小火箭策略组分流用的人多吗 最近感觉有使用场景 在研究🧐 优质「内容」 | 优质「资源」 | 你不知道的内幕消息🌳
Channel
优质信息收藏夹
@chunse1024
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
25,711subscribers
+4,431 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
Register entry
| Telegram ID | -1001854596532 |
|---|---|
| Type | Channel |
| Username | @chunse1024 |
| Created | Between 1 October 2022 and 30 September 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 19 September 2026 |
| Measurements held | 34 |
| Confirmed unchanged | 1 time, most recently 19 September 2026 |
| On Telegram | t.me/chunse1024 |
Topic
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 10 September 2026 and assigned it the closest of 31 fixed categories, at 88% 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.
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 19 Sept 2026, 01:40 | 25,711 | +41 |
| 16 Sept 2026, 17:17 | 25,670 | +60 |
| 14 Sept 2026, 20:01 | 25,610 | +96 |
| 13 Sept 2026, 03:18 | 25,514 | +63 |
| 11 Sept 2026, 07:16 | 25,451 | +86 |
| 8 Sept 2026, 08:20 | 25,365 | +116 |
| 5 Sept 2026, 03:57 | 25,249 | +55 |
| 3 Sept 2026, 09:03 | 25,194 | +56 |
| 2 Sept 2026, 01:41 | 25,138 | +56 |
| 1 Sept 2026, 04:44 | 25,082 | +44 |
| 31 Aug 2026, 02:24 | 25,038 | +35 |
| 30 Aug 2026, 01:54 | 25,003 | +31 |
| 29 Aug 2026, 03:47 | 24,972 | +12 |
| 28 Aug 2026, 00:53 | 24,960 | +48 |
| 27 Aug 2026, 01:05 | 24,912 | +63 |
| 26 Aug 2026, 02:44 | 24,849 | +47 |
| 25 Aug 2026, 01:22 | 24,802 | +46 |
| 23 Aug 2026, 15:49 | 24,756 | +81 |
| 22 Aug 2026, 00:24 | 24,675 | +56 |
| 20 Aug 2026, 18:55 | 24,619 | first reading |
Engagement
198 posts held, back to 2 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 68 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 3.87%
- avg views ÷ 25,711 subscribers
- Avg views / post
- 995
- 117 posts measured
- Reaction rate
- 0.147%
- reactions ÷ views · ER floor
- Posts in window
- 117
- of 198 held
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 44 of 117 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 3 September 2026 |
|---|---|
| Posts held | 198 (2 August 2026 – 3 September 2026) |
| Views total | 116,438 |
| Reactions total | 78 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 3 Sept 2026, 10:34 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.
What this channel posts
- Video runtime
- 24m 46s
- Average length
- 1m 27s
Measured directly from 17 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.
Reaction mix
220 reactions across 81 posts, in 13 distinct kinds. The most used accounts for 59.1% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 130 | 59.1% | |
| 👍 | 45 | 20.5% | |
| 💩 | 10 | 4.55% | |
| 🤡 | 10 | 4.55% | |
| 👏 | 6 | 2.73% | |
| 👎 | 4 | 1.82% | |
| 🤔 | 4 | 1.82% | |
| 😁 | 3 | 1.36% | |
| 🤬 | 2 | 0.909% | |
| 🤮 | 2 | 0.909% | |
| 🫡 | 2 | 0.909% | |
| 🖕 | 1 | 0.455% | |
| 🥴 | 1 | 0.455% |
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 100 of the 198 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 227 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 198 most recent posts we hold, published 2 August 2026 to 3 September 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.
Recent posts
这个开源项目收集了数万个 Mac 系统好用的软件 在线地址:https://wangchujiang.com/awesome-mac/README-zh.html https://github.com/jaywcjlove/awesome-mac/blob/master/README-zh.md 优质「内容」 | 优质「资源」 | 你不知道的内幕消息🌳
优质「资源⏬」收藏夹👇 https://t.me/meiriyishu 优质「信息✉️」收藏夹👇 https://t.me/chunse1024
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模型训练崩了可以重来,人崩了怎么重启? 发现一个看得脊背发凉,却又舍不得关掉的网站:Hacker News Burnout Guide 一个专门收集“硅谷老哥血泪史”的文档库,聚焦程序员职业生涯中最致命的杀手:枯竭(Burnout)。 这是硅谷老哥们的血泪自救指南 ,它不是教你如何年薪百万,也不是教你如何带团队,而是教你在灵魂烧成灰之前,如何完成一次成功的“赛博逃生”。 这里有几百位资深工程师血淋淋的复盘。 他们会详细描述那种“确诊”瞬间:面对一行写了三年的代码突然感到生理性作呕,或者盯着屏幕半小时,却发现大脑已经无法理解逻辑嵌套。 更硬核的是,它提供的不是“鸡汤”,而是“处方”。 你以为自救就是请假去大理?这组指南会告诉你那叫逃避。 它给出的战术极其具体且反直觉: 1️⃣ “只做 Ticket 不思考”: 暂时关闭创造力,把自己降级成执行机器,给过载的大脑降频。 2️⃣ “物理隔离”: 彻底断开一切带屏幕的设备,去感受真实的重…
❤1
对比Gpt 5.6 High和Opus 5在非编程是的推理能力,我把这张袁腾飞老师来到北美的照片喂给这两个模型,得出的以下结论: Gpt 5.6: 大概区域主观概率 加拿大南安省 / GTA 35–45% 美国东北部 / 五大湖地区 30–40% 加拿大大温地区 10–15% 美国其他温带地区10–15% 如果一定让我押一个城市圈,我仍然会首先押: Greater Toronto Area,尤其 Markham / Richmond Hill / Vaughan 一类社区。 Opus 5: 北美(大概率加拿大或美国)某郊区住宅区,独立屋/半独立屋密集排布,房屋两层以上,拍摄于顶层带阳台的房间,夏季晴天,居住者为亚裔家庭。 Gpt 5.6明显胜出 优质「内容」 | 优质「资源」 | 你不知道的内幕消息🌳
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Apple 新CEO火速入驻微博了 🤓 优质「内容」 | 优质「资源」 | 你不知道的内幕消息🌳
🖕1
纳瓦尔在2008年的时候分享了他眼中的人生公式 幸福 = 健康 + 财富 + 好的人际关系 健康 = 锻炼 + 饮食 + 睡眠 锻炼 = 高强度力量训练 + 竞技类运动 + 休息 饮食 = 自然食物 + 间歇性禁食 + 蔬菜饮食 睡眠 = 没有闹钟 + 8-9小时睡眠 财富 = 收入 + 当前财富 * 投资回报比 收入 = 责任感 + 杠杆 + 专业知识 责任感 = 个人品牌 + 个人平台 + 承担风险 杠杆 = 资本 + 利用他人时间 + 知识产权 专业知识 = 社会不能大规模培训的能力 ROI = 长期持有 + 合理估值 + 安全边际 或许在2026年我们需要更新一下: AI = 超级“人力杠杆” 稳定币 = 全球无限制跨境金钱的 API 新一代专项知识 = 判断力 × 品味 × 真实世界理解 × 跨域组合能力 一人公司收入 = AI工具箱 + 内容矩阵 + AllScale稳定币系统 私人优质信息收藏夹 https://…
我是全网第一个在2024年10月预测cerebras、sambanova、groq会被OpenAI、nvidia、google这些大厂收购的人。 现在看起来,距离这个预言越来越近了。 优质「内容」 | 优质「资源」 | 你不知道的内幕消息🌳
Showing the 12 most recent of 198 posts we hold for @chunse1024. 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.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Republishes
Channels on the register whose posts this channel has forwarded.
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.
Mentions
Named by 24 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.
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@taobao1024 · 2,7461 post新宇宙
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Names
Channels on the register whose handles appear in this channel's posts.
@meiriyishu · 12,035185 posts你不知道的内幕消息🅥
@inside1024 · 107,938168 postsAI探索指南
@aigc1024 · 37,13015 posts中年人生存报告
@dogdairy · 14,00814 posts互联网从业者充电站
@https1024 · 30,70914 posts你不知道的冷知识
@knowledge1024 · 19,42214 posts两性知识
@liangxing365 · 14,07714 posts书墨资源
@shumozy · 20,76914 posts副业赚钱探索指南
@text1024 · 19,74914 posts出海🚢&自媒体运营秘籍
@yunying23 · 21,08514 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.
Cite this entry
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 19 September 2026 — this entry's latest reading, not the date you are reading this.
“优质信息收藏夹” (@chunse1024), 25,711 subscribers as measured 19 September 2026. Telegram Register, tgregister.com/channel/chunse1024.
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.