Telegram RegisterThe public register of Telegram

Channel

NT³的自吹自擂

@nt_cubic

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

2,041subscribers

+17 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 1,000–3,162.

Register entry

Telegram ID-1001502227126
TypeChannel
Username@nt_cubic
CreatedBetween 1 July 2021 and 28 February 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live13 August 2026
Measurements held5
Confirmed unchanged1 time, most recently 13 August 2026
On Telegramt.me/nt_cubic

Growth

2,0242,0412,032.57 August 2026 — 2,024 subscribers7 August 2026 — 2,024 subscribers7 August 2026 — 2,026 subscribers10 August 2026 — 2,034 subscribers13 August 2026 — 2,041 subscribers7 August 202613 August 2026
5 measurements spanning 6 days, net +17. 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 2,021–2,044 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
13 Aug 2026, 07:542,041+7
10 Aug 2026, 16:522,034+8
7 Aug 2026, 23:272,026+2
7 Aug 2026, 01:462,024no change
7 Aug 2026, 01:362,024first reading

Engagement

19 posts held, back to 4 March 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
28.6%
avg views ÷ 2,041 subscribers
Avg views / post
583
6 posts measured
Reaction rate
1.86%
reactions ÷ views · ER floor
Posts in window
6
of 19 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.

What these figures were computed from
WindowRolling 30 days · latest post in window 7 August 2026
Posts held19 (4 March 20267 August 2026)
Views total3,498
Reactions total65
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken7 Aug 2026, 17:07 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

Video runtime
33s
Average length
33s

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

257 reactions across 19 posts, in 8 distinct kinds. The most used accounts for 38.1% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
9838.1%
😁4015.6%
🔥3513.6%
🤩218.17%
🤣207.78%
👍197.39%
🥰135.06%
😢114.28%

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 19 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 257reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 19 most recent posts we hold, published 4 March 2026 to 7 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

7 Aug 2026, 02:24 UTC249 views11 reactionsread 7 August 2026
Photo

这几天读了下Deepseek的官方论文和技术报告,获得了点灵感。 基于llama.cpp写了套MoE缓存的CUDA方案。 把输出提升到了约30t/s, 常驻显存下降到了12GB, 但计算时显存还是会进一步拉满,要后续进一步优化下。 CPU和GPU并行交换计算还可以进一步合并一下路由,感觉有望小幅度进一步提高。 群友:你啥时候才发游戏内容啊玛德

11

4 Aug 2026, 10:42 UTC433 views9 reactionsread 7 August 2026
Photo

↑ Nobody: 当全世界都没有办法用 Deepseek 时,我 昨晚靠官方llama更新+手动微调优化了下, 读取上升到平均630t/s,输出倒是没变化,应该是卡MoE专家缓存的传输瓶颈了,只能靠社区架构优化了。

🔥9

2 Aug 2026, 16:08 UTC616 views4 reactionsread 7 August 2026
Video

把梁叔叔大肥鱼0731搬回家了 速度还挺可观。 读取速度270-300t/s 输出20t/s

3🔥1

2 Aug 2026, 07:20 UTC662 views18 reactionsread 7 August 2026
Photo

家人们,用空余时间搞了个小东西 Marionette (提线木偶) 目标是:Agent Harness All in one 由于现在各大模型都有自家优化过的 Agent Harness,多模型搭配专属 harness 才最好用。 但问题是,如果使用多个提供商 = 多个 app 多开,CPU 内存告急,开发工具一同开启就更卡; 而且每个 harness 都要单独配一遍 skill、MCP,非常麻烦。 所以我花了两周多,搞了个不到1.5MB、五脏俱全的多 Agent 聚合工具: Opencode、Claude Code、Codex、Grok Build、Pi、Cursor……统统装进同一个壳里。 这就是 Marionette 让所有 Agents 坐在一起,共享同样的知识和经验。 你不用适应工具,而是让工具来适应你不断精进的做事方法。 除了轻巧外,还能实现以下的流畅工作: - 一个窗口,所有 Agent,按需启动不占资源

🔥126

1 Aug 2026, 03:50 UTC560 views11 reactionsread 7 August 2026
Photo

算命先生说我八字缺明水,家中需有润土之物相伴。 水为财,补水可旺。 朋友听闻后,连明连夜送来他的二手机箱,说这旺我。 家人们帮忙看看是不是这么回事

🤩7🥰31

23 Jul 2026, 05:59 UTC978 views12 reactionsread 7 August 2026

#思考 演出的质量,几乎和游戏能否成为主流、能否大卖成正比。 从最早的乒乓开始,进入街机、再进入FC、SFC、PS1……直到如今。 表面上看,这是图形技术的惊人进步。 许多历史上的名作,正是乘着图形技术爆发的东风实现了大卖。 但实际内容上,并没有太多出色点,也就随着时代而埋没了。 这一点,从某些作品即便完全重制也无人问津就能得到证明。 从玩家体验来看,图形技术的进步带来了镜头控制的革新和全新的交互方式:从传统的上帝视角、角色只占屏幕1/50像素的疏离感,进化到越肩视角和空间纵深。 换言之,代入感因技术进步而大幅提升。 但代入感只是演出质量的一个方面。 演出质量并不纯粹取决于图形技术是否精湛。 《女神异闻录5》的风格化演出就是最好的证明。 《时空之轮》的经典像素画面,也是至今仍未褪色的效果。 然而,演出质量也并不纯粹取决于美术质量。它可能来自听觉、构图、文字描述,甚至更多来自玩家在虚拟世界中的反馈层次。 譬如地球冒险、去月

🔥102

3 Jul 2026, 07:52 UTC≈1,620 views16 reactionsread 7 August 2026
Photo

肥波5,二十个提示词做出来的基于web的像素战旗。 耗时20分钟,基于TS,顺便手搓了个vite像素引擎。 https://fable5game.unsnow.org/ 甚至他还自带剧情、兵种、地形、音乐音效。 来源:https://linux.do/t/topic/2514773

7👍6🤩2🤣1

10 Jun 2026, 11:39 UTC≈2,380 views15 reactionsread 7 August 2026

#思考 游戏的机制,并不一定非要有趣。 开发者——尤其是「灵视」较低的开发者——很容易被一句话绊住:「游戏要好玩才行」 于是,战斗、资源、关卡、交互……方方面面都用力过头,觉得处处都要好玩,处处都得有趣。 使得这部分过于复杂,那部分又用力过猛,变得头重脚轻,比例不均。 这是一种典型的模块化思维和片面认知的错误。 我很喜欢拿生化危机举例,尽管恐怖游戏是我最讨厌的题材。 但生化危机1、2、4,都是作为开发者不得不去品的游戏。 如果我们抱着「游戏处处要有趣」的念头去做游戏,那生化危机是必然不会诞生的。 试想一下,把生化危机的一切都砍掉,只剩下一个缓缓走来的敌人,玩家举着手枪把它打倒—— 这好玩吗?这足以成为卖点吗? 甚至,光凭这一点,能发展成一款游戏吗? 若仅如此,这东西几乎可以和史上最烂的游戏相提并论。 即便不谈战斗,单独把生化危机的关卡拆开,单独拎出某一个解谜、某一段音乐、某一段剧情,都很无聊。 很难想象单靠这些,怎么能成就

15

31 May 2026, 07:09 UTC≈2,140 views14 reactionsread 7 August 2026
Photo

没有人。 当本周 Coding Plan 更新额度100%/100%时,我:

😁10🤣4

19 May 2026, 05:31 UTC≈2,460 views23 reactionsread 7 August 2026

#思考 近年来,喜剧与搞笑题材的故事、动画、电影和游戏,明显少了许多。 大量接触 20 年前(1990–2010)的作品,会发现那段时间几乎是喜剧的黄金期。 港剧、日剧、日漫、欧美剧,小品和相声,主流创作都必须让人娱乐和欢乐。 而如今,严肃、愤怒、狗血、讲求合理与逻辑的故事,基本成了主流。 我想,这可能是因为喜剧里的搞笑桥段,大多建立在冒犯之上。 嘲笑某一类人,嘲笑某个角色,嘲笑某一些行为,将其夸张化,借此获得讽刺与乐趣。 但是,喜剧的作用并不是纯粹的搞笑,也不仅仅是为了笑声而制作。 它是基于并强化大家所认定的美丑标准,再进行夸张,从而制造欢乐。 喜剧最重要的,是无时无刻都有个取笑的对象。 过去相当长一段时间里,公众共享一套相对稳定的审美和道德共识。 胖,可以被笑话;慢,可以被笑话;娘娘腔,可以被笑话;抠鼻屎不讲卫生,也可以被笑话。 创作者清楚哪些是“安全靶子”,大家也笑得了,也无负担。 因为笑的人心里都知道,这些笑料背后

12😢11

6 May 2026, 10:59 UTC≈1,620 views14 reactionsread 7 August 2026

家人们,黄金周放假自己做了个 opencode 终端的增强侧边栏插件。 已开源: https://github.com/nt-cubic/opencode-enhanced-sidebar/tree/main 由于 Deepseek 引入了缓存命中率的设计, 感觉每次对话都想八卦一下自己省了多少钱。 现在有了这个实时数据显示,就有种每次对话都有个作战目标的感觉(守望和moba按下tab看友方数据那种) 目前能显示的功能包括: • 7 个可折叠信息卡片(上下文健康 / 性能 / 花费 / 工具调用统计等) • 最近AI自己修改、读取的Top5文件 • 工具调用追踪 + 成功/失败率 + 动作链可视化 等等 详细功能介绍: 实时监控 • 会话总时长 / 平均每轮耗时 / 当前轮实时计时(每秒刷新) • TPS(tokens/秒)输出速度 成本 & 效率 • 已累积成本显示(精确到小数点后 4 位) • Output / Re

11🔥3

29 Apr 2026, 12:14 UTC≈1,760 views9 reactionsread 7 August 2026

说起来几个月前,用 AI Agent 就被人类文明的梦幻程度震惊到了。 虽然从以前到现在仍然有不停地看 SF 题材的故事,但这些事情发生在现实里,其现实的厚重感仍然让人觉得不可思议。 我通过键盘,输入了自己的需求。 然后—— 这串字符从键盘开关闭合开始,被操作系统的调度器切进队列,落进磁盘缓存,变成磁畴方向或闪存里的浮栅电荷,再爆成一串电磁脉冲。 接着,它涌入那枚从沙子里提纯出来的硅晶圆上,蚀刻出的几亿个纳米级晶体管里,以时钟周期的节奏翻跟头。 很快,信号又变成光,在比头发丝还细的玻璃纤芯里,以真空光速的三分之二狂奔,穿过海底——几千公里深的压强下,那根缆线里还藏着激光放大器和中继器,每个都精确到原子钟级别的同步。 然后,路由器和交换机的队列在微秒里作出判决,数据包可能经过十几个国家,被无数层协议拆了又装、装了又拆,加解密、校验和、拥塞控制、丢包重传——每跳都在几毫秒内完成。 最后,它到达某个数据中心:成千上万个核

🥰5🤣31

Showing the 12 most recent of 19 posts we hold for @nt_cubic. 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 — 894,805 of 1,340,412entries 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

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

“NT³的自吹自擂” (@nt_cubic), 2,041 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/nt_cubic.

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.