📣 #发现频道 慢慢 - 宝宝成长故事:把宝宝照片整理成连贯的成长故事(iOS) 这款应用从相册中发现成长变化,支持语音讲述、编辑故事,并生成回忆视频或 PDF 成长书。 支持 iOS 18.0 及以上;免费下载,部分功能需内购。照片和故事在本机处理保存,不上传;需注意免费额度和付费解锁条件。 阅读原文:https://meta.appinn.net/t/95680 💧小众软件 · 投稿 · 爆料
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Channel
@appinnfeed
On this record: Topic · Observations · Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Citations · Handles named that no longer answer · Telegram's recommendations · Cite this entry
34,958subscribers
+5,356 since we began measuring on 5 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
| Telegram ID | -1001073712343 |
|---|---|
| Type | Channel |
| Username | @appinnfeed |
| Description | 这里有小众软件和发现频道的内容更新,以及一个绑定的私有群组。 💬 吹水群:https://t.me/appinn 📖 RSS 订阅地址:https://feeds.feedburner.com/apipnntgfeed 📣 发现频道 @appinnfaxian 在这个频道发布广告(Buy ads on this channel): https://telega.io/c/appinnfeed |
| Created | 28 December 2016 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 10 November 2026 |
| Measurements held | 64 |
| Confirmed unchanged | 1 time, most recently 10 November 2026 |
| On Telegram | t.me/appinnfeed |
Technology — 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 99% 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 3 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 |
|---|---|---|---|
| @QQFXS/36835 Aug 2026, 11:00 UTC | @appinnfeed/17572 · this entry5 Aug 2026, 11:00 UTC | 1.00 | under a minute |
| @QQFXS/39911 Sept 2026, 11:00 UTC | @appinnfeed/17862 · this entry1 Sept 2026, 11:00 UTC | 1.00 | under a minute |
| @QQFXS/40778 Sept 2026, 11:00 UTC | @appinnfeed/17927 · this entry8 Sept 2026, 11:00 UTC | 1.00 | under a minute |
| @QQFXS/427123 Sept 2026, 11:00 UTC | @appinnfeed/18120 · this entry23 Sept 2026, 11:00 UTC | 1.00 | under a minute |
| @QQFXS/435230 Sept 2026, 11:00 UTC | @appinnfeed/18207 · this entry30 Sept 2026, 11:00 UTC | 1.00 | under a minute |
| @QQFXS/44377 Oct 2026, 11:00 UTC | @appinnfeed/18273 · this entry7 Oct 2026, 11:00 UTC | 1.00 | under a minute |
| Channel | Matching posts | Text overlap | Typical gap | Published first |
|---|---|---|---|---|
| @QQFXS | 10 (8/8 hand-verifiable sample passed) | 1.00 | under a minute | @QQFXS (6–4) |
| @QukanMovie | 8 (8/8 hand-verifiable sample passed) | 1.00 | under a minute | @QukanMovie (6–2) |
| @zhetengsha | 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. 12 of the 13 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 448 comparable posts for this entry, running 4 August 2026 to 10 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_copy · clone_mutual · clone_source, last confirmed 11 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 |
|---|---|---|
| 10 Nov 2026, 02:57 | 34,958 | +38 |
| 8 Nov 2026, 13:49 | 34,920 | +117 |
| 7 Nov 2026, 11:10 | 34,803 | +19 |
| 6 Nov 2026, 13:39 | 34,784 | +6 |
| 5 Nov 2026, 13:59 | 34,778 | +26 |
| 4 Nov 2026, 12:39 | 34,752 | +14 |
| 3 Nov 2026, 16:09 | 34,738 | +46 |
| 2 Nov 2026, 13:50 | 34,692 | +26 |
| 1 Nov 2026, 15:16 | 34,666 | +21 |
| 31 Oct 2026, 12:05 | 34,645 | +16 |
| 30 Oct 2026, 08:31 | 34,629 | -3 |
| 29 Oct 2026, 10:19 | 34,632 | +44 |
| 28 Oct 2026, 11:10 | 34,588 | +21 |
| 27 Oct 2026, 09:14 | 34,567 | +76 |
| 26 Oct 2026, 07:56 | 34,491 | +63 |
| 25 Oct 2026, 07:18 | 34,428 | +18 |
| 24 Oct 2026, 09:46 | 34,410 | +32 |
| 23 Oct 2026, 08:27 | 34,378 | +14 |
| 22 Oct 2026, 09:37 | 34,364 | +21 |
| 21 Oct 2026, 01:58 | 34,343 | first reading |
1,120 posts held, back to 3 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 173 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 273 of 449 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 11 November 2026 |
|---|---|
| Posts held | 1,120 (3 August 2026 – 11 November 2026) |
| Views total | 359,095 |
| Reactions total | 691 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 11 Nov 2026, 06:18 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 11 November 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 5 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.
1,156 reactions across 455 posts, in 24 distinct kinds. The most used accounts for 61.9% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 715 | 61.9% | |
| ❤ | 147 | 12.7% | |
| 😁 | 100 | 8.65% | |
| 🤯 | 33 | 2.85% | |
| 👎 | 32 | 2.77% | |
| 🤡 | 24 | 2.08% | |
| 🤣 | 24 | 2.08% | |
| 🐳 | 16 | 1.38% | |
| 🤔 | 10 | 0.865% | |
| 😢 | 9 | 0.779% | |
| 💩 | 8 | 0.692% | |
| 😱 | 7 | 0.606% | |
| 👀 | 4 | 0.346% | |
| 🔥 | 4 | 0.346% | |
| 😈 | 4 | 0.346% | |
| 🤬 | 4 | 0.346% | |
| 💊 | 3 | 0.26% | |
| ☃ | 2 | 0.173% | |
| 🆒 | 2 | 0.173% | |
| 👏 | 2 | 0.173% | |
| 4 further kinds | 6 | 0.519% |
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 490 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 1,156 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 15 August 2026 to 11 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: 9 of 449 posts held from the last 30 days (2.00%) 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).
📣 #发现频道 慢慢 - 宝宝成长故事:把宝宝照片整理成连贯的成长故事(iOS) 这款应用从相册中发现成长变化,支持语音讲述、编辑故事,并生成回忆视频或 PDF 成长书。 支持 iOS 18.0 及以上;免费下载,部分功能需内购。照片和故事在本机处理保存,不上传;需注意免费额度和付费解锁条件。 阅读原文:https://meta.appinn.net/t/95680 💧小众软件 · 投稿 · 爆料
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Posted without readable text
📣 #发现频道 轻轻-照片清理与相册整理:每次整理 20 张 iPhone 照片 轻轻是一款 iOS 相册整理工具,每次随机挑出 20 张照片供你逐张筛选,也可将照片归入相簿,并整理相似照片和视频。 滑动整理和视频整理免费不限次数;相似分组、下滑归档每天各免费 5 组,Pro 一次性买断 ¥68。照片在手机本地处理,无需注册;仅支持 iOS。 阅读原文:https://meta.appinn.net/t/95677 💧小众软件 · 投稿 · 爆料
📣 #发现频道 WidBar:将可自定义小组件放到 Windows 11 任务栏 WidBar 可在任务栏展示系统性能、媒体控制等小组件,支持交互式排列、多显示器和独立设置,让任务栏空间显示日常信息。 免费,适用于 Windows 11 22621.0 及以上版本。软件本体约 300 MB,组件约 200 MB,且组件需单独安装;用户反馈组件可能与任务栏内容重叠。 阅读原文:https://meta.appinn.net/t/95665 💧小众软件 · 投稿 · 爆料
发现频道日报 · 2026-11-10 昨日共 8 条 其他应用 6 条 · 桌面与系统 1 条 · 影音与游戏 1 条 其他应用(6) 小渔排班 一款为轮班人群设计的排班日历,支持周期排班、班次闹钟、工时薪资统计,现已永久免费。 追光快译 / GlowTrans 你打母语,译文跟到光标旁。 OpenSlides 一个在浏览器中运行的开源演示文稿工作室,可用拖拽或 TSX 代码制作动画幻灯片,并完成演示、旁白、视频、PDF 和静态 PPTX 导出。 表差 · AI业务表核对与本地工具 按关键字段核对两份 Excel/CSV,首发4.9元买断 小巧工具箱 小巧工具箱,一款汇聚200+实用功能的效率工具App。 时光匣 用口述、原声和照片记录自己与家人的故事,再慢慢整理成人生回忆录。 桌面与系统(1) Lofi Cozy Room 一款可以留在 Windows 桌面角落的陪伴游戏,带有 Lo-Fi 音乐、环境声和…
💧 #小众软件 小渔排班宣布永久免费:无广告、无订阅 小渔排班是为轮班、倒班工作者设计的日历应用,可安排规律或临时班次,并提供班次闹钟、工时薪资统计和班表分享等功能。 自 2026 年 10 月起,所有功能免费开放,仅保留自愿支持选项。应用支持 iPhone、iPad 和 Apple Watch,使用 iCloud 同步。 阅读原文:https://www.appinn.com/xiaoyu-shift-calendar-free/ 💧小众软件 · 投稿 · 爆料
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在 Mac 编译 Apple TV 上的 Super Mario Odyssey,第一次跑满了 CPU。 另外,我还看到了在 Linux 上跑 Mac 应用的项目,这个世界好混乱。
📣 #发现频道 Lofi Cozy Room:适用于 Windows 桌面的 Lo-Fi 陪伴游戏 这款游戏可缩成桌面小窗,提供 Lo-Fi 音乐、环境声和专注计时器,也能布置房间、观察小狗活动。 支持简体中文,通过 Steam 获取;中国区标准价 19.99 元,另有免费试玩版。试玩下载需要 Steam 账号和客户端。 阅读原文:https://meta.appinn.net/t/95621 💧小众软件 · 投稿 · 爆料
❤1👍1
📣 #发现频道 OpenSlides:在浏览器里制作、演示并导出带动画的幻灯片 OpenSlides 是一款浏览器端演示文稿工具,可拖拽编辑或编写 TSX,支持动画、演示者视图、旁白及视频、PDF、PPTX 导出。 基础功能免费,无需注册;支持桌面、平板和手机浏览器。目前只有英文界面,PPTX 导出为静态版本,动态组件会转为图片。 阅读原文:https://meta.appinn.net/t/95622 💧小众软件 · 投稿 · 爆料
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📣 #发现频道 表差:按关键字段核对两份 Excel/CSV,9.9 元买断 表差是本地表格核对工具,可按一个或多个关键字段比较两份 XLSX 或 UTF-8 CSV,识别新增、缺失和字段变化,并导出 XLSX 或 JSON。 9.90 元买断;需 Node.js 22、支付宝账号,并自行准备 AI 模型及额度。每个文件最多 10MB、200 列、20000 行;重复键和空键需处理。 阅读原文:https://meta.appinn.net/t/95615 💧小众软件 · 投稿 · 爆料
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📣 #发现频道 素刀 ClipKnife:用画面描述、截图和台词,找回硬盘里的素材 素刀是 Windows 本地素材管理软件,可按画面描述、参考图片和转写内容检索图片、视频片段与音频,并支持预览、分组和剪辑软件衔接。 支持 Windows 10 22H2/11 x64,内存至少 16GB;售价 99 元买断,目前公测限时免费。首次激活和许可验证需联网,macOS 与 Linux 暂无安装包。 阅读原文:https://meta.appinn.net/t/95608 💧小众软件 · 投稿 · 爆料
💧 #小众软件 福利:切问学术双十一会员积分翻倍 切问学术面向科研工作,提供文献检索与阅读、论文问答、科研 Agent 等功能,可协助跑实验、整理文献和撰写综述。 活动期间订阅任意套餐,会员每月积分翻倍,月付、年付及老会员升级均可参加;单账号最多购买12个月。Agent 等任务会消耗积分。 阅读原文:https://www.appinn.com/qiewenpaper-1111/ 💧小众软件 · 投稿 · 爆料
Showing the 12 most recent of 1,120 posts we hold for @appinnfeed. 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.
@appinnfeed edited 1 post 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.
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.
Named by 31 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. The 24 listed below are the most frequent namers; the rest are counted above but not each listed.
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.
@appinnfeed named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
We ourselves saw each of these resolve to a real page at some point before it went vacant — a genuine, evidenced change, not an inference from absence.
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 4 September 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 29 seed channels' Telegram-generated recommendation lists in total, of which the 24 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 10 November 2026 — this entry's latest reading, not the date you are reading this.
“Appinn Feed|小众软件” (@appinnfeed), 34,958 subscribers as measured 10 November 2026. Telegram Register, tgregister.com/channel/appinnfeed.
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.