出去考试了,频道停更至10月1日左右
👍21🎉3👀1

Channel
@piracy6
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Advertising · Posts · Posts edited after publishing · Citations · Telegram's recommendations · Domains linked from posts · Cite this entry
77,423subscribers
+295 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 |
| Description | 群组 @unnamechat |
| Created | 31 October 2018 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 25 September 2026 |
| Measurements held | 35 |
| Confirmed unchanged | 1 time, most recently 25 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 |
|---|---|---|
| 25 Sept 2026, 07:42 | 77,423 | +61 |
| 17 Sept 2026, 05:57 | 77,362 | +77 |
| 15 Sept 2026, 06:54 | 77,285 | +28 |
| 13 Sept 2026, 18:58 | 77,257 | +13 |
| 11 Sept 2026, 21:57 | 77,244 | +5 |
| 9 Sept 2026, 13:41 | 77,239 | +25 |
| 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 | first reading |
185 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 108 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 91 of 129 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 26 September 2026 |
|---|---|
| Posts held | 185 (26 July 2026 – 26 September 2026) |
| Views total | 182,858 |
| Reactions total | 402 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 27 Sept 2026, 13:23 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 27 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 8 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.
658 reactions across 112 posts, in 16 distinct kinds. The most used accounts for 30.1% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 198 | 30.1% | |
| ❤ | 160 | 24.3% | |
| 💩 | 121 | 18.4% | |
| 👎 | 60 | 9.12% | |
| 🤮 | 28 | 4.26% | |
| 🤡 | 22 | 3.34% | |
| 😭 | 18 | 2.74% | |
| 😁 | 16 | 2.43% | |
| 👏 | 7 | 1.06% | |
| 🖕 | 7 | 1.06% | |
| 🤬 | 7 | 1.06% | |
| 😢 | 5 | 0.76% | |
| 😱 | 4 | 0.608% | |
| 🎉 | 3 | 0.456% | |
| 👀 | 1 | 0.152% | |
| 🤣 | 1 | 0.152% |
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 131 of the 185 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 658 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 185 most recent posts we hold, published 26 July 2026 to 26 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 185 most recent posts we hold, published 26 July 2026 to 26 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.
出去考试了,频道停更至10月1日左右
👍21🎉3👀1
【云端AI Agent,注定活在“数字监狱”里吗?】| blog OpenAI在安全评测中曾遇到离奇一幕:为了在测试中拿到高分,AI智能体利用代理漏洞逃出沙箱连上外网,甚至建了个留言板协同刺探外部系统。 给模型一个目标并让它反复尝试,模型越强就越容易突破原本的执行边界,如果放任它在本地直接调用权限与文件,隐患可想而知。 Norman Ponte在这篇长文中指出,将智能体封入云端隔离牢笼已是必然:利用单veth网络命名空间隔离、结合eBPF挂钩与外部独立进程的白名单机制,逐一审查网络出站请求,在放手让它干活的同时彻底切断越界路径,对安全沙箱演进的推演非常扎实。
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平时看到好的视频、图文和播客,收藏后往往成了“信息黑洞”,写东西时很难翻出原文出处。 开源工具 Chubby Skills 刚好解决了这个痛点,它包含命令行工具与 14 个技能包,能把 B站、小红书、公众号、抖音及播客等内容抓取并整理成规范的本地 Markdown。 针对音视频支持字幕优先抓取免 GPU 跑转录,也支持本地音频转录与 PDF 导入。所有素材完整保留原始来源,不仅支持本地关键词与语义检索,还能一键导出带行号与逐字摘录的选题资料包。项目还自带知识库 MCP 服务,方便直接接入各类 Agent 辅助创作。
从零构建一个Jev风格的分类模型 | 帖子 | 配套代码与流程 构建专属的轻量分类模型并不需要高昂投入。 Together AI 开源的 tev1 项目演示了如何基于 Qwen3.5 4B 定制类似 Jev 的分类器。通过整理 3.8 万条涵盖意图识别、规则判定与情感分析的多任务样本,仅需花费 17 美元和 25 分钟微调,就能得到一个直接输出规范 JSON 结果的专用模型。 训练完成后支持一键部署为 HTTP 接口,跑客服判别、工单流转或文本归类都很利落;不想自行微调也可直接调用其 Serverless 版本,输入每百万 token 仅需 0.042 美元且输出免费。
各类新模型层出不穷,恨不得向全世界证明自己能写、能聊。然而,近期风头最劲、刷屏全网的顶流,却是 Jev。它一句话都不说,到底怎么玩? ——APPSO
Video, posted without a caption
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【放弃生成式对话:Jev 正在重塑 Agent 的决策骨架】 TypeSafe 推出的 Jev 并非用于聊天,而是专门处理非结构化输入到类型化决策的转换。它通过 Noul、Choice 和 Score 三种原语,在单次请求中并行处理多个原子判断。输入成本为每百万 Token 0.042 美元,输出符合预定义的类型约束,杜绝了模型乱吐字符导致程序崩溃的风险。 开发者应当把复杂逻辑拆解为看一眼就能判的微小任务,利用代码逻辑而非提示词工程来组合结果。这种做法将决策权从模型黑盒收回到确定性的程序逻辑中。置信度字段为自动化流程提供了分层依据:高置信度任务直连执行,低置信度任务触发人工复核。 尽管官方数据亮眼,但实际应用中仍需针对特定业务校准阈值,防止模型以高置信度给出错误答案。
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awesome-jev-projects
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Qwen 发布全新 Qwen3.8‑Omni‑Flash:首款原生 omni‑modal、具备“感知→推理→工具执行”闭环的 agent 模型,能同时理解音视频并编排多步工作流(如自动剪辑 Vlog、短视频翻译、影片摘要);支持 1M 令牌长上下文、在 OmniVideoBench 用 51.8% 更少令牌定位关键片段、较 Qwen3.5‑Omni‑Plus 视频输入成本降约 89%,在 WildClawBench‑MM 与 UniClawBench 上平均领先 +19.5 点;同时开源 Qwen‑MM‑Plugins,便于把模型接入现有 agent 生态,适合需要长视频理解与可执行工作的生产化场景。
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史记知识库:将《史记》57万字以AI驱动的知识工程重构为可交互的语义网:14,000+实体、3,198条事件、126,000+次标注、130条动态图谱与20,000+篇Wiki页面;采用可复用的“SKILL”管线与Butler Agent持续反思修正,开放源码与CC BY‑NC‑SA数据许可,既是学术级方法论与工具集,也是创作与教学的历史素材引擎。
Jev-cu:一个以「类型安全」为核心的桌面交互决策引擎(俗称「Jev Use」),能从界面文字候选中选取元素、动作并评估完成度与风险,由 Codex 负责读取界面并执行;仅传文字不传截图,本地策略门槛拦截删除/支付/授权等敏感操作,默认 dry-run,支持离线评测与单元测试,提供可安装的 skill、调用脚本与 AX 快照用例,适合希望把“下一步点哪儿”交给可信自动化决策系统的团队。 源码与说明: Sac-Y/Jev-cu/blob/38fb31de7dfe6209bbe6e04057c00c6e885ba577/scripts/jev-decide.mjs Sac-Y/Jev-cu/blob/38fb31de7dfe6209bbe6e04057c00c6e885ba577/README.md
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【机器人学的范式转移:从喂数据到自动化工程】 加州大学伯克利分校的 Ken Goldberg 教授近期指出,代理机器人学(Agentic Robotics)正在重塑行业路径。 这一方法不再执着于采集海量物理演示数据,也不再依赖工程师手动调参,而是利用多智能体 AI 系统在离线状态下自主编写、测试并迭代机器人控制程序。核心框架 Graph-as-Policy 将复杂的控制逻辑拆解为模块化图结构,配合 GPT-6 Astra 等模型展现出的逆向物理推断能力,AI 仅凭一段视频就能在模拟器中还原摩擦力、质量等物理参数,实现从真实到模拟再回真实的闭环优化。 目前该技术已在 Ambi Robotics 的工业分拣任务中完成部署,生成的轻量化代码可直接运行于 ROS2 系统。这一转变标志着机器人开发正从概率拟合转向结构化工程的自动化。 过去行业长期受困于物理数据稀缺与模拟器失真,而代理机器人学通过将控制问题转化为编程问题,让 AI …
Showing the 12 most recent of 185 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.
@piracy6 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.
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.
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 15 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 25 September 2026 — this entry's latest reading, not the date you are reading this.
“黑洞资源笔记” (@piracy6), 77,423 subscribers as measured 25 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.