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Channel

鑫源AI分享

@xinyuanai

On this record: Growth · Engagement · Reactions · Posts · Posts edited after publishing · Citations · Handles named that no longer answer · Cite this entry

19,573subscribers

+1,109 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1002387338565
TypeChannel
Username@xinyuanai
CreatedBetween 1 September 2024 and 31 March 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live5 September 2026
Measurements held27
Confirmed unchanged1 time, most recently 5 September 2026
On Telegramt.me/xinyuanai

Growth

18,46419,57319,018.57 August 2026 — 18,464 subscribers7 August 2026 — 18,464 subscribers8 August 2026 — 18,484 subscribers9 August 2026 — 18,523 subscribers10 August 2026 — 18,582 subscribers11 August 2026 — 18,609 subscribers12 August 2026 — 18,643 subscribers13 August 2026 — 18,722 subscribers15 August 2026 — 18,771 subscribers16 August 2026 — 18,849 subscribers17 August 2026 — 18,907 subscribers18 August 2026 — 18,972 subscribers19 August 2026 — 19,004 subscribers20 August 2026 — 19,045 subscribers21 August 2026 — 19,113 subscribers23 August 2026 — 19,170 subscribers24 August 2026 — 19,209 subscribers25 August 2026 — 19,240 subscribers27 August 2026 — 19,280 subscribers28 August 2026 — 19,336 subscribers29 August 2026 — 19,368 subscribers30 August 2026 — 19,381 subscribers31 August 2026 — 19,399 subscribers1 September 2026 — 19,430 subscribers2 September 2026 — 19,468 subscribers3 September 2026 — 19,523 subscribers5 September 2026 — 19,573 subscribers7 August 20265 September 2026
27 measurements spanning 29 days, net +1,109. 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 18,298–19,739 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 27
Measured (UTC)SubscribersChange
5 Sept 2026, 19:1919,573+50
3 Sept 2026, 16:1719,523+55
2 Sept 2026, 05:4819,468+38
1 Sept 2026, 06:5719,430+31
31 Aug 2026, 04:3819,399+18
30 Aug 2026, 04:0419,381+13
29 Aug 2026, 06:2319,368+32
28 Aug 2026, 05:0519,336+56
27 Aug 2026, 01:4519,280+40
25 Aug 2026, 22:3319,240+31
24 Aug 2026, 21:0819,209+39
23 Aug 2026, 06:5419,170+57
21 Aug 2026, 21:2919,113+68
20 Aug 2026, 16:4819,045+41
19 Aug 2026, 20:2119,004+32
18 Aug 2026, 22:5818,972+65
17 Aug 2026, 20:3518,907+58
16 Aug 2026, 16:4218,849+78
15 Aug 2026, 03:2318,771+49
13 Aug 2026, 23:2618,722first reading

Engagement

111 posts held, back to 31 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 53 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
11.4%
avg views ÷ 19,573 subscribers
Avg views / post
2,230
90 posts measured
Reaction rate
0.164%
reactions ÷ views · ER floor
Posts in window
91
of 111 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 81 of 90 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 2 September 2026
Posts held111 (31 July 20262 September 2026)
Views total200,574
Reactions total300
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken3 Sept 2026, 01:15 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.

Reaction mix

548 reactions across 96 posts, in 17 distinct kinds. The most used accounts for 55.7% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
30555.7%
👍8415.3%
🤣7613.9%
🤡203.65%
👎193.47%
👌162.92%
😱91.64%
🔥61.09%
🥴30.547%
🤔20.365%
🤩20.365%
👏10.182%
👾10.182%
😁10.182%
🤨10.182%
🤮10.182%
🥰10.182%

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 111 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 548 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 111 most recent posts we hold, published 31 July 2026 to 2 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

2 Sept 2026, 13:25 UTC871 views1 reactionsread 3 September 2026

想做 AI 博主不知道从哪入手?这期用我自己的频道举例:不要一上来做太深的东西,先做浅显易懂的 AI 工具分享。我发了 456 条视频才到 2.66 万订阅,热门视频几乎全是工具用法、剪映、数字人、插件这类实操。视频里会讲选题定位、每天更新的工具网站、班迪录屏、剪映会员、以及我用 Grok 做标题 / 时间轴 / 封面的流程。 这期没有神话,就是少走弯路:电脑不用买太好,前期唯一建议投资的是剪映会员(一年两百多),素材不用愁——工具站每天都在更新,国外工具大多有免费体验。等有了播放量,再谈广告、中转和打赏。 素材1:https://theresanaiforthat.com/ 素材2:https://top.aibase.com/ 素材3:https://www.toolify.ai/zh 素材4:https://futuretools.io/?pricing-model=free 素材5:https://topai.tools

1

Signed 源 哥

2 Sept 2026, 12:06 UTC933 views1 reactionsread 3 September 2026

🔥 【一条命令,把你的电脑变成专属私有 AI 服务器】 📌 项目定位: 如果你曾想自己部署大模型,一定被这套“组装流程”劝退过:先装 Ollama 拉模型、再配 Open WebUI 做界面、接 n8n 搞自动化、还得弄 ComfyUI 画图,最后还要处理隐私和鉴权……ODS(Osmantic Deployment System)把这一切打包成一个整体,**一条 curl 命令完成安装**,自动按你的硬件挑好模型、启动服务、打开本地网页就能聊天。它的本质是 **“本地 AI 全栈的一键编排工具”**,把散落的开源组件拧成一股绳,让你彻底告别手工维护依赖的噩梦。核心机制是**零前置依赖的引导式安装**——只要电脑里有 Docker,无论 Linux、macOS 还是 Windows,都能在几分钟内跑起一个功能完整的 AI 机房。 ⚡️ 核心优势: ▫️ **一键全家桶**:整合 Ollama/llama-server 推理、O

1

Signed 源 哥

2 Sept 2026, 08:26 UTC≈1,200 viewsread 3 September 2026

https://github.com/elder-plinius/CL4R1T4S/blob/main/ANTHROPIC/Claude-Fable-5.1.md claude-fable-5.1,系统提示词。

Signed 源 哥

1 Sept 2026, 11:06 UTC≈1,740 viewsread 3 September 2026

🔥 【DeepSeek Harness 插件全家桶:18 天涨粉 522,帮你从 0 到 1 搭起完整 Agent 生态】 📌 项目定位: 这可不是一个普通的"收藏夹",而是 DeepSeek 官方开源 Agent 框架 DSH(DeepSeek Harness)的**插件生态索引目录**。它解决的核心痛点是:DSH 刚发布不久,官方只给了框架骨架,真正好用的工具、模型接入、存储方案散落在 GitHub 各处,新手根本不知道该装什么、哪些靠谱。这个项目用**每日人工筛选 + 8 大分类(模型/工具/存储/调度/运行时/UI/集成/运维)**把优质插件一网打尽,每条收录都附**独立核验过的引用来源**,省去自己踩坑排查的时间。 ⚡️ 核心优势: ▫️ **每日精选机制**:只收录公开、有用、持续维护且明确为 DSH 打造的插件,质量有底线 ▫️ **8 大分类体系**:从 Models & Providers 到 Develo

Signed 源 哥

30 Aug 2026, 12:33 UTC≈2,260 views6 reactionsread 3 September 2026

🔥 【改词没用,sepia 直接修叙事架构,把 AI 味连根拔起】 📌 项目定位: 市面上的去 AI 工具都在改词汇和句式,但 sepia 基于 StoryScope 研究(arXiv:2604.03136,61,608 篇故事样本)发现了一个残酷事实:**仅靠叙事结构特征就能以 93.2% macro-F1 识别 AI 小说,而只改表面文风几乎没用**(95.5% → 93.9%)。真正暴露 AI 的是架构层面——主题被 narrator 讲透、因果链过于整齐、情感只写身体感受、没有真实世界参照……sepia 就是一台 **叙事架构修复机**,在动任何词句之前,先把小说骨架掰回人类的自然分布。 ⚡️ 核心优势: ▫️ **三层修复流水线**:叙事架构 → 话语流 → 表层风格,每篇小说只选 3-5 个关键动作,留足人类特有的松散度,而非无脑套用全部规则 ▫️ **四平台通吃**:一个标准 SKILL.md 适配 Claud

5👍1

Signed 源 哥

30 Aug 2026, 11:41 UTC≈1,990 views7 reactionsread 3 September 2026

🔥 【让 ChatGPT 替你思考,Codex 只负责干活——网页版额度不浪费,API 额度省着花】 📌 项目定位: 很多人订阅了 ChatGPT Plus/Pro,网页版额度大量闲置,而 Codex 编程代理却在烧紧张的 API 额度来做规划和 Review。这个项目把「思考」交给网页版 ChatGPT,Codex 只保留「执行权」。核心机制是 **只读 MCP 桥接**——不需要 API Key、不搞逆向代理,完全走官方网页 + OAuth 保护的安全连接。最狠的一点:**仓库永远不会被上传**,ChatGPT 只按需读取工作区里真正需要的几行代码。不是把 AI 接进来,而是把「大脑」和「手脚」彻底分开。 ⚡️ 核心优势: ▫️ **代码零上传**:ChatGPT 通过 OAuth 保护的只读 MCP 连接读取代码,且只读取当前工作区需要的部分,从机制上杜绝数据泄露。 ▫️ **一键自动安装**:不懂 git、Node、

7

Signed 源 哥

30 Aug 2026, 08:14 UTC≈2,220 views2 reactionsread 3 September 2026

孙哥《我的女友景甜》小作文全网爆火后,怎么用 AI 写出同款白描长文,还尽量去掉 AI 味? 本期用 OpenCode 桌面端,叠两个开源 Skill:卡兹克分享的「孙哥写作」+「拟人化改写」,从安装、配 API、斜杠调用到导出 TXT 一篇讲完。普通叙事文、回忆长文都能用;视频后半也演示了更细描写向的写法。 核心流程很简单:先让孙学 Skill 按短句、数字、物件、留白来起草,再用拟人化 Skill 补细节、去模板句。写得不满意就继续斜杠调用,让它改指定段落,不用从头重写。 孙割写作skill:https://github.com/KKKKhazix/sun-style-writing 拟人化skill:https://github.com/blader/humanizer?tab=readme-ov-file opencode下载:https://opencode.ai/zh/download 越狱模型api:https:

1🤡1

Signed 源 哥

30 Aug 2026, 05:18 UTC≈2,060 viewsread 3 September 2026
Photo

阿里Wan 3.0本周末全量免费:昆仑万维旗下天工工作台(skyproduction.cn)8月28日—30日对所有用户免费,新老用户、个人企业都适用。 周一恢复收费后0.05元/秒,一条30秒视频约1.5元,自称全网最低

Signed 源 哥

29 Aug 2026, 15:15 UTC≈2,220 views4 reactionsread 3 September 2026

https://fal.ai/tools/minimax-h3-max MiniMax H3 Max 可将文字描述在约3秒内转化为5秒长的768p视频片段。每日可免费生成5个视频。无需注册。

4

Signed 源 哥

29 Aug 2026, 14:28 UTC≈2,140 views4 reactionsread 3 September 2026

🔥 【自托管零运维的 A 股量化工作台:选股、回测、监控一套带走】 📌 项目定位: tick-stock-panel 是专为个人散户与量化爱好者打造的开源量化工作台,官方明确不对标同花顺/通达信,不搞「AI 荐股/涨停预测」。它解决的核心痛点是:想系统化做 A 股量化研究,却受制于数据源贵、运维重、策略验证门槛高。项目将 数据管道、策略引擎、回测约束、盘中监控 全部本地化封装,Docker 一键拉起,普通电脑即可运行一个「家庭版量化研究所」。 ⚡️ 核心优势: ▫️ **毫秒级全市场扫描**:基于 Polars 引擎,内置 18 个选股策略卡片,秒级扫全 A 股,并支持自定义信号与 LLM 生成策略 ▫️ **严谨的回测框架**:策略回测内置 T+1/手续费/滑点/止损约束,SSE 流式进度实时反馈,另有因子回测做 IC/IR 分层收益筛检 ▫️ **实盘级异动监控**:按交易所口径计算 3/10/30 日偏离值,盯住异动边

4

Signed 源 哥

29 Aug 2026, 11:52 UTC≈1,930 views2 reactionsread 3 September 2026

🔥 【世界首个开源企业级"世界模型",让知识库拥有可回放的时间维度】 📌 项目定位: 大多数知识库只能回答"是什么",而 Utopia 回答的是"什么时候是这样"。它是一个 自带时间轴的知识图谱平台**,每条事实都带有 `valid_from` / `valid_to` 有效区间,所有修改采用 **append-only 追加式存储**——修正不会覆盖旧版本,而是关闭上一个版本,让组织历史永远可重放。更关键的是,它不需要 Elasticsearch、向量服务或消息队列,**一个二进制 + 一个 Postgres 就能跑起完整的 RAG 检索、知识图谱抽取与多租户权限体系,还能完全跑在隔离内网环境。 ⚡️ 核心优势: ▫️ 时序知识图谱**:拖动时间轴,图谱即重绘为当时的状态;支持"谁在 2024 Q3 负责 X 项目?"这类穿越查询,每条边都链回原文证据 ▫️ **混合检索**:Tantivy 全文索引(内置 jieba 中

2

Signed 源 哥

29 Aug 2026, 11:22 UTC≈1,910 views5 reactionsread 3 September 2026

🔥 【AI 味太重被一眼识破?这个开源技能直接把"机器感"从根上拆掉】 📌 项目定位: sepia 是一个给 Claude Code、Codex、Grok Build、Antigravity 四款工具用的"去AI味写作技能包"。它不是简单换换词,而是基于 arXiv 论文 StoryScope(测了 6 万多篇人类+AI 小说)训练出的检测逻辑——从叙事结构层面动手,修掉那些"一看就是AI写的"隐藏特征。小说、技术文章、发布说明、工单回复,都能用。一份 SKILL.md 到处跑,不用为每个平台单独适配。 ⚡️ 核心优势: ▫️ 四层修复(叙事架构→话语节奏→表面风格→模型指纹修正),不是暴力换词,是结构性去AI味 ▫️ 支持写、审、改、重写四种操作:write / review / refactor / recreate,按需调用 ▫️ 内置专业文体模板:发版说明、PR回复、事故复盘、技术文章各有专属规则,贴合场景 ▫️

5

Signed 源 哥

Showing the 12 most recent of 111 posts we hold for @xinyuanai. 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.

Posts edited after publishing

@xinyuanai 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.

First edit seen
13 August 2026
Most recent edit
13 August 2026

Mentions

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.

“鑫源AI分享” (@xinyuanai), 19,573 subscribers as measured 5 September 2026. Telegram Register, tgregister.com/channel/xinyuanai.

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.