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Channel
@ytttt
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Handles named that no longer answer · Cite this entry
150,209subscribers
-6,768 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 100,000–316,228.
| Telegram ID | -1001093473592 |
|---|---|
| Type | Channel |
| Username | @ytttt |
| Created | Between 1 September 2016 and 30 September 2017 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 19 October 2026 |
| Measurements held | 41 |
| Confirmed unchanged | 1 time, most recently 19 October 2026 |
| On Telegram | t.me/ytttt |
Gambling & betting — 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 9 August 2026 and assigned it the closest of 31 fixed categories, at 86% 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.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 19 Oct 2026, 00:58 | 150,209 | +972 |
| 18 Oct 2026, 00:37 | 149,237 | +1,385 |
| 16 Oct 2026, 23:54 | 147,852 | +480 |
| 16 Oct 2026, 02:07 | 147,372 | -84 |
| 14 Oct 2026, 22:50 | 147,456 | -44 |
| 13 Oct 2026, 15:36 | 147,500 | +2,712 |
| 9 Oct 2026, 17:58 | 144,788 | +1,036 |
| 5 Oct 2026, 11:21 | 143,752 | +5,541 |
| 29 Sept 2026, 12:02 | 138,211 | +8,399 |
| 17 Sept 2026, 21:01 | 129,812 | +1,230 |
| 15 Sept 2026, 15:22 | 128,582 | +472 |
| 14 Sept 2026, 01:57 | 128,110 | +787 |
| 12 Sept 2026, 05:38 | 127,323 | +1,358 |
| 9 Sept 2026, 21:36 | 125,965 | +1,217 |
| 6 Sept 2026, 17:38 | 124,748 | -242 |
| 4 Sept 2026, 06:55 | 124,990 | +621 |
| 2 Sept 2026, 22:26 | 124,369 | +175 |
| 1 Sept 2026, 22:55 | 124,194 | +320 |
| 1 Sept 2026, 00:37 | 123,874 | +47 |
| 30 Aug 2026, 23:52 | 123,827 | first reading |
1,847 posts held, back to 7 August 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 148 pages of Telegram’s post history, 20 posts per page.
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 836 of 851 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 19 October 2026 |
|---|---|
| Posts held | 1,847 (7 August 2026 – 19 October 2026) |
| Views total | 8,097,208 |
| Reactions total | 73,322 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 19 Oct 2026, 00:45 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.
Measured directly from 295 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.
84,312 reactions across 975 posts, in 10 distinct kinds. The most used accounts for 12.8% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 10,806 | 12.8% | |
| 🤩 | 10,758 | 12.8% | |
| 🎉 | 10,553 | 12.5% | |
| 😍 | 10,525 | 12.5% | |
| 🥰 | 10,483 | 12.4% | |
| 🔥 | 10,438 | 12.4% | |
| ❤🔥 | 10,364 | 12.3% | |
| ❤ | 10,300 | 12.2% | |
| 😁 | 43 | 0.051% | |
| 👏 | 42 | 0.05% |
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 977 of the 1,000 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 84,312 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 1,000 most recent posts we hold (the sample is capped at 1,000), published 11 September 2026 to 19 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.
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#网友投稿 #提醒 买 iPhone 18 的时候,一定要先问清楚是 未激活(Non-active) 还是 已激活(Active) 的。 如果是已激活的,我们刚开始用的时候,显示开机激活的日期是跟当天对不上的。这种之前就被别人拆封查看过了,所以并不是全新的原封未拆机(原装未拆封),不过它的价格会相对便宜一些。 如果是未激活的,那就绝对是全新的原装拆封机,开机显示的激活时间和我们亲自撕膜/拆封的时间是一致的。以前他们那样说的时候,我都相信了,只能拿着手机回来了。但后来自己仔细去研究了解了一下,才发现原来是这么回事(被套路了)。 我把这些讲出来,就是为了告诉大家,提醒大家别再被他们一起合伙给坑了(套路了) ✅集团导航 @CAuuuu ✅投稿商务 @DIIII 😶有偿投稿机器人
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#网友爆料:将军山999园区B1老板阿豪公司,惨无人道!骗招、小黑屋打人、高压体罚、杠水桶。 你以为你在将军山就安全了吗?等着吧,我肯定会通过大使馆,把你们丑陋的一面告诉大使馆。 现在的将军山999园区,基本上全部都是做美国市场的,都在骗美国人的钱。大部分公司都是做高压体罚、杠水桶,没有业绩,每天就是噩梦。希望美国副总统跟FBI可以看到这条消息,铲除将军山999园区,铲除B1。 每天这个园区诈骗美国公民几百万美金,这都是最少的。这个园区现在彻底已经黑化了,大家千万不要来将军山999园区,正在演变成下一个KK园区。 ✅集团导航 @CAuuuu ✅投稿商务 @DIIII 😶有偿投稿机器人
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#群友爆光:外号祥子,华仔山东人,此人从公司离职。结果私下添加公司会员,给公司造成很大的影响以及损失。 更可笑的事情在东南亚做这种事情还不低调躲着一点,反过来先威胁带他出来的老乡。说他出事他老乡先死,来跟公司谈判 在公司多年偷偷拿到了同事的信息 拿同事的信息来威胁,以为威胁公司同事,就可以为所欲为。 威胁的当天晚上还去KTV庆祝,是有多嚣张,还跟同事说公司又不是你的,他让公司不要找他,他就不举报同事了。不知道哪里来的底气说大家是一条船上的人,谁他妈跟你一条船,这人真是恶心至极。 山东人都是如此不要脸的吗?真是丢尽了所有山东人的脸 ,他搞事情,第一反应是先搞死带他出来的人,带他出来的人跟他是老乡,还是一条村的人。 出事第一时间,想的是威胁带他4年,手把手带他的人, 刚来的时候是一个纯小白,平时嘴巴很会说 ,左一句师傅,又一句师傅,说得最多的话就是 老大你不走,我不走,很会装,就跟那个汉奸一模一样,皇协军。 平时喝酒都是叫一…
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#群友投稿:我讲过那些漂亮女孩去泰国接高薪业务被绑的事,今天有好消息了。 10月11日,缅东交克山DKBA总部附近抓了两男一女绑匪。其实,这是一个针对夜场女、外围女的团伙。主犯3男,共犯2女。绑匪团伙老大是福建人,其中团伙中的2女是其中2名主犯的女朋友。 通过各种聊天软件,以介绍高薪工作以及陪侍业务为由,将受害人骗至泰缅边境,强压受害人至交克山、瓦雷铁皮房实施敲诈,控制被绑者手机,进行各种转账套现、分期贷款。直到手机套不出一分钱,就逼迫受害人拿手机帮他们骗身边人或者陌生人,骗到绑匪头目人数满意为止。 之后,才跟受害人谈赔付多少钱放了他。没有骗到人就要挨打。实在骗不到人,就逼迫受害人给家里打电话,要骗赔付,一个人50万。其中还有几个男人,因骗不到人,家里也没有钱给赔付,便被绑匪以1万多美金卖给其他园区。 听说这帮绑匪已经在花高价保人。我相信,交克山DKBA的大胡子将军不会为了这点蝇头小利,就放了这种作恶之人吧? 国内的广…
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#群友投稿:在东南亚混了这么多年,才发现自己真的变了。 我十几岁就出来闯荡了,那时候什么都不懂,也没人教我怎么做人,怎么识人,更没人告诉我以后该走什么样的路。总觉得自己还年轻,只要肯吃苦,肯努力,总有一天能混出个人样。 这些年,一个人跑过不少地方,也经历了不少事。刚出来的时候,身边总有一群称兄道弟的人,吃饭有人陪,喝酒有人喊,遇到事情也有人说会帮你。可等到自己真的落难了,才知道有些人只是陪你热闹,从来没想过陪你渡过难关。 在东南亚待久了,见过太多今天还在一起喝酒,明天就因为一点利益翻脸的人。以前总觉得朋友之间应该讲感情,后来才明白,成年人的世界里,感情归感情,利益归利益。你没钱的时候,连说话都没多少分量;你混得好的时候,身边又突然多出一堆熟人。 说实话,这一路没少吃苦,也没少受委屈。有时候明明自己过得不好,还得装作没事一样,别人问起来就说挺好的。不是不想说,而是说了又能怎么样?谁的生活都不容易,自己的烂摊子,最后还得自己…
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缅东妙瓦底、三佛塔片区最新诈骗形势分析 长期以来大众舆论多聚焦缅北局势,却忽略了缅东妙瓦底的电诈现状。 目前缅北持续有专项打击、案件宣判、人员遣返动作,局势持续降温。反观缅东,仅在今年8月泰康园区有过一次小规模整治,数百名人员集中待遣返,此后近两个月几乎无大规模清剿动作。 结合最新实地动态,对妙瓦底及三佛塔片区现状做一次客观梳理: 1. 旧园区回流、新园区隐蔽扩建 此前被打掉的部分园区,目前已有零散诈骗团伙重新入驻恢复运作。整体管控暴力、拘禁虐待行为相比前两年略有收敛,但并未消失。 2. 人员核心聚集带 目前绝大多数电诈人员,不再扎堆妙瓦底城内,而是集中分布在: 妙瓦底城外—三佛塔沿路的交克、瓦来、三佛塔、亚太片区。 并非外界以为的“全部往最南部迁移”,相反:亚太片区上游几十公里,已新建多处隐秘园区。 老牌亚太园区目前也有少量人员回流入驻,只是规模、曝光度大幅降低,因此网络讨论热度下降。 3. 为什么现在亚太片区热度…
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#突发事件:西港南海酒店旁有一大楼被围了,现在下面全是警察,已经不让进出了 ✅集团导航 @CAuuuu ✅投稿商务 @DIIII
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Showing the 12 most recent of 1,847 posts we hold for @ytttt. 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.
Named by 7 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.
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.
@ytttt named 2 handles 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.
We ourselves saw each of these resolve to a real page at some point before it went vacant — a genuine, evidenced change, not an inference from absence.
References a handle that is not a live channel — we have no record it ever was one.
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 19 October 2026 — this entry's latest reading, not the date you are reading this.
“柬埔寨大事件|东南亚大新闻 @YTTTT” (@ytttt), 150,209 subscribers as measured 19 October 2026. Telegram Register, tgregister.com/channel/ytttt.
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.