Telegram RegisterThe public register of Telegram

Channel

折腾实验室频道

@TossLabChannel

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

16,401subscribers

+180 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-1002441821742
TypeChannel
Username@TossLabChannel
Description📢群组: @TossLab 🎈频道: @TossLabChannel 青龙面板玩转自动化,Task签到任务收割乐趣,Docker容器装满脑洞,VPS服务器折腾到底,Github项目探秘新世界! 热爱折腾,永不停歇,爱折腾就来!
CreatedBetween 1 September 2024 and 31 March 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live12 August 2026
Measurements held8
Confirmed unchanged1 time, most recently 12 August 2026
On Telegramt.me/TossLabChannel

Growth

16,22116,40116,3116 August 2026 — 16,221 subscribers7 August 2026 — 16,240 subscribers8 August 2026 — 16,257 subscribers8 August 2026 — 16,274 subscribers9 August 2026 — 16,302 subscribers10 August 2026 — 16,333 subscribers11 August 2026 — 16,365 subscribers12 August 2026 — 16,401 subscribers6 August 202612 August 2026
8 measurements spanning 7 days, net +180. 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 16,194–16,428 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 16:3616,401+36
11 Aug 2026, 16:1616,365+32
10 Aug 2026, 18:2116,333+31
9 Aug 2026, 21:5216,302+28
8 Aug 2026, 23:0416,274+17
8 Aug 2026, 00:5116,257+17
7 Aug 2026, 01:5116,240+19
6 Aug 2026, 04:0716,221first reading

Engagement

24 posts held, back to 1 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 17 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
18.5%
avg views ÷ 16,401 subscribers
Avg views / post
3,030
21 posts measured
Reaction rate
0.083%
reactions ÷ views · ER floor
Posts in window
21
of 24 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 16 of 21 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 12 August 2026
Posts held24 (1 July 202612 August 2026)
Views total63,624
Reactions total41
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken13 Aug 2026, 00:56 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
598
Videos
17
Links
660

Lifetime counters from Telegram’s own channel header, read 13 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

42 reactions across 16 posts, in 5 distinct kinds. The most used accounts for 66.7% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
2866.7%
👍1126.2%
👎12.38%
💩12.38%
🥰12.38%

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 19 of the 24 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 44reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 24 most recent posts we hold, published 1 July 2026 to 12 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

12 Aug 2026, 18:03 UTC462 views2 reactionsread 13 August 2026
Photo

#iOS27 #SiriAI #MobileGestalt #国行iPhone 📱 iOS 27 国行 iPhone 也能折腾 Siri AI 🤖 Siri AI 是重点 GestaltEdit 可以直接修改 iPhone 的 MobileGestalt 隐藏参数,其中就包括 Siri AI 相关 Flag。 简单说,就是通过修改系统底层配置,尝试解锁/开启部分 Siri AI 相关能力,这对国行 iPhone 用户尤其值得关注。 🛠️ 还能改什么? · Siri AI 相关功能 ⭐️ · 灵动岛相关功能 · AOD 息屏显示 · Camera Control · Stage Manager · 充电上限 · 轻点唤醒 · iPad App 兼容性 · 部分设备信息及系统隐藏开关 ⚠️ 注意 目前项目主要针对 iOS 27 Beta 1~Beta 4,需要自行用 Xcode 编译签名。 国行 Siri AI 能不能

2

Signed Steven Lee

12 Aug 2026, 15:04 UTC702 viewsread 13 August 2026
Video

#AI #开源项目 #Agent AI Agent 领域的开源 Palantir 决策与记忆框架 🌟 简介 专注于上下文管理与可解释 AI的开源图原生底座,为大模型系统提供具备因果推理与完整审计追踪能力的知识图谱。 📖 核心功能 统一上下文图 决策因果追溯 时序与规则治理 生态无缝无缝 适用场景 高合规/受监管行业 复杂 Agent / 多 Agent 协同 企业级 GraphRAG 构建 🔘 @TossLab 🔘 @TossLabChannel

9 Aug 2026, 18:09 UTC≈2,200 views3 reactionsread 13 August 2026
Photo

#AI #ComfyUI #文生图 #图生图 #Ai视频 #Ai音频 #3D模型 #自动化 ComfyUI:AI 创作神器,图片、视频、音频、3D 全能搞定 🧩 项目简介 ComfyUI 是一个开源、模块化的 AI 创作引擎,通过「节点 + 工作流」方式组合模型和处理流程。相比传统一键式 AI 工具,可以自行控制模型、参数、采样、提示词以及各个处理环节。 目前 GitHub 已超过 12 万 ⭐️ Stars,支持 Windows、Linux、macOS 🎨 能做什么 · AI 文生图、图生图 · Inpainting / Outpainting · ControlNet、T2I-Adapter · LoRA、模型合并、高清放大 · AI 视频生成 · AI 音频生成 · 3D 模型生成 · 自定义节点与复杂工作流 · API 接入自动化生产流程 🔘@TossLab 🔘@TossLabChannel

3

Signed Steven Lee

9 Aug 2026, 04:01 UTC≈2,210 views1 reactionsread 13 August 2026

#频道互推 #群组推荐 电影资源频道(4K、原盘) 爱游戏分享社 老司机之家 白嫖小仓库 丨 抽奖福利之家 小众机场测评 影视软件、TVBox接口分享 不是白嫖,这叫借鉴(备用) 极客分享2.0 [震撼回归] 零度资源分享-破解软件/游戏分享 电报频道&群组索引

💩1

Signed 喵喵互推

7 Aug 2026, 16:25 UTC≈2,570 views2 reactionsread 13 August 2026
Photo

#TaskReminder #提醒事项 #订阅服务 ⏰ TaskReminder:开源任务提醒工具,让重要事项不再遗忘⁠ 📌 项目简介 日常生活和工作中,经常会遇到定时提醒、周期任务、重要事项记录等需求。TaskReminder 是一个开源的任务提醒项目,帮助用户管理待办事项,并通过自动化方式进行提醒。 ⚙️ 功能特点 · 支持创建任务提醒,记录需要执行的事项 · 支持定时、周期性任务管理 · 提供简单直观的任务配置方式 · 适合个人日程管理、服务器维护、脚本任务提醒等场景 🛠️ 使用场景 · 定期检查服务器状态 · 提醒续费域名、VPS、订阅服务 · 日常打卡、学习计划 · 自动化任务提醒管理 🔘@TossLab 🔘@TossLabChannel

2

Signed Steven Lee

7 Aug 2026, 00:49 UTC≈2,610 views3 reactionsread 13 August 2026
Photo

#AI #腾讯云 #Agent 😖腾讯云开源的数据库智能 Agent 记忆组件 🌟 简介 腾讯云开源的 Agent 记忆管理组件,将对话、文档与代码转化为四类记忆资产(Chat Memory、Skill、LLM-Wiki、Code-Graph),解决多 Agent 协同和跨会话时重复输入背景、上下文丢失及 Token 浪费问题 📖 核心功能 分层记忆架构 资产权限管控 按需调用 agent 🔘 @TossLab 🔘 @TossLabChannel

👍3

6 Aug 2026, 00:30 UTC≈2,630 viewsread 13 August 2026
Photo

#视频下载 #视频号下载 🚀 wx_channels_download:微信视频号视频下载工具 🛠 使用方式 支持在线解析,也支持WIN/MAC双端下载使用。 🔘 @TossLab 🔘 @TossLabChannel

5 Aug 2026, 00:33 UTC≈2,760 views4 reactionsread 13 August 2026
Photo

#AI #编程助手 #DeepSeek ⭐ 专为 DeepSeek 优化的开源终端编程 Agent 🌟 简介 基于 Go 开发的原生终端 AI 编程助手,核心围绕 DeepSeek 的前缀缓存(Prefix-Cache)机制打造,长对话场景下缓存命中率超 90%,大幅降低 API Token 使用成本。 📖 核心功能 极限省 Token 单文件零依赖 多模态与插件扩展 🔘@TossLab 🔘@TossLabChannel

👍31

4 Aug 2026, 10:00 UTC≈2,870 views6 reactionsread 13 August 2026
Photo

#AI #大模型 #模型部署 #AirLLM ⭐ AirLLM:4GB 显存跑 70B 大模型的开源框架 🌟 简介:基于分层加载机制的 Python 推理库,将模型按层拆分并动态载入显存,突破硬件限制,无需量化即可在消费级显卡上运行百亿/千亿级超大模型。 📖 核心功能 极限省显存:4GB 显存跑 70B 模型,8GB 显存挑战 405B 参数。 无损推理:无需对原模型进行大幅量化、剪枝或蒸馏。 加速扩展:支持 4bit/8bit 分块量化叠加,进一步提升推理速度。 🔘 @TossLab 🔘 @TossLabChannel

👍41🥰1

2 Aug 2026, 01:03 UTC≈3,070 views0 reactionsread 13 August 2026
Photo

#字幕工具 #字幕翻译 #效率工具 🎬 MioSub:开源跨平台 AI 实时字幕与音视频翻译工具 🌟 简介 音视频翻译工具,支持音视频实时同传、字幕生成与离线翻译。 🛠 注意事项 视频支持在线视频及本地视频,默认仅支持Gemini API,但该工具支持本地语音模型Whisper,详见配置设置。 🔘 @TossLab 🔘 @TossLabChannel

1 Aug 2026, 12:36 UTC≈2,990 viewsread 13 August 2026
Photo

#视频下载 #媒体管理 📱 better-douyin:开源桌面端抖音浏览器与下载工具 🌟 简介 开源全功能抖音桌面客户端,内置沉浸式播放器,提供比网页端更顺畅的浏览与媒体管理体验。 📖 核心功能 解析下载:支持作品、图集、部分live photo及原声/BGM一键无损下载。 批量采集:支持按用户主页、搜索结果、推荐流及收藏/点赞列表批量下载。 沉浸播放:内置多媒体播放器,支持进度/音量控制与自动续播。 🛠 下载链接 🔘 @TossLab 🔘 @TossLabChannel

30 Jul 2026, 03:50 UTC≈3,210 viewsread 13 August 2026
Photo

Telegram必备的搜索引擎,极搜JISOU帮你精准找到,想要的群组、频道、视频、音乐 👉 t.me/jisou2?start=a_6787263594

Signed 极搜🔍资源搜索@JISOU

Showing the 12 most recent of 24 posts we hold for @TossLabChannel. 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 — 781,608 of 1,151,006entries 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 2 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.

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

“折腾实验室频道” (@TossLabChannel), 16,401 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/TossLabChannel.

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.