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 11 September 2026 and assigned it the closest of 31 fixed categories, at 100% 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 why it has no browse page of its own yet.
Growth
32 measurements spanning 43 days, net +168. 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 11,406–11,624 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 32
Measured (UTC)
Subscribers
Change
18 Sept 2026, 06:19
11,599
+1
15 Sept 2026, 22:41
11,598
+5
12 Sept 2026, 09:39
11,593
+15
10 Sept 2026, 05:18
11,578
+12
6 Sept 2026, 20:37
11,566
+4
4 Sept 2026, 09:56
11,562
+28
2 Sept 2026, 20:45
11,534
+6
1 Sept 2026, 18:37
11,528
+70
31 Aug 2026, 20:44
11,458
-2
30 Aug 2026, 18:39
11,460
-3
29 Aug 2026, 21:28
11,463
-2
28 Aug 2026, 22:34
11,465
-2
28 Aug 2026, 00:34
11,467
+4
26 Aug 2026, 23:35
11,463
-2
26 Aug 2026, 01:12
11,465
+1
25 Aug 2026, 01:08
11,464
+1
22 Aug 2026, 03:45
11,463
-5
20 Aug 2026, 22:55
11,468
-1
19 Aug 2026, 20:13
11,469
+1
18 Aug 2026, 22:05
11,468
first reading
Engagement
30 posts held, back to 3 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 44 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
33.2%
avg views ÷ 11,599 subscribers
Avg views / post
3,850
3 posts measured
Reaction rate
3.08%
reactions ÷ views · ER floor
Posts in window
3
of 30 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
Window
Rolling 30 days · latest post in window 1 September 2026
Posts held
30 (3 June 2026 – 1 September 2026)
Views total
11,540
Reactions total
355
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
2 Sept 2026, 23:51 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
44s
Average length
22s
Measured directly from 2 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
2,487 reactions across 29 posts, in 13 distinct kinds. The most used accounts for 55.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
1,381
55.5%
❤
515
20.7%
👍
478
19.2%
❤🔥
43
1.73%
🥰
28
1.13%
⚡
12
0.483%
✍
8
0.322%
👏
5
0.201%
🎉
4
0.161%
🙏
4
0.161%
🦄
4
0.161%
🤩
3
0.121%
🤝
2
0.08%
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 30 of the 30 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 2,567 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 30 most recent posts we hold, published 3 June 2026 to 1 September 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 Stars
Stars received
224
across the posts below
Posts paid on
28
of 30 we hold a reading for · 93%
Most on one post
103
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @nobilix. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 30 most recent posts we hold for this entry, published 3 June 2026 to 1 September 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
Qwen 3.8 27B, локальный инференс и мои эксперименты с подбором железа
Про эту модель уже несколько недель пишут "Opus 4.6 у себя дома". Нет, конечно. Но, черт, это первая небольшая локальная модель, которую я готов всерьез обсуждать в агентных сценариях, а не для чата по базе знаний.
Небольшие локальные модели (которые влезут в домашний GPU) на агентных задачах часто зацикливается, либо ломают tool-calling. Qwen 3.…
#ReDigest
Продолжаем субботнюю рубрику, тут я кратко рассказываю про новости из мира технологий и AI, которые привлекли мое внимание.
Дайджест недели:
- Nvidia покупает Hugging Face за $12,9 млрд (соглашение еще не подписано). Комьюнити тревожится за не-CUDA бекенды.
- Apple представила первый свой 2-нм чип M6 и M5 Ultra: Mac mini от $899, Mac Studio с памятью до 512 ГБ и полосой 1.2 ТБ/с. В продаже осенью.
- An…
Вот сколько бы ни хоронили промпт-инжиниринг (никогда не понимал этого пафосного названия), но то, как вы формулируете запрос, по-прежнему во многом определяет качество результата.
Например, мой частый совет из практики - Заменяй summarize на extract. "Суммаризируй" дает общий пересказ, а вот "Извлеки ключевые факты списком, по одному на пункт, без комментариев" - операция ближе к парсингу. Точный глагол = точный ре…
#ReDigest
Продолжаем субботнюю рубрику, тут я кратко рассказываю про новости из мира технологий и AI, которые привлекли мое внимание.
Дайджест недели:
- OpenAI официально притормозила обучение будущей фронтир-модели Astra (из-за киберспособности). Anthropic тем временем рассказала о внутренней Model 2 сильнее Mythos, которую выпускать не планирует.
- Z.ai выпустила GLM-5.3 с фронтирным кодингом, но придержала вес…
Железки - это весело. Пятничный пост про DIY.
Знаете, софт - это хорошо, но есть особый шарм именно у железяк: когда что-то щелкает, пищит, светится, включает и выключает вещи в комнате. Что-то осязаемое. Даже если девайс делает какую-то малополезную мелочь.
Еще вся эта DIY-электроника стала сильно проще. Помню, раньше написать нормальную прошивку занимало мгого дней, а сейчас: втыкаешь девайс по USB, говоришь Clau…
Как писать код с AI-агентами?
А если командой?
Что нужно учесть, чтобы этот код не положил продакшн?
Эти и другие не менее интересные вопросы мы обсудим на следующем стриме на моём YouTube-канале.
Для обсуждения я позвал очень интересных гостей:
Андрей Бреслав – один из создателей языка программирования Kotlin. Сейчас Андрей разрабатывает Agentic Engineering Toolkit под названием CodeSpeak.
Валера Ковальский – …
#ReDigest
Продолжаем субботнюю рубрику, тут я кратко рассказываю про новости из мира технологий и AI, которые привлекли мое внимание.
Дайджест недели:
- Anthropic встраивает невидимые водяные знаки во все тексты Claude, а следом планируют OpenAI, Google и другие.
- OpenAI: Computer History отдает ChatGPT и Codex историю работы в приложениях как контекст, вышла Linux-версия, а в Business появятся Premium-места з…
Evidence-based planning, или что не так с чистым SDD
TL;DR: Одно из самых недооцененных свойств AI-кодинга - это проектирование софта через ранние дешевые эксперименты.
Стандартный путь (часто) выглядит так: собрать весь input, скормить его в spec-driven framework или аналог, который нарежет все на тикеты, и погнали кодить агентами. Проблема в том, что спека, написанная до контакта с реальностью, фиксирует галлюцин…
#ReDigest
Продолжаем субботнюю рубрику, тут я кратко рассказываю про новости из мира технологий и AI, которые привлекли мое внимание.
Дайджест недели:
- Alibaba выпустила Qwen3.8-Max: 2,4T параметров, контекст 1M, вчетверо дешевле Opus 5.
- OpenAI обновила Sol, обещают меньше ошибок, а бесплатным дали безлимитную GPT-5.6 Luna.
- Agent Plugins - открытый стандарт упаковки скиллов и MCP-серверов в плагины; за ним …
bb - Codex-подобный оркестратор, который расширяется плагинами. И почему это делает его интересным.
В awesome-листе агентных оркестраторов сейчас под сотню проектов, они решают примерно одну задачу: собрать упряжки (Claude Code, Codex, OpenCode, Pi и тд) под одним интерфейсом, будь то терминал, TUI или десктоп.
bb хорош двумя вещами (помимо прочего).
Первая простая: он удобный и приятно выглядит. Интерфейс практич…
#ReDigest
Продолжаем субботнюю рубрику, тут я кратко рассказываю про новости из мира технологий и AI, которые привлекли мое внимание.
Дайджест недели:
- OpenAI снизила цены на GPT-5.6 и объяснила зачем.
- Anthropic обновила MCP: stateless-архитектура, официальный статус у Apps и Tasks и пр.
- Moonshot выложила веса Kimi K3 - теперь модель доступна на десятках провайдеров.
- OpenAI выкатила в API GPT Transcribe …
Фидбэк по фронту через запись экрана (с адаптацией для агентов).
Пока я весь в делах и не доходят руки до длинного бэклога с постами, поделюсь коротким лайфхаком.
Последнее время фидбэк по интерфейсу отдаю агенту короткими видео. Просто записываешь пару минут с экрана и наговариваешь голосом, что и где именно надо улучшить и отдаешь агенту в специальном формате (об этом позже). Разница в скорости и полноте: за эти…
🔥71❤10👍10❤🔥1🥰1
Showing the 12 most recent of 30 posts we hold for @nobilix. 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.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Posts edited after publishing
@nobilix edited 2 posts 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.
First edit seen
23 August 2026
Most recent edit
1 September 2026
Forward network
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.
Mentions
Named by 25 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.
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 18 September 2026 — this
entry's latest reading, not the date you are reading this.
“Refat Talks: Tech & AI” (@nobilix), 11,599 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/nobilix.
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.