Education — 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 8 October 2026 and assigned it the closest of 31 fixed categories, at 92% 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
8 measurements spanning 63 days, net +52. 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 229–301 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
10 Oct 2026, 02:21
293
+20
8 Oct 2026, 14:23
273
no change
29 Sept 2026, 03:58
273
+33
14 Sept 2026, 05:37
240
+2
6 Sept 2026, 10:21
238
+1
29 Aug 2026, 06:43
237
-2
14 Aug 2026, 20:26
239
-2
7 Aug 2026, 20:35
241
first reading
Engagement
19 posts held, back to 6 September 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 1 page of Telegram’s post history, 20 posts per page.
ERR · 30 days
96.4%
avg views ÷ 293 subscribers
Avg views / post
282
16 posts measured
Reaction rate
1.33%
reactions ÷ views · ER floor
Posts in window
16
of 19 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 6 October 2026
Posts held
19 (6 September 2026 – 6 October 2026)
Views total
4,519
Reactions total
60
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
8 Oct 2026, 14:23 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
3m 30s
Average length
3m 30s
Measured directly from 1 video 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
68 reactions across 19 posts, in 3 distinct kinds. The most used accounts for 95.6% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
65
95.6%
👍
2
2.94%
🔥
1
1.47%
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 19 of the 19 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 68 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 19 most recent posts we hold, published 6 September 2026 to 6 October 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.
🔴 #دانلود فایل صوتی اپیزود ۳۳ پادکست چراگاه: چرا بازی میکنیم؟ چرا میجنگیم؟
🎙
🗣اینستاگرام مهمان: دکتر مرسا ساعتچی
🎙️اینستاگرام میزبان: مهران حسنزاده
در این اپیزود درباره فیزیولوژی بازی و استرس و جنگ و دعوا و ارتباط عمیق آنها با سیستم عصبی خودمختار و تئوری پلیواگال پرداختیم..
با دکتر مرسا ساعتچی، دکترای مهندسی پزشکی ترمیمی از آلمان
و دانشآموخته تنظیم سیستم عصبی از استرالیا و انستیتو پلی واگال آمریکا
لینک تماشای ن…
🎙🎞 چراگاه ۳۳: چرا بازی میکنیم؟ چرا میجنگیم؟ گفتوگو با دکتر مرسا ساعتچی
در این اپیزود به بررسی فیزیولوژی بازی، ریشههای جنگیدن و ارتباط عمیق آنها با سیستم عصبی خودمختار و تئوری پلیواگال (Polyvagal Theory) پرداختیم.
—-
🎞لینک تماشا در یوتیوب
https://youtu.be/azkegXQrwhY?si=jSHNAfkqAebbj82K
——
🎧لینک شنیدن در اپهای پادکست
https://pod.link/1690024176
پادکست چراگاه شماره ۳۳: 🗣گفتوگو با دکتر مرسا ساعتچی ، متخصص تنظیم سیستم عصبی
امروز دوشنبه ساعت ۵ عصر در یوتیوب منتشر میشود.
«وقتی به اندازه کافی احساس امنیت نداشته باشیم که با هم بازی کنیم، با هم میجنگیم!»
WE FIGHT
WHEN WE DON'T FEEL SAFE ENOUGH
TO PLAY.
🎁⚠️فقط تا پایان امشب فرصت دارید برای خرید کتاب «مغز ایدئولوژیزده» از کد تخفیف ۲۰٪ استفاده کنید.
کد تخفیف: cheragah
🛒برای تهیه به نشر درنگ در تلگرام پیام دهید👈@Info_derang
📍یا از وبسایت خرید کنید👇
https://derangpub.com/product/ideological-brain/
پ.ن: این کتاب به زودی افزایش قیمت خواهد داشت. اگر قصد خرید دارید این فرصت را از دست ندهید.
📽️چرا ۲۵ درصد آدمها شکنجهگر نشدن؟
در آزمایش معروف میلگرام درباره اطاعت از قدرت، همه از نیمه تاریک اطاعت کورکورانه گفتند؛ اما راز آن ۲۵ درصدی که در بدترین شرایط نافرمانی و مقاومت کردند چه بود؟
🎬 نسخه کامل: اپیزود ۳۲ پادکست «چراگاه» | گفتوگو با دکتر مهدی شفا (مترجم کتاب مغز ایدئولوژیزده) را در یوتیوب چراگاه تماشا کنید یا در تمام پادگیرها بشنوید. 🎧✨
📚🎙️
https://youtube.com/shorts/agkKbVdnke4?si=9vUEXnzcyz0PAa-9
آیا واقعاً شستوشوی مغزی وجود دارد یا مغز بعضیها از قبل برای پذیرش ایدئولوژیها آمادگی بیشتری دارد؟ 🧠✨
در اپیزود جدید پادکست چراگاه با مرور کتاب «مغز ایدئولوژیزده»، سازوکار تعصب و امکانِ تغییرِ باورها را بررسی کردیم
🗣️در گفتوگو با دکتر مهدی شفا، متخصص ژنتیک، مترجم کتاب، و میزبان پادکست هشیوار👇
@houshivarpodcast
📚برای تهیه ترجمه رسمی این کتاب در ایران (که با خرید حق کپیرایت بینالمللی چاپ شده) به نشر درنگ پیام بد…
🔴 #دانلود فایل صوتی
اپیزود ۳۲ پادکست چراگاه: چرا مغزها را نمی توان شست؟
🎙
در این اپیزود از پادکست چراگاه، با دکتر مهدی شفا (میزبان پادکست هشیوار) گفتوگوی جذابی داریم درباره کتابی که به تازگی ترجمه کردهاند:
📖 کتاب مغز ایدئولوژیزده ، اثر دکتر لئور زمیگرود
@houshivarpodcast
-----------
من مهران حسنزاده هستم، مشاور تغذیه و میزبان شما در پادکست چراگاه
@cheragah_podcast
اینجا با دیدِ میانرشتهای (فرگشت، علوم اعصاب و…
سلام ودرود بههمراهان هشیوار،
مهمان پادکست «چراگاه» بودم؛ گفتوگویی درباره مغز ایدئولوژیزده
در پادکست «چراگاه»، به میزبانی آقای مهران حسنزاده درباره کتاب «مغز ایدئولوژیزده» (The Ideological Brain) اثر دکتر لئور زمیگرود گفتوگو کردیم؛ کتابی که ترجمه فارسی آن را بهتازگی انجام دادهام و در ایران توسط نشر درنگ منتشر شده است.
🧠 چرا بعضی ذهنها بیشتر در دام باورهای سخت و انعطافناپذیر گرفتار میشوند؟
🔍 چرا برخی افر…
❤6
Showing the 12 most recent of 19 posts we hold for @cheragah_podcast. 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
Republishes
Channels on the register whose posts this channel has forwarded.
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
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 10 October 2026 — this
entry's latest reading, not the date you are reading this.
“cheragah | چراگاه” (@cheragah_podcast), 293 subscribers as measured 10 October 2026. Telegram Register, tgregister.com/channel/cheragah_podcast.
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.