腾讯计划将半数员工部署到 AI 原生业务 腾讯创始人马化腾在年度演讲中敦促公司逾 10 万名员工拥抱 AI 技术,计划将 50% 的员工投入 AI 原生业务以拓展发展空间,另 50% 的员工则负责核心业务的提效。针对近期有关腾讯计划裁员 50% 的传言,知情人士称该说法完全不属实,实际为公司为适应 AI 时代而进行的资源重新调度。Solidot | 联合早报 🏷 #腾讯 #马化腾 #AI原生 📢 频道 👥 群组 📝 投稿
🤡14😁3❤🔥1

Channel
@kejiqu
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Posts recorded as deleted · Citations · Telegram's recommendations · Cite this entry
36,774subscribers
+503 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
| Telegram ID | -1001632084170 |
|---|---|
| Type | Channel |
| Username | @kejiqu |
| Description | 分享有价值、有趣的信息! 你也可以来投稿,包括但不限于科技新闻、知识、影视娱乐资讯.... 投稿 @kejiqubot 群组 @kejiquchat 联络 @SlowDayBot |
| Created | Between 1 December 2021 and 30 April 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 11 November 2026 |
| Measurements held | 64 |
| Confirmed unchanged | 1 time, most recently 11 November 2026 |
| On Telegram | t.me/kejiqu |
Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 9 September 2026 and assigned it the closest of 31 fixed categories, at 93% 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 the other topics.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 11 Nov 2026, 02:31 | 36,774 | -12 |
| 9 Nov 2026, 10:37 | 36,786 | +15 |
| 8 Nov 2026, 03:08 | 36,771 | +2 |
| 7 Nov 2026, 01:07 | 36,769 | +14 |
| 6 Nov 2026, 00:08 | 36,755 | +3 |
| 5 Nov 2026, 02:54 | 36,752 | +20 |
| 4 Nov 2026, 00:28 | 36,732 | +9 |
| 3 Nov 2026, 03:46 | 36,723 | +10 |
| 2 Nov 2026, 03:58 | 36,713 | +11 |
| 1 Nov 2026, 01:07 | 36,702 | +9 |
| 30 Oct 2026, 22:05 | 36,693 | -9 |
| 29 Oct 2026, 19:56 | 36,702 | +24 |
| 28 Oct 2026, 21:30 | 36,678 | +15 |
| 27 Oct 2026, 23:36 | 36,663 | +3 |
| 27 Oct 2026, 00:09 | 36,660 | +11 |
| 26 Oct 2026, 02:45 | 36,649 | +9 |
| 24 Oct 2026, 23:35 | 36,640 | -3 |
| 24 Oct 2026, 02:01 | 36,643 | -1 |
| 23 Oct 2026, 01:49 | 36,644 | -7 |
| 22 Oct 2026, 03:00 | 36,651 | first reading |
336 posts held, back to 1 August 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 182 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.
| Window | Rolling 30 days · latest post in window 12 November 2026 |
|---|---|
| Posts held | 336 (1 August 2026 – 12 November 2026) |
| Views total | 243,030 |
| Reactions total | 1,373 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 12 Nov 2026, 16:11 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 12 November 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.
5,215 reactions across 336 posts, in 53 distinct kinds. The most used accounts for 29.9% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 1,559 | 29.9% | |
| 😁 | 1,034 | 19.8% | |
| ❤ | 463 | 8.88% | |
| 🤡 | 299 | 5.73% | |
| 👀 | 248 | 4.76% | |
| 🤣 | 192 | 3.68% | |
| 👎 | 170 | 3.26% | |
| 👏 | 113 | 2.17% | |
| 👾 | 73 | 1.40% | |
| 🤓 | 70 | 1.34% | |
| 👌 | 66 | 1.27% | |
| 🔥 | 64 | 1.23% | |
| 💊 | 63 | 1.21% | |
| 👻 | 58 | 1.11% | |
| 👨💻 | 56 | 1.07% | |
| 🤯 | 56 | 1.07% | |
| 🌚 | 54 | 1.04% | |
| 🐳 | 49 | 0.94% | |
| 😱 | 43 | 0.825% | |
| 🍌 | 39 | 0.748% | |
| 33 further kinds | 446 | 8.55% |
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 336 of the 336 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 5,215 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 336 most recent posts we hold, published 1 August 2026 to 12 November 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.
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @kejiqu. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 336 most recent posts we hold for this entry, published 1 August 2026 to 12 November 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
Telegram’s own ‘edited’ marker: 39 of 102 posts held from the last 30 days (38.2%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
腾讯计划将半数员工部署到 AI 原生业务 腾讯创始人马化腾在年度演讲中敦促公司逾 10 万名员工拥抱 AI 技术,计划将 50% 的员工投入 AI 原生业务以拓展发展空间,另 50% 的员工则负责核心业务的提效。针对近期有关腾讯计划裁员 50% 的传言,知情人士称该说法完全不属实,实际为公司为适应 AI 时代而进行的资源重新调度。Solidot | 联合早报 🏷 #腾讯 #马化腾 #AI原生 📢 频道 👥 群组 📝 投稿
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俄罗斯云服务商 Yandex 表示无人机攻击其数据中心导致数据丢失 俄罗斯云运营商 Yandex 承认,10月初发生的无人机攻击导致其数据中心受损并造成数据丢失。其中 ru-central1-b 可用区发生火灾,导致部分云服务和支付服务中断。Yandex 恢复了 97% 以上的客户数据,但因关键硬件损坏及平台备份缺失,导致 2.84% 的客户数据丢失。目前 Yandex 已为所有 VMs 默认启用备份,并推出了新可用区 ru-central1-f,但受制裁影响,该区域目前设有资源配额且无法处理私有端点流量。The Register 🏷 #Yandex #VMs #ru #central1 #f 📢 频道 👥 群组 📝 投稿
🤷♂6😁4😢2
科学家审核其工作成果,并通过更新标志性的黑洞图像予以确认 4名澳大利亚 CSIRO 研究人员利用一种新的 machine learning 算法,对 2019 年 Event Horizon Telescope (EHT) 捕捉到的黑洞 M87 图像进行了独立验证。该研究采用闭合三元组方法,以减少 8 个全球望远镜采集数据中的仪器和环境误差。结果显示,新图像在大小和形状上与原图几乎完全相同,确认了标志性的环形结构。这是针对 EHT 图像的首次独立验证,相关研究已在 Nature 发表。The Register 🏷 #Event #Horizon #Telescope #EHT #M87 #machine #learning 📢 频道 👥 群组 📝 投稿
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Nvidia 和 Jensen Huang 面临股东起诉,指控其在间谍丑闻中误导投资者 Nvidia 及其 CEO Jensen Huang 面临股东起诉,被指在处理与涉嫌中国间谍 Alice Huang 的婚外情相关报道时误导投资者。原告称 Nvidia 否认 Alice Huang 旗下 Megaspeed 公司规避美国出口限制走私 Nvidia 芯片的行为,且未披露该关系,导致公司声誉受损及投资者损失。此外,Nvidia 曾向 Trump 的白宫舞厅基金捐赠 2500 万美元。The New York Times 另报道,Huang 与公司副总裁 Kimberly Powell 亦存在婚外关系。Slashdot 🏷 #Nvidia #Jensen #Huang #Shareholder #Lawsuit 📢 频道 👥 群组 📝 投稿
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澳大利亚科技教育电子游戏挑战赛将停办,主办方称生成式 AI 加大评审难度 澳大利亚 STEM 电子游戏挑战赛宣布将于 2026 年举办最后一届。该赛事创办于 2014 年,已举办 12 届,面向 3-12 年级学生(独立或最多 4 人团队)。主办方 ACER 表示,生成式 AI 的普及导致评委难以分辨作品是否由学生本人完成,增加了评审难度并使竞赛偏离原定目标。目前规则虽允许学生借助 AI 编写少量代码或排除错误,但要求详细记录使用情况,并提交源代码及 1 至 5 分钟的游戏录像。IT之家 🏷 #STEM电子游戏挑战赛 #生成式AI #ACER 📢 频道 👥 群组 📝 投稿
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DeepMind 发布 Gemini Robotics 2.5 及开放安全基准测试 Google DeepMind 发布新一代 AI 驱动的 Gemini Robotics 2.5 平台,显著提升了机器人在现实任务中的灵巧性。该模型经过 200 多项活动评估,部分 Gemini 机器人已在 Mercedes-Benz 工厂投入实际工作;搭载该系统的 Apptronik Apollo 2 人形机器人可完成打蝴蝶结等复杂操作。同时,DeepMind 推出了名为Ars Technica 🏷 #Gemini #Robotics #DeepMind #ASIMOV #jailbreak 📢 频道 👥 群组 📝 投稿
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OpenAI 扩展 ChatGPT 商业功能:企业可认领资料并创建 AI 智能体,为 Business Agent 投放广告 OpenAI于当地时间11月10日推出ChatGPT Businesses测试版,通过Business Hub为企业提供商家资料认领、资料管理及互动数据分析服务。目前ChatGPT全球每周有12亿人使用。企业可通过该平台更新产品价格和库存,Shopify商家可连接店铺简化验证。Business Hub提供提及次数和网站点击等汇总统计数据,且不分享用户对话内容。此外,符合条件的美国电商及在线服务企业可创建Business Agent用于产品介绍,并可购买“赞助展示位置”。IT之家 🏷 #OpenAI #ChatGPT #Businesses #Business #Agent 📢 频道 👥 群组 📝 投稿
👌4
谷歌迄今最强模型 Gemini 4 Carbon 登场:主打复杂编程、科学研究与网络安全 谷歌于当地时间11月10日正式开放Gemini 4 Carbon推理模型。该模型面向复杂软件工程、科学研究和网络安全,支持1,048,576 Token上下文窗口,最大输出可扩展至1,048,576 Token。2026年11月30日前,首发优惠价为每百万输入2美元、输出10美元。性能方面,SEC-bench Pro得分88.2%,CASP17同源序列数量提高40倍,Gray Swan IPI攻击成功率为0%。IT之家 🏷 #Gemini #Carbon #谷歌 #推理模型 📢 频道 👥 群组 📝 投稿
👍6
Google DeepMind 分析证明 Signal 和 WhatsApp 的加密是安全的 Google DeepMind 完成的一项分析正式验证了 Signal 协议的安全性。该协议被视为端到端加密消息传递的金标准,除 Signal 应用外,还被 WhatsApp 和 Google Messages 采用。此项研究成果被描述为一项里程碑式的发展。9to5Mac 🏷 #Google #DeepMind #Signal #WhatsApp 📢 频道 👥 群组 📝 投稿
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派拉蒙天舞 CEO:AI 可能成为电影史上最大创意突破,助力原创叙事诞生 派拉蒙天空之舞CEO大卫·埃里森在11月10日的电话会议中表示,AI有望成为电影史上最大的创意突破,其技术变革规模可比肩20世纪90年代初的CGI。他指出AI将极大释放创作潜力并降低制作成本。以《终结者》为例,该片当年制作成本为400万美元(约合2,674.6万元人民币),而如今重拍成本将达6,000万美元-7,000万美元(约合4.01亿元-4.68亿元人民币)。AI可通过降低预算降低项目风险,助力更多原创叙事诞生。IT之家 🏷 #AI #电影制作 #大卫·埃里森 📢 频道 👥 群组 📝 投稿
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FIA 主席苏拉耶姆:第 12 支 F1 车队应该来自中国,已和比亚迪、吉利接触谈判 国际汽联(FIA)已与比亚迪、吉利就成为第12支F1车队展开谈判。FIA主席穆罕默德 · 本 · 苏拉耶姆于11月9日表示,倾向于将中国制造商作为下一个参赛者,以利用其汽车产业及科技优势。目前F1阵容已扩至11支,包括凯迪拉克及通过收购索伯进入的奥迪。苏拉耶姆强调,技术框架不会为迎合中国新车而重写,且第12支车队的准入将基于提案质量及其带来的竞技与商业价值。比亚迪在讨论中拒绝收购现有车队,表示更倾向于自行运营。IT之家 🏷 #F1 #国际汽联 #中国制造商 📢 频道 👥 群组 📝 投稿
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Claude Fable 5.2 和 Mythos 5.2 在 IPO 之前巩固了 Anthropic 的 AI 领先地位 Anthropic 在 IPO 之前发布了其迄今最强大的模型 Claude Fable 5.2 和 Mythos 5.2,提供免费版及验证组织版。该模型能够解决开放物理问题,具备增强的网络安全能力并可设计可运行的操作系统;据 Anthropic 称,其核心代码中 90 percent 以上的合并更改由该模型编写。同时,Anthropic 已刻意限制 AI 加速自身开发的能力。THE DECODER 🏷 #Anthropic #Claude #Fable #IPO 📢 频道 👥 群组 📝 投稿
👍8
Showing the 12 most recent of 336 posts we hold for @kejiqu. 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.
We recorded 1 post on @kejiqu as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.
A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. 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 9 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.
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 1 September 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 21 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 11 November 2026 — this entry's latest reading, not the date you are reading this.
“科技&趣闻&杂记” (@kejiqu), 36,774 subscribers as measured 11 November 2026. Telegram Register, tgregister.com/channel/kejiqu.
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.