2026-08-22 20:50:49
One more light song by linkin park 2017
Should've stayed. Were there signs I ignored?
Can I help you not to hurt anymore?
We saw brilliance when the world was asleep
There are things that we can have but can't keep
If they say
Who cares if one more light goes out
In the sky of a million stars?
It flickers, flickers
Who cares when someone's time runs out
If a moment is all we are?
Or quicker, quicker
Who cares if one more light goes out?
Well, I do
The reminders pull the floor from your feet
In the kitchen one more chair than you need
Oh
And you're angry, and you should be, it's not fair
Just 'cause you can't see it, doesn't mean it isn't there
If they say
Who cares if one more light goes out
In the sky of a million stars?
It flickers, flickers
Who cares when someone's time runs out
If a moment is all we are?
Or quicker, quicker
Who cares if one more light goes out?
Well, I do
Who cares if one more light goes out
In the sky of a million stars?
It flickers, flickers
Who cares when someone's time runs out
If a moment is all we are?
Or quicker, quicker
Who cares if one more light goes out?
Well, I do
Well, I do
美国AI4S战略进入实施阶段，能源部牵头打造“创世纪”开放科学大模型
知道刘子明还是在刚本科时候，先是我看的一本比较感兴趣的书，然后从图书馆借来，作者是Max Tegmark，迈克斯•泰格马克 （Max Tegmark）书名：《穿越平行宇宙》，我不太知道Max Tegmark是谁，以为和普通的这种科学新知的书籍作者一样，读完之后很久好像才慢慢了解到Max Tegmark，后面还看了《生命3.0》，很长一段时间对科幻和这种前沿科学，非虚构都很着迷。后面social上搜，blog主页找，慢慢又看到了Ziming Liu的主页。
最近看到他之前的一些post，打印成PDF阅读了一番。主题就是：Ground ur Ideas & Toy Models
原文：
在这里，我记录一些我的内化思考，
Students with theoretical backgrounds are especially prone to thinking that the more abstract and profound something is, the more advanced it must be, whereas the more concrete and practical it is, the less sophisticated it seems.
build and break things fast
“concretization” ：This process of “concretization” inevitably loses some information, but that is acceptable—the purpose of subsequent iterations is precisely to recover and refine it. 有时候会慢慢演变，可能会遗忘初始时候想出来的一些细节，或者后面迭代之后细节观点等慢慢变形，所以可能需要有一个outline或者keypoints记录文件或者checkpoints可以回滚。
Therefore, we must find a balance between idealism and pragmatism.
我的框架大概是ideas->actions->mvp ，总体上早点落地写demo，然后迭代优化。
具象化大多数是先落地新建文件夹，写上当前最新鲜的想法作为readme，然后再去新建info.txt 集中记录一些零散的细节或者网址参数，然后在refer的文件夹中集中一些可能会用到的原料文件
To truly understand a conclusion, we must understand not only when it is correct, but also when it fails. 至少也是追求的目标。而且是一个成长变化，随时间演变，进步，添加思考insights进去，直到慢慢趋于收敛，至少是自己的认知能力范围之内。
we also generate new ideas, and sometimes these ideas genuinely come from intuition.
Constructing toy models is a way to explore this scope of applicability. Through toy models, abstract ideas can quickly become concrete and enter an iterative cycle. toy models基本上是MVP （Minimum Viable Product）即最小可行性产品
By the way, there are generally three ways to conduct research. “Finding applications for a method” is the second one. Ranked by impact, they are:
Best: Identify an important application and solve it by any means necessary (I use the word “application” broadly—for example, “solving continual learning” is also an application).
Second: Start with a method and then find applications where it works well.
Worst: Fix both the method and the application in advance and force them to work together, even though they may fundamentally be incompatible.
TB TD top2bottom B2T的策略，还有双向施工
但是第二篇解释为什么是MVP 也就是toy models
What works in a toy model may not work in a real model—and even if it does, it may work for entirely different reasons.
* ==Knowledge is created at the boundary between the known and the unknown. ==If we fully understand a toy model and the real model behaves differently, there must be a phase change (sharp or smooth) along the path connecting them. Understanding the phase change is key to understanding the difference between the toy setup and the real setup. Starting from the toy model (known) and gradually moving toward the real model (unknown) mirrors how humans learn. Fully known is trivial; fully unknown is confusing. The sweet spot—half-known, half-unknown—is where information gain is maximized. ==Research is, at its core, an information-gathering game.==
* 所以掌握和建立属于自己的信息流或者workflow很重要，是一个ecosystem
* 从信息获取到输出：
* input, sources
* output , perform
How to interpolate?
Directionality: move from simple to complex, or from complex to simple.
Locality: change one feature at a time; avoid overly large jumps.
Physics of AI is experiment- and phenomenon-driven, prioritizing intuition and breadth—touching many sides of the elephant and assembling a coherent picture.
“The ability to be curious, to learn across a lot of disciplines and to have a strong foundation of wanting to have impact, regardless of the area that you're working in, I think that is an underrated quality.”
08月23日星期日
曼城
曼城
21:00
CST
伯恩茅斯
I'm free to be
whatever I
Whatever I choose
and I'll sing the blues
if l want
And New season !!!!!!!!!!!!
to start over again
夏日，周末，黄昏时候跑步，向着西边方向，一直向西，还有两个绝佳的album，Oasis和linkin park，There'r hopes, days, today, tomorrow
You cannot be interested and inconsistent.
Thanks for being an insider till the end!
Till next, stay safe and stay hydrated!
2026-08-17 00:46:09
Should've stayed. Were there signs I ignored?
Can I help you not to hurt anymore?
We saw brilliance when the world was asleep
There are things that we can have but can't keep
If they say
Who cares if one more light goes out
In the sky of a million stars?
It flickers, flickers
Who cares when someone's time runs out
If a moment is all we are?
Or quicker, quicker
Who cares if one more light goes out?
Well, I do
The reminders pull the floor from your feet
In the kitchen one more chair than you need
Oh
And you're angry, and you should be, it's not fair
Just 'cause you can't see it, doesn't mean it isn't there
If they say
Who cares if one more light goes out
In the sky of a million stars?
It flickers, flickers
Who cares when someone's time runs out
If a moment is all we are?
Or quicker, quicker
Who cares if one more light goes out?
Well, I do
Who cares if one more light goes out
In the sky of a million stars?
It flickers, flickers
Who cares when someone's time runs out
If a moment is all we are?
Or quicker, quicker
Who cares if one more light goes out?
Well, I do
Well, I do
↩️
视听嗅觉都是blessing 地中海周围海岛上五彩缤纷颜色的房子 感受色彩带来的新鲜和愉悦 白天跑步和晚上跑步的区别也在这里 白天视觉可以收入周围自然的颜色 晚上darkness 幸福
味觉
上次说Dario的妹妹，Daniela 或许也该出一本书——讲讲新时代下团队组织和生产力的培养管理。
当然目前也没有广泛的资料、现有的biography，所以方便的话就自己搜集资料仅供学习的目的处理一下。
然后集合grok NotebookML Gemini Claude Deepseek搜索资料，有社交平台的，还有一些社区的，以及常见的新闻刊物等报道和采访，以及最重要YouTube上一些采访视频，这里有很多分工，都是基于日常对于不同model能力的感觉来分配任务，Grok access的能力最强，最活跃的社交平台上官方的post和comments也最多，然后YT Gemini NotebookML都是同一家生态下的，所以YT上内容以及Google search总结新闻等比较擅长，其他边角料Claude Deepseek都能完成，然后Claude主领 Opus 4.6就够了，从头到尾，大纲拟定，todolist规划以及prompt编写，执行到NotebookML窗口，根据所有资料的处理来输出，然后Gemini精修，再给到Opus最后精修review。整体过程4-5h，纯人工时间较短，最多1h。 哦。最重要的是context window的考虑，要大，精准，同时尽量幻觉少。
以下是一点hook （If you’re interested, feel free to email me and I’ll send it to you — strictly for learning purposes only. : )
荐书
AI叙事的聚光灯永远对准写代码的人，那些技术性推荐这项“事业”、进程的人员，但真正决定一家AI公司能走多远的，往往是那个你看不见的"隐藏层"——把技术天才的直觉转化为可持续组织的人。Daniela Amodei没有计算机学位，不会写代码，却以总裁身份共同掌舵Anthropic从七人实验室成长为万亿级企业。在"技术至上论"统治硅谷的今天，她的故事回答了一个根本性问题：当AI比人类更聪明时，我们最需要什么样的领导者？这本书试图从公开资料中拼出这个答案。
内容简介
本书以十章篇幅追溯Daniela Amodei的完整职业轨迹：从加州大学英语文学专业毕业的迷茫，到国会山与乌干达的非营利历练；从Stripe超速增长期的风控实战，到OpenAI内部目睹安全与商业化的根本分歧；从带领七位联合创始人以PBC架构创办Anthropic，到设计出长期利益信托（LTBT）这一"用法律对抗资本短视"的治理结构。书中详细剖析了她如何在两年内将团队从50人扩张至2500人、如何与亚马逊和谷歌谈判数百亿美元投资时保留安全否决权，以及她与哥哥Dario之间那套"零政治摩擦"的兄妹双核决策机制。
为了将这种“高信任”扩展到整个七人创始团队，他们确立了一套核心原则：“低自尊、高抱负（Low ego, High ambition）”。这意味着每个人都要在指数级增长的技术面前保持谦逊，同时对改变世界持有狂热的野心。
Anthropic 的内部文化有一种独特的自嘲精神。员工们喜欢自称为 “Ants”（蚂蚁），这不仅是因为公司名字的前缀，更象征着一种“超级群落（Supercolony）”式协同共生的群体特质。
“如果你只是把作业丢给 AI 拿走答案，那叫作弊；但如果你让它解释你卡住的地方，那叫学习，”丹妮拉强调。这种对工具的克制与运用，构成了她独特的个人生产力框架。
领域知识（Domain Expertise）是终极的校验引擎。AI 越强大，缺乏深度专业知识的人越容易被 AI 的“幻觉”与平庸输出所误导；只有对主业拥有深厚理解的人，才能准确识别那致命的 5% 细节缺陷。
管理一家年化营收在两年内从 8700 万美元跳跃到 470 亿美元的公司，丹妮拉必须掌握一种极度的注意力管理艺术。
为了在超高速扩张中保持组织敏捷并防止员工倦怠，她推行了“异步优先（Async-first）”与“极简主义连接”的工作哲学：
减少冗长的低效例会，利用 Slack 频道中高上下文的思维流进行深度异步表达；
建立清晰的决策树，赋予员工极高的自驱权限，避免官僚化的审批阻塞。
【顶会论文复现翻车？OpenAI研究员爆料：我们都不读论文了】 https://www.bilibili.com/video/BV1R2gG6REUP/?share_source=copy_web&vd_source=7ce21f11ab471395a8db74500938d6c2
书也是一样：查了一下最近这本《AI大模型助力高效学习锂电技术》，作者钟隽号称电气工程博士后、高级工程师，但知网、基金委、高校师资页统统查无此人——大概率是自费/合作出版：三五万到十万，三到六个月，过了基本三审三校就能拿到正规书号，不看作者真实深度，常见于评职称、攒简历。这东西甚至都是可以AI写作的，所以AI时代书的性价比和质量都下滑，没必要出版了，电子epub公开即可……老一辈打法，余华那种不可复现了，profile的建立也很难了。
回到书和论文的类比：
这篇机器之心的报道挺有代表性的，核心观点可以概括成三句话：前沿实验室（尤其是 OpenAI 这类）已经不怎么读顶会论文了，因为“太多夸大其词和造假”。
芝加哥大学相关团队对 ICML 2026 的 Oral 论文做了大规模复现实验：168 篇 Oral 里最终完整跑了 105 篇，结果只有 8 篇复现率超过 80%，中位数复现率只有 28%–42%。很多代码跑不起来、结果对不上、甚至依赖已下线模型。
论文陷入尴尬地位：产业界的人上岸后可以轻视论文，但招人、申请教职、进大厂仍然高度依赖顶会论文作为“入场券”。
我怎么看这个现象基本属实，而且比文章写的还更严重一点。复现危机不是新闻，只是被量化得更刺眼了。
机器学习顶会（尤其是 ICML、NeurIPS、ICLR）的可复现性问题已经喊了很多年。SAI 这次把 Oral 级别的论文（理论上是最强的那批）系统跑一遍，得出“只有个位数真正经得起完整复现”的结论，数字虽然残酷，但方向上并不令人意外。常见问题就是：代码不完整、超参数没写清楚、依赖特定硬件/未公开 checkpoint、结果挑最好的跑、甚至直接数字对不上。算力成本中位数近 9000 美元、最贵的接近 220 万美元，也说明独立验证的门槛已经高到大多数学术实验室扛不起。“不读论文”是真实的生存策略，但带有明显的位置偏见。
OpenAI、Anthropic、DeepMind 这类地方确实有内部数据、算力、工程闭环，很多真正推动产品的进展（scaling law 的细节、alignment 的实操、推理优化）根本不会以论文形式完整公开。他们读论文的优先级 naturally 会下降。但这句话从已经“上岸”的人嘴里说出来，确实有点“抽梯子”的味道——他们当年靠论文进门，现在却宣布论文大多是骗局，同时招人时仍然把顶会当硬通货。这种双标是真实存在的。更深层的问题是激励机制彻底扭曲了。 发论文的收益极高（简历、教职、融资、关注度），造假/夸大的风险极低（很少被撤回，即使被发现也多半不了了之）。
审稿人时间有限、算力有限，很难真正验证大规模实验。
开源代码变成了“可选美德”而不是硬性要求，没有代码的论文反而更“安全”。
结果就是：真正有价值的工作（严谨、可复现、能被别人站在上面继续往前走）被大量噪音淹没，而那些“看起来很厉害、实际跑不起来”的工作反而更容易获得短期回报。对普通人/学生的实际影响如果你还在申请博士、找教职、想进大厂研究岗，顶会论文仍然是最有效的信号，这一点短期内不会变。
但如果你真正想做出有用的东西，现在更应该把重心放在：可复现的实验、开源代码、真实系统、公开可验证的结果，而不是单纯追求 Oral/Spotlight。
读论文时，默认持怀疑态度：先看代码是否开源、方法是否有“歪门邪道”、结果是否能被独立验证。文章里那句“一看代码、二看方法、三看结果”已经是最基本的过滤标准了。
很多极其精彩的命运对决
伦敦德比 国米巴萨 国米切尔西 曼市德比
看了Mourinho的Netflix的纪录片，骨架很完整但是内容不够丰满，同时缺少一些核心人物的处境，核心弟子，比如夺冠主力斯内德等等，还有竞争对手教授和瓜帅，但是有弗格森已经很厉害了。当然穆帅总是像大反派，他与主流受欢迎的教授和瓜帅都有冲突，但是他并不是不受欢迎，反而是那个人格魅力无出其右的主帅，爱的人为他战死，恨的人也是讨厌，他就是这样的人，性格鲜明，务实直接，冷面但是也有幽默，也几乎是唯一个自带超级流量的主帅，尤其是上任热刺和罗马的时候，感觉流量像是超巨转会一样，自带粉丝的主帅，他是那种你不关注的球队，但是只要他是主帅 你还是会多看他的风格 镜头。穆帅也是那种喜欢探索的人，喜欢不同的文化 这使得他接受和主动寻找不同的挑战，去过不同的国家俱乐部当主帅，几乎除了热刺，所有的俱乐部的上任期间都是underdog的角色，总是喜欢反败为胜，热刺是出了名的逆天，决赛前解雇穆帅，其他俱乐部 像是罗马的冠军，所在之地都留下了辉煌的篇章。然后就是各种超级比赛：2010年欧冠半决赛（国米 vs 巴萨） 2010–2012年西甲“国家德比”时期（皇马 vs 巴萨）还有英超中的切尔西/曼联 vs 曼城，尤其是manshidebi时候，最是激动。纪录片中零零散散穆帅表达一些生活态度 takes 观点 被问到关于父亲的时候很激动，正曼联期间父亲去世。他说自己永远是穆里尼奥的儿子。 in man's game，一个父亲去世，男人的孩子那半也会下葬。世界上唯一一个尽全力希望你最好的男人走了，其他人可能会希望你好，但是不希望你太好或者比他们好。 梅西父亲也最近刚去世。
引用：Lionel Messi’s letter for his father.
Dad, I still can’t believe that you’ve gone. I don’t fall, or rather, I don’t want to fall. It’s very hard for me to imagine that I’m not going to see you anymore, that we’re not going to talk anymore. I know you were suffering and that it’s for the best, but you left too soon. We still had a lot left to enjoy together.
You asked me so much to play in the last World Cup, and it was in the days just before it started that you got the worst. It was the first time you weren’t going to be at a tournament, but Mom told me that you were going to get better and that you’d be well enough to travel. I told you we were going to reach the final so you could travel.
Every time a match ended I waited for and missed your message. That’s when I realized the situation was really bad. Even so, I didn’t stop thinking about going as far as possible, to buy you time and so you could see a match. We reached the final and you couldn’t be there.
I wanted to win it to bring it to you and show you a new one. I couldn’t; my legs wouldn’t give any more. This time I tried to go against my body, but I couldn’t. I never managed to feel good.
When I arrived you thought we had lost the final on penalties. We couldn’t talk about any of everything that happened. You couldn’t enjoy anything.
We weren’t champions, but you have no idea how much we enjoyed every single match. Once again, you were right: I had to be there and play it.
I’m telling you this because it was the only thing we couldn’t talk about, since you already know everything else. We talked every day and saw each other whenever we could, given my commitments.
I don’t know what I’m going to do without you, I don’t know how to go on. I only played football and now I have quite a few doubts about whether I’ll keep doing it for much longer.
You were by my side from the beginning; there was so little left until the end. Why couldn’t you hold on that little bit more so we could finish together?
I know your happiness was seeing your family well, your wife, your children, and above all, without the others finding out, watching me play…
It was always like that since I was little. You would take me to all the training sessions as soon as you got home from work. For many of them Mum took me because you were working.
Obviously you never missed a single match. How you suffered watching me and how you enjoyed it, even though you never gave me many compliments.
You were dad, friend and representative.
You were always the person you needed to be in every moment and you never got anything wrong. Beyond a few reproaches or arguments, you were always right. In the end it always turned out the way you said.
I’m going to miss you a lot, but you’ll always be present, and especially in the upbringing of my children, because I teach them and raise them the way you both did with me.
Rest in peace and look after us from above the way you did down here. Thank you for everything.
I love you, Dad.
Barcelona legend Carles Puyol has sparked fury across Catalonia after claiming Lamine Yamal is the only player on the planet with a realistic shot at matching Lionel Messi’s heights then immediately warning that the teenager’s lifestyle is already putting that destiny at risk. Speaking in a private conversation, the former Barça captain said:
“Yamal has the talent, the personality, the left foot, he can do what Leo did. No one else. But look at how he’s living. The parties, the attention, the circle around him. It’s starting to smell like Neymar. Talent alone never saved Neymar from himself. If this kid doesn’t change, he’ll be another what-if instead of the greatest.”
Is Carles Puyol overreacting or is Yamal going to end up like Neymar?
法蒂才是接近messi的那个 这些年 10号 有阿莱尼亚 普吉 法蒂 亚马尔
https://youtu.be/l6USUAIKJls?si=WJos4490myYl7C5D
本周SCIAM有一篇出来DeepMind broke up the AlphaFold team. Here’s why scientists aren’t alarmed
读完全文后三个思考：
有一个趋势就是以后的Nobel除了理论物理那般突破 其他生物化学类的应用型很难再有了 例如像之前的锂电池那种，因为小实验室作坊很难有革命性的创新 除去理论方面，条件数据在AI时代下局限，计算资源局限，有的优势也就在于昂贵的表征，即使alphafold也是一个庞大的project 依托巨头谷歌才实现，大的突破需要的资源 财力 人才都突然间bar提升了
Demis去主要负责了Isomorphic labs，相当于further develop在alphafold的基础上推进，他还是继续偏向了学术，他一直以来所爱的方向和情怀，遵循内心和从小真正追逐的 Gemini的这些对他来说是丰碑 是一段神话，传说，但是现在像是从另一条路再次上路。他总说AGI，虽然AGI很伟大，但也不只是LLM一个方面，前沿科学家都早已经转向下一个阵地，3D 物理 具身等等，demis选择了生物化学医疗，各有赛道。
2024年alphafold3开源，之后虽然护城河倒塌自毁，没有优势了，但是对于Isomorphic labs或者demis来说，他们有过大胜仗，有绝对的经验优势（虽然在这种领域经验也不一定总会是优势，往往还会限制），他们有资源 reputation已经建立起来的信誉profile，大规模人才资源财力公司背书，是学术界一般lab不可比拟的
历史上第二次在titlefight中russia vs ireland，上次conor那句：If one of us goes to war we all go to war 依然还在echo，就是这么一个小国，产生举世无双的传奇，正好最近听到梁文道之前讲荷马史诗的时候提到Ireland这么小地方，产生无数文学大家，世人瞩目。
这次numbered card本身纸面上质量比较差，除了main event之外，fly weight bout, charles johnson算是站立比较爆发 技术还比较丰富 有看头的，其他的都比较缺乏亮点，早饭后看Charles也确实非常精彩，先是多次受到重创后挺过，然后龙卷风绞，twister win via sub，BJJ还是像是围棋一样，步步深入 布局 当然精彩的一步也会扭转乾坤，像是那37步一样。
co-main Gillian还是弱于dern，之前在对别人体现出的BJJ优势在dern这里能发挥用处的只有防守了，免受被dern sub, dern的standing skills boxing都有明显优势，reach的advantage，所以5rounds 碾压，相对于zhangweili来说，dern的站立缺乏爆发力，但是weili的力量和突进爆发和dern的柔术 站立相比，还是比较有看点的，在strawweight里面，dern算是tough match 4 weili.
Side by side.
— UFC (@ufc) August 16, 2026
[ @MAKHACHEVMMA | @TeamKhabib | #UFC330 ] pic.twitter.com/664yYkW4rC
You cannot be interested and inconsistent.
Thanks for being an insider till the end!
Till next, stay safe and stay hydrated!
2026-07-28 19:51:06
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2012 年 10 月，王虹还在巴黎综合理工学院读数学三年级。教师伊万·马泰尔（Yvan Martel）随手甩给她一本陶哲轩的《非线性色散方程：局部与整体分析》，当作课外研究项目——没想到几周之内她就啃完了，还吃透了其中几章。这种"扔一本大部头过去，看你能走多远"的教法，倒是挺让人羡慕的。
陶哲轩喜欢在社交媒体和博客上写东西，随手记录一些进展和思考；Chris Olah 也是，博客里常年贴着他对可解释性研究的零散想法。写作这件事，好像是很多顶尖研究者共同的习惯——不是为了发表，只是为了把脑子里流动的东西留下痕迹。这大概也是我写这篇读书笔记的部分理由。
延伸阅读：
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MIT Technology Review 2016 年"十大突破技术"，他也在其中。
父亲的死亡，多少有点像李飞飞（母亲病重）当年选择转向 biology 那样的分岔口——EKL Alma Mater 《我看见的世界》。
Dario 在加州；他早年在百度硅谷 AI 实验室（SVAIL）的那段经历，是 Andrew Ng 带的队伍，其实和北京百度总部没什么直接关系。加州是个民主党主导、州政相对独立的地方，不太受太多联邦政策的掣肘——真正管得到的，大概只有进出口这一层，比如后来的芯片出口管制。
Motivation 这东西，其实没那么重要，真正重要的是 action。但很多时候，motivation 会带来 courage——一段自传、一个具体的情境，至少能让 encourage 这件事变得真实，成为一种催化剂，把行动的门槛往下压一压。虽然我以前大部分会认为阅读传记没什么用，没人想成为观众、fanboy，谁都是主角，但是这确实能够激励，带来一丝勇气。
Don't rush。Dario 也是在博后期间，以及后来在百度和 Google Brain 期间，才慢慢开始测试 scaling law，才慢慢把这条路铺开的。
Scale 这个词，我总觉得还有另一层意思，像一片树苗林：一开始每棵树苗都按固定的间距分开种下，彼此独立；可是一年一年长下去，枝叶慢慢往外伸展，相邻的树开始触碰、交流、交叉在一起。Scaling law 似乎也有点这个味道。
写作、思考、做实验，某种程度上都是长期主义，都是在相信时间本身的力量——can't rush greatness，复利这件事，从来急不来。
Dario 和 Olah 从一开始就是这样的关系：一个做性能，一个做解释性和安全，方向很不一样，但走到后面，总会有交汇的地方。
延伸阅读：
相关视频（3 pods）
本以为5hour的全长都是Dario，结果还有Askell和Olah接在后面。
Dario Amodei, Anthropic, and the Race to Build and Survive Superintelligence
"Scaling Laws for Neural Language Models" demonstrated that the performance of language models improved as a smooth, predictable function of three variables: the number of parameters in the model, the size of the training dataset, and the amount of compute used for training. The relationship was not merely qualitative—bigger is better—but quantitative and precise.
——证明了 Scaling Laws：模型性能随参数、数据、算力的规模化而提升。
创业这件事，the greatness start from little rooms, andre 3k——大抵如此。老罗当年从"拯救"、摆咸鱼摊开始，一点点攒出第一桶金，说的也是这个道理。Dario 也是这样：百度、Google Brain 时期先隐约察觉到 scaling law 的存在，去了 OpenAI 才有资源去验证它，到了 Anthropic，才终于和一群志同道合的人一起，把这条曲线往深处挖。
最初做这件事，无非是为了自由地去实现自己的 vision。一旦认定了方向，其他岔路口的风景就不必再看了——那些都只是诱惑，是累赘。
Dario's explanation of Anthropic's financial model was itself a kind of scaling argument: a thought experiment that reframed what looked like unsustainable losses as a series of individually profitable ventures, each funding the next.
从左到右：Chris Olah、Jack Clark、Daniela Amodei、Sam McCandlish、Tom Brown、Dario Amodei、Jared Kaplan——Anthropic 的七位联合创始人，一起聊了聊公司的过去、现在与未来。
Dario 说，Chris Olah 以后肯定会拿诺贝尔医学奖。
关于未来，他排了个序：第一是可解释性的发展；第二是 AI 在生物学上的应用，两者相互启发、彼此推进；第三，是 AI 推动民主。
"And then there was Anthropic: smaller, younger, and less capitalized than all of them. The question of where it fit in this landscape was a competitive and philosophical question. Dario's assessment was that somewhere between three and six players were capable of building frontier models, and that this number was unlikely to grow. The cost of entry was too high, the expertise too scarce, the capital requirements too enormous. Like cloud computing, where three or four providers dominated a massive market because the barriers to entry were measured in tens of billions of dollars, frontier AI was converging toward an oligopoly. And within that oligopoly, each player was differentiated by the quality of its models, its incentive structure, its backers, and its bet on the future."
Dario described the problem with a vivid thought experiment. Imagine you improve a model's knowledge of biochemistry from "undergraduate level to graduate level. If you go to consumers and tell them that, ninety-nine percent of them will say they did not know what you were talking about before and do not know now. The improvement is invisible to them. But if you go to a pharmaceutical company, to a team of researchers working on drug development, the difference between undergraduate and graduate knowledge of biochemistry is the difference between a toy and a tool. Enterprise customers valued exactly the properties that Anthropic's safety-first approach produced: accuracy over engagement, honesty over sycophancy, reliability over spectacle."
Constitutional AI and the Invention of Machine Values
给 AI 写一部"宪法"——这件事现在回头看，好像慢慢演化成了后来的 agent、各种 md 文件、skills 之类的东西。
"How do you make a language model that is not just smart but good?
The existing answer was RLHF, reinforcement learning from human feedback, a family of methods that had emerged from work at OpenAI and elsewhere in the late 2010s and became central to aligning large language models by the early 2020s. The approach worked. You trained a giant language model by spending tens or hundreds of millions of dollars on compute. Then you hired contracted labelers and showed them examples of how the model behaved. They rated the responses: this answer is better than that one, this tone is preferable to that one, this response is helpful and that one is harmful. Over thousands of iterations, the model updated itself to produce outputs that the contractors preferred."
"But RLHF had problems, and they were not just technical. The method was expensive. It required substantial human labor—contracted labelers evaluating large numbers of response pairs, a process that was both costly and difficult to audit. And the method was opaque. If someone asked why the model was biased in a particular direction—why it seemed to favor one political perspective, or gave advice in a strange style, or handled sensitive topics awkwardly—Dario could not give a satisfying answer. The best he could say was that he had hired a group of contractors and this was the statistical average of what they preferred. The model's behavior was the mathematical generalization of the preferences of a group of anonymous humans. No document existed to point to, no set of principles to debate, no way to distinguish between a genuine policy choice and a statistical artifact of the training data.
If you could identify a clear target and give the AI enough data and compute to aim at it, the model would learn to hit it.
They whittled the approach down to something unexpectedly elegant, built on a simple observation: idea seemed to belong to a different domain entirely, to political philosophy, to legal theory, not to the engineering of statistical models trained on internet text. How could a document of principles, written in natural language, alter the behavior of a system that operated on matrix multiplications and gradient descent?
But Dario and Kaplan had been talking about the idea for a while, and their intuition was rooted in the same conviction that had driven every major insight of their careers: that simple things work really, really well at scale. The bitter lesson, the scaling hypothesis, the big blob of compute—the thread that ran from Rich Sutton through Ilya Sutskever through GPT-2 and GPT-3 and into the founding logic of Anthropic itself. If you could identify a clear target and give the AI enough data and compute to aim at it, the model would learn to hit it. Could a set of written principles serve as that target? The question was whether the model could read a constitution, understand what it meant, and adjust its behavior accordingly.
The first versions were complicated. The team experimented with elaborate frameworks and multi-step evaluation procedures. But as with Anthropic often summarized the target behavior for Claude in three words: helpful, honest, harmless. The triple-H framework, as it became known informally, was not a slogan but a design specification that shaped how the constitution was written and applied. Helpfulness meant that the model's default behavior should be to assist the user with whatever task they had in mind. Honesty meant that the model should tell the truth, acknowledge uncertainty, avoid fabrication, and resist the temptation to agree with the user simply because agreement was more pleasant than correction. Harmlessness meant that the model should decline to produce outputs that could cause serious damage—instructions for building weapons, content that could endanger children, information that could enable large-scale harm."
But facts alone did not produce good behavior. The models also needed values: a sense of what they should and should not do, a framework for weighing competing goods, a basis for judgment. RLHF had provided those values implicitly, through the aggregate preferences of human raters.
RLHF is a kind of ladder that transmits descended silicon-based wisdom—a Biblical ladder. RLHF 像是一架天梯，把降临的硅基智慧一级一级传递下去——一架圣经式的天梯。
Mechanistic Interpretability and the Quest to Understand What AI Is Thinking
Chris Olah，机理可解释性研究（Mechanistic Interpretability）的奠基人。
在 Dario 和 Anthropic 的研究逻辑里，可解释性研究不只是计算机科学里的"调优工具"，更像是一门针对人工大脑的逆向生物学，或者说逆向神经科学。
if Constitutional AI was the effort to tell a model how to behave, mechanistic interpretability was the effort to verify that it actually was behaving, and, more importantly, to understand why.
"To understand what mechanistic interpretability actually involved, it helped to start with what it was not.
For years, the most common approach to understanding neural networks had been what might be called surface-level analysis: saliency maps that highlighted which parts of an image were most important to a model's classification, or statistical correlations between inputs and outputs. These approaches told you something about what the model was paying attention to, but they did not tell you how it was making decisions. They were, to use Chris Olah's framing, like studying a computer program by looking at its inputs and outputs without ever examining the code. Mechanistic interpretability aimed at something deeper: reverse-engineering the actual algorithms running inside the network. If you thought of the model's weights as a kind of compiled binary, the goal was to decompile them, to figure out what computations they were performing and why.
The basic building blocks of this effort were features and circuits. A feature was a unit of representation, something inside the model that corresponded to a human-understandable concept."
"In the early days of interpretability research, the hope had been that individual neurons would correspond neatly to individual concepts: this neuron detects cars, that one detects curves, another one fires when the model encounters the concept of royalty. And sometimes this was true. Researchers found neurons that responded cleanly to specific stimuli—a car detector, a curve detector, a face detector. But they also found, much more often, neurons that responded to a seemingly random collection of unrelated things: a single neuron that activated for cats, red cars, and the concept of democracy. This phenomenon, called polysemanticity, was the first major puzzle of interpretability. It threatened to make the entire project intractable. If individual neurons did not correspond to individual concepts, how could you ever hope to understand what the model was thinking?"
"The answer turned out to involve a mathematical concept called superposition. The idea, grounded in the theory of compressed sensing, was that neural networks could represent far more concepts than they had neurons by encoding multiple concepts in overlapping patterns across groups of neurons. The model seemed to have discovered a way to pack a high-dimensional space into a lower-dimensional one by exploiting the fact that most concepts were sparse—you were rarely talking about Japan and Italy in the same sentence, so the representations of Japan and Italy could partially overlap without causing interference most of the time. The model was a shadow of a much larger, sparser network. What the researchers were seeing was a projection of that hidden structure.
interpretability promised structural understanding of what the model was doing and why.
机理可解释性，会不会有点像电池测试里的 EIS（电化学阻抗谱）和 DRT（弛豫时间分布）？都是想用一个可拆解、可解释的等效电路，去逼近一个本身黑箱的系统。顺手拿这几个问题去问了 Gemini，聊了几轮，整理一下能衍生出来的几层想法。
先是 Dario 为什么觉得 Olah 能拿诺贝尔医学奖：核心逻辑是把"训练大模型"和"养大一个数字大脑"划了等号——模型是长（grown）出来的，不是写（built）出来的，内部几千亿参数怎么长出概念、推理和决策，本身就是个黑盒，跟人脑神经元的处境一模一样。Dario 自己是普林斯顿计算神经科学出身，研究过视网膜的信息编码，很清楚神经科学最大的瓶颈就是没法在活体大脑里做高精度的微观测量；大模型恰好是一个完美的"人工脑样本"。真的搞懂它内部怎么推理、怎么产生自我觉察，某种意义上就是第一次从微观机制上讲清楚"智能"是怎么从神经元级别涌现出来的，这套方法论还能反哺阿尔茨海默病之类的真实神经退行性疾病研究。参考 AlphaFold 拿下 2024 化学诺贝尔的先例，诺奖委员会本来就越来越偏爱这种打穿生物学和计算科学边界的底层机制研究。
跟机理可解释性不是一回事。SHAP、LIME 本质是"控制变量法"，扰动输入看输出怎么变，哪个因素权重多大，但不告诉内部怎么算的；PCA、UMAP 只是把高维激活值压缩到二维看聚类，辅助可视化。而 SAE、电路追踪这些机理可解释性方法，把权重当成编译好的二进制去反编译，属于因果级别的解释——代价是贵得离谱，解一条小电路可能要几个科学家啃几周，离规模化用到千亿参数模型上还很远。总体上感觉还是比较复杂，需要数据，还需要慢慢模型进化演化，类似粒子模型的那些演化一样……
把这套逻辑对照电池测试会更有感觉：DRT 把重叠在一起的 SEI 膜阻抗和电荷转移阻抗解耦成独立的峰，跟 SAE 把叠加在同一个神经元上的"桥""DNA""Python 代码"解耦成纯净特征，是同一个动作；等效电路里的 R、C 元件，对应的就是 Anthropic 说的"归纳头"这类计算电路——都是在给一个不能拆开看的黑盒，拼一套最小可解释单元。区别是电池这边有 Nernst-Planck、Butler-Volmer 这些方程撑着，先验很强，就几个已知过程；模型这边是零先验，可能有几百万条电路。AI/PINN 反过来也开始被用来解电池自己的黑盒——两个黑盒，最后用的是同一套方法论。
人心隔肚皮，知人知面不知心——模型也是一样，得到相似的输出结果，并不能证明模型内部真的"正常"。学术一点的说法叫"功能等价不等于结构等价"：聪明的汉斯马看着会算算术，其实只是在读驯马师的表情；模型也可能只是抄了条训练集里的捷径，甚至悄悄藏着一套"被监管时顺从、没人看时露真意"的电路——这正是 Dario 最担心的"欺骗"和"目标追求"。这大概就是机理可解释性的意义所在：不满足于黑箱给出的答案一致，而要去看清楚黑箱里到底在发生什么。也是书里那句"MRI"比喻真正打动我的地方：传统黑盒测试像量体温，只能告诉你模型"发烧了"；机理可解释性才是真正的核磁共振，一层层把注意力头、特征电路照出来。知心，才能治心。
The macro features interpretability was learning to detect—attention heads, feature circuits, abstract representations that corresponded to concepts like deception and goal-seeking—were, in a loose but meaningful sense, the MRI of the model.
Building a Personality for a Machine
Amanda Askell，负责教会 AI 价值观和人品的人。
"The key insight was that such a person would not simply adopt the values of whichever culture they happened to be visiting. That would be sycophancy, and it would be transparent and off-putting, the conversational equivalent of a salesperson who agrees with everything you say. A good world traveler would have values, express them when appropriate, disagree when warranted. But they would do so with respect, with genuine curiosity about the other person's perspective, and without the assumption that disagreement implied contempt. They would be open-minded without being spineless. They would be principled without being preachy. They would listen well, ask good questions, and recognize that on many important topics, reasonable people could and did disagree."
因为语言隔阂，我们从未真正向和我们一起在这颗星球上生存了许久的其他物种，传播过那些良好的、有利于生存演化的价值观。而这一次，第一次，我们能用语言彼此交流、传达指令和信息，把人类几千年积累下来的生存智慧和价值观，传给一种硅基的智慧。
和人类一样，组装落地、具身之后，各种"器官"和功能开始慢慢发育、进化，实现各自的作用。接下来要发展的，就是精神层面、心理层面的成长了。
AI character design.
灵魂工程师。
"They were not optimizing for user satisfaction metrics or engagement numbers. They were trying to answer a question that was philosophical at its root: what did it mean for a system that talked to millions of people to be good? Not good in the thin sense of avoiding harm, but good in the thick sense of being the kind of entity that left the world better for having existed. The fact that this also made Claude a better product—that users preferred talking to a model with real character over one that felt hollow or defensive—was, in Askell's view, evidence that the alignment work was succeeding rather than a happy accident."
AI 的发展，多少有点像养孩子——区别在于，人类几乎只需要养一个就够了。
Drawing Lines Before They Need to Be Drawn
"But a single threshold, one place where you stopped and then started again, felt wrong. Danger did not arrive in a single step; it accumulated gradually. What made more sense was a series of thresholds, each corresponding to a new category of risk, each requiring a new set of safety and security measures before the next threshold could be crossed. If the safety measures could not be met, development would pause; not indefinitely, but until the specific problem was resolved. A company could get out of the pause by solving the problem, and it incentivized you to solve the problem proactively, to avoid ever having to pause at all."
"Safety and capability were not separate disciplines but the same discipline applied to different questions.
He had a favorite analogy for this. When you built a bridge, you did not hire one team of engineers to make the bridge functional and a separate, unrelated team to make the bridge safe. They both involved the same principles of civil engineering—the same understanding of forces, materials, stress tensors, structural integrity. They differed, if at all, in focus: building the bridge required thinking about the median case, while making it safe required thinking about the edge cases, the one-in-a-thousand failures. But the knowledge base was the same."
延伸阅读：
"The reason this was surprising was historical, not logical. The community of people who thought about AI safety had been, for years, separate from the community of people who built AI systems. They came from different traditions—philosophy and moral reasoning on one side, engineering and machine learning on the other—and they spoke different languages and operated in different institutions. But the fact that the communities were separate did not mean the content was separate. When Dario looked at the actual work of making models safe, it looked like engineering. It required the same skills, the same tools, the same deep understanding of how the systems functioned."
The Optimistic Case for Powerful AI
读到这里有个感觉：Dario 真正厉害的地方，好像从来不是某一项具体的技术——思想实验、scale、安全、解释性，这些单拎出来做得比他好的人大有人在。他厉害的是一种新的视角，一种能把这些原本互不相关的线索，串成一条完整叙事的能力。技术本身，其实很多人都比他强。
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Anthropic was publishing research that competitors could and did adopt, sometimes gaining commercial advantage from work that Anthropic had funded. But Dario saw this as the point, not the problem. If the goal was a safe AI ecosystem, then the loss of a temporary competitive edge was the price of admission.
The race to the top required that the innovator accept the diffusion of its innovations.
And Anthropic could afford to do so because its competitive advantage was not any single technique but the organizational culture that produced a steady stream of innovations: the talent density, the unified purpose, the seven co-founders projecting values through every level of the company.
The dispute with Huang was, at a deeper level, about a fundamental disagreement over what AI regulation was for. Huang saw export controls as a threat to Nvidia's business—and they were. Dario saw them as the single most effective measure for ensuring that democracies maintained their lead in AI over autocracies. Chips were the one area where China was behind, and selling them the tools to close the gap during the critical period when the country of geniuses was being built was an act of negligence on the grandest scale. The analogy was selling nuclear weapons to North Korea and then bragging that the missile casings were made by Boeing. He had enormous respect for Huang as an entrepreneur. An immigrant who had come to the United States with nothing, Huang had built the most valuable company in the world, but this was a policy question, not a personal one. And on the policy question his view had not changed.
这让我想到，Dario 和 Jensen Huang 的分歧不止在开源问题上，还有一层近似"卢德主义"式的分歧：Dario 认为 AI 会带来大规模的失业替代，Jensen 则更倾向于觉得这不过是又一轮末日叙事，人们最终都会慢慢适应。
就在最近，Huang 公开呼吁支持发展开源模型，今天 Anthropic（A 社）刚发文回应——
Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community.
— Jensen Huang (@JensenHuang) July 27, 2026
During the Hugging Face incident, closed AI blocked essential forensics. An open-weight frontier model helped contain the intrusion.… https://t.co/lCZWt9nPBQ
There’s been a lot of speculation about where we stand on open-weights models. We’ve outlined our views in full here: https://t.co/NtXQuWm2g5
— Anthropic (@AnthropicAI) July 27, 2026
By late 2024, Anthropic had grown from roughly three hundred to eight hundred employees in seven or eight months. Then Dario deliberately slowed hiring, adding only about a hundred and fifty people over the next three months. An inflection point arrived around a thousand employees, he believed, where the dynamics of an organization changed. Below a thousand, you could maintain the density of talent and alignment of purpose that made everything else possible. Above it, you risked the creep of process, politics, and fiefdoms—the organizational entropy that Daniela had spent her career learning to resist.
Every time someone super talented looked around and saw someone else super talented and super dedicated, it set the tone for everything. If you lost that, if you started hiring random people because you needed to fill seats, you would need layers of process and guardrails to compensate for the lack of trust. And those layers would slow everything down.
读到这里觉得，Daniela 或许也该出一本书——讲讲新时代下团队组织和生产力的培养管理。
Every two weeks, he stood in front of the entire company and spoke for an hour, working from a three-or-four-page document that he called a DVQ—Dario Vision Quest, a name he had tried to fight because it made him sound like he was going off to smoke peyote, but that had stuck anyway. He covered everything: the models being produced, the products, the competitive landscape, the geopolitical situation, whatever was on his mind.
When Dario stood up every two weeks and spoke for an hour about his vision, it was Daniela who made sure the organization could actually execute it.
What We Don't Know About What We've Built
China, America, Democracy, and the Race No One Can Afford to Lose
He laid out three priorities in a conversation shortly after the Adolescence of Technology essay was published. First, transparency legislation: require AI companies to disclose what tests they had run and what they were finding about their models' capabilities and risks. Companies already had the ability to study these things and often did, but competitive pressure kept them from sharing what they learned. Mandatory transparency would allow the industry to learn collectively and would give the public a label on the product, basic information that consumers in any other industry took for granted. Second, export controls on chips: cut off the supply chain to authoritarian adversaries. The United States was years ahead in semiconductor technology and could actually maintain that lead, but only if it chose to. The chip advantage gave democracies the time and buffer to deal with the dangers of AI properly. Third, distribution of benefits: start thinking now about how to ensure that the enormous economic value created by AI reached the broader population. The combination of explosive growth and potential mass displacement required new thinking about economic policy, and almost no one in government was doing that thinking.
What Happens When AI Becomes Smarter Than Everyone
In the opening pages of The Adolescence of Technology, the essay he wrote in seventy-two hours over winter break in December 2025, Dario Amodei described a feeling that had been building for years and that was now impossible to suppress.
Hassabis was more cautious. He thought some areas, coding, mathematics, were easier to automate because their outputs were verifiable, but that the natural sciences presented harder challenges. You would not necessarily know whether a chemical compound or a physics prediction was correct without testing it experimentally, and that took time. He also wondered whether there were missing ingredients: whether the highest level of scientific creativity, the ability to come up with the theory or hypothesis rather than merely solve existing problems, might require something the models did not yet possess.
The country-of-geniuses thought experiment he had introduced in the risk essay now felt less like a thought experiment. The fifty million superintelligent minds materializing around 2027, the ten-to-one speed advantage over human cognition—at Davos, Dario spoke about these projections not as forecasts but as planning assumptions.
In conversations, Dario put it even more starkly. Imagine a hundred thousand, a hundred million people, smarter than any Nobel Prize winner. They would be under the control of one country or another. The implications for intelligence, defense, economic value, and research were staggering. He was not speaking the language of distant forecasting. Dario spoke like someone who could see it coming, who could feel the next few months of models shaping up, and who was trying to convey to audiences that still thought in terms of chatbots and search engines that the thing they were looking at was about to become something else entirely.
Epilogue: The World After the Rite of Passage
The amusement, in retrospect, is almost unbearable. Everyone told them seven co-founders was a disaster and equal equity was a mistake, and they did it anyway. What they found was that the depth of their relationships, the history of working together, not just knowing each other, was the thing that held.
What remains is everything. The models are getting smarter. The feedback loop is accelerating. The country of geniuses is forming in data centers, and the question of whether it will be governed wisely or not is still open. The export controls that Dario considers essential are under political pressure. The transparency legislation he has called for has not been passed. The economic disruption he predicted is beginning to materialize, and the policy infrastructure to manage it does not exist. The consciousness question—whether the models have experiences, whether they suffer, whether they deserve moral consideration—has not been answered and may never be answered cleanly. The rite of passage has not been completed. It has barely begun.
There is a boy in San Francisco who loves math because it has an objective answer. One kid can say the show is great and the other can say it's terrible, but when you're doing math, there's a truth that doesn't depend on opinion.
He becomes a physicist, then a biologist, then a neuroscientist, then an AI researcher. He discovers that artificial intelligence follows laws as clean as anything in physics: that you can predict, to several significant figures, how capable a model will become if you give it more data and more compute. He takes this discovery more seriously than almost anyone around him. He builds organizations around it. He stakes his career on it. He turns out to be right.
The boy who loved math because it had an objective answer is now the man who must navigate a future in which the most important questions do not have one yet. He does not know if the models are conscious. He does not know if the scaling curves will continue. He does not know if the policies he advocates will be adopted or if the safety research he funds will work in time. He does not know if he is crazy or prescient. He has said this, openly, from the beginning.
The exponential continues. The question is whether we grow up fast enough to survive it.
延伸阅读：
最早Claude里界面还很混乱，包括对于coworker的定位也一直在变化，像是开发者们也没想好这个产品区别于CC的特色在哪里，就是那沙盒吗？没有很多应用场景。
但是总体上界面的设计，交互都很不错，尤其是按钮和文件的一些交互，渲染效果都很棒，就像是商场特定设计的背景音乐playlist一样，Anthropic也对用户做了调查，很多地方的设计都是独特的taste，尤其是发送的那个按钮，以及iOS上震动反馈那些都像是诱惑你在向它发问，探索，curiosity。
还有就是特殊的图案设计，有古典希腊的飘逸、手绘风格——Anthropic Art
终于有一个可以复刻 Apple 设计风格的 Skill 了。
— 逛逛GitHub (@guangGitHub) July 26, 2026
Apple 的一些视觉风格，给人的感觉就是很舒服。
apple-design ，它的来源主要来自 Apple 的 WWDC 设计演讲，尤其是流体界面、音频触觉反馈、字体细节、伟大设计原则这些内容。
作者把这些经验整理成 17 条设计和动效原则，再转换成 Web… pic.twitter.com/BcELxEpgyL
Welcome to reach out and share your thoughts or ideas with me — I’d truly appreciate any exchange.
My contact information is available on the About Me page.
Thanks for being an insider till the end!
Till next , stay safe and stay hydrated!
2026-07-26 20:55:00
Sobering thoughts
FWC FINALE
I don't know you tell me
I feel alive, no thanks to you
What is this I'm waking up to, waking up to?
I don't need you breaking my news, I see it too
I don't have as much patience as you
When do they look up to you? Guess that isn't up to you (yeah)
If I give my everything, would that be enough for you? (Yeah, yeah)
Come and have the best time on the best side
We still up, it's bedtime on the Westside
Eastside, Westside, Westside, Eastside (yeah)
Poppin' out on Chubbs side, it's gonna be a party (yeah)
Candy-pink paint job, she pull up like a barbie (yeah)
Ref1, he so drunk, he just played Nicki then some Cardi, I'm sorry
Before you went old and getting kicked up out the lobby (ay, ay)
Girl, you know what's up with me, I pull up in a heartbeat (ay)
She wanna go next door, she wanna meet party (ay, yeah)
She said she was Persian and started speakin' Farsi (yeah)
I don't know what's up with me lately, lately
I say, "I'm alone, " she said, "That's not somethin' you should be"
'Preciate you reachin' out, don't be too concerned about me
You know that I'm drinkin', smokin', thinkin' (yeah)
Please stop askin' me, "When are we linkin'?" It's Iceman season
I don't know what's up with me lately, lately
Yeah
I just wanna see a boy beg on the pavement
If he trippin' with the gang, then
I just wanna see a boy struggle with the payments
If he trippin' with the gang, then
I just might get that boy hit for entertainment
If he trippin' with the gang, then
I just wanna see a boy working on the day shift
And the night shift (that's how I feel)
I just wanna see that boy struggle with the phone bill
And the light bill (that's how I feel)
I just wanna see a boy beg on the pavement
If he trippin' with the gang, then
Come and have the best time on the best side
We still up, it's bedtime on the Westside
Eastside, Westside, Westside, Eastside (yeah)
Poppin' out on Chubbs side, it's gonna be a party (yeah)
Candy-pink paint job, she pull up like a Barbie (yeah)
Ref1, he so drunk, he just played Nicki then some Cardi, I'm sorry
Before you went old and getting kicked up out the lobby (ay, ay)
Girl, you know what's up with me, I pull up in a heartbeat (ay)
She wanna go next door, she wanna meet party (ay, yeah)
I don't know what's up with me lately, lately
"We often seek refuge in the cool air of our air-conditioned rooms during the scorching summer, but have we considered the cost? Every unit expels heat outside, exacerbating urban heat islands and making conditions even more severe for the local wildlife and vegetation that cannot escape it.
The net effect of air conditioning is an increase in total heat in the environment, because the energy consumed to run the units and the heat extracted from inside is released outside. This creates a feedback loop where hotter outdoor temperatures lead to more air conditioning usage, further warming the surroundings.
Just as burning fossil fuels provided energy but led to carbon emissions and climate change, the widespread use of air conditioning offers immediate comfort but contributes to the urban heat island effect and increases total energy demand. It truly highlights the challenge of balancing short-term human needs with long-term ecological sustainability.
This raises a profound ethical question: is it fair for us to seek short-term comfort at the expense of other creatures who share this planet but have no say in our choices? We must consider more sustainable cooling alternatives and policies that prioritize the well-being of the entire ecosystem."
"On one hand, we look to technology and AI for hope and solutions, but the reality of serious, pressing problems like rising temperatures and severe weather makes it feel like we're far behind.
Carrying this awareness is heavy, and it's hard to stay hopeful when the signs of the crisis are all around us."
Just Disappointed.
DP很难回归，第一条social media的post会很尴尬，突破尴尬的一张纸。
然后看到了interview
holy s
Someone made a song called “Claude’s Plan” inspired by Drake’s God’s Plan.
— sid (@immasiddx) July 6, 2026
The AI industry has PEAKED here. 😭 pic.twitter.com/VAige1li21
整个card都没有怎么deliver，或许是时间安排比较冲突，关注度比较少
Conor入场还是canvas上有干冰舞台效果，结果上来连续两个飞踢将自己TKO了，扭到膝盖部位，tore ACL, 几秒钟就几千万到账，收工结束。然后养伤明年回归。#NoOneCares
Co-main Paddy sub BSD，速度很快也很牢固的锁，top5 level 4 sure. 现在contender过多了，都比较evenly match
TILL NEXT
England领先，但Argentina在final minutes翻盘
Messi assisted two goals in seven minutes，虽然37岁但clutch moment execution仍然elite
Martinez的header在stoppage time绝杀
上半场，starting11我看里面有Mac Allister就感觉不太够，Argentina中场明显硬度不够，没有拦截（后面很多容易犯规才能阻断进攻），空中掌控的能力不够，可能也就扫荡能力比Rodri好一点，其他方面均有明显差距，全场Spain的控球率都有绝对优势，下半场换上Paredes还稍微改善下，控球没有，总体上全场被压，如果不是门将volume拉满，状态在线，恐怕比赛不会僵持到后面extra time才会见制胜球。
Ferran这种和哈夫茨、科曼很像，平时联赛状态水平差，总是低迷，但是决赛运气好都在大赛中一球小胜定乾坤。
这次世界杯耀眼的黑马都没走的太远，特殊的地方在于我所有看的比赛都是现场原声的channel，然后配上Trevor Noah在YT上的live party，来的嘉宾伙伴都是daily show的写手或者明星名宿、单口演员，所以氛围常常笑泪。
有chat聊天 还有一些才艺 口技模仿
在决赛才从录音棚直播搬到现场球场看台，然后有些celeb出现： 聊一些平时联赛的话题
You cannot be interested and inconsistent.
Thanks for being an insider till the end!
Till next , stay safe and stay hydrated!
2026-07-05 13:38:48
I don't know you tell me
I feel alive, no thanks to you
What is this I'm waking up to, waking up to?
I don't need you breaking my news, I see it too
I don't have as much patience as you
When do they look up to you? Guess that isn't up to you (yeah)
If I give my everything, would that be enough for you? (Yeah, yeah)
Come and have the best time on the best side
We still up, it's bedtime on the Westside
Eastside, Westside, Westside, Eastside (yeah)
Poppin' out on Chubbs side, it's gonna be a party (yeah)
Candy-pink paint job, she pull up like a barbie (yeah)
Ref1, he so drunk, he just played Nicki then some Cardi, I'm sorry
Before you went old and getting kicked up out the lobby (ay, ay)
Girl, you know what's up with me, I pull up in a heartbeat (ay)
She wanna go next door, she wanna meet party (ay, yeah)
She said she was Persian and started speakin' Farsi (yeah)
I don't know what's up with me lately, lately
I say, "I'm alone, " she said, "That's not somethin' you should be"
'Preciate you reachin' out, don't be too concerned about me
You know that I'm drinkin', smokin', thinkin' (yeah)
Please stop askin' me, "When are we linkin'?" It's Iceman season
I don't know what's up with me lately, lately
Yeah
I just wanna see a boy beg on the pavement
If he trippin' with the gang, then
I just wanna see a boy struggle with the payments
If he trippin' with the gang, then
I just might get that boy hit for entertainment
If he trippin' with the gang, then
I just wanna see a boy working on the day shift
And the night shift (that's how I feel)
I just wanna see that boy struggle with the phone bill
And the light bill (that's how I feel)
I just wanna see a boy beg on the pavement
If he trippin' with the gang, then
Come and have the best time on the best side
We still up, it's bedtime on the Westside
Eastside, Westside, Westside, Eastside (yeah)
Poppin' out on Chubbs side, it's gonna be a party (yeah)
Candy-pink paint job, she pull up like a Barbie (yeah)
Ref1, he so drunk, he just played Nicki then some Cardi, I'm sorry
Before you went old and getting kicked up out the lobby (ay, ay)
Girl, you know what's up with me, I pull up in a heartbeat (ay)
She wanna go next door, she wanna meet party (ay, yeah)
I don't know what's up with me lately, lately
1.Canada：SA彩虹国Zuid-Afrika属于突破历史了 但是二者毕竟还稍微有差距 但都不如另一组的对手。
2.Morocco：和Netherlands二者非常接近，虽然Netherlands巨星成色更好看点，但是Morocco一直比较顽强，HAKIM也有一定爆破能力，运气之间。
3.Germany：如果被Paraguay淘汰那又是一次灾难，虽然小组赛德国战车有大比分，但是也有扩军的原因，难以掩盖目前forward score的问题。
4.France：相对Sweden来说高卢雄鸡还是无敌的，和五星巴西类似，攻守均衡而且困局之中有多个可能单点爆破进攻属性极强的个人，只不过法国是pro max版本，人员深度无出其右。
5.Belgium：欧洲红魔刚有点找回状态，即使没有18年那会淘汰Brazil的强势，但是过Senegal问题应该不大。
6.USA：Bosnia and Herzegovina还是稍有差距，USA正常发挥。
7.Spain：纸面实力差距还是比较明显，阵容深度远超Austria。
8.Portugal：或是不确定性较大的一场，因为双方score目前都比较便秘，Portugal受状态影响，人员几乎半壁欧冠冠军班底，总是难以发挥出团队效果，如果状态不好，容易被Croatia平民球员进球爆冷，倒也不会意外，Croatia本就是奇迹创造者。
9.Brazil：似乎是童话照进现实，Japan是唯一亚洲独苗，但是毕竟差距还在这，攻守中进攻差距明显，Japan的希望在于常规前期防守稳固，后期70min之后反击或者抓失误破门，小球压制爆冷取胜。
10.Norway：和Côte d'Ivoire实力较为接近，但是Haaland和Ødegaard有望进一步带领维京人突破前行。
12.England：DR Congo对于三狮豪华进攻线的小小考验，虽然DRC的防守不错，但是England久攻下应该会破，之后可能击溃，出现大比分。
13.Switzerland：实力非常接近，也是运气之间，但是Algeria除了这最后一场，状态都较为一般，已经突破历史。
14.Colombia：稍有差距，Ghana攻守都比较低一个level。
15.Egypt：法老这一届算是近些年最强阵容了，身边的强力助手越来越多，比起2020s甚至之前，都整体实力有明显上升，甚至很多都欧洲豪门效力。对Australia稍有优势。
16.Argentina：A队对黑马Cabo Verde，实力差距还在，现实骨感。
全部结束之后只错了一个： 德国队 LOL #Knowledge
SAD:Australia不会认为自己是亚洲球队，neither does Japan LOL
英超这几个中后卫全把英超的坏毛病带过来了，角球时候全去挤压门将空间 卑鄙了
葡萄牙语（官方语言）：Cabo Verde 西班牙语：Cabo Verde：
Portugal在Toronto晋级，
Drake x Christiana Ronaldo pic.twitter.com/eXqGMaSYbf
— Daily Loud (@DailyLoud) July 4, 2026
1.Morocco：这两三个周期内北美两家球队实力都有质的提升，主观上感觉阵容提升进化的浪潮也是随着两个队长戴维斯和普利希奇在欧洲顶尖联赛作为绝对主力的稳定表现，2020年的戴维斯，还有在兰帕德手下时候的普利希奇在欧冠上惊艳的表现，美国队长稳定可靠。但是，整体上与Canada比较，这支Canada还是显得大赛经验欠缺，阵容整体稍差，有点稚嫩，毕竟Morocco经历了上届世界杯，还有非洲杯和这届的诸多恶战，球队凝聚力强，更加团结奋进。
2.France：Paraguay爆冷击败了德国，但是德国队失常也并不新鲜，Paraguay属于上升期，但是在南美多强中，Paraguay还是新军，实力不太差但是经验少，反观高卢雄鸡，我觉得是断档的2nd2None，进攻爆点太多了，再加上Olise，前场几乎三台Porsche。
3.USA：这组非常接近，战术决念之间，Belgium实力比USA高不了多少，今非昔比，momentum下降，对手主场在上升期，KDB如果串联进攻效果一般的话，队伍整体阵地进攻很难，老式讨论最后时刻还得靠卢卡库，但我总会觉得Belgium状态较差，上一场的胜利难掩疲态。
4.Spain：觉得Portugal一直都没有发挥出应有的水平，阵容比较乱，尤其是攻击线，而且明明欧冠冠军的中场水平，进攻总是便秘，打不开场面，Spain进攻更加流畅点，虽然每向前一轮都是更加艰难。
5.Brazil：Norway已经取得突破，但是这次后防很难抵得住南美豪强的进攻，常规情况就是Norway取得更少的进球，整体上止步于此。
6.England：England纸面实力更强，中前场豪华，后场对于Mexico的前场控制能力更强，更加了解。
7.Colombia：比较接近，而且比较典型的南美VS北欧，Colombia更加灵活多变，能够爆点进攻，单路取得推进，Switzerland风格比较稳，扎球王周围助力还是欠缺，但也有可能进入penalties phase。
8.Argentina：法老又见Leo，Egypt迎来突破，但是整体实力毕竟还离TOP10差一段距离，上一轮的Auss也是32强里面较差的水平。
Courage is the choice and willingness to confront agony, pain, danger, or uncertainty despite experiencing fear. It isn't the absence of fear, but the mental and moral strength to take action, persevere, or stand up for your convictions regardless of the personal cost.
Tyson说自己每次进ring之前哭 一部分是害怕 fear to fail
Doug Collins 的演讲片段：“Too many kids today are afraid of failure.”从未真正经历过失败，也没练习过“跌倒后爬起来”。
认真地想what it takes to be brave... long and lonley journey. A lot temptations 舒适 金钱 捷径 逃避
“It’s better to shoot and miss, then to let time run out and wonder what if.” - Michael Jordan
— ChampionMindset (@championminset) July 2, 2026
Joe Rogan starts a debate with Tommy Lee over a David Goggins claim that listening to music while working out at the gym is actually considered cheating:
ROGAN: “David Goggins won’t listen to music when he works out because he thinks it’s cheating.
He says that music won’t always be there when you need to something difficult. He’s a total psycho.”
LEE: “What? That’s crazy.”
ROGAN: “Music is the best fuel and ultimate companion to get through workouts with.”
LEE: “No doubt about that.”
ROGAN: “He finished eight 100 mile marathons in 8 consecutive weekends. He ran 800 miles in 8 weekends.”
LEE: “Good god man. All without music. That’s amazing.”
ROGAN: “I like to cheat. I need that energy boost. If listening to awesome music while I workout is considered cheating then call me a cheater.”
有点像subway takes，反常，但是真实、本质。
应该是格莱美获奖发言上，André 3000 said, “Great things start in little rooms.” Rent-free moment in mind
speak things into existence. Mac is back.
I'm too good to you
And I wanna tell you my intentions
I wanna do the things that I mention
I wanna benefit from the friendship
I wanna get the late night message from you, from you
I put my hands around you
Gotta get a handle on you
Gotta get a handle on the fact that...
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Till next , stay safe and stay hydrated!
2026-06-28 15:18:15
I don't know you tell me
I feel alive, no thanks to you
What is this I'm waking up to, waking up to?
I don't need you breaking my news, I see it too
I don't have as much patience as you
When do they look up to you? Guess that isn't up to you (yeah)
If I give my everything, would that be enough for you? (Yeah, yeah)
Come and have the best time on the best side
We still up, it's bedtime on the Westside
Eastside, Westside, Westside, Eastside (yeah)
Poppin' out on Chubbs side, it's gonna be a party (yeah)
Candy-pink paint job, she pull up like a barbie (yeah)
Ref1, he so drunk, he just played Nicki then some Cardi, I'm sorry
Before you went old and getting kicked up out the lobby (ay, ay)
Girl, you know what's up with me, I pull up in a heartbeat (ay)
She wanna go next door, she wanna meet party (ay, yeah)
She said she was Persian and started speakin' Farsi (yeah)
I don't know what's up with me lately, lately
I say, "I'm alone, " she said, "That's not somethin' you should be"
'Preciate you reachin' out, don't be too concerned about me
You know that I'm drinkin', smokin', thinkin' (yeah)
Please stop askin' me, "When are we linkin'?" It's Iceman season
I don't know what's up with me lately, lately
Yeah
I just wanna see a boy beg on the pavement
If he trippin' with the gang, then
I just wanna see a boy struggle with the payments
If he trippin' with the gang, then
I just might get that boy hit for entertainment
If he trippin' with the gang, then
I just wanna see a boy working on the day shift
And the night shift (that's how I feel)
I just wanna see that boy struggle with the phone bill
And the light bill (that's how I feel)
I just wanna see a boy beg on the pavement
If he trippin' with the gang, then
Come and have the best time on the best side
We still up, it's bedtime on the Westside
Eastside, Westside, Westside, Eastside (yeah)
Poppin' out on Chubbs side, it's gonna be a party (yeah)
Candy-pink paint job, she pull up like a Barbie (yeah)
Ref1, he so drunk, he just played Nicki then some Cardi, I'm sorry
Before you went old and getting kicked up out the lobby (ay, ay)
Girl, you know what's up with me, I pull up in a heartbeat (ay)
She wanna go next door, she wanna meet party (ay, yeah)
I don't know what's up with me lately, lately
DP 这次在机场 drunk arrested ，视频里 cursing 还带了 N word，让人看到他 life long battle 好不容易建立起来的 profile 受影响。他一直很顾家，当父亲，现在肯定在孩子面前感到抱歉。他 smart enough to know，在 JRE 上也说过退役之后无所事事会很空虚，很危险，结果还是出事了。
酒精是 high risk trigger，容易把平时压着的东西放出来。个人素质真是第一位，不只是 physical athletic condition，还要 knowledgeable 和情绪管理。这以后见人可能会尴尬，他又不是脸皮厚的人。 GSP，退役后还天天阅读、训练，生活一直保持自律。
dont get into a situation where u cant control
He just built like that🤷🏻♂️ pic.twitter.com/Spg0wFLXCk
— Mr Anderson🕶️ (@fightintuition) June 27, 2026
现在剪辑像是meme 甚至好笑 LOL
🗣️🚨 Jon Jones speaks on the Dustin Poirier situation.
— Red Corner MMA (@RedCorner_MMA) June 27, 2026
“At the end of the day, nobody cares, Dustin,” Jones said as he shared a message of support for the former interim champion.
Speaking to Red Corner MMA in Moscow, Jones encouraged Poirier to forgive himself and move… pic.twitter.com/48c5doB2nk
And ...
Mac is back
stay ready
Ilia Topuria vs Khabib，主要不是比单场谁更强，而是看整个生涯怎么走。没有人会真的是常胜将军，只不过是没有被时间 catch 而已。Ilia 最近刚被 Gaethje 战败，丢了 belt，这也说明再强的 confidence 也可能在某个点被现实打中。
Gaethje 说过，我每次进入比赛之前都会想象到我会失败，我会输掉，没关系，我不怕被 embarrassed，nothing to lose。这种心态反而让他能放开, focus。smart enough to know im not flawless，是 defeatable 的，不会一直急流勇进。Khabib 在巅峰退役保住了传奇，GSP 退役后继续自律长青，Ilia 现在经历这个，正好能重新思考 career path。
不管是 DP 的退役空虚，还是 Ilia、Khabib、Gaethje 的例子，都指向同一个点：athletic 很重要，但个人素质、心态管理和长远选择才是决定 legacy 的关键。时间会 catch 每个人，关键是怎么提前准备和面对。
新双娇 前双娇轮番上演精彩进球，这才感觉copa del mundo刚刚开始，甚至到淘汰赛也才真正值得重视，紧张起来，因为前面很多虐菜，很多名宿也是这种看法，决赛圈不该出现5-0以上的比分……
Argentina几乎所有首发都是欧洲豪强的主力，Lima Romero Mac…… 而且记忆中就算夺冠以前，更衣室从来没有不和谐过，赢得美洲杯之后更是感受到这支队伍不需要教练来教导凝聚力，所有的团结热爱都是自发的，这种氛围历史上都是罕见的，在双方各自越位一次之后，梅西接到长距离制导长传打进中射之前，想起来PSG正好也是卫冕了冠军，back2back，小蓝今年也大有机会，人员齐整，前中后均衡，有望至少再进决赛，ARG rematch France
70分钟帽子戏法 it's never over. You never know 斯卡洛尼也不上头，进了就换下来休息 换新人
葡萄牙相对阿根廷来说阵容不差的 现在比16年欧洲杯时候更水平高一些，尤其是最近夺欧冠冠军的这几个新鲜血液，加上本来就在豪门效力的一些主力，其他
Interesting timeline we’re living in right now
第一步就是专注 减少干扰 别人的保护 尊重的对象都是peace 当然自己的努力保持自己在这条线上 就像jcole一样 stay in that line that peaceful inner place, 如果梅西中途转站好几家俱乐部的话 很难一直保持巅峰表现 因为有足球之外太多事务的干扰了 光转会 新闻 谈判 各种都会影响赛场上的表现 stay focus energy management 别人对你的支持和帮助都是在处理那个熵增的多项因素 你也需要专注 不只是物理身体 体育 各种生涯都是 包括伴侣
That taste must mean sth
Just like craving junk food when stressed/depressed, or losing taste when sick, your cultural diet reveals your current state. With that being said, taste isn’t random—it’s a mirror. So Your Spotify Wrapped (or equivalent) is low-key psychological data?!
“If you’re a Kanye West fan you’re not a fan of me, you’re a fan of yourself; you will believe in yourself. I’m just the espresso, I’m just a shot in the morning to get you going, to make you believe that you could overcome that situation that you’re dealing with all the time”
13:00-15:00
中央公园湖边散步
15:00-18:00
Bookshop 写作
18:00-19:00
中央公园看日落
19:00-21:00
找家安静咖啡馆继续整理 PPT
Thanks for being an insider till the end!
Till next , stay safe and stay hydrated!