38% | |
| ❤🔥 | 5 | 3.85% | |
| 👏 | 3 | 2.31% | |
| 💯 | 2 | 1.54% | |
| 🤔 | 2 | 1.54% | |
| 🏆 | 1 | 0.769% | |
| 🤝 | 1 | 0.769% |
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 20 of the 20 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 130reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 9 July 2026 to 7 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.
LoRA: ожидание и реальность Всем же очень хочется доучить модель под свои нужды? Она не тупит, просто мы ее не научили! В теории всё выглядит прекрасно. Берём готовую языковую модель, не трогаем её миллиарды весов, добавляем небольшие обучаемые матрицы и за сравнительно небольшие деньги меняем поведение модели под свою задачу. Это и есть LoRA. Вместо полноценного переобучения модели мы обучаем небольшой адаптер, ко…
🔥5👍3
Большой могучий грандиозный и не только труд уважаемых коллег по теме внедрения наших нейронных друзей в стройку. Горжусь, что приложил чуть чуть руку!
🔥3
Готова ли ваша организация к применению ИИ? Часто внедрение ИИ начинают с выбора сервиса или модели. На практике результат сильнее зависит от другого: насколько описаны процессы и роли, структурированы данные, доступны корпоративные знания, определены критерии качества и правила безопасной работы. Мы в ROSECO (РУСЭКО-СТРОЙПРОЕКТ) собрали чек-лист ИИ-готовности для проектных, BIM- и обследовательских организаций: 9…
🔥8
По сути, RAG — это костыль. Очень полезный, местами виртуозно настроенный, но всё-таки костыль. Языковые модели почти ничего не запоминают после обучения. Мир меняется, данные обновляются, проект развивается, а модель продолжает жить с тем, что однажды записали в её веса. До недавнего времени многие современные модели, например, продолжали считать НДС в России равным 20% и уверенно называли это истиной. Чтобы ИИ не …
🔥4👍3
Никто не знает зачем, но Гугл встроил в Google Earth нейронку для генерации избражений прямо поверх карту. Лимитка маленькая, но можно побаловаться. UDP А все!
👏3
Posted without readable text
👍3🔥2
Ну что, виварий отработал. Основная интрига для меня была не в том, кто победит. Я хотел понять, насколько вообще различаются современные STT-модели. После мира LLM уже как-то привыкаешь, что разница между соседями по рейтингу часто измеряется парой процентов. Здесь оказалось совсем не так. Первым, естественно, запустил Whisper. Просто потому, что именно с него сегодня начинают почти все локальные проекты. Потом до…
🔥2
Я не люблю видео, я люблю читать. А сейчас почему-то всем проще записать видео на час вместо того, чтобы дать краткий емкий текст, который можно прочитать где угодно, быстро просмотреть по диагонали, найти нужное через поиск и через полгода вернуться ровно к тому месту, которое понадобилось. Особенно странно это выглядит в технических материалах. Человек сорок минут показывает экран и рассказывает то, что спокойно у…
👍4
Как дать домашней нейросети топор Вы решили завести домашнюю языковую модель. Скачали Qwen, Gemma или Llama, поставили Ollama и запустили: ollama run gemma4 Поздравляю: у модели появился дом. Ollama загружает её в память, принимает запросы и даёт другим программам доступ по API. Модель живёт локально и отвечает без чужого облака. Но по хозяйству она пока бесполезна. Она не может открыть папку с проектом, прочитать Ex…
🔥6❤1
Добавил нормальное обучение, добавил багов, добавил прокачку и экономику с кассовым разрывом.
👍2
Все же видели посты "Я написал Клоду/ГЛМ/ГПТ сделай мне игру и он сделал копию ГТА про Нижневартовск где можно зайти в каждый дом!"? Разумеется такое можно попросить и разумеется ничего у вас путного так не выйдет. Все таки какие то усилия надо прилагать. Я решил усилий не прилагать, накидал идей на пол страницы, скормил кодексу вместе с каналом Олега и за пару часов он мне собрал симулятор стройки офисов в стиле Evi…
❤🔥5🔥3💯2🤝1
Мучаю я тут модуль расчета сметной стоимости, потом покажу что получилось. И самое сложное - найти тонкую грань мжду кодом, промтом и свободой модели. В работе с ИИ постоянно хочется то мешать модели, то помогать ей, и очень легко впасть в одну из двух крайностей. Первая крайность состоит в том, чтобы настолько не доверять модели, что попытаться заменить её мышление правилами. Для этого пишется огромный промпт, зат…
👍10🏆1
Showing the 12 most recent of 20 posts we hold for @wearefired. 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 — 788,312 of 1,350,102entries 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.
Republishes
Channels on the register whose posts this channel has forwarded.
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 2 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.
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.
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.
“Мы пилим сук, на котором сидим” (@wearefired), 340 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/wearefired.
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.