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 why it has no browse page of its own yet.
Growth
36 measurements spanning 54 days, net -1,184. 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 100,065–101,605 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 36
Measured (UTC)
Subscribers
Change
29 Sept 2026, 01:58
100,243
-170
17 Sept 2026, 19:39
100,413
-102
15 Sept 2026, 18:37
100,515
+12
14 Sept 2026, 03:58
100,503
-52
12 Sept 2026, 14:17
100,555
+5
10 Sept 2026, 09:18
100,550
+243
7 Sept 2026, 01:17
100,307
-53
4 Sept 2026, 07:18
100,360
-78
2 Sept 2026, 21:07
100,438
-63
1 Sept 2026, 21:25
100,501
-20
1 Sept 2026, 00:07
100,521
-24
31 Aug 2026, 00:33
100,545
+30
29 Aug 2026, 23:48
100,515
-5
28 Aug 2026, 23:13
100,520
-29
27 Aug 2026, 21:16
100,549
-40
26 Aug 2026, 23:43
100,589
-78
26 Aug 2026, 02:25
100,667
-17
25 Aug 2026, 02:06
100,684
-50
23 Aug 2026, 17:47
100,734
-25
22 Aug 2026, 00:23
100,759
first reading
Engagement
256 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 116 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
1.33%
avg views ÷ 100,243 subscribers
Avg views / post
1,330
106 posts measured
Reaction rate
0.464%
reactions ÷ views · ER floor
Posts in window
106
of 256 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 98 of 106 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 30 September 2026
Posts held
256 (31 July 2026 – 30 September 2026)
Views total
141,034
Reactions total
613
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
1 Oct 2026, 23:39 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
Photos
≈2,130
Videos
≈343
Links
≈1,970
Lifetime counters from Telegram’s own channel header, read 1 October 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Video runtime
29m 35s
Average length
1m 03s
Measured directly from 28 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
1,129 reactions across 226 posts, in 35 distinct kinds. The most used accounts for 47.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
536
47.5%
👍
184
16.3%
🔥
114
10.1%
👎
81
7.17%
🤣
61
5.40%
👏
25
2.21%
❤🔥
22
1.95%
🤯
19
1.68%
🥴
10
0.886%
🤔
9
0.797%
🍌
8
0.709%
🌭
7
0.62%
🆒
5
0.443%
😍
5
0.443%
🥰
5
0.443%
🥱
4
0.354%
⚡
3
0.266%
💘
3
0.266%
😡
3
0.266%
😨
3
0.266%
15 further kinds
22
1.95%
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 239 of the 256 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 1,169 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 256 most recent posts we hold, published 31 July 2026 to 30 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.
📌 گوگل مدل Gemini 4 Argon را معرفی کرد
گوگل از مدل جدید Gemini 4 Argon رونمایی کرد؛ مدلی که برای کارهای پیچیده و طولانی در مهندسی نرمافزار، امور حقوقی و مالی و دفاع سایبری طراحی شده است.
💻 عملکرد در بنچمارکها
• امتیاز ۷۷.۹٪ در DeepSWE v1.1 برای وظایف طولانی مهندسی نرمافزار؛ بهگفته گوگل، رکورد جدید این آزمون
• امتیاز ۵۱.۳٪ در AutomationBench برای کارهای سازمانی
• امتیاز ۹۱.۷٪ در LVBench برای درک ویدئوهای طولا…
🛡️ انویدیا یک سیستم امنیتی برای کنترل عاملهای هوش مصنوعی ساخته است
انویدیا پلتفرمی به نام Open Agent Safety Platform معرفی کرده که اجازه میدهد عاملهای هوش مصنوعی در یک محیط امن و ایزوله کار کنند.
🔹 چرا این پلتفرم لازم است؟
عاملهای هوش مصنوعی وقتی مدت طولانی کار میکنند، ممکن است از وظیفه اصلی خود منحرف شوند. مثلاً دستورهای مبهم را اشتباه بفهمند یا وقتی به یک ممنوعیت میخورند، راه دور بزنند. به این حالت «رانش» …
🤖 ۸ کاربرد جالب مدل Jev: از بررسی کد تا دستهبندی مقالات
مدل Jev از شرکت TypeSafe AI، برخلاف مدلهای زبانی معمول، بهجای تولید متن، به پرسشهای ساختاریافته پاسخ میدهد و تصمیمها و احتمالها را برمیگرداند. همین ویژگی آن را بسیار سریعتر و ارزانتر کرده است. در ادامه ۸ نمونه از کاربردهای واقعی آن را میبینید.
۱. بررسی درخواستهای ادغام کد با هزینه نزدیک صفر
یک کاربر یک بررسیکننده کد ساخته که تفاوتهای کد را به Jev…
🧠 هوش مصنوعی بهعنوان چهارمین انقلاب «مرکززدایی»: از کیهان تا ذهن
پژوهشی جدید با همکاری جفری هینتون و پژوهشگران دانشگاههای مختلف، استدلال میکند که هوش مصنوعی صرفاً یک فناوری تحولآفرین نیست، بلکه چهارمین انقلاب مرکززدایی در تاریخ خودشناسی انسان است.
🔭 سه انقلاب قبلی
· انقلاب کوپرنیکی (قرن ۱۶): زمین را از مرکز جهان برداشت و نشان داد انسان در مرکز کیهان نیست.
· انقلاب داروینی (قرن ۱۹): انسان را از قلهی آفرینش پای…
📌 مهمترین معرفیهای OpenAI در DevDay 2026
شرکت OpenAI علاوه بر Dots، بیش از ۲۰ محصول و قابلیت جدید معرفی کرد. مهمترین موارد عبارتاند از:
🚀 مدل GPT-6.1 Sol
نسخه جدید Sol برای کدنویسی عاملمحور، کار با کامپیوتر و وظایف حرفهای عرضه شد. OpenAI میگوید عملکرد آن به Astra نزدیک است، اما هزینه استاندارد توکنهایش یکپنجم Astra است.
⚡️ حالت Ultrafast
نسخه Ultrafast مدل Astra در Codex تا ۳۰۰ توکن در ثانیه تولید میکن…
📌 شرکت OpenAI دستیارهای شخصی Dots را معرفی کرد
شرکت OpenAI از Dots رونمایی کرد؛ دستیارهای هوش مصنوعی شخصیسازیشدهای در ChatGPT که با مدل GPT-6 Astra کار میکنند و برای انجام وظایف طولانی و چندمرحلهای طراحی شدهاند.
⚙️ دستیار Dots چه کاری انجام میدهد؟
• هر Dot یک رایانه مجازی اختصاصی در فضای ابری دارد و میتواند حتی پس از بستن ChatGPT، کار را در پسزمینه ادامه دهد.
• کاربران میتوانند نام، ظاهر، دستورالعملها …
🔍 فریکشنمکسینگ چیه؟
اصطلاح «Friction-maxxing» (ترکیبی از «اصطکاک» و پسوند اصطلاح اینترنتی «maxxing» به معنای بیشینهسازی) در ژانویه ۲۰۲۶ توسط کاترین جزر-مورتون، ستوننویس مجله The Cut، در مقالهای با عنوان «در سال ۲۰۲۶، ما فریکشنمکسینگ میکنیم» ابداع شد.
ایده ساده است: بهجای اینکه همه کارها رو با کمک فناوری آسانتر کنیم، عمداً کمی سختترشون کنیم تا تحملمون نسبت به ناراحتی بالا بره و حس رضایت و معنای بیشتری تجربه…
🎮 دیتاست صداهای طراحیشده NCSOFT: ابزاری برای توسعهدهندگان بازی
شرکت NC AI (زیرمجموعه NCSOFT) دیتاست و بنچمارکی به نام Designed Vocalizations Dataset منتشر کرده که در کنفرانس Interspeech 2026 پذیرفته شده است. هدف این پروژه، پر کردن شکاف در حوزه تبدیل صداهای غیرانسانی مانند غرش هیولا، صداهای رباتیک و افکتهای طراحیشده است — حوزهای که تا پیش از این بهدلیل نبود منابع عمومی، کمتر بررسی شده بود.
📦 محتوای دیتاست
دیت…
⚡️ مدل Claude Sonnet 5.5: سریعتر، ارزانتر، دقیقتر
شرکت Anthropic دومین مدل از خانواده Claude 5.5 را پس از Opus 5.5 معرفی کرد. Sonnet 5.5 بهعنوان مکملی سریعتر و ارزانتر برای Opus 5.5 طراحی شده و برای کارهای روزمره با تعریف مشخص — مانند رفع باگ، تهیه اسناد، ارائه و جدول — ایدهآل است.
بر اساس اندازهگیریها، Sonnet 5.5 پاسخها را ۳۰٪ سریعتر از Sonnet 5 تولید میکند و به دلیل مصرف توکن کمتر، هزینه هر کار تا ۳۰٪…
🧠 چرا مدلهای کوانتیزهشده «زیادی فکر میکنند»؟
پژوهشی از Meta AI نشان میدهد که فشردهسازی (کوانتیزاسیون) مدلهای استدلالی، آنها را «کندذهن» نمیکند، بلکه به «شکاک مزمن» تبدیل میکند.
📌 یافته اصلی
در ۵۲ درصد از خطاهای مدلهای کوانتیزه، مدل در یک مرحله میانی استدلال به پاسخ درست رسیده بود، اما آن را نهایی نکرد و به «فکر کردن» ادامه داد تا پاسخ درست را از دست بدهد.
طول زنجیره استدلال پس از کوانتیزاسیون تا ۴.۵ برا…
📉 سقوط قیمت تفکر: چرا هوش مصنوعی اینقدر سریع ارزان میشود؟
گزارش جدیدی از Epoch AI نشان میدهد هزینه دستیابی به یک سطح مشخص از عملکرد هوش مصنوعی، در سه سال گذشته هر فصل حدود ۴۷٪ کاهش یافته است — یعنی سالانه ۱۳ برابر ارزانتر شده است. این سرعت کاهش، از هر فناوری تحولآفرین دیگری در تاریخ سریعتر است.
📊 مقایسه با سایر فناوریها
برای درک بزرگی این عدد، مقایسهاش با نرخ کاهش قیمت فناوریهای دیگر مفید است:
· توالییا…
🌐 اخبار هوش مصنوعی
---
⚖️ دادگاه تجدیدنظر آمریکا Anthropic را در لیست سیاه پنتاگون نگه داشت
دادگاه منطقه کلمبیا قانونی بودن قرار گرفتن Anthropic در فهرست ریسک زنجیره تأمین را تأیید کرد. این وضعیت، استفاده نظامیان و پیمانکارانشان از Claude را در کارهای مربوط به وزارت دفاع ممنوع میکند. دادگاه ادعاهای شرکت درباره خودسرانه بودن تصمیم، تجاوز از اختیارات و نقض قانون اساسی را رد کرد. به نظر دادگاه، ارزیابی نسبت ریسک بر …
❤3
Showing the 12 most recent of 256 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.
Posts edited after publishing
@asrgooyeshpardaz edited 6 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
18 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 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 · 22,553 Telegram ranks this channel #8 of 62 here — alongside 61 others — read 20 September 2026
هوش مصنوعی [ AI ] @iFutureAI · 43,242 Telegram ranks this channel #13 of 89 here — alongside 88 others — read 28 August 2026
آکادمی آیولرن | هوش مصنوعی و برنامه نویسی @aiolearn · 160,013 Telegram ranks this channel #20 of 86 here — alongside 85 others — read 24 August 2026
هوش مصنوعی | AI @AI_University · 32,281 Telegram ranks this channel #29 of 83 here — alongside 82 others — read 5 September 2026
علی سیاه کلاه | Ali Siahkolah @alisiahkolah · 30,365 Telegram ranks this channel #52 of 75 here — alongside 74 others — read 18 September 2026
پولسازی با هوشمصنوعی @polsazi_abbasi · 20,900 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 29 September 2026 — this
entry's latest reading, not the date you are reading this.
“عصر گویش | هوش مصنوعی” (@asrgooyeshpardaz), 100,243 subscribers as measured 29 September 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.