Photo, posted without a caption
❤1

Channel
@piracy6
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Advertising · Posts · Citations · Handles named that no longer answer · Telegram's recommendations · Domains linked from posts · Cite this entry
77,214subscribers
+86 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
| Telegram ID | -1001466835758 |
|---|---|
| Type | Channel |
| Username | @piracy6 |
| Created | 31 October 2018 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 6 September 2026 |
| Measurements held | 29 |
| Confirmed unchanged | 1 time, most recently 6 September 2026 |
| On Telegram | t.me/piracy6 |
Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 20 August 2026 and assigned it the closest of 31 fixed categories, at 86% 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.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 6 Sept 2026, 06:40 | 77,214 | +33 |
| 4 Sept 2026, 03:01 | 77,181 | -1 |
| 2 Sept 2026, 18:45 | 77,182 | -9 |
| 1 Sept 2026, 17:44 | 77,191 | -17 |
| 31 Aug 2026, 20:36 | 77,208 | +26 |
| 30 Aug 2026, 17:05 | 77,182 | -4 |
| 29 Aug 2026, 14:17 | 77,186 | +3 |
| 28 Aug 2026, 11:53 | 77,183 | -4 |
| 27 Aug 2026, 12:07 | 77,187 | +6 |
| 26 Aug 2026, 12:15 | 77,181 | +12 |
| 25 Aug 2026, 09:17 | 77,169 | -17 |
| 24 Aug 2026, 09:17 | 77,186 | -9 |
| 22 Aug 2026, 20:35 | 77,195 | +6 |
| 21 Aug 2026, 13:18 | 77,189 | +14 |
| 20 Aug 2026, 16:21 | 77,175 | +22 |
| 19 Aug 2026, 14:14 | 77,153 | +9 |
| 18 Aug 2026, 11:49 | 77,144 | +10 |
| 17 Aug 2026, 14:39 | 77,134 | -3 |
| 16 Aug 2026, 06:44 | 77,137 | +25 |
| 14 Aug 2026, 18:37 | 77,112 | first reading |
113 posts held, back to 26 July 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 71 pages of Telegram’s post history, 20 posts per page.
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 71 of 98 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 8 September 2026 |
|---|---|
| Posts held | 113 (26 July 2026 – 8 September 2026) |
| Views total | 142,061 |
| Reactions total | 390 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 9 Sept 2026, 06:52 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.
Lifetime counters from Telegram’s own channel header, read 9 September 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Measured directly from 6 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.
439 reactions across 69 posts, in 10 distinct kinds. The most used accounts for 28.2% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 124 | 28.2% | |
| 💩 | 104 | 23.7% | |
| 👍 | 84 | 19.1% | |
| 👎 | 51 | 11.6% | |
| 🤡 | 26 | 5.92% | |
| 🤮 | 26 | 5.92% | |
| 😁 | 12 | 2.73% | |
| 🖕 | 7 | 1.59% | |
| 👏 | 4 | 0.911% | |
| 🤣 | 1 | 0.228% |
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 81 of the 113 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 439 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 113 most recent posts we hold, published 26 July 2026 to 8 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.
An ad marker, not a judgement about a post. A post is counted here because it carries one of two explicit markings: an erid token, which Russian law has required on paid placements since 2022 and which is issued against a specific advertising contract, or a #реклама / #ad hashtag in the body, which is the channel declaring it itself. The first is documentary; the second is a self-declaration and is weaker. No classifier reads the text and decides — nothing on this site guesses that a post is an advertisement.
This is a floor, and it can only ever be a floor. A channel that runs paid placements without marking them produces no marker for us to count, and an unmarked ad is indistinguishable from an ordinary post on the public surface. The ad load above therefore means “the share of posts that declared themselves”, never “the share of posts that were paid for”. A low figure is not evidence of a channel that runs few ads.
Both figures are medians, and no ratio between them is published. Each is a view reading that actually occurred on a post, picked by percentile_disc rather than averaged, so one viral post cannot move it and no interpolated value is invented between two readings. They are printed side by side with the count behind each rather than as a ratio: an ad and an ordinary post are not otherwise matched — for topic, for length, for hour of day — so the gap between them is a description of two groups and not the effect of one being an ad.
Measured over the 113 most recent posts we hold, published 26 July 2026 to 8 September 2026. Views are the latest single reading held for each post, and any reading at or above 1,000 is rounded by Telegram to three significant figures.
Photo, posted without a caption
❤1
会员制已经取消了,以后没会员了,课程只能联系 @ashbur_bot 单买。 补充一句:办理过的不影响权益
🤡19👍1
#ad 【Telegram官方推荐】无极高端vpn加速器 中国大陆用户光速上网!8k视频秒开! 看任何片都不卡,专线解锁,只做高端! 【群主自用款】点击下方网址注册体验⬇️ vv2pn.com 官方频道:https://t.me/wujivpnvpn 在线客服: @wujivpn @wujivpnvip
🤮4🤡2❤1
🚀 从零构建 AI 工程(ai-engineering-from-scratch):涵盖 20 个阶段、523 节课、342 小时,从线性代数到自主智能体,为读者提供一条连贯的学习主干。 📚 课程结构:20 个阶段、523 节课、约 342 小时。依赖关系呈分层结构,数学是基础,Agent 和生产是上层。 1️⃣ 基础 P0–P3 环境工具、数学基础(22 课)、经典 ML(18 课)、深度学习核心(13 课,含手写 autograd 与 mini 框架)。 2️⃣ 感知与语言 P4–P9 计算机视觉(28 课,到 3DGS、SAM3、世界模型)、NLP(29 课)、语音(17 课)、Transformer 精讲(16 课)、生成式 AI(15 课)、强化学习(12 课,直到 RLHF)。 3️⃣ LLM P10–P12 从零训练 LLM(24 课,含 DeepSeek-V3、NSA、MTP、投机解码等 2025–26 …
❤3
一个提供各种类型英文绘本的公益性网站:Book Dash 词汇量小、句子结构简单、配图丰富,适合英语初学者或者正在陪孩子一起学英语的家长。比起成人英语材料,理解门槛低很多,不会一上来就被生词劝退。还有一些带真人朗读以及视频配套。
❤1
FluentRead-流畅阅读:FluentRead 支持在原网页中对照阅读原文与译文,并提供划词翻译、AI 阅读辅助、图片翻译、文档翻译和视频双语字幕。翻译卡接入了 DeepSeek Harness 会话内核的浏览器适配,支持结合上下文解释选中文字并连续追问。
如果你想从零开始学习 Agent 架构,这三个 GitHub 项目一定不能错过: 1. all-agentic-architectures:看懂不同架构怎么工作 收录了 35 种 Agent 架构,覆盖工具调用、任务规划、记忆、RAG 和多 Agent 协作,配有代码、Notebook 和评测工具。适合拿同一个任务换几种架构运行,观察它们怎么拆任务、调用工具、检查结果。 2. ai-agents-from-scratch:亲手搭出一个 Agent 用 JavaScript 和本地模型,从基础的模型调用开始,逐步加入工具、记忆和 ReAct 循环。跟着示例,可以看清 AI 如何选择工具、读取执行结果,再决定下一步。适合有一点编程基础、想弄懂 Agent 运行过程的人。 3. harness-books:理解编程 Agent 背后的工程设计 两本围绕 Claude Code 和 Codex 的第三方架构分析书,讲模型之外…
❤2
腾讯团队把内部用了半年的 TeamAI CLI 开源了! 🚀 TeamAI CLI:一个 Coding Agents 的"团队配置与知识分发中枢,让团队沉淀的 AI 经验成为公共资产,项目 Slogan「Make Every Team AI Native」。 它用 Git 仓库作为唯一事实来源,把团队统一的 Skills、Rules、Hooks、MCP 配置和经验知识,同步到每个成员本地的 Claude Code、Codex、Cursor、Qoder、CodeBuddy 等十余种 AI 编码工具中;一次配置,全员生效,且不绑定任何单一 AI 工具。 🛠 TeamAI CLI 要解决的问题: 1. 配置碎片化:同一个团队有人用 Claude Code、有人用 Codex,每个工具的 skills、rules、hooks 格式互不兼容,最佳实践散落在各人本地,无法共享,更无法统一治理。 🧩 2. 经验蒸发:某个人花两小时踩透…
👍1👎1
探索多模态人工智能的前沿课来了——MIT「Modeling: MultiModal AI (MMAI)」春季课程视频已发布。| YouTube 课程涵盖跨模态表示、对齐、多步推理与生成、跨域迁移及伦理安全等核心主题,由Paul Liang 等多位导师授课,结合实验作业与团队研究项目,面向真实世界多模态数据与落地创新。 周二与周四下午授课(MIT Media Lab),期待对语言、图像、音频、传感等多模态交叉应用有兴趣的你加入,共同把“如何(几乎)让AI做到任何事”变成可能。
👍1
【读者正在发动一场针对AI味儿的审美起义】 最近圈内对“AI代笔”的厌恶已经从私下吐槽演变成了公开决裂。Bryan Cantrill的一篇博文引爆了讨论:调查显示近八成开发者一旦发现AI痕迹会立刻停止阅读,甚至从此拉黑作者。 这件事的本质不是技术歧视,而是写作背后的“社交契约”崩塌了——如果你连写都不愿亲自写,凭什么要求读者付出注意力去读?那些“不仅是X,更是Y”的陈词滥调,正成为文字信用破产的标志。 这不仅仅是审美疲劳,更是一场关于“真实性”的军备竞赛。当人类开始用Pangram这类检测器去反击AI灌水时,文字的价值坐标正在重塑。虽然有人担心检测器会误伤非母语作者或特定文风,但共识已经很明确:AI可以当磨刀石,但不能当厨师。 在Token泛滥的时代,那些带着瑕疵、温度和思考折痕的人类原笔,反而成了最稀缺的奢侈品。如果你有话要说,请直接说,别给读者的脑子喂“电子预制菜”。
Posted without readable text
【瓶中云:终结“云端数字农奴制”的尝试】 云计算这二十年,开发者从“FTP传文件”的自由民变成了AWS们定义的“数字农奴”,连给静态网页加个密码都要折腾半天IAM。 Cloud in a Bottle的出现,本质上是想把云服务的便利与本地软件的自主权重新缝合。它在Ubuntu上构建了一个类似“云端智能手机”的系统:应用以根权限隔离的容器运行,但通过统一身份认证(SSO)和平台级API打破了容器孤岛。这意味着你登录了主控台,就自动登录了所有自托管应用,不同应用间还能安全地交换数据,而不是像传统方案那样让每个应用都成为信息孤岛。 这件事的价值在于它试图降低“数字主权”的门槛。目前自托管领域存在断层:要么是像Coolify这种纯粹的容器管理工具,缺乏集成感;要么是像Nextcloud这种笨重的企业级套件。Cloud in a Bottle想走第三条路——用AI辅助打包应用,配合S3存储层解决数据备份痛点,让普通人也能像装App一…
Showing the 12 most recent of 113 posts we hold for @piracy6. 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.
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.
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.
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.
@piracy6 named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
We ourselves saw each of these resolve to a real page at some point before it went vacant — a genuine, evidenced change, not an inference from absence.
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 19 August 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 12 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
8 domains this channel’s own posts have linked to, measured by scanning the post bodies themselves — not the channel’s description, which is the separate Declared links section below when this entry has one. Appearing here is not a claim about who runs the linked site or why the channel linked to it; an advertisement, a news citation and a malicious link all leave the same kind of row.
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 6 September 2026 — this entry's latest reading, not the date you are reading this.
“黑洞资源笔记” (@piracy6), 77,214 subscribers as measured 6 September 2026. Telegram Register, tgregister.com/channel/piracy6.
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.