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Channel

冰器库(Bing's Toolkit)

@ifanbing

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry

6,822subscribers

-3 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001459895572
TypeChannel
Username@ifanbing
DescriptionXDash 的分享频道,主观推荐有料有趣的自我提升/生产力/投资方向的工具/影视/书/报告等资源。
CreatedBetween 1 April 2019 and 30 September 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live16 August 2026
Measurements held5
Confirmed unchanged1 time, most recently 16 August 2026
On Telegramt.me/ifanbing

Growth

6,8226,8256,823.57 August 2026 — 6,825 subscribers7 August 2026 — 6,825 subscribers10 August 2026 — 6,823 subscribers12 August 2026 — 6,825 subscribers16 August 2026 — 6,822 subscribers7 August 202616 August 2026
5 measurements spanning 10 days, net -3. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 6,822–6,825 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
16 Aug 2026, 17:476,822-3
12 Aug 2026, 19:336,825+2
10 Aug 2026, 00:476,823-2
7 Aug 2026, 02:156,825no change
7 Aug 2026, 02:056,825first reading

Engagement

21 posts held, back to 19 February 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 11 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
19.3%
avg views ÷ 6,822 subscribers
Avg views / post
1,310
6 posts measured
Reaction rate
0.556%
reactions ÷ views · ER floor
Posts in window
6
of 21 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 4 of 6 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 14 August 2026
Posts held21 (19 February 202614 August 2026)
Views total7,888
Reactions total28
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken18 Aug 2026, 10:54 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

Photos
113
Links
549

Lifetime counters from Telegram’s own channel header, read 18 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.

Reaction mix

60 reactions across 15 posts, in 9 distinct kinds. The most used accounts for 46.7% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍2846.7%
1423.3%
🔥58.33%
🥰58.33%
😁35.00%
💩23.33%
👏11.67%
🙏11.67%
🤔11.67%

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 15 of the 21 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 60reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 21 most recent posts we hold, published 19 February 2026 to 14 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.

Recent posts

14 Aug 2026, 01:10 UTC668 views9 reactionsread 18 August 2026

我前不久把自己私下研究「FDE(前沿部署工程师)」(这个被 Palantir 带火的岗位)的二十万字资料,整理成了一本开源书,免费放到 Github。才短短三周,已经将近 4k star。 这周,我又为它创建了一个官网,地址是: https://fde4.ai 你可以在网站上阅读本书(与 github 项目的迭代实时同步),适配手机端,在中国大陆的阅读速度和体验更优。 此外,网站上新增了三个板块: a、案例库。目前已收录到一百六十多个案例,每条一段,标注信息源头,有公开原文的附链接。 b、生态地图。这个生态的玩家按层排开:谁发明了模式(Palantir 系),谁在自营(OpenAI、Anthropic 同日对垒),谁在垂直落地,谁在中国实践。 c、名录。FDE 生态的人物、团队与知识源头。 书里承载不下的,就放在上面三个库里了。也是持续更新的。 再说 FDE 社群。它本不在我的计划里,是需求催生的。 书开源之

👍63

6 Aug 2026, 06:41 UTC≈1,180 views4 reactionsread 18 August 2026

本周适合个人上手的教程/评测/资源: 《用 Vercel Eve 构建 AI 代码审查机器人》How I AI 频道主持人 Claire 演示用 Vercel Eve 框架构建 PR 风险评分机器人「Merge Mommy」。 《每日 UI 改进自动 PR 系统》一位日本开发者用 Claude Code 搭了套每日自动改进机制:每天早 8 点 GitHub Actions 触发,从 GA4、Search Console 拉指标,Claude 分析异常生成改善方案,去重后建 Issue,低风险代码变更自动出 PR。 《代码库知识图谱化工具解析》日本作者用 graphify 把自己 407 个文件、约 1800 万词的「散乱工厂」项目转成知识图谱,全程基于 tree-sitter 本地解析、不用 LLM,token 成本为 0。 《重构经济收益:Token 消耗降 83%》经过 15 步重构后,让智能体执行同一变更的输入 t

👍3😁1

30 Jul 2026, 08:08 UTC≈1,530 views14 reactionsread 18 August 2026

我把一本二十多万字的新书免费开源在 Github 了——《前置部署工程师:人工智能时代的客户价值交付秘籍》。 全本在线读,一分钱不收,地址在文末。 为什么要写这本书?从两组数字说起。 一组来自麻省理工学院:全球企业在生成式 AI 上烧了三四百亿美元,95% 的项目没能产生可衡量的回报。演示很惊艳,落地全阵亡。 另一组来自招聘市场:一个叫「前置部署工程师」(FDE)的岗位,发布量九个月涨了 800%。OpenAI 在招,Anthropic 在招,YC 一百多家创业公司在招。a16z 直接管它叫「科技行业最热门的岗位」。 两组数字放在一起,结论不难猜:模型已经不稀缺了,能把模型塞进客户真实业务里的人,才稀缺。 FDE 就是被创造出来填这道鸿沟的角色——驻扎在客户现场、把「产品能做的」和「客户需要的」接起来的工程师。Palantir 二十年前在情报机构的保密室里发明了它,如今 OpenAI 为它专门成立了一家估值百亿美元的

👍92🔥1🤔1🙏1

24 Jul 2026, 03:49 UTC≈1,620 viewsread 18 August 2026

火凤资本官网上提供的这个小工具:「AI Bubble Watch」挺好的, AI 一二级市场投资人都可以没事刷刷: https://bubble.huofengcap.com/

23 Jul 2026, 06:20 UTC≈1,660 views1 reactionsread 18 August 2026

本周推荐的适合个人上手的教程/评测/资源 2026/07/23—— 《腾讯悄悄上线了他们第一个设计 Agent - Miora》:腾讯的设计 Agent 也来了,背后是 WorkBuddy 团队。老板们终于可以自己涂抹五灿斑斓的黑了? 《想用 Codex 做科研,可以先从这 9 个 Skills 开始》:作者在 GitHub 上筛选并实测了九个学术科研类 Skill,从学科覆盖、功能设计和质控水平三个维度分为夯、顶级、人上人、NPC 四个档次评级。 《Agent 不下班:随时随地随设备的开发体系》:我社群成员张立行近期文章,完整分享了如何随时让 Coding Agent 24 小时在线、任何设备都能接入的工具和方案。 《Use My /No-AI-Slop Skill to Remove 20+ Patterns of AI Slop From Your Writing》:作者 Peter Yang 公开了自己的 /no

1

16 Jul 2026, 07:45 UTC≈1,960 views5 reactionsread 18 August 2026

本周推荐的适合个人上手的教程/评测/资源 2026/07/16—— 1、《Vista 電子報 No.124:知識工作者的數位資產:我用 AI 蓋了簡報庫與工具實驗室》 https://iamvista.substack.com/p/vista-no124-ai 知识工作者应停止「倒水式」的发文,转而搭建「蓄水式」的数位资产。Vista Cheng 利用 AI 工具以 Vibe Coding 方式,亲手搭建了两个网站:第一个是公开简报库 slides.vista.tw,将每场演讲的简报即时上架,成为立体履历、邀课入口与 GEO 的养分;第二个是 AI 工具实验实 lab.vista.tw,将重复给出的咨询建议转化为 32 个可自助使用的互动工具。对想开始累积数位资产的人,他的建议是:先盘点过去两年的产出与重复建议;接着挑一个最小的开始;最后让 AI 当工程师,用中文描述需求做出第一版。 2、《我與 AI 各自需要一套第二大腦

4👍1

22 Jun 2026, 15:21 UTC≈2,200 views4 reactionsread 18 August 2026

https://x.com/SUOHA_AI/status/2068726088608428180 测试下分享这类投资辅助类的资源,看看大家感兴趣吗,毕竟现在大 A 炒股这么火

🔥4

15 Jun 2026, 14:43 UTC≈2,820 views2 reactionsread 18 August 2026

各大平台都在搞 6.18 大促,我也来凑个热闹:把我之前做的两个视频课,限时降价一波,让平时忙碌又爱学习的人,可以在端午小假期默默地卷。 以下课程,原价都是 299 → 99 元(优惠限时 6.15-6.20)。 1、《我如何用 AI 打造 100X 知识萃取系统》: https://zerodaybook.mikecrm.com/LjEzDNf 2、《我如何实践打造私人 AI 贾维斯助手》: https://zerodaybook.mikecrm.com/kctVTes 另外还有个预售中的:《我如何用 AI 开发虚拟内容商品的经验和复盘》,早鸟价也是 99 元(6.18 正式发货): https://zerodaybook.mikecrm.com/0SydFc1 Enjoy it!

😁2

11 Jun 2026, 04:09 UTC≈2,520 views1 reactionsread 18 August 2026

https://www.youtube.com/watch?v=_O6amadG-8Y

1

4 Jun 2026, 08:06 UTC≈2,950 views3 reactionsread 18 August 2026
File

李笑来老师新书,期待简体中文版(得到正在制作)

👍3

29 May 2026, 03:38 UTC≈3,240 views6 reactionsread 18 August 2026

各位群友,我最新的 AI 课程,预售早鸟票现在开售啦。 这次听取了学员们的普遍需求痛点反馈,会分享大家最关心的、离赚钱近的那些事儿!像往常一样,我个人的实战经验和复盘会在这门课里毫无保留地分享。 详情介绍:https://www.zengzhang.ai/p/ai 直接购买の传送门:https://zerodaybook.mikecrm.com/nOjaRWg 感谢各位支持~

🥰51

Showing the 12 most recent of 21 posts we hold for @ifanbing. 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.

Citation-graph rank

Citation-graph rank — 1,257,255 of 1,550,220entries 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.

Forward network

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.

Mentions

Named by 1 registered channel — 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.

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 16 August 2026 — this entry's latest reading, not the date you are reading this.

“冰器库(Bing's Toolkit)” (@ifanbing), 6,822 subscribers as measured 16 August 2026. Telegram Register, tgregister.com/channel/ifanbing.

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.