Other / unclassifiable — 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 13 September 2026 and assigned it the closest of 31 fixed categories, at 29% 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
13 measurements spanning 42 days, net -26. 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 4,708–4,745 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
17 Sept 2026, 00:20
4,715
+1
12 Sept 2026, 18:39
4,714
+2
9 Sept 2026, 02:55
4,712
-4
31 Aug 2026, 19:06
4,716
-1
28 Aug 2026, 19:23
4,717
-8
26 Aug 2026, 01:12
4,725
+1
23 Aug 2026, 07:33
4,724
-2
19 Aug 2026, 15:52
4,726
+1
16 Aug 2026, 20:45
4,725
-2
13 Aug 2026, 11:57
4,727
-4
10 Aug 2026, 06:05
4,731
-8
7 Aug 2026, 07:11
4,739
-2
6 Aug 2026, 11:02
4,741
first reading
Engagement
21 posts held, back to 2 December 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 7 pages of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 21 posts for this entry, the most recent from 10 August 2026. An engagement rate over an empty window would be a number about nothing.
What this channel posts
Video runtime
2m 10s
Average length
33s
Measured directly from 4 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
283 reactions across 20 posts, in 12 distinct kinds. The most used accounts for 43.1% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
122
43.1%
👏
32
11.3%
👍
27
9.54%
😁
22
7.77%
🤣
22
7.77%
🙏
16
5.65%
😢
14
4.95%
👌
13
4.59%
💔
11
3.89%
😴
2
0.707%
❤🔥
1
0.353%
👾
1
0.353%
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 21 of the 21 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 283 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 21 most recent posts we hold, published 2 December 2025 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.
📱 گوشی شما زمان رو دو بار ازتون میدزده!
فرض کنید صبح گوشی رو فقط برای چک کردن یه پیام برمیدارید...
یه ویدئو میبینید، بعد یکی دیگه، یه خبر، یه پست، یه استوری...
سرتون رو که بلند میکنید، میبینید یک ساعت گذشته! 😐
حالا از خودتون میپرسید: «من توی این یک ساعت دقیقاً چی کار کردم؟»
و عجیب اینجاست که خیلی وقتها جواب مشخصی ندارید. همهچیز یه جور مبهمه.
🔺این فقط یه حس شخصی نیست.
🔬پژوهشها درباره ادراک زمان و رسانهه…
📱 گوشی شما زمان رو دو بار ازتون میدزده!
⏳ بار اول، همان لحظه: چون متوجه نمیشیم چقدر زمان گذشته.
🧠 بار دوم، در حافظه: چون وقتی به عقب نگاه میکنیم، چیز مشخصی برای چسبیدن به آن یک ساعت نداریم.
و حتی عجیبتر...
@onlinebme
سلام اقای دکتر وقتتون بخیر مقاله من هم چاپ شد خواستم ازتون تشکر کنم راهنمایی ها و دوره های شما نبود من هیچوقت نمیتونست این مسیر رو شروع کنم.
یه دنیا تشکر برای لطف و محبتتون
این پژوهش یک مدل یادگیری عمیق ترکیبی با نام ResSPNet برای احراز هویت افراد بر اساس سیگنالهای EEG ارائه میکند. در این مدل، شبکه SPCNN ویژگیهای زمانی و مکانی سیگنال خام EEG را استخراج میکند و شبکه ResNet-101 نیز ویژگیهای فرکانسی را از تصاویر طیفنگار حاصل از تبدیل STFT یاد میگیرد. سپس این ویژگیها با یکدیگر ترکیب میشوند تا فرایند احراز هویت با دقت و اطمینان بیشتری انجام شود.
برای افزایش کاربردپذیری سیستم، تعداد…
💡متا از نسل جدید رابط مغز و کامپیوتر غیرتهاجمی خود رونمایی کرد.
✍پژوهشگران متا از Brain2Qwerty v2، پیشرفتهترین سامانه غیرتهاجمی برای تبدیل فعالیت مغز به متن، رونمایی کردهاند. این سامانه با ترکیب سیگنالهای مغزی ثبتشده توسط دستگاه MEG، مدلهای یادگیری عمیق و مدلهای زبانی بزرگ (LLMs)، میتواند جملات را تقریباً بهصورت لحظهای رمزگشایی کند.
نکته جالب اینجاست که Brain2Qwerty v2 بدون نیاز به کاشت الکترود در مغز کار م…
📚 اگر کسی از من بپرسد برای یادگیری Deep Learning از کجا شروع کنم، این سه کتاب جزو اولین پیشنهادهای من خواهند بود😉
📘 Neural Networks and Learning Machines
💡کتابی که برای من سنگ بنای یادگیری شبکههای عصبی بود. Heykin فقط شبکههای عصبی رو آموزش نمیده؛ بلکه یاد میده هنگام مطالعه یک الگوریتم جدید، از دید فنی به دنبال چه چیزهایی باشیم و چگونه آن را از روی روابط ریاضی به پیادهسازی برسانیم.
📗 Deep Learning
اثر ارزشمند Go…
🧠 چین یک گام مهم در مسیر تجاریسازی رابطهای مغز و کامپیوتر برداشت.
نهادهای نظارتی چین مجوز استفاده پزشکی از سامانه BCI موسوم به NEO را صادر کردهاند؛ سامانهای که به افراد مبتلا به آسیبهای نخاعی کمک میکند تنها با سیگنالهای مغزی، یک دستکش رباتیک را کنترل کرده و بخشی از توانایی حرکت دست خود را بازیابند. این نخستین بار است که یک رابط مغز و کامپیوتر تهاجمی، خارج از چارچوب آزمایشهای بالینی مجوز استفاده تجاری دریافت …
✅ توضیح لایه Pooling در شبکههای عصبی کانولوشنالی CNNs
✍لایه Pooling یکی از اجزای کلیدی در معماری شبکههای عصبی کانولوشنال است که با کاهش ابعاد تصویر، ویژگیهای مهم آن را حفظ می کند. این لایه نه تنها پیچیدگی محاسباتی را به شدت کاهش میدهد، بلکه باعث بهبود تعمیمپذیری (Generalization) مدل و مقاوم بودن آن در برابر تغییرات مکانی نیز میشود. لایه پولینگ (pooling) به شبکه های عصبی CNNs کمک میکند که به جای فشرده سازی ساد…
❤6👍1
Showing the 12 most recent of 21 posts we hold for @onlinebme. 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.
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 1 registered channel — 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.
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.
Appears in Telegram’s recommendations for other channels
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.
Menschen | منشن @MenschenA1bisC1 · 26,777 Telegram ranks this channel #46 of 66 here — alongside 65 others — read 12 September 2026
This channel appears in 1 seed channel's Telegram-generated recommendation list in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 17 September 2026 — this
entry's latest reading, not the date you are reading this.
“Onlinebme” (@onlinebme), 4,715 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/onlinebme.
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.