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Channel

🅥杭州老师🧑‍🏫臻选(免费上榜)

@gndntcg

On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry

5,795subscribers

+3,806 since we began measuring on 31 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1003532568586
TypeChannel
Username@gndntcg
CreatedBetween 1 December 2025 and 31 May 2026 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded31 August 2026
Last confirmed live4 November 2026
Measurements held18
Confirmed unchanged1 time, most recently 4 November 2026
On Telegramt.me/gndntcg

Topic

Adult — 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 18 September 2026 and assigned it the closest of 31 fixed categories, at 62% 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.

Growth

1,9836,9924,487.531 August 2026 — 1,989 subscribers31 August 2026 — 1,989 subscribers1 September 2026 — 1,983 subscribers5 September 2026 — 6,992 subscribers10 September 2026 — 6,757 subscribers14 September 2026 — 6,625 subscribers18 September 2026 — 6,446 subscribers26 September 2026 — 4,964 subscribers5 October 2026 — 5,128 subscribers11 October 2026 — 5,724 subscribers14 October 2026 — 6,330 subscribers17 October 2026 — 6,832 subscribers20 October 2026 — 6,988 subscribers23 October 2026 — 6,964 subscribers26 October 2026 — 6,928 subscribers29 October 2026 — 6,706 subscribers1 November 2026 — 6,267 subscribers4 November 2026 — 5,795 subscribers5,79531 August 20264 November 2026
18 measurements spanning 65 days, net +3,806. 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 1,232–7,743 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
4 Nov 2026, 15:185,795-472
1 Nov 2026, 23:506,267-439
29 Oct 2026, 18:266,706-222
26 Oct 2026, 18:186,928-36
23 Oct 2026, 11:186,964-24
20 Oct 2026, 10:166,988+156
17 Oct 2026, 17:316,832+502
14 Oct 2026, 21:376,330+606
11 Oct 2026, 08:015,724+596
5 Oct 2026, 06:405,128+164
26 Sept 2026, 21:004,964-1,482
18 Sept 2026, 04:166,446-179
14 Sept 2026, 09:156,625-132
10 Sept 2026, 11:196,757-235
5 Sept 2026, 09:356,992+5,009
1 Sept 2026, 11:361,983-6
31 Aug 2026, 23:151,989no change
31 Aug 2026, 23:001,989first reading

Engagement

16 posts held, back to 31 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 3 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
33.9%
avg views ÷ 5,795 subscribers
Avg views / post
1,960
10 posts measured
Reaction rate
0.821%
reactions ÷ views · ER floor
Posts in window
10
of 16 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
WindowRolling 30 days · latest post in window 17 October 2026
Posts held16 (31 August 2026 – 17 October 2026)
Views total19,620
Reactions total161
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken4 Nov 2026, 12:44 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
49s
Average length
12s

Measured directly from 4 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

186 reactions across 12 posts, in 16 distinct kinds. The most used accounts for 23.7% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤4423.7%
🎉2714.5%
🔥2513.4%
👏2211.8%
🤔147.53%
🥰136.99%
👻94.84%
👍84.30%
🦄73.76%
🫡52.69%
💯42.15%
👀31.61%
🐳21.08%
💘10.538%
😘10.538%
🤗10.538%

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 12 of the 16 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 186 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 16 most recent posts we hold, published 31 August 2026 to 17 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.

Recent posts

Telegram’s own ‘edited’ marker: 0 of 10 posts held from the last 30 days (0%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).

17 Oct 2026, 08:45 UTC≈1,610 views17 reactionsread 4 November 2026
Photo

爱称:#小雨 身材:高168重92🐻B 地点:#拱墅 课费:500p半套 电报:@xiaoyuaz1527 特征:呈自然微卷的深棕色波浪发,发丝柔顺有光泽,两侧自然披散,刘海自然分到两边。脸型偏鹅蛋脸,皮肤白皙细腻,五官精致:大而明亮的眼睛,双眼皮,眼神温柔;鼻梁挺直,嘴唇饱满,涂着淡淡的橘粉色口红,整体妆容清透自然。

❤9🥰4🎉3💘1

16 Oct 2026, 09:02 UTC≈1,910 views14 reactionsread 4 November 2026
Photo

爱称:#摇摇 身材:高166重90🐻B 地点:#萧山 课费:800p 1500pp 电报:@yaoyao5656 特征:长直黑发,白色波点抹胸上衣,蓝色沙发背景自拍,皮肤白嫩,五官精致,大眼睛,气质清纯温顺。

❤9🔥3👀2

15 Oct 2026, 08:09 UTC≈2,190 views12 reactionsread 4 November 2026
Photo

爱称:#柚子 身材:高165重100🐻B 地点:#拱墅 课费:900p 1700pp 电报:@lvqian5201314 特征:长波浪棕发,紫色蕾丝吊带上衣,床上自拍,皮肤白净,五官精致,气质温柔甜美,人美声甜。 来自西北的妹妹,性格开朗听话(颜值嫩妹),初恋般的感觉,逼紧,情绪价值拉满。

❤7👏4🤗1

14 Oct 2026, 08:48 UTC≈2,140 views13 reactionsread 4 November 2026
Photo

爱称:#竹川萤 身材:高170重100🐻C 地点:#拱墅 课费:1000p 1900pp 电报:@zhuchuanying 特征:长直黑发,米色一字肩针织衫,室内自拍,皮肤白净,五官精致,气质温柔可甜可盐,S身材,马甲线。

👏9🥰3🎉1

13 Oct 2026, 09:35 UTC≈1,950 views13 reactionsread 4 November 2026
Photo

爱称:#桐桐 身材:高163重90🐻C 地点:#上城 课费:1000p 1800pp 电报:@tongbaby8 特征:长卷发,白色短袖上衣配牛仔裤,室内坐姿,气质清纯可爱。活泼开朗,真人真照,逼逼小紧致,个人自聊,学生兼职,本人比照片好看。皮肤白,待客如男友,热情主动。

🎉8👍3🥰1🫡1

12 Oct 2026, 09:00 UTC≈2,000 views16 reactionsread 4 November 2026
Photo

爱称:#颜小雨 身材:高168重92🐻C 地点:#萧山 课费:1300p 2400pp 电报:@yxy55599 特征:长波浪棕发,黑色荷叶边上衣搭配牛仔裤,街头自拍风格,妆容精致,皮肤白,身材高挑。

❤8💯4🐳2🎉1👀1

11 Oct 2026, 09:33 UTC≈1,790 views16 reactionsread 4 November 2026
Photo

爱称:#糖果 身材:高159重93🐻B 地点:#西湖 课费:600p 1100pp 电报:@tangguoABA 特征:戴眼镜,黑长发,白底小碎花短袖裙,坐在公园长椅上,旁边放着一杯奶茶。看起来乖,皮肤白。

🤔9🦄5🎉2

10 Oct 2026, 07:41 UTC≈1,980 views19 reactionsread 4 November 2026
Photo

爱称:#可爱多 身材:高168重108🐻G 地点:#萧山 课费:1300p 2500pp 电报:@keaiduo8845 特征:皮肤雪白,娃娃音,配合度高,双商在线。栗色长发,灰高领背心配百褶短裙,溪边石栏前拍的,胸大,腰细。上课热情超级配合,童颜巨乳蜜桃臀。主动配合,自动摇摇机,榨汁机。

👻9🥰5👍3❤1😘1

9 Oct 2026, 08:47 UTC≈2,000 views20 reactionsread 4 November 2026
Photo

爱称:#柠柠 身材:高168重112🐻D 地点:#上城区 课费:800p 1600pp 电报:@ningningxnin 特征:23岁,168,56kg,D杯,纯天然白皮。黑长发,眼线浅,嘴红,海军蓝睡衣领,下巴有一颗小痣。室内自拍,妆干净,看着温柔。

🔥8🤔5👏4❤2🎉1

8 Oct 2026, 09:46 UTC≈2,050 views21 reactionsread 4 November 2026
Photo

爱称:#娜美 身材:高176重110🐻C 地点:#上城区 课费:1100p 2100pp 电报:@dfghd2467 特征:夜路自拍,长发栗棕,发量多,头顶有一点蓬,披到胸前。脸小,下颌收着,眉眼长,眼尾微挑,鼻梁挺,唇粉,妆很淡。锁骨和肩线露在外面,穿浅粉吊带,藏青包边,外面一件黑开衫滑到臂弯,米白包带斜挎。

❤8🎉7🫡3👍2👏1

7 Oct 2026, 10:04 UTC≈1,420 views13 reactionsread 4 November 2026
Photo

📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 👀老师:柠檬 👙身高:159年龄:29体重:47kg胸围:D 🗺位置:#杭州 上城区 💴课费:700p 1300pp 📗标签:kiss 调情 制服诱惑 舔🥚 鸳鸯浴 🛫联系:@ningmengaa6 📮好评: 【报告01】【报告02】【报告03】 🏰 小鸡🐥臻选 🎥 福利视频

🔥7👏4🦄2

7 Oct 2026, 08:38 UTC≈1,280 views12 reactionsread 4 November 2026
Photo

📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 👀老师: 娜娜 👙身高:172年龄:24体重:52胸围:D 🗺位置:#杭州 #上城 💴课费:1400p 2500pp 📗标签:kiss 调情 制服诱惑 舔🥚 鸳鸯浴 各姿势均🉑 情绪价值提供满满 🛫联系: @daxnana 📮好评: 【报告01】【报告02】【报告03】 🏰 小鸡🐥臻选 🎥 福利视频

🔥7🎉4🫡1

Showing the 12 most recent of 16 posts we hold for @gndntcg. 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.

Mentions

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 4 November 2026 — this entry's latest reading, not the date you are reading this.

“🅥杭州老师🧑‍🏫臻选(免费上榜)” (@gndntcg), 5,795 subscribers as measured 4 November 2026. Telegram Register, tgregister.com/channel/gndntcg.

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.