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Telegram profile photo for 深圳枫林榜 @szflw

Channel

深圳枫林榜 @szflw

@szflwbd

On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Citations · Handles named that no longer answer · Cite this entry

40,598subscribers

+12,685 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 31,623–100,000.

Register entry

Telegram ID-1003397917787
TypeChannel
Username@szflwbd
Description关注频道🍁嫩妹首发! 私聊老师发送“枫林晚”,优惠福利等着你🍁 🚗修车导航:https://t.me/szfenglinwan 🍁查找老师🔍 https://t.me/mingyue1bot 👮‍♀️本群管理:https://t.me/qwer3456
CreatedBetween 1 November 2025 and 31 March 2026 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live25 October 2026
Measurements held48
Confirmed unchanged1 time, most recently 25 October 2026
On Telegramt.me/szflwbd

Topic

Other / unclassifiable — 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 9 September 2026 and assigned it the closest of 31 fixed categories, at 74% 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

27,91340,59834,255.56 August 2026 — 27,913 subscribers7 August 2026 — 27,948 subscribers8 August 2026 — 28,175 subscribers9 August 2026 — 28,533 subscribers10 August 2026 — 28,786 subscribers11 August 2026 — 29,013 subscribers12 August 2026 — 29,120 subscribers13 August 2026 — 29,343 subscribers14 August 2026 — 29,824 subscribers15 August 2026 — 30,323 subscribers17 August 2026 — 30,676 subscribers18 August 2026 — 31,023 subscribers19 August 2026 — 31,502 subscribers20 August 2026 — 31,772 subscribers21 August 2026 — 32,195 subscribers23 August 2026 — 32,692 subscribers24 August 2026 — 32,799 subscribers25 August 2026 — 32,918 subscribers26 August 2026 — 33,282 subscribers27 August 2026 — 33,423 subscribers28 August 2026 — 33,439 subscribers29 August 2026 — 33,612 subscribers30 August 2026 — 33,760 subscribers31 August 2026 — 33,969 subscribers1 September 2026 — 34,104 subscribers2 September 2026 — 34,242 subscribers3 September 2026 — 34,483 subscribers5 September 2026 — 34,505 subscribers9 September 2026 — 34,697 subscribers11 September 2026 — 35,292 subscribers13 September 2026 — 35,667 subscribers15 September 2026 — 35,812 subscribers17 September 2026 — 35,982 subscribers25 September 2026 — 36,892 subscribers4 October 2026 — 36,696 subscribers8 October 2026 — 36,745 subscribers13 October 2026 — 38,043 subscribers14 October 2026 — 38,499 subscribers15 October 2026 — 38,602 subscribers16 October 2026 — 38,833 subscribers17 October 2026 — 39,312 subscribers18 October 2026 — 39,983 subscribers19 October 2026 — 40,163 subscribers20 October 2026 — 40,257 subscribers22 October 2026 — 40,241 subscribers23 October 2026 — 40,332 subscribers24 October 2026 — 40,406 subscribers25 October 2026 — 40,598 subscribers6 August 202625 October 2026
48 measurements spanning 79 days, net +12,685. 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 26,010–42,501 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 48
Measured (UTC)SubscribersChange
25 Oct 2026, 04:0940,598+192
24 Oct 2026, 02:3940,406+74
23 Oct 2026, 04:2940,332+91
22 Oct 2026, 01:2040,241-16
20 Oct 2026, 16:3640,257+94
19 Oct 2026, 15:2840,163+180
18 Oct 2026, 12:5439,983+671
17 Oct 2026, 11:1139,312+479
16 Oct 2026, 08:1038,833+231
15 Oct 2026, 11:3038,602+103
14 Oct 2026, 14:3838,499+456
13 Oct 2026, 03:1638,043+1,298
8 Oct 2026, 18:5536,745+49
4 Oct 2026, 04:5836,696-196
25 Sept 2026, 09:4036,892+910
17 Sept 2026, 06:5635,982+170
15 Sept 2026, 07:4035,812+145
13 Sept 2026, 16:4135,667+375
11 Sept 2026, 20:2135,292+595
9 Sept 2026, 07:1934,697first reading

Engagement

530 posts held, back to 6 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 147 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
0.986%
avg views ÷ 40,598 subscribers
Avg views / post
400
237 posts measured
Reaction rate
0.175%
reactions ÷ views · ER floor
Posts in window
237
of 530 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. It is computed over the 47 of 237 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 25 October 2026
Posts held530 (6 August 2026 – 25 October 2026)
Views total94,891
Reactions total33
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken25 Oct 2026, 23:11 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

Photos
≈8,550
Videos
≈1,160
Links
≈1,210

Lifetime counters from Telegram’s own channel header, read 25 October 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.

Video runtime
32m 34s
Average length
10s

Measured directly from 195 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

112 reactions across 90 posts, in 8 distinct kinds. The most used accounts for 55.4% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤6255.4%
👍1917.0%
🔥98.04%
👏76.25%
💯76.25%
🎉54.46%
👀21.79%
👎10.893%

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 124 of the 530 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 112 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 530 most recent posts we hold, published 6 August 2026 to 25 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

25 Oct 2026, 15:02 UTC358 viewsread 25 October 2026
Photo

花名:哎呀哎呀(SM) 位置:深圳 #福田 课费:1000p 1800pp SM : 1500p 2800pp 私聊:@ayay123123 频道:https://t.me/ayay7788i 双向:@mm50321_bot

25 Oct 2026, 12:01 UTC521 viewsread 25 October 2026
Photo

花名:南山小嫩妹 位置:深圳 #南山 课费:1300p 2200pp 私聊:@YY55153 频道:https://t.me/+eHKCoGg3sMQ3ODdl 双向:@YY556bot

25 Oct 2026, 12:00 UTC497 viewsread 25 October 2026
Photo

🍁深圳枫林晚 - @szflw 私聊老师发送“枫林晚”,优惠福利等着你🍁

25 Oct 2026, 11:31 UTC444 viewsread 25 October 2026
Photo

花名:王梓欣(舞蹈老师) 位置:深圳 #南山 课费:1500p 2500pp 私聊:@WangZiXin9 频道:https://t.me/+LALJiNIx98wzMWMx 双向:@WangZiXinbot

25 Oct 2026, 11:01 UTC472 viewsread 25 October 2026
Video

深圳 #嫩妹 中高端(全区) #千元档 #可外出(需要车费🚘) #福田 #南山 #宝安 #沙井 #龙华 #龙岗 #罗湖 妹子自带工作室,小区公寓,安全卫生(见到妹妹合适付费给妹妹,不合适可以离开,没有看见人之前不收一分钱) 靠谱面付❤️趁年轻多做爱 无任何隐形消费,无套路❤️ 频道链接 https://t.me/uu1567888 客服 @uu7979111 双向联系机器人 @uu7979111bot 🍁枫林晚上榜编号:9830

25 Oct 2026, 09:05 UTC617 views2 reactionsread 25 October 2026
Photo

花名:萱儿(巨乳) 位置:深圳 #龙岗 #坂田 课费:600p 1100pp 私聊:@Xer88o 频道:https://t.me/xuaner001xx 双向:@xuaner88_bot

❤2

25 Oct 2026, 08:16 UTC237 viewsread 25 October 2026
Photo

花名:淼淼 位置:深圳 #福田 课费:700p 1300pp 私聊:@miaomiao9889 频道:https://t.me/miaomiao22889 双向:@miaomiao6868_bot

25 Oct 2026, 07:02 UTC266 viewsread 25 October 2026
Photo

Telegram 中文圈搜索引擎,积木 JIMU 帮你查询任何您想要的群组、频道(不论公开与私密) 🧩 积木:@jimu · @jimux · @jimuxx 👉 所有入口都能助力提升排名🚀

25 Oct 2026, 07:01 UTC259 viewsread 25 October 2026
Photo

花名:源源 位置:深圳 #南山 课费:1400p 2500pp 私聊:@aayuanu 频道:https://t.me/Ecy003x 双向:@Szyyjqrbot

24 Oct 2026, 12:25 UTC515 viewsread 24 October 2026
Photo

花名:雪儿 位置:深圳 #龙华 课费:600p 1100pp 私聊:@xueer168_pl 频道:https://t.me/xueer168_88 双向:@xueer1238_bot

24 Oct 2026, 12:00 UTC519 viewsread 24 October 2026
Photo

🍁深圳枫林晚 - @szflw 私聊老师发送“枫林晚”,优惠福利等着你🍁

24 Oct 2026, 09:00 UTC663 viewsread 24 October 2026
Photo

花名:玟玟 位置:深圳 #南山 课费:1400p 2500pp 私聊:@Ww5208888 频道:https://t.me/Szjxyfy1 双向:@Sedchjqrzbot

Showing the 12 most recent of 530 posts we hold for @szflwbd. 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 edited after publishing

@szflwbd edited 6 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.

An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.

First edit seen
15 August 2026
Most recent edit
1 October 2026

Forward network

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 17 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.

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 25 October 2026 — this entry's latest reading, not the date you are reading this.

“深圳枫林榜 @szflw” (@szflwbd), 40,598 subscribers as measured 25 October 2026. Telegram Register, tgregister.com/channel/szflwbd.

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.