Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 10 August 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 the other topics.
Growth
63 measurements spanning 95 days, net -1,829. 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 99,324–101,701 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 63
Measured (UTC)
Subscribers
Change
8 Nov 2026, 12:57
99,598
-35
7 Nov 2026, 08:25
99,633
-56
6 Nov 2026, 06:46
99,689
-48
5 Nov 2026, 04:27
99,737
-22
4 Nov 2026, 04:30
99,759
+17
2 Nov 2026, 10:04
99,742
-27
1 Nov 2026, 09:06
99,769
-14
31 Oct 2026, 06:59
99,783
-37
30 Oct 2026, 07:49
99,820
+3
29 Oct 2026, 10:50
99,817
-47
28 Oct 2026, 07:40
99,864
-9
27 Oct 2026, 07:46
99,873
-31
26 Oct 2026, 04:27
99,904
-33
25 Oct 2026, 00:50
99,937
-46
24 Oct 2026, 00:36
99,983
-21
23 Oct 2026, 02:10
100,004
-4
22 Oct 2026, 02:40
100,008
-15
20 Oct 2026, 19:58
100,023
+6
19 Oct 2026, 20:37
100,017
+4
18 Oct 2026, 20:18
100,013
first reading
Engagement
311 posts held, back to 31 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 190 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
1.87%
avg views ÷ 99,598 subscribers
Avg views / post
1,860
38 posts measured
Reaction rate
0.261%
reactions ÷ views · ER floor
Posts in window
38
of 311 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. It is computed over the 37 of 38 measured posts that carry a reaction reading, and over those same posts' views.
What these figures were computed from
Window
Rolling 30 days · latest post in window 7 November 2026
Posts held
311 (31 July 2026 – 7 November 2026)
Views total
70,820
Reactions total
181
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
8 Nov 2026, 05:09 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
40m 18s
Average length
1m 11s
Measured directly from 34 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
1,421 reactions across 278 posts, in 36 distinct kinds. The most used accounts for 47.9% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
681
47.9%
👍
215
15.1%
🔥
136
9.57%
👎
84
5.91%
🤣
66
4.64%
👏
41
2.89%
🤯
34
2.39%
❤🔥
22
1.55%
🤡
17
1.20%
🥴
17
1.20%
💔
12
0.844%
😍
12
0.844%
🤔
10
0.704%
🍌
9
0.633%
🌭
7
0.493%
🥰
6
0.422%
🆒
5
0.352%
🤷♀
5
0.352%
😭
4
0.281%
🥱
4
0.281%
16 further kinds
34
2.39%
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 291 of the 311 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 1,461 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 311 most recent posts we hold, published 31 July 2026 to 7 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 Stars
Stars received
2
across the posts below
Posts paid on
2
of 311 we hold a reading for · 0.6%
Most on one post
1
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @asrgooyeshpardaz. 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 311 most recent posts we hold for this entry, published 31 July 2026 to 7 November 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.
Recent posts
Telegram’s own ‘edited’ marker: 21 of 38 posts held from the last 30 days (55.3%) 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).
👾 تلهپاتی دستهای: «تلهپاتی» برای هوش مصنوعی از خانوادههای مختلف
محققان از موسسات MIT و MIT-IBM، روش SwaT را معرفی کردهاند: روشی برای تبادل مستقیم حافظه کش (KV-cache) بین مدلهای زبانی بزرگ (LLM) از خانوادههای مختلف، بدون تولید متن و بارگذاری مجدد (prefill).
💡 اصل کار چیست؟
به جای خلاصههای متنی که ممکن است معنا را تغییر دهند، SwaT به طور مستقیم وضعیت داخلی (حافظه کش) مدل فرستنده را به فضای مدل گیرنده منتقل م…
📊 آنتروپیک یک مدل قدرتمند دارد که هنوز منتشرش نکرده است
سازمان مستقل METR که مدلهای هوش مصنوعی را ارزیابی میکند، در گزارش تازهای که پنجم نوامبر منتشر شد، خبر داده که آنتروپیک مدلی در دست دارد به نام «Model 4» که هنوز بهصورت عمومی عرضه نشده است. این مدل در تمام ۱۵ آزمون استانداردی که METR استفاده میکند، امتیازی بالای ۵۰ درصد گرفته — چیزی که تا حالا برای هیچ مدلی اتفاق نیفتاده بود.
🔍 این ارزیابی چطور انجام شد؟
…
🔥 آیا افزایش طول متن ورودی در مدلهای زبانی بزرگ (LLM) مزیتی واقعی است؟
محققان شرکت NVIDIA نشان دادهاند که پنجرههای حافظه بسیار بزرگ در مدلهای هوش مصنوعی (تا بیش از 1 میلیون توکن) برای حل مسائل پیچیده ضروری نیستند.
🧠 نکات کلیدی:
⚡️ گران و کند: تولید توکنها با پنجره حافظه 256K، 3.6 تا 7 برابر گرانتر و دو برابر بیشتر طول میکشد تا زمانی که از پنجره حافظه 16K استفاده شود.
✂️ فشردهسازی متن: فشردهسازی منظم تاریخ…
🔐 معرفی FlashProve توسط Anthropic: اثبات رمزنگاری هویت مدلهای هوش مصنوعی
شرکت Anthropic یک سیستم پژوهشی متنباز به نام FlashProve رونمایی کرد که با استفاده از رمزنگاری، امکان اثبات هویت مدلهای هوش مصنوعی را فراهم میکند؛ بدون اینکه نیازی به افشای وزنهای محرمانه مدل باشد.
🔍 مشکل کجاست؟
وقتی کاربران یا شرکتها از طریق API به یک مدل زبانی درخواست میفرستند، راه مطمئنی برای اطمینان از اینکه پاسخ واقعاً توسط همان مد…
📌 شرکت Tencent نسخه نهایی مدل پرچمدار Hy4 را با قابلیت بینایی و کار با کامپیوتر معرفی کرد
تیم Tencent Hunyuan نسخه نهایی مدل Hy4 را منتشر کرد؛ مدلی که برای نخستین بار به درک تصویر و قابلیت کار با کامپیوتر مجهز شده و برای وظایف عملی در حوزه مالی، فرانتاند، بازیسازی، کارهای اداری و پژوهش بهینه شده است.
⚙️ قابلیتهای جدید
• درک بصری و Computer Use: تحلیل دقیق تصاویر و نمودارها، کار با محیطهای گرافیکی و خط فرمان، …
🎙 روایت داریو و دانیلا آمودی از جدایی از OpenAI، خط قرمزهای اخلاقی و سایه استفاده نظامی از Claude
داریو و دانیلا آمودی، بنیانگذاران شرکت Anthropic، در گفتوگویی با والتر آیزاکسون در پادکست «The Builders» از پشتصحنه جدایی خود از OpenAI، فلسفه ساخت مدل کلود و نگرانیهایشان از آینده هوش مصنوعی گفتهاند. اما این مصاحبه زمانی منتشر میشود که خبر استفاده نظامی از Claude در حملات به ایران، سایه سنگینی بر ادعاهای اخلاقی ا…
علّت عاشق ز علتها جداست
عشقْ اصطرلاب اسرارِ خداست
عاشقی گر زین سر و گر زان سرست
عاقبت ما را بدان سَر رهبرست
هرچه گویم عشق را شرح و بیان
چون به عشق آیم خجل باشم از آن
گرچه تفسیرِ زبان روشنگرست
لیک عشقِ بیزبان روشنترست
چون قلم اندر نوشتن میشتافت
چون به عشق آمد قلم بر خود شکافت
عقل در شرحش چو خَر در گِل بخفت
شرحِ عشق و عاشقی هم عشق گفت
آفتاب آمد دلیلِ آفتاب
گر دلیلت باید از وی رو متاب
چون حدیث روی شمسالدّین ر…
📌 گوگل مدل Gemini 4 Carbon را معرفی کرد؛ فقط دو هفته پس از Argon
🛡 امنیت سایبری
ادعای اصلی گوگل، بهترین عملکرد جهان در کارهای سایبری و وظایف طولانیمدت است:
• امتیاز ۸۸.۲٪ در SEC-bench Pro
• امتیاز ۵۴.۸٪ در ExploitGym، حدود ۱۳ واحد بالاتر از Astra
• موفقیت صفر درصدی حملات تزریق پرامپت در بنچمارک Gray Swan
💻 کدنویسی و وظایف طولانی
• امتیاز ۶۵.۲٪ در Terminal-Bench 4.0؛ بالاتر از همه رقبا در جدول رسمی گوگل
• صعود ۸…
📌 شرکت DeepMind مدل Genie 4 را معرفی کرد؛ شبیهساز دنیای واقعی و تعاملی
🌐 مدل Genie 4 برخلاف مدلهای معمول تولید ویدئو، صرفاً یک کلیپ از پیشتعیینشده نمیسازد؛ بلکه یک محیط زنده است که در لحظه به فرمانها پاسخ میدهد. کاربر میتواند با زبان طبیعی صحنه را تغییر دهد؛ مثلاً هوا را عوض کند، دوربین را جابهجا کند یا شخصیت و شیء جدیدی به صحنه اضافه کند.
🔵 ویژگیهای اصلی
• فیزیک پیشرفته: شبیهسازی آیرودینامیک، برخوردها و…
🧩 معرفی SimplexLoRA: توزیع هوشمند بودجه پارامترها برای آموزش بهتر مدلهای زبانی
پژوهشگرانی از شرکتهای T-Technologies، MFTI، Sber و دانشگاه Innopolis روشی جدید به نام SimplexLoRA را در کنفرانس EMNLP 2026 معرفی کردهاند که به مدلهای زبانی کمک میکند بدون افزایش تعداد پارامترهای قابل آموزش، با دقت بیشتری برای وظایف خاص تنظیم شوند.
🔍 مشکل کجاست؟
در روش استاندارد LoRA، وزنهای اصلی مدل ثابت میمانند و آموزش از طریق م…
🧮 شرکت OpenAI مدعی اثبات «حدس هموارسازی موضعی سوگه» شد: زنجیرهای از مسائل کلاسیک آنالیز هارمونیک
شرکت OpenAI در تاریخ ۲۶ اکتبر ۲۰۲۶ اعلام کرد که مدلهای داخلی آن یک اثبات ۲۵ صفحهای برای حدس هموارسازی موضعی سوگه (Sogge's Local Smoothing Conjecture) تولید کردهاند؛ حدسی که کریستوفر سوگه در سال ۱۹۹۱ مطرح کرد و بهعنوان یکی از مسائل بنیادین در آنالیز هارمونیک شناخته میشود.
📜 زنجیره استنتاج: از یک حدس تا چهار حدس بزر…
📌 شرکت OpenAI مدل GPT-6.1 Terra را معرفی کرد
مدل GPT-6.1 Terra برای انجام کارهای روزمره در نرمافزارها طراحی شده است؛ محتوای صفحه را میفهمد، با کمک ابزارها در برنامههای مختلف اقدام میکند و در نگارش و ویرایش متن نیز طبیعیتر عمل میکند.
📊 عملکرد
• سرعت تکمیل وظایف کامپیوتری بیش از سه برابر GPT-6 Astra و GPT-6.1 Sol گزارش شده است
• عملکرد در Agents’ Last Exam با سطح استدلال medium نسبت به GPT-5.6 Terra حدود ۱۵ د…
❤1
Showing the 12 most recent of 311 posts we hold for @asrgooyeshpardaz. 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
@asrgooyeshpardaz edited 7 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
17 August 2026
Most recent edit
13 October 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 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.
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.
Channels Telegram recommends alongside this 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 24 August 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.
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.
راز پرش / هوش مصنوعی @razeparesh · 22,234 Telegram ranks this channel #8 of 62 here — alongside 61 others — read 20 September 2026
هوش مصنوعی [ AI ] @iFutureAI · 42,541 Telegram ranks this channel #13 of 89 here — alongside 88 others — read 28 August 2026
آکادمی آیولرن | هوش مصنوعی و برنامه نویسی @aiolearn · 156,839 Telegram ranks this channel #20 of 86 here — alongside 85 others — read 24 August 2026
هوش مصنوعی | AI @AI_University · 31,935 Telegram ranks this channel #29 of 83 here — alongside 82 others — read 5 September 2026
علی سیاه کلاه | Ali Siahkolah @alisiahkolah · 29,518 Telegram ranks this channel #52 of 75 here — alongside 74 others — read 18 September 2026
پولسازی با هوشمصنوعی @polsazi_abbasi · 20,627 Telegram ranks this channel #54 of 79 here — alongside 78 others — read 29 September 2026
This channel appears in 6 seed channels' Telegram-generated recommendation lists 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 8 November 2026 — this
entry's latest reading, not the date you are reading this.
“عصر گویش | هوش مصنوعی” (@asrgooyeshpardaz), 99,598 subscribers as measured 8 November 2026. Telegram Register, tgregister.com/channel/asrgooyeshpardaz.
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.