Education — 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 11 August 2026 and assigned it the closest of 31 fixed categories, at 71% 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.
Observations
These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.
Content that also appears on other registered channels
Posts published here appear word for word on 4 other registered channels. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.
Matching posts — open both and compare (6 of the pairs behind the counts below)
Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.
What this cannot establish
MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.
Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. None of the 29 matches recorded here fall after that date, so for this entry we cannot say whether any of them carried a credit. The duplication is measured; the absence of attribution is not.
“Published first” means first in this corpus. We hold 12 comparable posts for this entry, running 4 November 2023 to 18 July 2026. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.
The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.
Verbatim republication has three causes and the text separates only two: a clone/mirror, unattributed copy-paste, or BOTH channels copying a common third source that neither attributes. The spread filter (content held by at most 8 channels) reduces the third and does not remove it.
'Earliest' means earliest IN THIS CORPUS. A channel ingested one page deep will look younger than a neighbour ingested in full; corpus_coverage above is there to be checked before the direction is believed.
shared_verified_est extrapolates the sampled pass rate over the full narrow match count; sampled/passed are the numbers actually measured.
Absence of a forward header is not proof of intent: Telegram lets a channel disable forward attribution, and a credit written in the body is not parsed as attribution here (mention_edge_either_way above is the closest available signal).
Across the whole group of 5, the earliest publisher we hold is @adobestar. That is a statement about our reading window, not a claim of authorship.
Recorded under the keys clone_copy · clone_source, last confirmed 7 August 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.
Also posting the same content
This channel’s posts match, word for word or near enough, posts on 4 other registered channels, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.
Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.
3 measurements spanning 7 days, net +1. 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 204–205 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
12 Aug 2026, 23:06
205
+1
7 Aug 2026, 04:00
204
no change
5 Aug 2026, 17:26
204
first reading
Engagement
18 posts held, back to 4 November 2023 — 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 pageof Telegram’s post history, 20 posts per page.
ERR · 30 days
2.93%
avg views ÷ 205 subscribers
Avg views / post
6.0
1 post measured
Reaction rate
—
this channel exposes no reaction counts
Posts in window
1
of 18 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 18 July 2026
Posts held
18 (4 November 2023 – 18 July 2026)
Views total
6
Reactions total
—
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Aug 2026, 04:00 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.
آموزش های فارسی و رایگان هوش مصنوعی
تعدادی از آموزش های فارسی و رایگان هوش مصنوعی من رو هم اینجا میتونید ببینید:
کانال یوتوب من:
https://www.youtube.com/@ImanAI24/videos
فقط 6 کلمه در روز! شاید کوچیک به نظر برسه، ولی بعد از سه ماه ، با جمع زدن اون 6 کلمه ، به عدد 540 می رسی ، که یه دستآورد بزرگ هست! با این روش به فهم و یادگیری زبان انگلیسی خیلی نزدیک تر میشی!
تفاوت اینجاست :
❤️ تمرکز در این کانال بر روی کلمات انگلیسی رایج و متداول در دنیای تولید محتوا ، افترافکت ، پریمیر پرو ، فتوشاپ و یوتیوب است . ❤️
آدرس کانال در سروش
https://splus.ir/ZabanEnglishSade
به تازگی در تلگرام هم را…
سلام دوستان
تصویر آنالیتیکس یوتیوب من:
درآمد : 6700 دلار
بازه زمانی : 365 روز گذشته
تعداد بازدید : 2 میلیون و چهارصد هزار
موضوع کانال : آموزش هوش مصنوعی
امیدوارم شما هم بتونید یک کانال یوتیوب راه اندازی بکنید و با تخصص خودتون از توی خونه درآمد خوب دلاری داشته باشید .خیلی دلم میخواد که در زمینه کسب درآمد از کانال یوتیوب براتون ویدیو بسازم ولی متاسفانه درگیر تولید محتوا هستم .از طریق این کانال با اسپانسرهای خیلی بزرگ…
پک فونت های فارسی جذاب که همه باید داشته باشن 👆👆👆
❤️ نصب فونت ها 👆👆 :
فقط کافیه فونت ها رو از حالت فشرده خارج کنید ، همه فونت ها رو انتخاب کنید و روی یکی از اونها کلیک راست کرده و گزینه Install رو بزنید تا فونت ها توی ویندوز نصب بشن و بعدش در تمام نرم افزارهای گرافیکی میتونید از این فونت ها استفاده کنید .
در آموزش قبلی ( ادیت ویدیو با موشن برو ) دیدین که موشن برو در تایپ متن فارسی مشکل داره !!! اما توی آموزش یک ابزار خیلی سبک و ساده و کاربردی رو معرفی کردم که مشکل رو حل میکنه !!!
ابزار تایپ فارسی در افترافکت / پریمیر / تری دی مکس / سینمافوردی و تمام نرم افزارهای ادوبی تمام نسخه ها !!!! بهمراه فونت 👇👇👇
😍 آموزش ادیت ویدیو با ترنزیشن و افکت های Motion Bro
دراین آموزش یاد میگیرید که با موشن برو ویدیوهای خودتون رو ادیت کنید و از افکت ها و ترنزیشن های موشن برو به صورت رایگان برای تدوین استفاده کنید .
تماشای آموزش در یوتیوب 👇👇
✅ https://youtu.be/i4rNzWgUjxs
❤️ نسخه جدید Motion Bro 4.5.1
💥 پکیج رایگان افکت ویدیویی و ترنزیشن وافکت متنی
👌 برای تدوین با پریمیر پرو
آموزش نصب موشن برو برای پریمیر پرو نسخه Motion Bro 4.5.1 که یک پلاگین و بانک رایگان افکت ویدیویی و ترنزیشن برای تدوین است را در ادامه خواهیم داشت .پلاگین Motion Bro 4.5.1 دارای یک کتابخانه گسترده از افکت های ویدیویی، الگوها، ترنزیشنها، افکتهای متنی ، افکت های صوتی ، موزیک متن و موارد دیگر است که میتوان آنها ر…
سلام سال نو مبارک : سالی پر از سلامتی ، موفقیت و پیشرفت برای تک تک شما آرزو میکنم.
در سال 1402 ، کانال یوتوب خارجی من در زمینه هوش مصنوعی مانیتایز شد ، به درآمد دلاری رسید و رشد عجیبی داشت ، از طریق کانال یوتوب خارجیم ، برند بین المللی و معروف Skillshare ، اسپانسرم شد . این مهمترین اتفاق زندگیم در سال 1402 بود که درهای جدیدی رو به روم باز کرد .می خوام شما رو هم با این فضا آشنا بکنم . براتون توصیه هایی برای سال 14…
سلام دوستان
💥 جهت یادآوری 💥 امشب 21-بهمن ساعت 8 تا 10 جلسه دوستانه آنلاین در مورد راه اندازی کانال یوتیوب و درآمدزایی دلاری داریم .
جلسه در گوگل میت - Google Meet برگزار میشه . اپلیکیشن ، گوگل میت فیلتر نیست ! احتیاج به VPN نداره ! رایگان هست و در موبایل های اندروید به صورت پیش فرض هست و جهت برگزاری کلاس آنلاین و تماس تصویری استفاده میشه . لینک ورود به جلسه آنلاین رو ساعت 8 توی کانال میزارم . روی لینک کلیک …
Showing the 12 most recent of 18 posts we hold for @tarsimpress. 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 12 August 2026 — this
entry's latest reading, not the date you are reading this.
“ترسیم پرس | TarsimPress.com” (@tarsimpress), 205 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/tarsimpress.
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.
