Just a nobody. I Have No Mouth, and I Must Scream.
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a nobody retweeted
和美宝老师 @mablejiang @mablemeibao 聊了 5 万块钱的天！聊了聊 Hyperliquid 生态中现实资产放量崛起的现象（为社么？），聊了聊永续合约如何成为“交易一切”的基础设施，还有背后的运行逻辑（美宝老师小课堂）。
youtube.com/watch?v=oO9r4HOL…
“我们这一代人，希望在任何时候用钱来表达对事物的观点，万事万物都可以被大宗商品化。”
当然，美宝老师也讲了讲她的新孩子 @trasiaxyz 要怎么干；还有，她怎么看 Hyperliquid 生态中 HIP3 玩家的竞争和这类交易产品获胜的关键。
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碳基消费观察18
经济下行往往利好Live House，比如国内live house票房规模在最近这些年持续保持三位数，大两位数的同比增长。
国内的live house里，这两年我印象最深的是上海的Cozy MUSIC LAB，非常现象级的live house（私以为在国内只有上海有这个土壤），感觉目前在国内已几乎没有对手：
1）每晚爆满，每周定时放票，但每次都是一票难求；
2）Cozy最卓越的点在于，表面上感觉它本质上是个音乐类TOP MCN的线下店，每晚驻场的4-5个演唱嘉宾都是百万粉丝级音乐类实力“网红”且外表形象气质佳，自带粉丝和流量。
但更深入来看，这家店可能本质上是老板的个人IP的线下延伸：这家店的老板同样是百万粉丝级音乐类网红，能唱能跳有流量有人脉关系，能推荐驻场嘉宾上《歌手》；极致的社区运营，每天亲自在60多个微信群&各个社交媒体和客户互动；丝滑的流量打法：每天产生无数现场切片，持续放大传播效果...
3）在它之前的Live house，感觉不管是去哪家，听了多少次，都很难记住当晚是谁唱了什么歌。Cozy不仅完全解决了这个问题，氛围感非常强，能让听众印象非常深刻，甚至完美解决了这个问题：当天演唱结束后，它又会通过无数精华切片在私域/公域传播，不断增强听众的印象和记忆，和忠诚度。
“熊市”可能催生不了伟大的音乐，但会产生卓越的LIVE HOUSE
碳基消费观察17
香港半岛酒店母公司宣布投21亿港币翻新香港半岛和东京半岛，以迎接香港半岛一百周年（2028）及东京半岛20周年（2027）。
香港半岛+东京半岛总共600间房，21亿翻新预算对应每间房350万港币/44万美金。
这个翻新预算非常夸张。在国际上，一般奢华酒店翻新，10万刀每间房对应基础式翻新，如果能花到20万刀每间房，那能翻新的非常夯。这平均每间房44万刀的“纯翻新预算”，极度炸裂。在国内，每间房纯装修预算在百万就已经是宝格丽安缦MO...级别（诸多老哥喜欢的亚朵4.0/全季5.0，平均每间房“装修成本”也就是13-15万人民币）。
目测香港半岛翻新完后可以直接和卓越的Rosewood（平均每间房装修成本1M USD）扳扳手腕。
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在股市最差的时候给人生最后的雇主提交了离职申请😂
生活方式和工作领域都会带来巨大的惯性，身处字节获得的惯性更大（协作工具、AI工具、灵活的作息和远程、现金流），所以一有契机就要抓住，让世界线分叉发散但又不可逆转的通向真正自由和广阔未知
不过我的打工生涯好像一直有4年魔咒：
土豆：近4年
豆瓣：4年
Flipboard+Strikingly：近3年
字节架构：近3年
PICO：4年半
虽然迟了很多，但终于能「从周期股的诅咒中解脱，跳出价值股的泥潭，成为成长股去承担更多风险和不确定性」了😊，很兴奋，人生的每分每秒又开始恢复成可触可及清晰可辨的样子，用动画里的中二台词来说，这才是生の実感
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期待已久的世界杯行情最终落下了帷幕，不舍中还是要恭喜西班牙夺得总冠军。
复盘一下：
1️⃣ 币安的瓜分 400万 USDT 活动实际上多号是能够赚很多的，群里也出现过两次的 2026 USDC 的奖励，我自己这里比较懒了，只做了一个号，最终收益应该是在30U左右。
2️⃣OKX 的世界杯零撸活动，一直在梭哈冷门中不断归零😅最终没有拿到任何的单场奖励，只靠进入前3万名获得了15U的低保。
3️⃣最大的毛来自于 @predictdotfun 的瓜分 200 万 USDT 的粉丝积分活动。大毛定律要求活动格局大奖励多+竞争小，而这个活动总奖励200万USDT，即使2万人参与，人均的收益也在100U左右，而通过过往和 Predict 的 Ding 老板的接触，能感受到老板的格局很大，所以我义无反顾的参加了，还带上了一群的群友。
这个活动开始之前我在群里预估人均奖励在1000U左右，最终群友们也纷纷砍下几千U等不错的成果，可喜可贺。
我本人由于运气加持，在32进16和16进8的时候连续两次获得了摩洛哥的第一名拿到了大毛，Predict 的粉丝积分奖励最终整体收益在12000U。
4️⃣世界杯期间，各个预测市场平台流动性非常充足，各种定价错误和尾盘数不胜数。为了这波行情我也是准备了半年的时间深度体验预测市场和搭基建，最终通过主观交易和搬砖套利扫尾盘等形式，纯收益约8000U。
综上所述，世界杯期间的一个半月期间我的预测市场收益为 20000 USDT，比我上半年的预测市场总收益还要多。
盛夏结束了，预测市场的行情却不会停止，Predict 的 TGE 也一定会是越来越热的话题，而我更关心的下一站，中期选举，也越来越近了。
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Without my permission, my AI agent harness and loop engineering video got downloaded, reskinned, re-uploaded by much bigger X accounts. 400K+ views between them, none linked to the original X post. @nikitabier
Bigger accounts on X tend to attract more attention and I am fine with that. But the native video has no provenance on X and whoever re-uploads fastest gets the reach and the source disappears.
This just penalizes anyone trying to share original work on this platform.
This is not only making me feel uncomfortable, but also this is unfair to all creators who try to do original work on this platform.
Attribution should travel with the video. The system should reward the source instead of the rip.
Accounts I found who re-uploaded my video without asking: @0xMovez @LunarResearcher @0xCodez @servasyy_ai
x.com/ShenSeanChen/status/20…
@elonmusk
You Can Learn AI Agent Harness & Loop Engineering In 19 Min, with LLM Ops, Eval, Tracing and RAG.
They went viral not because they're complicated but because they're simple building blocks, and once you see them you can prompt your way to building real systems.
🎬YouTube: youtube.com/watch?v=GrNbuWWJ…
Here's the whole thing in one picture.
An LLM is a powerful brain that knows everything about humanity and nothing about you or the software you're running.
The harness is the set of tools you put on that horse so it runs where you want.
Memory gives it context: who you are, what happened before, how to act.
The loop lets it call tools again and again, with guardrails so it knows when to stop.
Eval and LLM Ops trace every run, score it, and feed the fixes back in so the system keeps improving itself.
Master these four and you can read almost any AI agent repo or paper and actually know what's going on.
You Can Build Anything. You Can Learn Anything. 💪
Chapters:
Intro: the 4 AI agent buzzwords
What an AI agent run actually is
The memory system: procedural, semantic, episodic
What "harness" really means (the horse)
Storing and updating memory (databases, skills, summarizer agent)
Retrieval: RAG, SQL vs semantic search
Tool calling and why agents loop
Loop engineering and end-loop guardrails
A Claude Code hooks example
Eval and LLM Ops: why you need them
Tracing every run (Langfuse, LangSmith)
Evaluation: LLM as a judge
Diagnosing what broke
The gate: ship the fix or fix the bug
Zoom out: the full system
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It's Sunday night, and I'd love to share some of my favorite podacasts. All of them have the great and unique insights:
With tradable insights:
1. Macro voice(@MacroVoices by @ErikSTownsend and @PatrickCeresna)
2. Renmac(@RenMacLLC, macro trading)
3. Goodeye 股癌(Taiwanese, great semi supply chain insights: podcasts.apple.com/tw/podcas…)
Somewhat tradable:
1. 硅谷101(@thevalley101 Chinese, very deep insights, for example the recent episode about the feasibility and cost of space data center by former spacex engineer: podcasts.apple.com/cn/podcas…)
2. "Watch your bids" by @kevinmuir. Although he is a perma bear, it's always great to understand the thesis from the other side as traders love volatility ;) Note: this is a private podacast.
3. The intrinsic Value(@TIV_Podcast)
Non-tradable but still great:
1. macromicro 财经M平方(Taiwannese, great macro insights @MacroMicroMe)
2. BG2(@BG2Pod)
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对了。做自媒体一直有个热门的而且经久不衰，取之不竭的选题，就是罪案类的。公众号，podcast，YouTube。
基本上不会翻车，题材取之不尽。阅读量，观看量，互动量都是非常高的。
不过现在大家有个趋同化的情况。
但是很少有人去做经济犯罪类的罪案犯罪节目，以及商战类节目。
经济犯罪类节目，可以参考这个American Greed，翻译加演绎，这种题材取之不尽。
商战类题材，可以参考 business war这个podcast，估计做个几百期没啥问题。
我自己没精力做这些了，感兴趣的，可以做做这些。大概率能做起来的。
美国做正经生意，不需要喝酒，不需要商K，不需要送礼。
😂
但是不代表美国人不讲人情，人情属于good to have，但不是必须的。大家都比较守规矩。
当然骗子也有，推荐电视纪录片《american greed》各种诈骗教科书。
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Everyone asks if Atlas can bring them a drink, but this robot can bring you the whole fridge. Using AI-driven behaviors, Atlas is doing hard work and coordinating its whole body to manage heavy objects, balancing complex contact points with accuracy and reliability.
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I'm traveling the world for a bit, starting with China but then hopping around the globe, anywhere. Open to any adventure. No plans, only a backpack. Hoping to meet & get to know humans from all walks of life. The pic is from a long hike on the Great Wall. For me, as a fan of history, this was an epic experience.
In China, first I'm visiting a few big cities & talking to engineers at the heart of China's AI revolution. After that, if feeling crazy enough, I'm hitchhiking (first time) across rural China for a few weeks. Hitchhiking because I think it's the best way to meet rural folks who I would otherwise never get the chance to meet. I hope to do the same in US and other places.
I have a request, if you have a travel recommendation, fill out the form(s) below if you feel like it. Or share with folks who might have advice about such travel.
Form 1 - travel recommendation:
If you can, recommend to me an interesting place I should visit anywhere in the world. For this, fill out form 1. Not touristy stuff, but something off the beaten path, that tourists may not know about, but is legendary. It could be as remote as meeting a herder in the mountains who is a local legend. Asia, Middle East, Europe, India, South/North America, Africa, Australia, anywhere. In China, I'm hoping to visit maybe Heibei, Shanxi, Shaanxi, Gansu, Sichuan, Yunnan, etc, so recommendations for spots to visit are helpful.
Form 2 - coffee:
If you want to grab a coffee with me anywhere in the world, fill out form 2 (please don't use form 1 for that).
Anyway, I hectically tossed stuff in backpack. Realizing I don't have a clear plan of any kind, which is probably the only way to do it. LFG.
Love you all ❤️
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What is intelligence? What will it take to create AGI? What happens once we succeed?
The Scaling Era: An Oral History of AI, 2019–2025 by @dwarkesh_sp and @gleech explores the questions animating those at the frontier of AI research.
It’s out today: press.stripe.com/scaling
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Imagine every pixel on your screen, streamed live directly from a model. No HTML, no layout engine, no code. Just exactly what you want to see.
@eddiejiao_obj, @drewocarr and I built a prototype to see how this could actually work, and set out to make it real. We're calling it Flipbook. (1/5)
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【速報】コーエーテクモ、資産運用が絶好調すぎて業績予想を大幅に上方修正、上振れ額はなんと約135億円
- 当初の予定（令和7年4月30日発表）
・「運用で60億円稼ぐ予定です」
- 今回の修正（令和8年4月20日発表）
・「運用で195億円稼げそうです」に変更
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These are the materials I read every week:
- BofA Simon Wu’s memory report
- Mirae Asset Securities’ AI Weekly report
- Global Semi Research Substack
- My own weekly recap and retrospective of TrendForce’s news
- Lately, The Diligence Stack as well
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📈ティザーPV公開📈
2026年TVアニメ化決定！
TVアニメ
「FX戦士くるみちゃん」
ティザーPVを公開しました💴
ほんわかゆるふわＪＤによる ちょっとアブナイ（！？）ＦＸのおはなしの始まりです！
▼YouTube▼
youtube.com/watch?v=Hg3mStr7…
#FX戦士くるみちゃん
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