Channel
@gledos_microblogging
On this record: Growth · Engagement · Reactions · Stars · Posts · Citations · Telegram's recommendations · Cite this entry
6,582subscribers
+13 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1001298979108 |
|---|---|
| Type | Channel |
| Username | @gledos_microblogging |
| Created | Between 1 March 2018 and 30 June 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 13 August 2026 |
| Measurements held | 5 |
| Confirmed unchanged | 1 time, most recently 13 August 2026 |
| On Telegram | t.me/gledos_microblogging |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 13 Aug 2026, 06:25 | 6,582 | -1 |
| 10 Aug 2026, 16:52 | 6,583 | +15 |
| 7 Aug 2026, 11:30 | 6,568 | -1 |
| 6 Aug 2026, 20:15 | 6,569 | no change |
| 6 Aug 2026, 20:05 | 6,569 | first reading |
20 posts held, back to 28 May 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 9 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.
| Window | Rolling 30 days · latest post in window 4 August 2026 |
|---|---|
| Posts held | 20 (28 May 2026 – 4 August 2026) |
| Views total | 9,310 |
| Reactions total | 69 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 12 Aug 2026, 03:01 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.
426 reactions across 20 posts, in 18 distinct kinds. The most used accounts for 40.1% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 171 | 40.1% | |
| 🤔 | 78 | 18.3% | |
| 👀 | 49 | 11.5% | |
| 👍 | 26 | 6.10% | |
| 🆒 | 22 | 5.16% | |
| 😁 | 14 | 3.29% | |
| 🥰 | 13 | 3.05% | |
| 🤡 | 12 | 2.82% | |
| 🤨 | 12 | 2.82% | |
| 🔥 | 7 | 1.64% | |
| 🌚 | 5 | 1.17% | |
| 😢 | 4 | 0.939% | |
| 🤬 | 4 | 0.939% | |
| 🤣 | 3 | 0.704% | |
| 👎 | 2 | 0.469% | |
| 🙏 | 2 | 0.469% | |
| 💅 | 1 | 0.235% | |
| 🥱 | 1 | 0.235% |
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 20 of the 20 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 426reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 28 May 2026 to 4 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.
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @gledos_microblogging. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 20 most recent posts we hold for this entry, published 28 May 2026 to 4 August 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
色觉辨认障友好医院 色觉辨认障碍 具体有色弱、色盲。具有色觉辨认障碍的人们，可能不适合一些对颜色要求高的行为，比如当印刷厂的一些工作可能就不太适合。 近年人们对弱势群体的关注上升，所以色觉辨认障也迎来了无障碍设计，即 色彩通用设计。这是使其辨识不单独依赖颜色的设计，比如信号灯红色是行人停止的形状，而绿灯是行人行走的动态效果。 如果信号灯总是依次排序（事实上确实是有统一标准的），又或者有形状，其实对于色觉辨认障碍而言，是能分清的。不过仍有一些地区，仍然对色觉辨认障有许多障碍，比如大陆和俄罗斯。 大陆虽然只规定了「无红绿色盲」即可，但部分做体检医院只会给看《色盲检测图》，看到色弱就不给过。以至于有人会去 背《色盲检测图》，或者寻找 色觉辨认障友好医院。 俄罗斯 在 2025 年的新规后，不再允许色觉辨认障友人士获得驾照。另外，俄罗斯在过去已经禁止给抑郁症、跨性别者颁发驾照。 而其它的常见地区，如 英国、法国、德国、澳大利…
❤9👎1
积极虚无主义 存在主义危机（existential crisis）的特征，通常是感觉生活缺乏意义，并对自身身份感到困惑。由于其可能导致焦虑和压力，甚至带来抑郁症，所以这是一个在近年常被讨论的议题。 过去宗教氛围浓郁的时期，存在主义危机没有那么普遍，因为大家都被赋予了个目标。这是一种只要遵守什么什么戒律，就能获得救赎的，与神的约定。 但过去还是有存在主义危机。当时的神职人员、知识阶层和识字者，还是会在自己的文字记录里，留下了不少怀疑，怀疑的背后都是在问「上帝为什么沉默？」「我是否已经被上帝抛弃？」「我的苦难究竟有什么意义？」等问题。 用游戏来比喻的话，宗教氛围浓郁的时期其实是有明确任务引导的氪金游戏。而现的世界秩序像是个低指引的开放世界游戏，比如《塞尔达传说：旷野之息》那样。（不过现实没有那么好玩） 前者具有既定的轨道，所以玩家能找到事情做，较少思考自己应该怎么玩。只要有时间，玩到满级通常不是问题。但低指引的开放世界游戏…
👍9❤4
全局神经工作空间与 J-空间 Global Neuronal Workspace 是个有趣的意识理论，大意是大脑里许多专门系统在后台并行工作，只有少数信息可以进入一个容量有限的工作空间，然后才能被报告、控制，并广播给许多其他系统使用。而意识大概就是这个工作空间，工作空间之外则是某种潜意识。 也有实验符合 GNW 理论，实验 过程是让单词的屏幕上出现 29 毫秒，然后使用其他图形覆盖。虽然实验参与者报告没看见，但参与者的加工书面文字的核心区域，视觉词形区却被激活了。 这时给两个单词，让参与者来判断之前出现的单词是什么，结果正确率是 52.9 %，说明单词信息没有进入 GNW。有趣的是之后出现同一个词，参与者会更快作答，相关脑区的反应也会减弱，说明大脑已经在无意识中提取了部分词形信息，只是该内容暂时未被放入 GNW。 最近 Anthropic 发现 Claude 也存在类似 GNW 的空间，并将其命名为 J-空间（J-spa…
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视频博主推荐 ⭐ 无关茉莉星（订阅者数量 2.5 万） 科普一些冷知识的博主，幽默感与节奏感挺有趣的。只是感觉许多议题本来还能继续深挖，而博主的视频已经到结尾了。 ⭐ 派数据PaiData（订阅者数量 33.6 万） 像回形针那样的可视化科普，不过更新很慢。其中关于近视手术和牙齿矫正的节目很好，基本打消了 Lia 考虑这类手术、矫正的想法。 📹 志祺七七（订阅者数量 167 万） 视角很全面的实事议题频道，因为是台湾频道，所以还能了解到台湾近期流行的议题，以及台湾文化。 ⭐ 耳畔的猫头鹰（订阅者数量 12.9 万） 聊的疾病都是外国的社会议题，但实际上都聊了。比如《有一个国家，半数人灵活就业，劳动法形同废纸》（墨西哥）、《有一个国家，彩票头奖号码能提前预测，经营者却说是巧合》（美国）等。 ⭐ 金融民工吉胖子（订阅者数量 23.9 万） 他做了许多现在为什么不能买房的视频，视频主要会用房价一直在跌的官方数据来讲…
❤5
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🥰8❤1
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在《小小的我》里疗愈「内在小孩」 在 VOCALOID 文化圈的音乐《小小的我》『ちっちゃな私 feat. 重音テトSV』里，许多听众表示很感动，甚至听哭了。 这种情况，可以用心理治疗中「内在小孩」的概念来理解。比如心理治疗师让人回想小时候的自己，然后问：「如果小时候的我坐在这里，我会怎么照顾 TA？」 许多人在面对自己时，非常苛刻，但面对一个受伤的小孩，却会很温柔。所以治疗师会用内在小孩卸下盔甲，帮助培养自我关怀，促进自我接纳等。 音乐《小小的我》也是这样，歌曲一开始是在探索过去的自己，然后发现过去痛苦的心情（以及这个心情至今仍然存在），最后通过歌词「小小的我，一直，一直，啊 存在在这里」，承认自己的心情。 这种过程，也接近心理治疗中所说的「给情绪命名」：将原本混杂而模糊的难受，标识成悲伤、羞耻、害怕或想哭等名称。这样可以改善心理状态，原因尚不明确，但这也算是某种〈名字的力量〉吧。（笑） #杂谈
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双胞胎禁忌 游戏《零 ～紅蝶～》中，村庄将双胞胎的一位作为祭品献祭，以防止灾难发生。《寒蝉鸣泣之时》里的园崎家族，有杀死双胞胎之一的传统。游戏《死魂曲》里的双胞胎，也是同样的命运。（怎么都是双胞胎姐妹） 在现实中，这种献祭仍有少量地区存在，比如非洲的 奈及利亚部分部落。在 Bassa Komo 部落的 Ubo Saidu 村，有村民表示双胞胎不属于人类，而是会危害整个部落的存在。 另一个部落认为，双胞胎具有非常强大的力量，超越了掌管部落的巫女或巫师，所以统治者不会让双胞胎留在部落里。有的部落甚至会为了丰收而献祭小孩。 维克多·特纳在《仪式过程：结构与反结构》里，对这类行为有个解释：一般情况下，一次出生事件对应一个孩子、一个亲属位置、一个长幼顺序。但双胞胎是一次出生出现两个孩子，导致两个身体挤占了一个亲属位置、长幼顺序。 在小聚落的人很少遇到双胞胎，通常的规则不生效，于是只能杀掉其中的一个，解决异变。亦或是神圣化、赋予特…
❤11🙏2👍1🤣1
3X 择偶 日本 20 世纪 80 年代的经济繁荣／泡沫时期，女性择偶的选择，出现了 3 高 的说法：高等教育、收入高、身材高大。（即高富帅） 不过泡沫经济破裂，引发经济长期停滞后的 2003 年，有心理学家描述了日本女性的新择偶标准：3C。 3C 是指 comfortable、communicative 和 cooperative。含义分别是足够的薪水，相近的价值观和乐于帮忙做家务。 不过近年似乎又有变化，变成了 3 低：低姿势、低束缚和低风险。即非男性沙文主义、能独立并分担家务和育儿责任，以及从事稳定职业、被裁员风险较小（铁饭碗）。（不过这些要求还是一点都不低） 中国的经济走势，跟日本有点相似（测试服），类似的社会现象也在发生。多年前经济好的时候，流行高富帅、潜力股、富二代、官二代等词语，而现在流行三观合、布男、独生子、体制内（编制、铁饭碗）等说法。 #杂谈
🤔16😁10👀7❤2👍2🤣2👎1
家用电器的线上线下款 TCL 电视具有明显的线上线下隔离。线下店铺看到的型号，无法在电商平台上搜索到。同样，线上的型号无法在线下店铺里看到。 原因是线下店铺只有 X、C、98 等开头的型号，而线上只有 Q、T 和 V 开头的型号。线上与线下完全被区别为了两个生态。 不只是 TCL，常见的经营了很多年线下店铺的厂家，都有相同的操作。比如美的、海尔和苏泊尔等，同样是具有型号隔离。 原因也很简单，这能防止消费者轻松比价，保护线下门店经销商的利润，免受电商的冲击，以免得罪经销商。 但厂家也不能简单的把同一个商品，按线上与线下来分别定下不同的型号，因为用户很快就能发现。所以厂家往往会让外形有较大差异。 比如美的冰箱，线上通常是金属面板，线下多数都是漂亮的玻璃面板，因为顾客在线下更容易注意到外观。以及同价位的线上比线下配置好，因为线上更容易对比功能、参数。 近年厂家也在尝试绕开经销商，比如让子品牌做纯线上。海尔的统帅，美的的华…
🤔22🤡4🥱1😁1
利用 AI 压缩截图 之前给博客网站配图时，希望将网页或软件的截图压缩到很小。考虑到矢量图就很小，所以研究了一下 HTML 转 SVG 或 PDF 的方法。 一些现有的工具，其压缩率还是不够，并且有比截图还要大的情况发生，于是便停止跟进这一想法了。 最近把这个问题向 Gemini 咨询了一下，结果 Gemini 表示现在多模态模型十分强大，可以实现这种截图转 SVG 的想法。 然后使用 ChatGPT 测试 了一下效果，发现确实很棒。原始 544 KB 的 PNG 截图，压缩到 AVIF 会有 35 KB，而被 AI 重绘为了 SVG 后，只要 2.2 KB，细节多一点也就 5.7 KB。 在一些指导软件操作的教程里，可能需要很多截图配图，如果使用 AI 绘制 SVG 截图，大概能节省不少空间吧。 不过缺点也很明显，LLM 生成 SVG 可能比较费 token，又因为有 LLM 参与，所以还需要手动检查生成的图片是否…
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Showing the 12 most recent of 20 posts we hold for @gledos_microblogging. 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.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Citation-graph rank — 475,914 of 1,350,102entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
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 3 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.
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 3 seed channels' Telegram-generated recommendation lists 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 13 August 2026 — this entry's latest reading, not the date you are reading this.
“gledos Lia green 的微型博客” (@gledos_microblogging), 6,582 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/gledos_microblogging.
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.