新加坡网民热议带初访客去哪吃 近日,一名新加坡网民在社交平台X发帖,询问同胞会带首次到访的外国朋友去哪里用餐,引发讨论。有人建议滨海湾花园、克拉码头、牛车水及武吉士等游客集中区域的餐馆,也有人提到老巴刹,但承认那里是“常见的游客陷阱”。不少网民认为小贩中心最能体验本地饮食文化,推荐旧机场路、中峇鲁、牛车水大厦及黄金坊。另有网民推荐本地美食,如美芝路虾面、森林广场咸蛋及新民路鸡饭,并鼓励探索小印度和登布西山。也有人建议请客人吃家常菜,或选择麦当劳、鼎泰丰等连锁餐厅。
❤8👍8🎉4

Channel
@dnyuuu88
On this record: Topic · Observations · Growth · Engagement · What this channel posts · Reactions · Posts · Posts recorded as deleted · Citations · Telegram's recommendations · Cite this entry
121,554subscribers
+6,944 since we began measuring on 8 August 2026
Risers and fallers across the register · movement among entries of 100,000–316,228.
| Telegram ID | -1001430856828 |
|---|---|
| Type | Channel |
| Username | @dnyuuu88 |
| Created | Between 1 April 2019 and 31 October 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 14 November 2026 |
| Measurements held | 64 |
| Confirmed unchanged | 1 time, most recently 14 November 2026 |
| On Telegram | t.me/dnyuuu88 |
Gambling & betting — 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 9 August 2026 and assigned it the closest of 31 fixed categories, at 98% 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.
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 in near-verbatim form on 2 other registered channels. 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 |
|---|---|---|---|
| @dnyuuu88/26334 · this entry8 Aug 2026, 13:33 UTC | @dubaixinwen2/2521212 Aug 2026, 09:06 UTC | 1.00 | 4 days |
| @dnyuuu88/26390 · this entry10 Aug 2026, 14:52 UTC | @dubaixinwen2/2525414 Aug 2026, 11:02 UTC | 1.00 | 4 days |
| @dnyuuu88/27344 · this entry13 Sept 2026, 07:25 UTC | @dubaixinwen2/2576715 Sept 2026, 02:39 UTC | 1.00 | 43 hours |
| @dnyuuu88/27341 · this entry13 Sept 2026, 07:09 UTC | @dubaixinwen2/2577715 Sept 2026, 07:11 UTC | 1.00 | 2 days |
| @dnyuuu88/27340 · this entry13 Sept 2026, 06:59 UTC | @dubaixinwen2/2577815 Sept 2026, 09:03 UTC | 1.00 | 2 days |
| @dnyuuu88/27642 · this entry27 Sept 2026, 14:19 UTC | @dubaixinwen2/2603629 Sept 2026, 09:06 UTC | 1.00 | 43 hours |
| Channel | Matching posts | Text overlap | Typical gap | Published first |
|---|---|---|---|---|
| @dubaixinwen2 | 7 (7/7 hand-verifiable sample passed) | 1.00 | 2 days | this entry (7–0) |
| @dsj665 | 5 (5/5 hand-verifiable sample passed) | 1.00 | under a minute | this entry (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 1,674 comparable posts for this entry, running 7 August 2026 to 13 November 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.
Recorded under the keys clone_mutual · clone_source, last confirmed 14 November 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.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 14 Nov 2026, 23:47 | 121,554 | -549 |
| 11 Nov 2026, 23:20 | 122,103 | -873 |
| 9 Nov 2026, 22:57 | 122,976 | -642 |
| 8 Nov 2026, 15:55 | 123,618 | -606 |
| 7 Nov 2026, 12:48 | 124,224 | -1,674 |
| 6 Nov 2026, 10:18 | 125,898 | -69 |
| 5 Nov 2026, 10:39 | 125,967 | -125 |
| 4 Nov 2026, 08:26 | 126,092 | +1,260 |
| 3 Nov 2026, 07:14 | 124,832 | -110 |
| 2 Nov 2026, 06:37 | 124,942 | -333 |
| 1 Nov 2026, 08:16 | 125,275 | -479 |
| 31 Oct 2026, 07:01 | 125,754 | +906 |
| 30 Oct 2026, 03:48 | 124,848 | -3,122 |
| 29 Oct 2026, 00:30 | 127,970 | -1,939 |
| 27 Oct 2026, 19:49 | 129,909 | -536 |
| 26 Oct 2026, 20:27 | 130,445 | -1,049 |
| 25 Oct 2026, 22:40 | 131,494 | -380 |
| 24 Oct 2026, 23:16 | 131,874 | -971 |
| 23 Oct 2026, 22:06 | 132,845 | -5,435 |
| 22 Oct 2026, 21:47 | 138,280 | first reading |
1,744 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 206 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 473 of 479 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 14 November 2026 |
|---|---|
| Posts held | 1,744 (7 August 2026 – 14 November 2026) |
| Views total | 2,359,597 |
| Reactions total | 8,511 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 14 Nov 2026, 18:42 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 28 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.
17,446 reactions across 988 posts, in 12 distinct kinds. The most used accounts for 17.1% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 2,976 | 17.1% | |
| 🎉 | 2,221 | 12.7% | |
| ❤ | 2,163 | 12.4% | |
| 🔥 | 2,143 | 12.3% | |
| 🤩 | 1,956 | 11.2% | |
| 😍 | 1,550 | 8.88% | |
| 💯 | 1,437 | 8.24% | |
| 🥰 | 1,409 | 8.08% | |
| ❤🔥 | 1,356 | 7.77% | |
| 👏 | 210 | 1.20% | |
| 😁 | 19 | 0.109% | |
| ⚡ | 6 | 0.034% |
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 989 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 17,446 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 16 September 2026 to 14 November 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.
Telegram’s own ‘edited’ marker: 0 of 479 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).
新加坡网民热议带初访客去哪吃 近日,一名新加坡网民在社交平台X发帖,询问同胞会带首次到访的外国朋友去哪里用餐,引发讨论。有人建议滨海湾花园、克拉码头、牛车水及武吉士等游客集中区域的餐馆,也有人提到老巴刹,但承认那里是“常见的游客陷阱”。不少网民认为小贩中心最能体验本地饮食文化,推荐旧机场路、中峇鲁、牛车水大厦及黄金坊。另有网民推荐本地美食,如美芝路虾面、森林广场咸蛋及新民路鸡饭,并鼓励探索小印度和登布西山。也有人建议请客人吃家常菜,或选择麦当劳、鼎泰丰等连锁餐厅。
❤8👍8🎉4
数据:美国政府持有 30.679 万枚比特币、1.743 万枚 ETH 及其他加密资产,总价值约 205.7 亿美元 据 Onchain Lens 监测,美国政府持有 30.679 万枚比特币、1.743 万枚 ETH 及其他加密资产,总价值约 205.7 亿美元。
👍3🔥2🤩2
萨法维否认故意离队 称有难言之事 马来西亚国家队边锋萨法维·拉希德就他离开在中国集训的“马来亚虎”营地一事发声,称并非随意决定。他在Threads发文表示,有些事可以说明,有些事不能说明,并强调自己职业生涯中从未拒绝国家队征召,穿上黑黄球衣是他的骄傲,帖文附有#familysafetyfirst”标签。此前球迷对他离开广东集训感到意外。代理主帅陈清和确认,萨法维已向教练组说明问题,因家庭事务获准返国,管理层理解并尊重这一决定。
❤5👍5🤩5
⚡️⚡️⚡️⚡️1️⃣1️⃣1️⃣1️⃣1️⃣ 😀😀😀😀😀😀 ➡️ 全网已押千万u点击传送门 👈 ⚡️⚡️⚡️⚡️数字网投领导者! ⚡️⚡️⚡️⚡️ 始终坚持“用户至上,信誉为本”的运营理念,力求为玩家带来公平公正、真实专业的高品质游戏竞技,让你体验顶级博彩乐趣! 【🇺🇦:🇺🇦😻🤔🥴🤧😷👿,创造人生新财富!】 1️⃣1️⃣1️⃣选择 ⚡️⚡️ 必须发 1️⃣1️⃣1️⃣选择 ⚡️⚡️ 必须发 1️⃣1️⃣1️⃣选择 ⚡️⚡️ 必须发 1️⃣1️⃣1️⃣选择 ⚡️⚡️ 必须发 1️⃣1️⃣1️⃣选择 ⚡️⚡️ 必须发 1️⃣1️⃣1️⃣选择 ⚡️⚡️ 必须发 1️⃣1️⃣1️⃣1️⃣ 顶级博彩娱乐尽在⚡️⚡️⚡️⚡️ 😀😀😀😀😀😀😀😀 🙄😶😄😶 😀全网诚邀:个人代理,SEO,技术/劫持/主播/推广团队,最高顶级福利待遇。😀 💎代理扶持计划 😀 会员注册即可申请赠送彩金【18u-888u】 😀 新人首存即可申请首存百…
❤7👍7❤🔥4
马来西亚移民局突袭吉隆坡两处住宅,22名中国及菲律宾籍外国人被捕 据马来西亚国家通讯社(BERNAMA)11月13日报道,此次行动于11月11日至12日展开。执法人员在两处住宅内共发现22名外籍人员,其中男性18人、女性4人。 初步调查显示,涉事住宅疑似被用于开展非法网络活动。部分被扣留人员声称,他们此前在本国收到工作邀请,对方承诺提供每月2000至8000令吉不等的薪资。 此次行动由吉隆坡移民局联合马来西亚通讯及多媒体委员会(MCMC)展开。执法部门目前正进一步调查相关人员的实际工作性质、组织分工及是否涉及其他违法活动。 目前,案件仍在调查阶段,相关人员是否涉及网络诈骗或其他非法网络活动,尚待执法部门进一步确认。
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加拿大与菲律宾完成自贸谈判 加拿大总理卡尼11月14日在马尼拉表示,加拿大已结束与菲律宾的自由贸易协定谈判。该协定将取消超过90%的关税税目,覆盖农业、航空航天和人工智能等领域,预计约六个月后签署。卡尼称这是菲律宾签署的最全面协定,预计未来十年双边贸易额将增长两倍。2025年两国贸易总额约为24亿美元。卡尼还表示,加拿大与东盟11国的更广泛协定谈判也已“基本完成”,该协定将覆盖每年超过500亿美元的双向贸易。马尼拉方面称,预计该贸易协定将在未来一年内完成。
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微软服务全球故障波及新加坡政府系统 微软服务出现全球性中断,新加坡警察部队、移民与关卡局、国内税务局等多个政府系统受影响,当地多家银行、新加坡航空、市镇理事会及三大公共医疗集群也报告邮件通信受阻。故障自13日早间开始,Outlook问题报告在9时13分达1136起。税务局建议改用在线聊天或税务门户,警方称收发邮件能力受影响,法院在线立案系统仍正常。微软14日表示“深表歉意”,称故障为技术问题,与网络攻击或AI无关,源于早前网络容量升级中的问题,将在审查完成后发布完整报告。
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安华称1MDB等遗留债务推高马来西亚国债 吉隆坡11月14日消息,马来西亚总理拿督斯里安华·易卜拉欣当日回应有关其担任财政部长期间国家债务上升的说法,称债务增加源于前几届政府遗留的债务,包括与一马发展公司(1MDB)相关的债务。安华表示,联邦政府自2022年以来持续缩减预算赤字,赤字从当年约1000亿林吉特降至约900亿、800亿和700亿林吉特,但总债务仍在增加,原因是须偿还1MDB、联邦土地发展局(Felda)等旧债,其中Felda债务每年须偿还9.8亿林吉特,朝圣基金局债务须偿还100亿林吉特。他同时谴责腐败,强调领导人诚信的重要性,称腐败文化将毁掉国家。
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马六甲文打希里选区投票率最低 截至今日下午4时30分,马六甲文打希里州选区投票率仅略低于56%,在28个州选区中垫底,与最高投票率的士兰加(74.09%)相差20.72个百分点。该选区自上午8时开放投票以来投票率持续偏低。全州普通选民投票率为67.78%,即669,211名选民中已有433,944人投票。文打希里共有23,042名登记选民,其中华裔约占73%,巫裔12.9%,印裔6.4%,其他6.8%。该选区上演三角战,由行动党原任议员林秀凌、国阵候选人骆慧仪及马来西亚全民党候选人莫哈末诺拉希迪角逐。2021年该选区投票率为57.55%,目前数字尚未达到。投票将于下午6时结束,选举委员会整体目标为70%。
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迪拜文化投资与住房计划并行推进 迪拜本周公布多项进展:在阿联酋政府年度会议上,谢赫·曼苏尔·本·扎耶德·阿勒纳哈扬表彰了一批文化界人士,政府将文化定位为吸引旅游、培养人才和多元化收入的增长引擎。交通方面,迪拜出租车公司第三季度净利润下降25.3%,至5710万迪拉姆,主因是融资成本上升和客运需求疲软。该公司运营超过5000辆车,正面临车队更新与资金成本的双重压力。住房方面,迪拜将推出包含15个独立街区的新社区,计划今年晚些时候开工,提供经济型公寓、高端别墅及零售休闲配套,以应对人口增长带来的住房需求。
🤩5😍5🥰4
选委会提醒马六甲选民勿用非官方链接查登记 马来西亚选举委员会提醒第16届马六甲州选举选民,不要使用未经授权的链接查询选民登记资料,应通过官方网站 www.spr.gov.my 核实。选委会在脸书帖文中强调,分享信息前应先核实。本届州选投票于今日上午8时至下午6时在240个投票中心进行,共有669,211名合格选民,将选出28个州议席的代表。选委会建议选民浏览官方选举网站获取最新进展。
❤🔥6💯5😍4
迪拜国际机场获评全球最佳机场 据Worldworthy 100排名,迪拜国际机场以9.26分位居全球机场榜首。迪拜在城市榜单中位列第19位,被评为最值得造访的城市之一;阿布扎比的总统府(Qasr Al Watan)在全球宫殿排名中位列第六。该榜单同时涵盖阿联酋多座城市和地标。
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Showing the 12 most recent of 1,744 posts we hold for @dnyuuu88. 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.
We recorded 11 posts on @dnyuuu88 as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.
A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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.
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 24 seed channels' Telegram-generated recommendation lists in total, of which the 23 where it ranks highest are shown above. 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 14 November 2026 — this entry's latest reading, not the date you are reading this.
“迪拜新闻大事件-马来西亚吉隆坡吃瓜” (@dnyuuu88), 121,554 subscribers as measured 14 November 2026. Telegram Register, tgregister.com/channel/dnyuuu88.
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.