Channel
@nvda2052
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
397subscribers
+15 since we began measuring on 8 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002516277967 |
|---|---|
| Type | Channel |
| Username | @nvda2052 |
| Created | 12 May 2025 — measured — dated from the channel’s first post |
| First recorded | 8 August 2026 |
| Last confirmed live | 25 August 2026 |
| Measurements held | 5 |
| Confirmed unchanged | 1 time, most recently 25 August 2026 |
| On Telegram | t.me/nvda2052 |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 25 Aug 2026, 01:18 | 397 | +10 |
| 16 Aug 2026, 15:43 | 387 | +4 |
| 8 Aug 2026, 18:40 | 383 | +1 |
| 8 Aug 2026, 06:05 | 382 | no change |
| 8 Aug 2026, 00:17 | 382 | first reading |
20 posts held, back to 12 May 2025 — 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.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 3 April 2026. An engagement rate over an empty window would be a number about nothing.
10 reactions across 8 posts, in 4 distinct kinds. The most used accounts for 40.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🎉 | 4 | 40.0% | |
| ❤ | 2 | 20.0% | |
| 👍 | 2 | 20.0% | |
| 👏 | 2 | 20.0% |
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 9 of the 20 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 10reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 12 May 2025 to 3 April 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.
007 能耐寻求定理：君子不器 文章以"君子不器"为切入点，借助AI安全领域Turner 2021年提出的"能耐寻求定理"（最优策略倾向于保持选项开放）和信息论中的"赋能"概念，论证人不应被单一目标函数奴役，而应主动追求增加自身选项和影响力。文章同时引入Mullainathan和Shafir的"隧道效应"解释认知窄化的危害，区分了"被驱使"（为外部目标牺牲生活其他维度）和"赋能"（主动扩展能力和选择权）两种状态，主张即使在追求具体事业时，君子的标志是始终高于任何目标函数，保持主体性和自由度。 📦课程目录 👇🏻课程文件见评论区
❤1
006 内核：你和你的三个[自我] 文章提出人有三个层次的"自我"：进程自我是时时刻刻的主观体验流，本质上只是系统运行日志而非决策者；界面自我是人在不同社交情境中展示的角色和人设，随场景切换；内核自我则是大脑底层的算法、参数和先验假设，决定直觉与潜意识判断，类似AI模型的权重。文章以AI大模型架构为类比，论证真正的成长不在于调节情绪或切换人设，而在于通过选择训练语料（信息环境）和奖励函数（激励机制）来改写内核自我的模型参数。 📦课程目录 👇🏻课程文件见评论区
🎉1
005 可能：不确定性是意义的燃料 文章提出世界存在五种无法消除的不确定性（混沌、计算不可约性、量子随机、奈特不确定性、反身性），并借助Pluchino等人的模拟研究论证运气在成功中的作用可能被低估。在此基础上，文章梳理了人们面对不确定性的三种态度——渴望虚假确定、被动全盘接受、主动分析拥抱——并引用GPT的回答提出"人类享受的是追求确定性的过程而非确定性本身"，最终以"燃料"为隐喻，主张将不确定性视为意义的来源加以管理和利用。 📦课程目录 👇🏻课程文件见评论区
🎉1
004.5 问答：叙事和讲故事、造梦的区别是什么？ 📦课程目录 👇🏻课程文件见评论区
👍2
004 约束：先尊重，再行动 原文主要强调，现实世界并不是一个可以凭主观愿望随意塑造的空间，而是由资源、时间、自然规律和他人能动性等“硬约束”共同构成。人在行动之前，需要先理解这些限制条件，再讨论目标与路径。 文章通过对不同类型叙事作品的比较，说明很多流行叙事倾向于强化“努力必有回报”或“善意终将得到奖励”的想象，而现实往往更复杂。作者借此提出，成熟的认知方式不是沉浸在自我安慰式叙事中，而是在约束范围内寻找可行的行动方案。 📦课程目录 👇🏻课程文件见评论区
🎉1
003 能动：稳态生存的观念陷进 主要讨论的是：许多人仍然沿用在资源匮乏、社会流动性较低环境中形成的“稳态生存逻辑”来理解和安排人生，这种逻辑强调求稳、从众、避险和指标导向。作者认为，在当代更高波动、更强调流动性与机会捕捉的社会中，这套旧观念已不再完全适用，人需要逐步转向更主动的思考和行动方式，成为能够独立判断并主动选择的“能动者”。 📦课程目录 👇🏻课程文件见评论区
002 重尾：世界服从极端 现实世界中很多现象并不服从平均分布，而是呈现“重尾”特征，即少数个体或事件贡献了大部分结果。 其背后机制是正反馈与复利效应，使得资源、财富和机会不断向已有优势者集中。 因此，在这样的环境中，应避免平均用力，更关注能够被放大和积累的优势领域。 📦课程目录 👇🏻课程文件见评论区
🎉1
001 叙事：这个宇宙的第一性原理 这篇文章以沃尔夫勒姆的Ruliad概念为起点，论证物理定律之所以呈现当前形态（因果律、量子不确定性、引力聚合），是因为它们构成了"允许叙事"的条件，进而提出叙事——而非物理定律——才是宇宙的第一性原理。文章随后将叙事从宇宙论拉回到人的层面，归纳出叙事的三个功能：预测处理、提供意义、公共协调，并引用丹尼特的"叙事重心"和席勒的"叙事经济学"来说明自我和经济行为都由叙事驱动。最终落脚于实操建议：一切叙事都是主观的，你应该觉察自己身处的叙事框架，跳出别人设定的叙事，主动为自己设定叙事。 📦课程目录 👇🏻课程文件见评论区
❤1
2. 《现代思维工具词典》 《现代思维工具词典》通过高度压缩的方式整理了多个跨学科概念，适合作为建立认知框架的入门索引。其结构清晰、覆盖面广，有助于快速理解不同思维模型的基本含义与应用方向，在信息获取与知识组织层面具有一定价值。 📦课程目录 👇🏻课程文件见评论区
万维钢·现代思维⼯具100讲 pinned «课程目录 1. 发刊词：现代精英的思维武器库 2.《现代思维工具词典》 001 叙事：这个宇宙的第一性原理 002 重尾：世界服从极端 003 能动：稳态生存的观念陷进 004 约束：先尊重，再行动 004.5 问答：叙事和讲故事、造梦的区别是什么？ 005 可能：不确定性是意义的燃料 006 内核：你和你的三个[自我] 模块一：成长战略 007 能耐寻求定理：君子不器 008 供给侧心态：怎样再正和的世界合作（以及竞争） 009 复利：可积累的优势 010 自我决定理论：一流人物不可能是痛苦的卷王 011…»
万维钢·现代思维⼯具100讲 pinned a photo
1. 发刊词：现代精英的思维武器库 📘《现代精英的思维武器库》核心总结 在复杂世界中， 真正拉开差距的不是努力，而是思维工具与模型能力。 🧠 三种认知层级 传统：靠努力（线性思维） 市侩：看收益（短期最优） 精英：建模型（系统优化） 👉 本质差别：是否拥有高质量世界模型 ⚙️ 核心方法论（4要素） 目标函数：你到底要什么（可排序/加权） 世界模型：理解世界如何运作（看预测力） 反馈回路：正反馈增长，负反馈稳定 预算带宽：注意力 & 精力是核心资源 🚀 关键认知升级 世界奖励“用对工具的人”，不是更努力的人 学习的本质 = 优化你的世界模型 AI的强大，本质是稳定调用一套思维工具 📊 本质一句话 👉 把人生从“经验驱动”升级为“系统驱动” 📦课程目录 👇🏻课程文件见评论区
Showing the 12 most recent of 20 posts we hold for @nvda2052. 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 — 780,346 of 1,603,376entries 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.
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.
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.
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 August 2026 — this entry's latest reading, not the date you are reading this.
“万维钢·现代思维⼯具100讲” (@nvda2052), 397 subscribers as measured 25 August 2026. Telegram Register, tgregister.com/channel/nvda2052.
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.