Channel
@Gaoxiaoman
On this record: Topic · Observations · Also posting the same content · Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry
6,422subscribers
+27 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1002317058928 |
|---|---|
| Type | Channel |
| Username | @Gaoxiaoman |
| 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 | 14 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 14 August 2026 |
| On Telegram | t.me/Gaoxiaoman |
Memes & entertainment — 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 11 August 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 why it has no browse page of its own yet.
These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.
Posts published here appear word for word on 1 other registered channel. They sit inside a group of 8 channels that share the same post bodies with each other. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.
| Posted first | Then | Overlap | Gap |
|---|---|---|---|
| @Gaoxiaoman/285778 · this entry7 Aug 2026, 14:48 UTC | @DNYQWCG/2321507 Aug 2026, 16:42 UTC | 1.00 | 1.9 hours |
| @DNYQWCG/2321517 Aug 2026, 16:42 UTC | @Gaoxiaoman/285851 · this entry7 Aug 2026, 16:42 UTC | 1.00 | under a minute |
| @DNYQWCG/2321537 Aug 2026, 16:42 UTC | @Gaoxiaoman/285853 · this entry7 Aug 2026, 16:42 UTC | 1.00 | under a minute |
| @DNYQWCG/2321567 Aug 2026, 16:43 UTC | @Gaoxiaoman/285855 · this entry7 Aug 2026, 16:43 UTC | 1.00 | under a minute |
| @DNYQWCG/2321577 Aug 2026, 16:43 UTC | @Gaoxiaoman/285856 · this entry7 Aug 2026, 16:43 UTC | 1.00 | under a minute |
| Channel | Matching posts | Text overlap | Typical gap | Published first |
|---|---|---|---|---|
| @DNYQWCG | 5 (5/5 hand-verifiable sample passed) | 1.00 | under a minute | @DNYQWCG (4–1) |
Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.
What this cannot establish
MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.
Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. 4 of the 4 matches recorded here fall after that date and carried no header when we read them. The rest predate reliable capture and are not evidence either way.
“Published first” means first in this corpus. We hold 9 comparable posts for this entry, running 7 August 2026 to 7 August 2026. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.
The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.
Across the whole group of 8, the earliest publisher we hold is @gtkx1. That is a statement about our reading window, not a claim of authorship.
Recorded under the key clone_copy, last confirmed 8 August 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.
This channel’s posts match, word for word or near enough, posts on 7 other registered channels, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.
Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 14 Aug 2026, 11:58 | 6,422 | +4 |
| 11 Aug 2026, 01:45 | 6,418 | +17 |
| 8 Aug 2026, 04:58 | 6,401 | +6 |
| 7 Aug 2026, 15:01 | 6,395 | first reading |
86 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 8 pagesof 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 1 of 86 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 12 August 2026 |
|---|---|
| Posts held | 86 (7 August 2026 – 12 August 2026) |
| Views total | 2,102 |
| Reactions total | 0 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 12 Aug 2026, 03:10 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 43 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.
一位投资人晚上向我展示他目前的AI使用情况。 他会自己写程序，一个月在AI订阅上花1000美元，从Claude、OpenAI、Gemini、Grok、Cursor等等全部顶配买一遍。他从七月开始游泳，用Cursor自己写了一整套数据检测程序，从距离、速度、心率、摆臂效率等等，极为详尽的数据分析，由程序自动生成。 他还有专门分析股票的专业程序，由多个模型配合；其中，需要实时分析的部分由Grok承担，因为Grok接入了实时社交媒体数据。每次看一家公司，他的AI程序会用巴菲特、彼得林奇、达里奥、芒格等传奇投资人的视角来给公司打分并进行分析。 他是程序员出身，私下仍是极客，比如私下他会改装自家音响，自己动手操作Cursor写码，给里面装上语音AI助手。 想想看，这两天Claude推出的Co-worker就是Anthropic程序员两天的机器代码做出来的产品，一推出就震撼全网。在人类向AGI转型的时代，可能不会操作AI coding to…
穷人的钱不好赚，一块钱都叽叽歪歪。但是穷人的钱好骗，很容易就倾家荡产。
海外创业信息差 万亿旅游市场。 做跨境电商，大家对渠道的理解还是太浅显了。 如果你是卖个护或者3C电子的，在电商平台上卷生卷死，还不如直接all in旅游零售，闷声发大财。 比如说下面这几个卖货渠道大家就可以试试： 1。 航班零售。 2。 机场自动贩卖机。 3。 Airbnb民宿。 4。 滑雪场商店。 5。 景点Gift Shop。 自媒体/出海经验/赚美金/运营技巧 @zhjingxuan
今天听说一老哥带了40万港币入境被拦了。 因为他走的无申报通道，钱又装在行李箱里，被扫出来很容易，最后罚了6%了事。 之前还有人带100多万没申报被罚10%的，太傻了。 上限是罚20%，但主动配合金额不大，一般1-6%。 其实带外币入境是无上限的，单人超过5000美元你走申报通道就行了，可以小程序预约，也可以现场直接去。 不会盘问你，填个表盖个章就结束了，你拿着纸去国内银行存外币，很方便。 海关不会收税，唯一的收税可能性，就是信息传给税务局，未来再按资金来源收。 不过没看谁因此被税务打过电，我听说是目前系统只同步给外管局，不主动给税务，但税务可以要求协助调取。 最后一个冷知识，一个28寸托运行李箱可以装500万美元。 ｜ |
早上看到一个超级强的 Chrome 扩展「Language Reactor」，真正的实现看 Youtube、Netflix 的时候学习英语，将每一句台词直接中英文纯正的给标出来，并支持反复听和鼠标移上去查看含义，比单纯记单词好多了，将你看视频过程给变成了一个有趣的英语学习。 🤖 互联网从业者专属 @zhjingxuan
这本书在同类里简直是“异类级别”。 《普通人的财富自由之道》在亚马逊 714 条评分里拿到 4。8 分，几乎满分。 作者采访了 3000 多位创业者，把他们的路径、失败点、底层方法统统拆开。 它不是鸡汤，很多内容创业者把它当成“行动手册”。 🔗： @meiriyishu
远离那些不出钱、不出力，意见很特别多的人，你符合他们的利益，他们就会夸赞你、抬举你，反之，他们就会打压诋毁你。 副业赚钱探索 @zhjingxuan
😁😁😁😁😁😁😁 😃😃😃😃 😀 😀😀😀😀😀😀😀 😍
承德市附属医院十字路口碰到了 #开门杀 #车祸 吓了一跳
夏天穿成这样 散散味 ～
金华G60 给货车干墙上去了，小米车主有点猛 #车祸
君子动口不动手
Showing the 12 most recent of 86 posts we hold for @Gaoxiaoman. 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.
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.
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 14 August 2026 — this entry's latest reading, not the date you are reading this.
“吃瓜快讯” (@Gaoxiaoman), 6,422 subscribers as measured 14 August 2026. Telegram Register, tgregister.com/channel/Gaoxiaoman.
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.