一号 #舔狗 确实有点东西 👍 等着吧!下回分手选你 !#聊天记录
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Channel
@aichiguaba
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
210,186subscribers
-42,344 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 100,000–316,228.
| Telegram ID | -1001819259766 |
|---|---|
| Type | Channel |
| Username | @aichiguaba |
| Description | 吃瓜 广告投稿机器人👉 @BGTGOBOT |
| Created | Between 1 October 2022 and 30 September 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 24 October 2026 |
| Measurements held | 47 |
| Confirmed unchanged | 1 time, most recently 24 October 2026 |
| On Telegram | t.me/aichiguaba |
Sports — 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 8 August 2026 and assigned it the closest of 31 fixed categories, at 72% 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 |
|---|---|---|
| 24 Oct 2026, 21:46 | 210,186 | -115 |
| 23 Oct 2026, 21:10 | 210,301 | -182 |
| 22 Oct 2026, 20:37 | 210,483 | -122 |
| 21 Oct 2026, 15:27 | 210,605 | -141 |
| 20 Oct 2026, 11:18 | 210,746 | -90 |
| 19 Oct 2026, 11:37 | 210,836 | -111 |
| 18 Oct 2026, 09:37 | 210,947 | -148 |
| 17 Oct 2026, 10:44 | 211,095 | -94 |
| 16 Oct 2026, 12:37 | 211,189 | -320 |
| 15 Oct 2026, 15:56 | 211,509 | -228 |
| 14 Oct 2026, 15:37 | 211,737 | -275 |
| 13 Oct 2026, 04:00 | 212,012 | -30,935 |
| 8 Oct 2026, 22:58 | 242,947 | -1,202 |
| 4 Oct 2026, 14:18 | 244,149 | -1,281 |
| 26 Sept 2026, 07:39 | 245,430 | -2,063 |
| 19 Sept 2026, 10:38 | 247,493 | -844 |
| 17 Sept 2026, 02:57 | 248,337 | -823 |
| 15 Sept 2026, 03:41 | 249,160 | -644 |
| 13 Sept 2026, 11:18 | 249,804 | -1,293 |
| 11 Sept 2026, 17:16 | 251,097 | first reading |
1,289 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 162 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 372 of 460 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 25 October 2026 |
|---|---|
| Posts held | 1,289 (7 August 2026 – 25 October 2026) |
| Views total | 309,307 |
| Reactions total | 1,042 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 25 Oct 2026, 12:26 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.
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.
Measured directly from 757 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.
1,727 reactions across 684 posts, in 28 distinct kinds. The most used accounts for 55.1% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 951 | 55.1% | |
| ❤ | 478 | 27.7% | |
| 😁 | 71 | 4.11% | |
| 💩 | 60 | 3.47% | |
| 🍌 | 38 | 2.20% | |
| 🤡 | 20 | 1.16% | |
| 🤮 | 18 | 1.04% | |
| 🌭 | 12 | 0.695% | |
| 😈 | 10 | 0.579% | |
| 🤣 | 10 | 0.579% | |
| 🕊 | 7 | 0.405% | |
| 😍 | 7 | 0.405% | |
| 😱 | 7 | 0.405% | |
| 🙏 | 7 | 0.405% | |
| 👏 | 6 | 0.347% | |
| 🤬 | 5 | 0.29% | |
| 🌚 | 4 | 0.232% | |
| 🤓 | 3 | 0.174% | |
| 🔥 | 2 | 0.116% | |
| 🖕 | 2 | 0.116% | |
| 8 further kinds | 9 | 0.521% |
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 704 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 1,727 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 23 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.
一号 #舔狗 确实有点东西 👍 等着吧!下回分手选你 !#聊天记录
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#蛋疼 #摩托 #蛋蛋 受伤了,有点肿。
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#反差 #女大 #酒吧 #母狗 #清纯 在学校里还是一副青春学生模样,一进酒吧就彻底放飞成骚母狗,清纯女大人设瞬间崩塌,浪得不行。
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日剧把日军罪行嫁接给中国人 日本TBS电视台的电视剧《Vivant》(《别班》),剧情有多离谱?剧中设计了中方人员对日本情报人员实施切指、刺穿腹部的情节,这些手段和侵华日军对中国抗日志士使用过的酷刑高度重合,而剧中则颠倒黑白,将其嫁接到现代中国人的形象之上;整部剧悄悄美化日本海外越界行动,模糊自卫队的职能边界,变相输出扩张式叙事,更可笑的是,剧中一个自卫队情报人员坐着飞机逃离时,还对中方人员说,“要是这架飞机被攻击,就等着西方大举出动把你们碾碎……”更值得警惕的是:日本外务省斥巨资搞对华舆论抹黑和歪曲历史、用AI视频篡改历史、用AI大模型“投毒”、持续篡改教科书——从屏幕到课本,这是一套系统性叙事工程,与右翼政客参拜“战犯神社”背后是同样的历史观。娱乐化包装的敌意,比赤裸裸的宣传更危险。
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就喜欢这种功能型美女
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#漫展 开淫趴 ?
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男子抢了5万个餐厅排队号 “最多的一天有900多个空号,我们一个空号叫过去,需要有3秒时间……”热门餐厅刚开门,线上取号系统就显示数百人排队,叫号不断却没人进店用餐。这不是系统故障,而是新型技术黄牛在作祟。上海警方侦查发现,雷某利用技术手段破解取号系统,批量抢占号源,累计非法获取号源5万余个,在网络平台加价倒卖,非法获利1万余元。目前,雷某因涉嫌非法控制计算机信息系统罪,已被刑事拘留。
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Video, posted without a caption
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Photo, posted without a caption
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恒大昔日向哈佛捐出上亿美元,如今清盘人追讨200万美元 10月22日,据英国《金融时报》报道,负责清理恒大资产、替债权人追款的清盘人已向哈佛大学发出催款函,要求退还2020年科研资助款中的200万美元,目前双方正在协商。 清盘报告披露,恒大在2013年和2016年还向该校支付过约1.56亿美元,部分用于设立研究中心并取得冠名权。 清盘人称,未能找到这些巨额付款的明确商业理由,公司似乎并未从中获益。
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凯迪拉克 车友会? 好骚 !
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一段视频画面显示,中国解放军与印度军队在边境对峙。 据印度媒体报道称,对峙从7月下旬起已经持续至今。
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Showing the 12 most recent of 1,289 posts we hold for @aichiguaba. 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 6 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.
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 14 August 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
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.
This channel appears in 1 seed channel's Telegram-generated recommendation list in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 24 October 2026 — this entry's latest reading, not the date you are reading this.
“全网吃瓜🍉” (@aichiguaba), 210,186 subscribers as measured 24 October 2026. Telegram Register, tgregister.com/channel/aichiguaba.
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.