Telegram RegisterThe public register of Telegram
Telegram profile photo for Лаборатория ИИ

Channel

Лаборатория ИИ

@unrealneural

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

2,722subscribers

+29 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 1,000–3,162.

Register entry

Telegram ID-1001305499623
TypeChannel
Username@unrealneural
Created6 July 2019measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded6 August 2026
Last confirmed live15 August 2026
Measurements held5
Confirmed unchanged1 time, most recently 15 August 2026
On Telegramt.me/unrealneural

Growth

2,6932,7222,707.56 August 2026 — 2,693 subscribers6 August 2026 — 2,693 subscribers9 August 2026 — 2,697 subscribers12 August 2026 — 2,696 subscribers15 August 2026 — 2,722 subscribers6 August 202615 August 2026
5 measurements spanning 10 days, net +29. 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 2,689–2,726 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
15 Aug 2026, 16:142,722+26
12 Aug 2026, 11:082,696-1
9 Aug 2026, 05:032,697+4
6 Aug 2026, 05:192,693no change
6 Aug 2026, 02:322,693first reading

Engagement

17 posts held, back to 22 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
34.8%
avg views ÷ 2,722 subscribers
Avg views / post
947
16 posts measured
Reaction rate
1.10%
reactions ÷ views · ER floor
Posts in window
17
of 17 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 7 August 2026
Posts held17 (22 July 20267 August 2026)
Views total15,148
Reactions total167
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken7 Aug 2026, 16:14 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

Video runtime
11m 17s
Average length
52s

Measured directly from 13 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

147 reactions across 15 posts, in 7 distinct kinds. The most used accounts for 47.6% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍7047.6%
2718.4%
🔥2416.3%
1610.9%
❤‍🔥53.40%
🤗32.04%
🤝21.36%

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 17 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 167reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 17 most recent posts we hold, published 22 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.

Recent posts

7 Aug 2026, 07:50 UTC263 views7 reactionsread 7 August 2026
Video

#unrealneural #vibecad Multi-Agent CAD - конвейер text2CAD для генерации CAD геометрии Примерно за $0.15 каждая модель и это примерно за 1/13 стоимости CAD Skills, с более быстрым выводом и большей точностью ⚡️⚡️⚡️ Открытый исходный код https://github.com/Pan-Chera/Multi-Agent-CAD

👍32🔥2

Signed Артур Ишмаев

6 Aug 2026, 06:18 UTC430 views10 reactionsread 7 August 2026
Video

#unrealneural #вайбпроектирование Интерактивные инструкции для IKEA ⚡️ Мне безумно понравился этот кейс✨ Автор с помощью ИИ в том числе превратил инструкции по сборке мебели IKEA в интерактивные 3d сцены 🤗 Монтажные работы с такими инструкциями должны стать менее трудоемкими 👍🏻

7👍21

Signed Артур Ишмаев

5 Aug 2026, 06:20 UTC432 views8 reactionsread 7 August 2026
Video

#unrealneural Андрей Карпатый дал Opus 5 первый абзац «Властелина колец», бюджет 1M токенов (~$10) и попросил сделать интерактивную сцену этой истории. 🤯 Модель работала ~2 часа и написала 5500 строк кода, которые процедурно отрисовали сцену. Получилось шатковато, но забавно. Самое впечатляющее: LLM сама размещает полигональные ассеты в 3D-координатах и пишет код анимации. И это вообще работает.⚡️ Никто в здравом

👍61🔥1

Signed Артур Ишмаев

4 Aug 2026, 06:06 UTC459 views9 reactionsread 7 August 2026
Video

#unrealneural #вайбпроектирование Приложение по 3D-анатомии человека с помощью вайб-кодинга и не только Первый шаг - это изображение концепт (GPT Image 2.0). Затем - генерация картинок каждого органа → превращение их в 3D-модели через Tripo AI → и передача всего в Codex вместе с мастер-промптом. Codex собирает первую версию красиво, но модели весили по 120–150 МБ каждая (почти 900 МБ в сумме). Для веба это не подхо

6🔥21

Signed Артур Ишмаев

3 Aug 2026, 06:10 UTC494 views6 reactionsread 7 August 2026
Video

#unrealneural #вайбпроектирование Drawing2CAD в SolidWorks Отличное демо воссоздания модели по чертежам с помощью Fable 5 + кастомного harness. Казалось бы зачем решать обратную задачу, ведь чертеж это побочный продукт модели и очень редко бывает, что есть чертежи без модели. Однако, именно это самый адекватный бенчмарк проверки работы интеллекта ИИ модели, который позволит потом проделать путь обратно ⚡️ P.S. Есте

👍4🔥1🤝1

Signed Артур Ишмаев

2 Aug 2026, 13:44 UTC506 views16 reactionsread 7 August 2026
Video

#unrealneural #вайбпроектирование Revit + Claude и вайб-проектирование в Revit Официальный MCP от Autodesk вышел в июне 2026 к Revit 2027, но пока даёт только доступ на чтение, а запись намеренно закрыта (Tech Preview). Еще один очередной вайб-проектировщик из интернета за 3 часа вручную настроил неофициальный community MCP-сервер для Revit, чтобы создавать и редактировать элементы. ⚡️ В результате ему удалось за

👍8🔥53

Signed Артур Ишмаев

31 Jul 2026, 16:30 UTC747 views7 reactionsread 7 August 2026
Video

#unrealneural Технология пространственной памяти для роботов «FARM» ⚡️⚡️⚡️ Структурированная память, которая учитывает характеристики объектов и их расположение относительно окружения, создаётся в реальном времени 👍🏻 Даже на большой стройплощадке площадью 15 000 м² система с высокой точностью находит и автоматически исследует нужный объект по простым голосовым командам с указанием расположения — например, «туалет р

👍42🤗1

Signed Артур Ишмаев

30 Jul 2026, 13:45 UTC602 views6 reactionsread 7 August 2026
Video

#unrealneural #вайбмоделирование Dream-Cubed Sakana AI совместно с NYU представили работу, где большие трансформеры обучаются генерации воксельных миров Minecraft, используя кубы как токены. 🧐 Ключевое: создан крупный датасет из десятков миллиардов кубов (процедурка + человеческие постройки) и обучены модели, способные генерировать coherentные, сразу playable структуры и ландшафты с высоким уровнем контроля. Отлич

👍321

Signed Артур Ишмаев

29 Jul 2026, 18:00 UTC≈6,080 views11 reactionsread 7 August 2026
Video

#unrealneural #вайбмоделирование TRELLIS 2 опубликовали в open source ⚡️ Microsoft открыли исходный код модели на 4 миллиарда параметров 👍🏻 Главное что открыты не только веса, но и весь код обучения. Можно дообучить на своей библиотеке ассетов и получить генерацию точно в стиле вашей студии. Ещё один мощный шаг к настоящему AI 3D-продакшену ⚡️ https://github.com/microsoft/TRELLIS.2

👍541🔥1

Signed Артур Ишмаев

28 Jul 2026, 15:45 UTC842 views7 reactionsread 7 August 2026
Video

#unrealneural #vibecad Claude Code (Opus 5) полностью построил 3D-модель стальной лестницы в *Tekla Structures по обычным 2D-DXF чертежам. ⚡️ 184 стальных элемента + 30 элементов ж/б каркаса. Что сделал Claude Code: 1. Правильно определил систему координат 2. Снимал размеры напрямую по геометрии линий 3. Сгенерировал все элементы пачкой через скрипт (Tekla Open API) - MCP рычаги 4. Проверил модель наложением на все

👍3🔥31

Signed Артур Ишмаев

28 Jul 2026, 15:12 UTC645 views13 reactionsread 7 August 2026
Photo

#вайбпроектирование #ЛабораторияИИ Вайб-проектирование в массы⚡️⚡️⚡️ Прошли замечательные выходные в Нижнем Новгороде Спасибо большое Команде "Ниша" за замечательное мероприятие и приглашение 🤗 Мы всегда рады делиться своим опытом, показывать реальные кейсы формирования ИИ-трансформации проектирования, дискутировать на тему развития архитектуры и проектирования. Это очень важно, потому что мы каждый день создаем б

👍82🤗2🔥1

Signed Артур Ишмаев

27 Jul 2026, 06:56 UTC600 views8 reactionsread 7 August 2026
Video

#unrealneural #вайбмоделирование Img2threejs v1.4 Это инструмент, который с помощью ЛЛМ превращает изображения в полноценные процедурные 3D-модели на Three.js В этой версии разработчики сильно улучшили качество реконструкции. В отличие от большинства других ИИ-решений, которые создают только внешнюю оболочку, здесь модель строит объекты с настоящими внутренними структурами, точными пропорциями, хорошими материалами

❤‍🔥5👍2🔥1

Signed Артур Ишмаев

Showing the 12 most recent of 17 posts we hold for @unrealneural. 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

Citation-graph rank — 126,107 of 1,481,217entries 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.

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

Named by 4 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.

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.

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

“Лаборатория ИИ” (@unrealneural), 2,722 subscribers as measured 15 August 2026. Telegram Register, tgregister.com/channel/unrealneural.

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.