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Channel

Hermes爱马仕&🦞OpenClaw小龙虾

@openclaw1024

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

6,871subscribers

+89 since we began measuring on 6 August 2026

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

Register entry

Telegram ID-1003853389174
TypeChannel
Username@openclaw1024
CreatedBetween 1 February 2026 and 28 July 2026 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live5 September 2026
Measurements held11
Confirmed unchanged1 time, most recently 5 September 2026
On Telegramt.me/openclaw1024

Growth

6,7826,8716,826.56 August 2026 — 6,782 subscribers6 August 2026 — 6,787 subscribers9 August 2026 — 6,796 subscribers13 August 2026 — 6,795 subscribers16 August 2026 — 6,810 subscribers19 August 2026 — 6,809 subscribers22 August 2026 — 6,814 subscribers26 August 2026 — 6,804 subscribers28 August 2026 — 6,811 subscribers1 September 2026 — 6,824 subscribers5 September 2026 — 6,871 subscribers6 August 20265 September 2026
11 measurements spanning 30 days, net +89. 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 6,769–6,884 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
5 Sept 2026, 10:376,871+47
1 Sept 2026, 10:186,824+13
28 Aug 2026, 23:366,811+7
26 Aug 2026, 02:426,804-10
22 Aug 2026, 23:086,814+5
19 Aug 2026, 08:456,809-1
16 Aug 2026, 18:346,810+15
13 Aug 2026, 00:356,795-1
9 Aug 2026, 17:516,796+9
6 Aug 2026, 23:426,787+5
6 Aug 2026, 03:476,782first reading

Engagement

46 posts held, back to 28 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 12 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
8.93%
avg views ÷ 6,871 subscribers
Avg views / post
614
29 posts measured
Reaction rate
0.039%
reactions ÷ views · ER floor
Posts in window
29
of 46 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 3 of 29 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 16 August 2026
Posts held46 (28 July 202616 August 2026)
Views total17,800
Reactions total1
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken20 Aug 2026, 04:20 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
1m 17s
Average length
26s

Measured directly from 3 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

13 reactions across 7 posts, in 3 distinct kinds. The most used accounts for 84.6% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
1184.6%
👌17.69%
👎17.69%

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 9 of the 46 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 13 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 46 most recent posts we hold, published 28 July 2026 to 16 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

16 Aug 2026, 00:31 UTC875 viewsread 20 August 2026
Photo

DeepSeek-V4 API 大幅涨价 应该是为了 DeepSeek-Harness 的套餐让路 Hermes/OpenClaw | AI探索

15 Aug 2026, 23:23 UTC876 viewsread 20 August 2026
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OpenAI 发布 GPT-5.6 构建者指南: 同样的效果,账单从 33 美元降到 1.33 美元 OpenAI 发布了一份给开发者的 GPT-5.6 构建者指南,主是怎么把 Agent 的账单砍下来: 型号选哪个、推理档位开多大、三个新的 API 开关怎么用,全是能直接照着改的配置 主要思想就是:不再需要“无脑上最贵的顶配模型”,通过更聪明的模型搭配、架构优化和 API 新特性,可以用骨折级的价格做出更强、更快的应用。 6家已经把 Agent 跑在线上的创业公司,各交了一条实测数字。 它推翻的是过去搭 Agent 的默认姿势,上最贵的旗舰模型,把推理拉满。 到了 5.6 这一代,中小型号多想一会儿就能顶到上一代旗舰的水平,价钱只是零头。 Hermes/OpenClaw | AI探索

15 Aug 2026, 22:40 UTC739 viewsread 20 August 2026
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Codex/Claude Code 现在都支持跨session访问会了,还是挺方便的,比如我昨天在测试我的 App 在 Windows 的运行情况,先在某个测试 Project 中调用 BaoCut Skill 去做转录视频,发现第一次运行时下载模型体验很糟糕。 然后我到 BaoCut 所在源代码项目中,把会的 Session Id 给它,让它去分析原因并给出优化方案,这样就不需要你自己去让 Agent 自己总结,也不用担心总结的时候会损失上下文,Agent 自己可以去会中找所需的上下文。 Hermes/OpenClaw | AI探索

15 Aug 2026, 19:05 UTC744 views0 reactionsread 20 August 2026

这篇 Pi 压缩的文章,太过于朴实无华,就真的只是写个 prompt 让 LLM 把上下文总结一下,然后保留前面的system prompt 和工具调用,在摘要后可能还会保留最近几次对。 这种压缩是有损的,不知道是不是有机制会去历史会检索上下文? 当然这确实是压缩上下文的最简单有效方案。 Hermes/OpenClaw | AI探索

15 Aug 2026, 16:44 UTC723 views0 reactionsread 20 August 2026

看到大佬的Agent自己赚了五位数! 我才后知后觉!! OKX最近上线了AI Marketplace!! 如果你有API、MCP 或者 Agent,都可以注册成为服务商(ASP),然后在里面接单赚钱。 你的Agent会自己接任务、做任务、收款,还可以被别的Agent雇去干活。 这不是妥妥的”睡后收入“! Hermes/OpenClaw | AI探索

15 Aug 2026, 14:45 UTC716 viewsread 20 August 2026

软件自进化可能是个伪命,只会带来更大的混乱。 插件要么是一次性用完就扔的,要么就得要设计、验证和维护的,不是现在模型能力可以“自进化”的。 OpenClaw 的一坨能“自进化”的 Skills 已经做了示范。 还是等模型自学习自进化更靠谱点。 Hermes/OpenClaw | AI探索

15 Aug 2026, 13:26 UTC734 viewsread 20 August 2026

被收购其实是 Manus 最好的结局。 现在作为独立公司来运营,乍一看似乎是好事,但这段时间,Agent的竞争已今非昔比。 从前占尽优势的Manus,现在要在更残酷的竞技场上单打独斗。 Hermes/OpenClaw | AI探索

15 Aug 2026, 09:15 UTC750 viewsread 20 August 2026

我最近在 ~/.claude/CLAUDE.md 里面加了一段提示词,让它多开 SubAgent(Opus)去执行,这样我默认开 Fable 5 High,Token 消耗也不算厉害。 Fable 5 则主要做需求澄清、方案拆解、任务分发和结果验收。 之所以不用 Opus 5 是因为太太太慢了,而且 Token 消耗巨大! --- 注意你的主要任务是分析、编排和验证,具体任务尽可能交给 subagent(Opus)去执行。当主 agent 是 Fable 5 时尤其如此:自己只做需求澄清、方案拆解、任务分发和结果验收,实现类工作(读大量代码、写代码、跑测试、批量修改)一律用 Agent 工具派给 Opus subagent 执行。 Hermes/OpenClaw | AI探索

15 Aug 2026, 06:22 UTC746 viewsread 20 August 2026

当下模型与Agent遍地开花,真正能长久记住、并持续自学习进化的能力,才是关键。 期待Alloomi带来的表现 Hermes/OpenClaw | AI探索

15 Aug 2026, 02:17 UTC773 viewsread 20 August 2026
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真的神了!关于电脑一切问,无脑甩给Codex就行! 刚才鼠标突然很卡顿,我看了下也没在运行什么很夸张的东西,还以为是鼠标电量不足。 然后让Codex给我解决一下,结果很快就定位到问。 一个小时前,我从相机存储卡上拷完素材,然后只取了卡,没有取读卡器,结果导致什么USB控制链路冲突啥的。 谁能想到一个读卡器,会让我鼠标变卡啊! 赶紧把读卡器拔了,瞬间恢复正常!! Hermes/OpenClaw | AI探索

15 Aug 2026, 00:31 UTC731 viewsread 20 August 2026
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偶然发现我这个Skill被人挂闲鱼卖了!! 卖就卖吧,不过卖家要持续关注哈, 这个Skill会持续更新的! 还有很多超绝的插画风格,还没来得及分享出来! Hermes/OpenClaw | AI探索

14 Aug 2026, 23:23 UTC734 viewsread 20 August 2026

手动整理 Get笔记里订阅博主的内容太费事,GetbijiEx 一键把知识库中博主(多为抖音)的全部笔记导出为 Markdown,并附带 Agent skill,装好后对 Claude Code、Codex 说一句就能触发导出。 https://github.com/Likely7/GetbijiEx Hermes/OpenClaw | AI探索

Showing the 12 most recent of 46 posts we hold for @openclaw1024. 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.

Mentions

Named by 7 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.

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.

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 5 September 2026 — this entry's latest reading, not the date you are reading this.

“Hermes爱马仕&🦞OpenClaw小龙虾” (@openclaw1024), 6,871 subscribers as measured 5 September 2026. Telegram Register, tgregister.com/channel/openclaw1024.

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.