Telegram RegisterThe public register of Telegram

Channel

黑洞资源笔记

@piracy6

On this record: Growth · Engagement · What this channel posts · Reactions · Advertising · Posts · Citations · Domains linked from posts · Cite this entry

77,125subscribers

-3 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 31,623–100,000.

Register entry

Telegram ID-1001466835758
TypeChannel
Username@piracy6
Created31 October 2018measured — cross-checked against a third-party dataset (TGDataset)
First recorded6 August 2026
Last confirmed live13 August 2026
Measurements held9
Confirmed unchanged1 time, most recently 13 August 2026
On Telegramt.me/piracy6

Growth

77,12377,13977,1316 August 2026 — 77,128 subscribers6 August 2026 — 77,128 subscribers7 August 2026 — 77,139 subscribers8 August 2026 — 77,128 subscribers9 August 2026 — 77,123 subscribers10 August 2026 — 77,129 subscribers11 August 2026 — 77,133 subscribers12 August 2026 — 77,129 subscribers13 August 2026 — 77,125 subscribers77,1256 August 202613 August 2026
9 measurements spanning 7 days, net -3. 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 77,121–77,141 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
13 Aug 2026, 06:5377,125-4
12 Aug 2026, 10:0777,129-4
11 Aug 2026, 07:3677,133+4
10 Aug 2026, 04:3477,129+6
9 Aug 2026, 05:0277,123-5
8 Aug 2026, 04:0077,128-11
7 Aug 2026, 03:2077,139+11
6 Aug 2026, 04:4577,128no change
6 Aug 2026, 04:3777,128first reading

Engagement

24 posts held, back to 26 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 19 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
3.16%
avg views ÷ 77,125 subscribers
Avg views / post
2,440
24 posts measured
Reaction rate
0.174%
reactions ÷ views · ER floor
Posts in window
24
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 24 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 13 August 2026
Posts held24 (26 July 202613 August 2026)
Views total58,527
Reactions total78
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken13 Aug 2026, 07:49 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
19,700
Videos
898
Links
16,700

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. 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.

Video runtime
2m 41s
Average length
2m 41s

Measured directly from 1 video 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

78 reactions across 13 posts, in 5 distinct kinds. The most used accounts for 62.8% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
4962.8%
👎1519.2%
👍78.97%
😁45.13%
💩33.85%

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 16 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 78reactions 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 26 July 2026 to 13 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.

Advertising

Ad load
4.17%
1 of 24 posts carry an ad marker
Regulatory tokens
0
none — marked by hashtag only
Median views · ads
2,700
over 1 measured post
Median views · rest
1,810
over 23 measured posts

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. The sample on one side is under five posts, which is too thin to compare. The two figures are shown side by side with the count behind each, and deliberately not divided into a headline like “ads get x% fewer views” — an arithmetic that is easy to print and, at this sample size, means nothing.

Measured over the 24 most recent posts we hold, published 26 July 2026 to 13 August 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.

Recent posts

13 Aug 2026, 01:49 UTC827 views8 reactionsread 13 August 2026
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【AI进入"够用就好"时代:DeepSeek的低价并非巧合】 近期DeepSeek的新模型动态在海外社区引发了一场有趣的碰撞:虽然官方连个像样的发布页面都没准备,但开发者们已经在用真金白银投票了。 这波热议的核心不在于它是否全面超越了前沿大模型,而在于它通过极致的上下文缓存技术把成本压到了地板上。社区里最流行的套路是让昂贵的Claude写方案,让DeepSeek跑执行,这种"大脑+苦力"的协作组合正成为降本增效的标配。这背后藏着一个被很多人忽视的趋势:当模型能力在大多数场景下都达标后,智力的边际溢价会迅速消失。 社区评论中有一个深刻的预判:2027年的赢家或许不是那个账面最聪明的巨头,而是能把Open Weights模型做到最廉价、最易得的一方。对大多数现实业务场景来说,最强模型往往只是昂贵的装饰,能让逻辑闭环跑起来的高性价比Token才是真正的生产力。

5💩3

11 Aug 2026, 14:27 UTC≈2,700 views13 reactionsread 13 August 2026
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👎13

11 Aug 2026, 14:21 UTC≈1,890 views4 reactionsread 13 August 2026
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【别再让AI说废话:这个插件把编程代理变成“极简行动派”】 针对 ADHD(注意力缺陷多动障碍)用户的编程助手插件 i-have-adhd 走红。它通过 10 条硬核指令强行重塑 Claude Code 等 Agent 的输出风格:禁止寒暄(没有“希望这能帮到你”)、严禁废话、任务必须编号、且首句必须是具体的执行命令。它把原本充满社交辞令的 AI 建议,压缩成了“第一步运行 npm,第二步修改某行”的纯粹干货,甚至要求 AI 给出具体到分钟的时间估算。 这不只是对特定人群的关照,更是在 Agent 时代对 AI 社交属性的一种“认知剥离”。AI 为了显得像人,常在答案中埋藏大量低信息量的客套话,在连续作业中这其实是一种昂贵的注意力税。 社区评论认为,这种工具的流行反映了开发者对 AI 工具化纯度的极致追求:当程序员进入高强度状态时,需要的是确定性的执行路径而非情绪价值。既然是工具,就请闭嘴干活,让信息密度回归到行动本身。

👍31

11 Aug 2026, 14:19 UTC≈1,450 viewsread 13 August 2026
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【把AI装进REPL:Prime Agent正在让编程代理长出经验】 Prime Intellect开源的Prime Agent是一款基于递归语言模型RLM的长效编程与研究代理。它最核心的架构改变是将Prompt变量化,并将子代理的调用视作函数执行。 它运行在一个持久化的IPython环境下,支持后台挂起、断线重连和心跳检查。通过所谓的持续座架,代理能利用/refine命令根据历史操作证据自主微调其技能描述和操作习惯,解决了AI在长链路任务中容易丢失上下文的痛点。 这种设计让AI处理大规模重构或深度调研时,能够维持一套跨会话的生物履历。这预示着AI开发正在从对话框模式切换到环境模式。 以往我们靠复杂的Prompt工程来防止AI出错,现在则通过构建一个可生长的程序化环境让AI自主提炼经验。你不再是单纯地向它提问,而是在编排一个能够自我修正的系统。 虽然直接执行模型生成的代码依然存在安全边界问题,但Prime Agent所

11 Aug 2026, 14:15 UTC≈1,160 views1 reactionsread 13 August 2026
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【Ry再出重拳:把Cloudflare的分布式“黑科技”搬回你家】 Deno团队开源了celld,这是一个能让你在自建服务器上运行类似Cloudflare Durable Objects服务的守护进程。 它的设计极具极简主义色彩:摒弃了复杂的Raft或Paxos共识协议,完全依靠S3存储桶来实现节点间的状态协调与所有权锁定。每个对象本质上都是一个独立的SQLite数据库,这种“一对象一数据库”的模式让应用在构建之初就实现了天然分片,规避了传统共享数据库的锁竞争与单点故障。 在AI代理井喷的当下,这种架构极度契合Agent的状态管理——每个代理的记忆和上下文都能封装进一个独立的Cell,不用时自动“冬眠”至近乎零能耗。 更有意思的是,由于厌恶AI生成的低质PR干扰,Ry明确宣布仓库只接受邮件补丁。这不仅是一次基础设施的降维打击,更是对工程纯粹性的宣言:在云厂商的黑盒之外,开发者终于拥有一种低成本、高并发且完全自托管的分布式

1

11 Aug 2026, 14:12 UTC≈1,140 views1 reactionsread 13 August 2026
Video

【拒绝“面试题八股”:这份DevOps实战题库正在撕开大厂的面纱】| YOUTUBE 开源项目DevOps-Interview-Guide收录了2025至2026年间真实的DevOps与SRE面试记录。不同于市面上常见的泛泛而谈,这套指南包含151份针对85家具体公司的面经实录,涵盖了Kubernetes、Terraform、云原生架构以及SRE指标体系等核心领域。 它以公司为维度组织资料,从全球技术巨头到初创企业应有尽有,直接还原了求职者在现场面对的压力测试。解剖者视角来看,这份资料的价值在于它揭示了行业人才筛选标准的动态演变——面试官不再死磕基础指令,而是更看重SLI/SLO等稳定性治理实操和CI/CD流水线的故障恢复逻辑。 社区评论认为,这种原始素材比经过修饰的“标准答案”更有参考意义,它让求职者能像读谍报一样观察不同规模企业的技术偏好。 在技术招聘门槛不断抬升的当下,掌握这些第一手信息意味着你能从被动的“背题家”

1

11 Aug 2026, 13:57 UTC≈1,250 views2 reactionsread 13 August 2026
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【拒绝框架崇拜:一份把 Agent 拆解到原子级的“源码小说”】| BLOG Antinomie Lab 推出的 pi-agent 源码导读本周正式上线。 这并非传统意义上的说明书,而是一份将 pi-agent 这种非框架、积木式 Agent 循环彻底拆解的技术手册。其最硬核之处在于每个技术论断都精准关联了源码行号,这种逐行溯源的写法极大提升了文档的可验证性。 针对目前开发者普遍存在的“LangChain 疲劳”,它的核心逻辑是:Agent 不该是沉重的框架套娃,而应是模块化的积木组合。 这种文档风格兼具了代码深度与阅读的轻快感,对于想从零构建智能体而非被黑盒框架束缚的开发者来说,这种回归代码本质的剖析是极其稀缺的养分。它不仅在讲透一个项目,更在传递一种构建 Agent 的底层思想:成为系统架构的建筑师,而非臃肿框架的搬运工。目前该书保持每周更新频率,正逐步放出从 pi 迁移到任何 Agent 的通用方法论。

2

11 Aug 2026, 13:51 UTC≈1,110 viewsread 13 August 2026
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【智能体的“技能商店”:333个精选 Skill 让 Agent 告别野路子】 skills.qiaomu.ai 是一个专门为 AI Agent 打造的技能推荐网。该站精选了 333 个涵盖前端开发、数据库优化、Git 工作流以及行业专用的 Skill,并直接关联了 GitHub 原始文档。 每个 Skill 本质上是一个 SKILL.md 配置文件,让 Agent 能够习得如 Vercel 部署、Supabase 调优或强制代码验收等专业级 SOP。这标志着 Agent 开发正在进入“标准化模块”时代。过去我们靠拼凑长 Prompt 来碰运气,现在通过加载特定的 Skill,我们可以为 Agent 注入确定的行业知识与执行标准。社区评论认为,这种按需安装的逻辑正在把 Agent 塑造成真正的数字员工。 对开发者而言,最有价值的不是大模型的泛泛而谈,而是这种能把“Vercel 最佳实践”或“复杂任务拆解”变成开箱即用能力的

11 Aug 2026, 13:47 UTC≈1,300 viewsread 13 August 2026
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【微调全家桶:Liquid AI 的这份食谱把门槛拆了】| github Leonie 分享的这份开发者资源把散落各处的微调技术集成到了一起,从传统的 SFT 监督微调到时下热门的 DPO 和 GRPO 强化学习方案,不仅支持文本还覆盖了音视频多模态模型。 借助 Unsloth 和 TRL 等工具,这份指南实际上为那些想在有限算力下定制垂直领域模型的开发者提供了一套完整的工程方案。 与其说这是一份文档,不如说它解决了开发者最头疼的选型焦虑。社区一针见血地指出,选择微调方法往往比训练本身更耗时,而这份资源让方案对比变得直观。 虽然目前还缺少显存占用和评估基线的量化标注,难以实现完全的横向比对,但对于正在处理小模型或者冷门语种任务的人来说,这依然是一座避开无效实验的宝藏。它给出的信号很明确:AI 开发正在从玄学调优向标准工程化转变,比起纠结参数,直接上手跑通一套成熟的路径才是正经事。

11 Aug 2026, 10:14 UTC≈1,900 viewsread 13 August 2026
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【药房AI“劝退”潮:当救命的处方撞上不靠谱的智能体】| blog 连锁药房 Kinney Drugs 近期紧急下线了其 AI 助手 Burt,原因是该系统在处理处方药时表现糟糕:语音混乱、甚至搞错药剂数量。尽管官方强调其符合隐私标准,但在数以百计的投诉面前,药房被迫退回了那个被时代嫌弃的“按 1 续药、按 2 咨询”的按键音频时代。 这一翻车现场揭示了 AI 落地深水区的残酷现状。许多非技术企业引入 LLM 并非为了提升体验,而是将其作为抵御人力成本的“数字墙”——通过增加沟通损耗让用户感到无助,从而放弃寻找昂贵的真人药剂师。 社区评论指出,药房这种容错率极低的行业,正面临利润缩减与技术泡沫的双重夹击。缺乏领域专家深度介入的 AI 实现,往往只是在脆弱的业务逻辑上加了一层不确定的伪装。当 AI 的非确定性碰撞到刚性的医疗契约,所谓的高效率便会沦为危险的噪音。 对于这类行业,AI 不该是万能锤子,而应是在专业闭环内被严密

26 Jul 2026, 13:27 UTC≈10,400 views20 reactionsread 13 August 2026
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【提示词工程正在死亡,AI 正在吞噬它的工具箱】 Anthropic 近期发布了 Claude Cookbook,系统性地展示了从代理工作流、上下文压缩到前端美学引导的实战指南。 然而社区反馈却揭示了一个残酷的真相:2023 年那些被奉为圭臬的 Prompt Engineering 技巧,如 CoT 或 ReAct,正在被新一代推理模型迅速降维打击。 现在的趋势是,要么复杂的功能被直接内置进模型,要么开发者回归到最朴素的清晰表达。这意味着所谓的“提示词工程”本质上更接近“大学英语写作 101”:如果你无法向人类同事清晰地描述需求,AI 同样救不了你。 更值得警惕的是,过度依赖特定的 Agent 框架或复杂的“咒语”可能是在做无用功,因为模型的进化周期仅有三个月,它会不断吸收并取代周边的工具链。与其钻研那些半衰期极短的技巧,不如关注如何构建确定性的流程。 另外,官方那个被寄予厚望的“前端美学”指南正面临口碑翻车,被不少圈

16👍4

26 Jul 2026, 13:25 UTC≈6,040 views10 reactionsread 13 August 2026
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【别让“黑灯工厂”毁了你的代码库】| github AI编程正从“提效神器”变成“屎山制造机”。 Dex Horthy指出,所谓“无人值守、自动出片”的代码工厂在现实中行不通:虽然AI能刷爆各类Benchmark,但目前的强化学习(RL)只奖励“修好Bug”和“跑通测试”,却对“代码可维护性”完全没有惩罚。结果就是AI为了通过测试会疯狂写try-catch、胡乱强转类型,导致代码库在3-6个月内迅速腐烂。 当系统崩溃时,你会发现自己不得不花两周时间去清理AI堆砌的逻辑垃圾。 底层逻辑在于,好的架构设计需要跨越数月的长线思考,而目前的Token消耗模型只在乎当下的输出。国内开发者最该带走的认知是:不要试图把思考外包给AI,真正的效率来自“重回设计”。在让AI动手前,先花30分钟对齐产品逻辑、系统架构和程序设计,这能省下后续数小时的痛苦Review。 AI可以当你的高级绘图员,但你必须永远是那个掌握“审美”和“直觉”的总工程

4😁4👎2

Showing the 12 most recent of 24 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.

Citation-graph rank

Citation-graph rank — 366,565 of 1,176,251entries 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

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

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.

Domains linked from posts

8 domainsthis 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.

x.com4 posts · 26 July 2026 – 26 July 2026 · example post
github.com3 posts · 26 July 2026 – 26 July 2026 · example post
www.reddit.comregistrable domain: reddit.com2 posts · 26 July 2026 – 26 July 2026 · example post
artificialanalysis.ai1 post · 26 July 2026 – 26 July 2026 · example post
brevio.pro1 post · 26 July 2026 – 26 July 2026 · example post
platform.claude.comregistrable domain: claude.com1 post · 26 July 2026 – 26 July 2026 · example post
drive.google.comregistrable domain: google.com1 post · 26 July 2026 – 26 July 2026 · example post
karimjedda.com1 post · 26 July 2026 – 26 July 2026 · example post

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

“黑洞资源笔记” (@piracy6), 77,125 subscribers as measured 13 August 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.