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 14 September 2026 and assigned it the closest of 31 fixed categories, at 87% 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.
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 in near-verbatim form on 1 other registered channel. This entry also forms a group of 2 channels linked by matching posts. 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 (3 of the pairs behind the counts below)
Channels this entry shares post bodies with, where at least one matching post pair has no forward credit between the two channels, or to a common source, recorded in our data
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 0 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 19 comparable posts for this entry, running 5 April 2026 to 7 August 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).
Of the group of 2 channels, the one whose matching posts we hold earliest is @RetroProduct — which is this entry. That is a statement about our reading window, not a claim of authorship.
Recorded under the key clone_source, last confirmed 11 November 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.
Channels linked by matching posts
This channel is in a group of 2 registered channels linked by matching posts. Links are found by comparing text fingerprints across every channel on the register.
Which channel, if either, published first is deliberately not shown in this list. “First” could only mean first in our own crawl, which depends on how deep we happened to reach into each channel’s history, not on when the content was actually first posted. Our one hand-check read 45 of the highest-confidence flagged pairs against the live Telegram pages. In 18 of them the live page showed a “Forwarded from” credit that our database had no record of, so reading that pair as an unattributed copy was wrong; the check did not measure who published first. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Near-verbatim 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.
24 measurements spanning 93 days, net +6. 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 3,748–3,780 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 24
Measured (UTC)
Subscribers
Change
7 Nov 2026, 12:28
3,758
+3
4 Nov 2026, 01:47
3,755
-1
1 Nov 2026, 08:06
3,756
+4
29 Oct 2026, 15:36
3,752
-6
26 Oct 2026, 18:18
3,758
-3
23 Oct 2026, 17:15
3,761
+4
21 Oct 2026, 03:15
3,757
-2
18 Oct 2026, 09:37
3,759
-1
12 Oct 2026, 14:36
3,760
+1
5 Oct 2026, 16:59
3,759
-11
27 Sept 2026, 21:00
3,770
+1
18 Sept 2026, 04:58
3,769
+5
14 Sept 2026, 05:39
3,764
-4
10 Sept 2026, 04:41
3,768
-8
4 Sept 2026, 22:36
3,776
+3
29 Aug 2026, 07:13
3,773
+3
26 Aug 2026, 09:34
3,770
+2
23 Aug 2026, 02:04
3,768
+7
19 Aug 2026, 16:12
3,761
+2
16 Aug 2026, 05:48
3,759
first reading
Engagement
20 posts held, back to 5 April 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 5 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 20 posts for this entry, the most recent from 7 August 2026. An engagement rate over an empty window would be a number about nothing.
Reaction mix
444 reactions across 19 posts, in 14 distinct kinds. The most used accounts for 42.3% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
188
42.3%
💔
83
18.7%
👌
38
8.56%
👍
30
6.76%
😢
28
6.31%
😡
22
4.95%
🔥
19
4.28%
🤯
16
3.60%
👏
8
1.80%
🥴
6
1.35%
🤩
3
0.676%
😍
1
0.225%
😐
1
0.225%
🤔
1
0.225%
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 20 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 444 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 5 April 2026 to 7 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.
سلام به همگی
براساس صحبتهایی که این مدت با خیلی از بچه ها داشتیم و همینطور مصاحبه ها و جلسات منتورشیپ موارد پرتکراری رو در بچهها دیدیم.
به همین دلیل یک نظرسنجی طراحی کردیم که اگر چند دقیقه وقت بذارید و تکمیلش کنید خیلی عالی میشه و حتما در آینده نزدیک نتایجش و احیانا مسیری که بتونه به دغدغه ها کمک کنه رو با شما به اشتراک میذاریم.
نهایتا ۱۰ دقیقه هم از شما وقت نمیگیره:
https://survey.retro.college/
@RetroProduct
پاسخ به مهمترین #سوالات_مصاحبه
(ویژه تمام پوزیشنهای حوزه تک)
سوال ۱: از خودت بگو
Tell me about yourself..
+ دلیل این سوال چیست؟
+ چه پاسخی میتواند پاسخ صحیح باشد؟
+ رویکرد پاسخ به سوال؟
🔻بطور خلاصه باید گفت:
- کوتاه پاسخ دهید، صادقانه و جذاب
- نقاط قوت خود را نشان دهید
- داستان خود را به نیازهای آنها گره بزنید
اسلایدهای کامل را در این پست لینکدین ببینید.
@RetroProduct
یک مدتی بود که ذهنم درگیر این موضوع بود و حالا توی پست آخر لینکدین درباره فرق سابقه کاری و تجربه نوشتم و خیلی دوست دارم که نظر شما رو هم دربارهش بدونم.
بیاید دربارهش صحبت کنیم
لینک پست👇👇
https://lnkd.in/eijuimhq
@RetroProduct
خلاصه کتاب تست مامان
(The Mom Test)
حالا در ۵۰ صفحه، بدون از دست دادن حتی یک ایدهی مهم
این کتاب یه سؤال ساده رو جواب میده: چطور سؤال بپرسیم که حتی مادرمون هم نتونه بهمون دروغ بگه؟
توی این خلاصه میبینید:
🔹 ۳ قانون طلایی آزمون مامان
🔹 تحلیل ۱۴ سؤال رایج (کدومش خوبه، کدومش فاجعهست)
🔹 چطور تعارف، حاشیه و ایدههای گمراهکننده رو از دادهی واقعی جدا کنیم
🔹 دهها دیالوگ واقعی از خود کتاب (نسخهی غلط / نسخهی درست…
#CaseStudy
برای مساله بالا (دیجی کالا) این نکات رو میتونید به یاد داشته باشید.
خرد کردن مساله و فهم دقیق مشکل قدم اصلی و مهم برای حل مساله ست.
@RetroProduct
#CaseStudy
❇️ نظرتون چیه که گاهی یک سری مسائل واقعی رو مطرح کنیم تا بهش فکر کنیم؟
کیس اول:
دیجیکالا - طراحی فیچر جدید
🔹تحقیقات نشان داده:
حدود ۶۰٪ کاربران قبل از خرید کالا، قیمت آن را در چند سایت مختلف بررسی میکنند.
مدیر محصول پیشنهاد داده:
قابلیت Price Alert اضافه شود تا وقتی قیمت کالا کاهش یافت، به کاربر اطلاع داده شود.
🔶 قبل از ساخت این قابلیت چه مراحلی را طی میکنید؟
خروجی مورد انتظار
• ارزش پیشنها…
#وبینار #رایگان
⭐️ کسبوکارهای بزرگ چطور تصمیمات دادهمحور میگیرند؟
🔻آنچه در این وبینار مطرح خواهد شد:
◀️ یک تصمیمگیری داده محور چه خصوصیاتی دارد؟
◀️ چگونه میتوان با کمک گرفتن از دادهها تصمیم گرفت؟
◀️ تصمیمسازی و تصمیمگیری در سازمانهای بزرگ بر عهده چه کسانی است؟
در گفتگو با: روزبه داداش زاده
Pricing & Data Science Manager @ Snapp!
ویدیو این وبینار به آپارات اضافه شد:
https://www.aparat.com/v/tvxgd40
@Ret…
گوگل یه دوره ۵ روزه کاملا رایگان برای ساخت AI Agent
گذاشته که اسمش AI Agents Intensive هست و از ۱۵ تا ۱۹ ژوئن برگزار میشه.
نه پیشنیاز خاصی داره، نه هزینهای. فقط ۵ روز یاد میگیری چطور از صفر یه Agent هوشمند بسازی.
با همون مفاهیمی مثل Vibe Coding که تا همین چند وقت پیش فقط توی تیمهای تخصصی بهشون میپرداختن، حالا توی این دوره قابل دسترسه.
لینک ثبتنام: 👇 🔗
https://www.kaggle.com/competitions/5-day-ai-agents-i…
این دو هفته حدود 200 #رزومه برام ارسال شد که در برخی موارد فقط فایل رزومه رو باز می کردم و میبستم! بدلیل زیاد بودن رزومه ها هم نمیشد به هر کسی فیدبک بدم و خب این الگویی هست که در ارسال رزومه به شرکت ها هم زیاد پیش میاد و اصلا وارد فانل نمیشه و در چند ثانیه رزومه بسته میشه و تمام!
حالا یک رزومه خوب چه خصوصیاتی داره؟
دوست دارم یک جلسه آنلاین بذاریم و با بررسی چند رزومه واقعی این موارد رو چک کنیم اما مواردی که به نظرم…
#موقعیت_شغلی
مدیرمحصول
طراح محصول
جاوا دولوپر
در شرکت راهگزین رایانه، پروژه سایت و اپلیکیشن همراه من
بچه ها به دیده شدن این پست در لینکدین کمک کنید.
@RetroProduct
واقعا ناراحت و اذیتم از حماقت مسئولینی که این حجم تعدیل و بیکاری آدما رو به حساب شرایط جنگ میذارن و نه به حساب قطع کردن اینترنت!
همون اینترنتی که حدود ۷۰ روزه قطع کردند و راه درامد صدها هزار آدم رو به راحتی بستن و بعد به قیمتی چند برابر همون اینترنت قبلی رو با پر کردن یک فرم بمون میفروشن!
یعنی اینترنت قبلی امنیت کشور رو به خطر مینداخت و اگر دو میلیون حق اشتراک بگیریم و هر گیگ رو ۵۰ تومن بفروشیم اتفاقی نمیفته!
لعنت…
❤52😢11👏5🥴2
Showing the 12 most recent of 20 posts we hold for @RetroProduct. 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 recorded as deleted after publishing
We recorded 1 post on @RetroProduct as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.
A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.
First seen missing
7 August 2026
Most recent seen missing
7 August 2026
Median time before seen missing
15 days
Seen missing in the last 30 days
0
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 4 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.
Handles this channel named that no longer answer
Dead references
1
handles named in this channel’s posts, vacant today
Evidenced gone
0
we ourselves saw one of these resolve, at some point
Never seen alive
1
vacant every time we have ever looked
@RetroProduct named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
@retroptoduct named in 2 posts, 8 August 2026 – 9 August 2026
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.
امید امانی | Omid Amani @dr_amaniiiii · 97,775 Telegram ranks this channel #6 of 91 here — alongside 90 others — read 16 August 2026
هنر ایده خلاقیت @upcyclingidea · 26,288 Telegram ranks this channel #7 of 67 here — alongside 66 others — read 15 September 2026
Arman Garshasbi @Armangarshasbiofficial · 49,870 Telegram ranks this channel #26 of 90 here — alongside 89 others — read 27 August 2026
IELTS Channel @PARGARIELTS · 25,790 Telegram ranks this channel #45 of 87 here — alongside 86 others — read 14 September 2026
Share the Joy @sharethejoy · 30,231 Telegram ranks this channel #48 of 87 here — alongside 86 others — read 6 September 2026
| پادکست فارسی انسانک | @ensanak · 31,418 Telegram ranks this channel #48 of 86 here — alongside 85 others — read 6 September 2026
IEM Twitter | توییتر مهندسی صنایع و مدیریت @IEMTwitter · 26,751 Telegram ranks this channel #50 of 91 here — alongside 90 others — read 12 September 2026
UI/UX Tutorial @uigraphic · 33,865 Telegram ranks this channel #57 of 87 here — alongside 86 others — read 3 September 2026
Mindq @mindqinfo · 27,384 Telegram ranks this channel #59 of 84 here — alongside 83 others — read 10 September 2026
برنامه ریزی رایگان مطالعه زبان ارشد و دکتری @GeneralEnglishPlan · 33,423 Telegram ranks this channel #65 of 90 here — alongside 89 others — read 5 September 2026
پادکست پلی لیست|Playlist @Playlistpodcas · 156,453 Telegram ranks this channel #68 of 95 here — alongside 94 others — read 12 August 2026
پادکست آقای مالیات 📻 @MrTax_podcast · 22,528 Telegram ranks this channel #75 of 95 here — alongside 94 others — read 20 September 2026
گفت و چای | فهیم عطار @fahimattar · 43,207 Telegram ranks this channel #83 of 96 here — alongside 95 others — read 28 August 2026
Kooshiar @kooshiar3 · 25,589 Telegram ranks this channel #86 of 93 here — alongside 92 others — read 14 September 2026
This channel appears in 14 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 7 November 2026 — this
entry's latest reading, not the date you are reading this.
“↻ Retro Product | رترو پروداکت” (@RetroProduct), 3,758 subscribers as measured 7 November 2026. Telegram Register, tgregister.com/channel/RetroProduct.
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.