Channel
@peopleofscreen
On this record: Growth · Engagement · Posts · Citations · Cite this entry
852subscribers
-1 since we began measuring on 8 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001150590140 |
|---|---|
| Type | Channel |
| Username | @peopleofscreen |
| Created | Between 1 November 2019 and 31 January 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 8 August 2026 |
| Measurements held | 2 |
| Confirmed unchanged | 1 time, most recently 8 August 2026 |
| On Telegram | t.me/peopleofscreen |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 8 Aug 2026, 16:01 | 852 | -1 |
| 8 Aug 2026, 10:18 | 853 | first reading |
20 posts held, back to 18 May 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 1 pageof 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 4 August 2026 |
|---|---|
| Posts held | 20 (18 May 2026 – 4 August 2026) |
| Views total | 294 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 8 Aug 2026, 10:18 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.
https://www.theguardian.com/media/2026/jun/14/have-i-been-influenced-personal-taste-out-of-fashion-algorithm 作者指出一个悖论：平台以“个性化”为卖点，却通过数据和相似度推荐把个人品味合成、打包、自动化，结果反而磨平差异，让人们变成“被喂养”的消费者。 信息和内容的数量又极其庞大，以至于我们很难有足够精力认真消化，从而更依赖算法的默认选项，进一步削弱自我选择。
https://yourbrainonmoney.substack.com/p/everything-is-private-equity-3 逆半人马：人被资本、算法双向支配 医疗机构在华尔街眼里只是可拆分变卖的金融标的，治病救人的公共属性完全让位于短期资本套现，属于典型的企业劫掠式私募玩法，也是多克托罗所说 “劣化（Enshittification）” 在医疗行业的具象体现。 以为美国梦只代表一己顺遂、个人富足。历经种种变故才幡然醒悟，真正的美国梦，是让整片社区里的所有人，都能安稳体面地生活。普通人不愿看着邻里深陷苦难的本心 —— 这份守望互助的执念，才是美国梦最本真的内核。
https://buttondown.com/maiht3k/archive/how-to-talk-about-ai-without-adding-to-the 去拟人化的基本原则 作者建议从“功能”角度描述系统，比如说它执行计算、生成概率文本，而不是“思考”或“理解”。 能动性应当归于使用系统的人而不是系统本身，避免过度夸张的认知隐喻
https://kevinkelly.substack.com/p/latent-space-as-a-new-medium 文字生成动作本身就是思考过程：模型每输出一个字，等价于在潜在高维空间持续移动向量坐标； 用户输入提问的每一个字词，都会直接拉扯、改变向量前进轨迹，全程动态修正空间移动方向。 LLM 可以在数十亿维概念空间自由游走、组合从未在现实出现过的向量组合； 所有全新故事、艺术风格、逻辑框架、跨界创意，本质是在潜在空间走出一条人类文字历史里不存在的向量路径。 "个人潜在空间：用你选择的书籍、经历、创作和关系训练出的私人AI" 这是文章中最具个人色彩的部分。凯利设想： 策展即艺术：选择训练材料本身就是一种创作行为，"训练材料的顺序至关重要" 教育即塑造："教育模型的教学进程会产生不同的模型属性" 微调即自我：用你自己的经历、创作、关系、半成品想法、日记——" basically 你的生活"——来微调 输出即品牌：…
Americans are more likely to place a gambling bet than read a single book https://www.theatlantic.com/magazine/2026/08/reading-crisis-postliterate-age/687618/
People reading was actually a 500 year glitch The 500-year print era was a temporary historical anomaly, and society is reverting to a post-literate, oral mindset where volume and social repetition define truth over verifiable text AI accelerates this shift by driving content generation costs to zero and delegating deep reading to automated agents, transforming deep literacy from a universal baseline into a niche l…
https://lizleatrice.substack.com/p/the-year-is-2063-and-you-were-never 一生被 Instagram、网红审美、社交展演式人生逐步吞噬、自我主体性慢慢消解的全过程 一辈子始终在旁观、模仿，从未认真经营属于自己的真实生活。
https://www.theideasletter.org/essay/the-cult-of-optimization/ 指标崇拜
“认知游击战”。 科技巨头在建造宏大的“系统”（如庞大的AI模型），而我们作为个体，最好的反击不是去造更大的系统，而是在微观层面重建“工具”与“感知”的连接。 这里有三个具体的行动方向： 拒绝“毁灭论”的恐吓（破除“应该”）： 下次听到有人说“AI会让所有人都失业”时，不要陷入恐慌，也不要盲目反驳。试着问一句：“对谁有利？” 这种问题能瞬间戳破谎言，让你看清这到底是趋势，还是某种商业策略。 支持“小作坊”式的创新（寻找佛罗伦萨）： 关注那些开源的、去中心化的、由小型团队开发的AI工具。这些工具往往更透明，更注重隐私，且允许你修改（就像用可擦除的墨水写字）。它们不是要接管你的生活，而是要帮你更好地生活。 坚持“低效”的人文关怀（感知苦行）： 无论AI写诗多快，坚持亲手给朋友写一张卡片；无论AI总结多准，坚持亲自读完一本书。这种“低效”不是落后，而是对“人”的主体性的坚守。正如你所说，辨别力是关键——我们要辨别出哪些是机器的“高效…
https://stringinamaze.net/p/rich-guy-quote-journalism
Posted without readable text
https://syndekit.substack.com/p/the-butlerian-jihad-has-begun 在AI出现前，我们已经习惯用KPI量化价值、用数据替代感知、用流程消解责任，这种“非人化”的逻辑早就渗透在社会肌理里了。 首要敌人： ● 技术官僚体系：AI被少数人用作权力工具、监控手段、压迫机器的风险。 ● 统治逻辑：支配欲、利益至上对创造力的践踏。 非技术本身：AI的“恶意”并非根源，关键在于人类如何运用技术。人工智能是工具，其伦理风险源于掌控者的意图与制度的失范。
Showing the 12 most recent of 20 posts we hold for @peopleofscreen. 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 — 1,016,247 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.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Republishes
Channels on the register whose posts this channel has forwarded.
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.
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 8 August 2026 — this entry's latest reading, not the date you are reading this.
“屏浅隐狱（何究）” (@peopleofscreen), 852 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/peopleofscreen.
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.