浪子回头 (1967) #电影 #剧情 主演: 亚娜·布赖霍娃 尼娜·蒂维斯科娃 扬·卡切尔 豆瓣: 7.3 JanPoutnik(VladimirPucholt)wasborninCzechoslovakiabuthasemigratedtotheUnitedStates.. 🎥 更多网盘 | 🧲 资源 🧶 相关推荐: 魔鬼的陷阱 @seedhub_pro

Channel
影视动漫分享|SeedHub|夸克|网盘|🧲
@seedhub_pro
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Cite this entry
35,363subscribers
+887 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
Register entry
| Telegram ID | -1002455883223 |
|---|---|
| Type | Channel |
| Username | @seedhub_pro |
| Description | 频道每日推送热门和新上架影视动漫资源 (为了安全,不发英语作品) 搜索/查看全部资源前往: sidhub.cc 公告/导航: t.me/seedhub_pro/7861 灌水讨论: t.me/seedhub_chat |
| Created | Between 1 September 2024 and 31 March 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 21 August 2026 |
| Measurements held | 14 |
| Confirmed unchanged | 1 time, most recently 21 August 2026 |
| On Telegram | t.me/seedhub_pro |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 21 Aug 2026, 22:38 | 35,363 | +48 |
| 20 Aug 2026, 18:23 | 35,315 | +68 |
| 19 Aug 2026, 15:16 | 35,247 | +72 |
| 18 Aug 2026, 16:33 | 35,175 | +75 |
| 17 Aug 2026, 13:46 | 35,100 | +84 |
| 16 Aug 2026, 04:16 | 35,016 | +98 |
| 14 Aug 2026, 14:27 | 34,918 | +81 |
| 13 Aug 2026, 06:27 | 34,837 | +43 |
| 12 Aug 2026, 08:32 | 34,794 | +71 |
| 11 Aug 2026, 08:53 | 34,723 | +74 |
| 10 Aug 2026, 05:36 | 34,649 | +80 |
| 9 Aug 2026, 05:01 | 34,569 | +71 |
| 8 Aug 2026, 07:07 | 34,498 | +22 |
| 7 Aug 2026, 19:45 | 34,476 | first reading |
Engagement
342 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 34 pagesof Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 0.564%
- avg views ÷ 35,363 subscribers
- Avg views / post
- 200
- 342 posts measured
- Reaction rate
- 0.448%
- reactions ÷ views · ER floor
- Posts in window
- 342
- of 342 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 60 of 342 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 23 August 2026 |
|---|---|
| Posts held | 342 (7 August 2026 – 23 August 2026) |
| Views total | 68,266 |
| Reactions total | 93 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 23 Aug 2026, 04:40 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
- ≈17,900
- Links
- ≈17,900
Lifetime counters from Telegram’s own channel header, read 23 August 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.
Reaction mix
93 reactions across 57 posts, in 2 distinct kinds. The most used accounts for 84.9% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 79 | 84.9% | |
| ❤ | 14 | 15.1% |
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 60 of the 342 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 93reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 342 most recent posts we hold, published 7 August 2026 to 23 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.
Recent posts
阿纳克莱托:特务密探 (2015) #电影 #动作 #喜剧 主演: 基姆·古铁雷斯 萝西·德·帕尔马 卡洛斯·阿雷塞斯 豆瓣: 6.0 阿道夫(奎姆·古铁雷斯饰)年方三十,却依然只是个前途渺茫的底层保安。他懒散不负责任,最终失去了女友。一天,阿道夫遭到刚越狱的黑帮老大巴斯克斯(卡洛斯·阿雷塞斯饰)的.. 🎥 更多网盘 | 🧲 资源 🧶 相关推荐: 列车怎么办 @seedhub_pro
兄弟情 (2009) #电影 #同性 #剧情 主演: 托尔·林德哈特 西涅·埃格霍尔姆·奥尔森 尼可拉斯·布若 豆瓣: 7.5 这部讲述2个新纳粹成员同性恋的影片,是来自丹麦的年轻时尚摄影师尼科洛·多纳托执导的第一部剧情长片,获得2009年第4届罗马电影节最高奖。 影片中年轻的男主人公.. 🎥 更多网盘 | 🧲 资源 🧶 相关推荐: @seedhub_pro
刀侠 (1999) #电影 #动作 #剧情 主演: 孟飞 张晞 狄威 豆瓣: 5.2 一位英俊潇洒的边陲侠士王刚,家中突遭不幸,爱妻被杀,小屋被烧,自己也被前来寻仇的马贼毒打重伤。在前恋人的治疗鼓励下,王刚奋起复仇,将凶狠残暴的马贼通通消灭殆尽。.. 🎥 更多网盘 | 🧲 资源 🧶 相关推荐: 霹雳十杰 | 飞龙斩 | 盗皇陵 @seedhub_pro
空之境界 第七章 杀人考察(后) (2009) #动漫 #动作 #奇幻 #剧情 #动画 #夸克 #迅雷 #百度 主演: 坂本真绫 铃村健一 本田贵子 豆瓣: 9.0 1999年2月,杀人鬼回来了! 1995年在日本发生的连续猎奇杀人事件,因嫌疑人遇到车祸住院而草草结案。如今疯狂的犯罪再次出现,恐怖的气氛来袭,原嫌疑人的嫌疑.. 🎥 夸克网盘 | 迅雷网盘 | 百度网盘 | 更多网盘 | 🧲 资源 🧶 相关推荐: 凉宫春日的消失 | 福音战士新剧场版:序 | 新世纪福音战士剧场版:死与新生 @seedhub_pro
依然绿茵的日子 (2015) #电视剧 #剧情 #百度 主演: 宋昰昀 金正山 朴贤淑 豆瓣: 0 KBS2日日剧接档一片丹心蒲公英.. 🎥 百度网盘 | 更多网盘 | 🧲 资源 🧶 相关推荐: @seedhub_pro
第十一个妈妈 (2007) #电影 #剧情 #夸克 #百度 主演: 金惠秀Hye-su Kim 柳承龙 黄政民 豆瓣: 7.9 在洙(金英灿饰)的父亲(柳承龙饰)是个皮条客,所以他家总是出入浓妆艳抹、心怀不轨的女人。虽然年幼的在洙并不完全了解父亲和这些女人的职业,但他的直觉和本能却让他对她们.. 🎥 夸克网盘 | 百度网盘 | 更多网盘 | 🧲 资源 🧶 相关推荐: 家族 @seedhub_pro
有毒蜂蜜 (2015) #电影 #剧情 #爱情 主演: 察合台·乌鲁索伊 蕾拉·丽迪雅·吐古特鲁 哈塞·阿维尼·丹亚尔 豆瓣: 7.9 巴里斯(恰塔伊·乌鲁索伊饰)出生在一个非常民主自由的家庭。他与父亲的关系既是师生又是朋友;他成长在一个宽容的环境和浓浓的家庭关爱之中。而芙松(莱拉·莉迪亚·图古特鲁.. 🎥 更多网盘 | 🧲 资源 🧶 相关推荐: 寂寞芳心 | 心碎的感觉 | 阿德尔曼夫妇 @seedhub_pro
龙之战 (2017) #电影 #历史 #战争 #动作 #夸克 #迅雷 主演: 刘佩琦 曹云金 罗昱焜 豆瓣: 6.4 1885年,法军入侵越南谅山。驻守在那里的清军不战而退,清政府的统治岌岌可危。慈禧太后召集大臣商议由谁来统领军队。最终,她任命了年近七十的退役将领冯子才(刘培奇饰).. 🎥 夸克网盘 | 迅雷网盘 | 更多网盘 | 🧲 资源 🧶 相关推荐: 鸣梁海战 | 血战湘江 | 勇士 @seedhub_pro
米良与麦青 (2026) #电视剧 #爱情 #剧情 #夸克 #迅雷 #百度 主演: 赵波 瑛子 来喜 豆瓣: 0 一条网线连接着中国鲁明村和英国牛津。麦香通过视频告诉米良:婚事取消了。鲁明村顿时一片混乱,村民们纷纷指责麦香是叛徒。麦香在婚前检查中被诊断出不孕,从此开始了她痛苦的.. 🎥 夸克网盘 | 迅雷网盘 | 百度网盘 | 更多网盘 | 🧲 资源 🧶 相关推荐: 太行谣 | 汴京上元局 | 龙婆虎 @seedhub_pro
新上海滩 (2007) #电视剧 #爱情 #剧情 #夸克 #百度 主演: 黄晓明 孙俪 黄海波 豆瓣: 7.2 故事发生在20世纪30年代的上海,这座商界纷争不断的城市里,商会之间的阴谋诡计层出不穷。初来乍到的徐文强(黄晓明饰)邂逅了上海商会大佬冯景尧(李学健饰)的爱女冯澄澄.. 🎥 夸克网盘 | 百度网盘 | 更多网盘 | 🧲 资源 🧶 相关推荐: 玉观音 | 甜蜜蜜 @seedhub_pro
危险情人 (1992) #电影 #犯罪 #剧情 #爱情 #夸克 #百度 主演: 郭富城 刘青云 袁洁莹 豆瓣: 6.4 汉(徐锦江饰)、希、官、Bonnie(苑琼丹饰)是四个银行劫匪。他们劫持了一辆运钞车,汉与希阻断追捕的警察,官和Bonnie在逃亡中遭遇小警察家辉(郭富城饰),官不.. 🎥 夸克网盘 | 百度网盘 | 更多网盘 | 🧲 资源 🧶 相关推荐: 乱世儿女 | 皇家女将 | 海狼 @seedhub_pro
Showing the 12 most recent of 342 posts we hold for @seedhub_pro. 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
@seedhub_pro edited 5 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
- 12 August 2026
- Most recent edit
- 19 August 2026
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 21 August 2026 — this entry's latest reading, not the date you are reading this.
“影视动漫分享|SeedHub|夸克|网盘|🧲” (@seedhub_pro), 35,363 subscribers as measured 21 August 2026. Telegram Register, tgregister.com/channel/seedhub_pro.
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.