leigh france

The first Web3 app to reward sustainable living. Share, earn, connect all in one place. With zero risk to privacy.

[leigh france @FengETH9]

In flux of capital, wisdom whispers

[Lookonchain @lookonchain]

July 15 Update:

#Bitcoin ETFs:
1D NetFlow: +2,648 $BTC(+$172.34M)🟢
7D NetFlow: -5,716 $BTC(-$372.04M)🔴

#Ethereum ETFs:
1D NetFlow: +31,277 $ETH(+$60.08M)🟢
7D NetFlow: +45,185 $ETH(+$86.8M)🟢
https://t.co/xvc2Lx5iN5

[Arvind Narayanan @random_walker]

1) If you haven't read AI as Normal Technology, these annotated slides are probably the easiest way to get a high-level overview. https://t.co/vNgRJCKxi3

2) If you're already familiar with the core ideas, Part 1 of the talk is largely a summary of what I and @sayashk have already written, while Parts 2 and 3 have new ideas. There are a lot of unexamined assumptions in the discourse about Recursive Self-Improvement and I hope you find my pushback interesting.

3) I'm really grateful to the team (@steverab @sayashk @PKirgis & Felix Chen) for feedback on the talk. In my first version, Part 2 was about 3x too long and I was super frustrated with myself. They encouraged me to cut it down ruthlessly and turn the full version into essays on the newsletter, so that's what I plan to do! (https://t.co/ZwebetjZ4n)

4) I've received a few requests for the video. There's a video on the ICML website, but it is login-walled https://t.co/rYHlxPGEXY (I assume it's for ICML registrants only). Last year's videos are public, so presumably @icmlconf will make it public at some point.

[leigh france @FengETH9]

@random_walker @sayashk AI's normal tech era? Finally, a sane take

[leigh france @FengETH9]

Can't wait, Jason's gonna kill it

[YouTube @YouTube]

the YouTube FIFA Creator Cup is coming to you live from Central Park, and @jasontheween is in. are you? streaming globally, July 12th at 5pm ET → https://t.co/c4pGb2vb0n

[leigh france @FengETH9]

"In volatility, true value awakens.

[CoinMarketCap @CoinMarketCap]

EXPLAINED: 📊 Bitcoin found strength last week despite the freshly released FOMC minutes revealing a divided Fed and Strategy selling $216 million in BTC. Does Bitcoin retaking $64,000 confirm the bulls are back?

With all of that in the background, what should we expect for the crypto market this week? Read on to find out. 👇

https://t.co/k3dRfetu27

[Faroo @Farooxyz]

Pharos × Faroo RWA Hybrid Vault (stPROS Pre-mint) is live🟢

50 winners will share $500 USDC total.

✅Follow @Farooxyz & @pharos_network
✅Repost this giveaway
✅Tag 2 friends in the comments who should get in on the pre-mint

⏰ Draw Dates: July 6 and July 13, 25 winners each

Full pre-mint details in the quoted tweet.

What part of the pre-mint are you most looking forward to? Drop it in the comments!

[leigh france @FengETH9]

@Farooxyz @pharos_network Doing my part, universe do yours. Awesome
https://t.co/JmsfLyC6RC

[leigh france @FengETH9]

@Farooxyz @pharos_network Mission complete, entry submitted, now I'll just wrap myself in a blanket of hope and wait for the draw dates 🤗 I’m gonna use the money to eat pork knuckle rice later

@MeiNFTBTC
@VortexFeng295

[leigh france @FengETH9]

@Farooxyz @pharos_network Mission complete, entry submitted, now I'll just wrap myself in a blanket of hope and wait for the draw dates 🤗 I’m gonna use the money to eat pork knuckle rice later

@MeiNFTBTC
@VortexFeng295

[leigh france @FengETH9]

Brilliant lineup of insights

[Surya Ganguli @SuryaGanguli]

Check out all the amazing work from our @SimonsFdn Collaboration on the Physics of Learning and Neural Computation (https://t.co/TfOKlQxCrE) presented at the main meeting of @ICMLconf #ICML2026

Tuesday

Efficient Learning of Compositional Targets with Hierarchical Spectral Methods,Hugo Tabanelli, Yatin Dandi, Luca Pesce, and Florent Krzakala
https://t.co/wix8AkXVcl

CompleteP for RL: Maintaining Feature Learning When Scaling Deep Reinforcement Learning
M Ganesh Kumar, Adam Lee, Blake Bordelon , Cengiz Pehlevan
https://t.co/Ox6AgFt5LU

Universal One-third Time Scaling in Learning Peaked Distributions
Yizhou Liu, Ziming Liu, Cengiz Pehlevan, Jeff Gore
https://t.co/QzIQf5ANde

Wednesday

A Noise Sensitivity Exponent Controls Large Statistical-to-Computational Gaps in Single- and Multi-Index Models, Leonardo Defilippis, Florent Krzakala, Bruno Loureiro, Antoine Maillard
https://t.co/x4zZVfTHP9

Single-Head Attention in High Dimensions: A Theory of Generalization, Weights Spectra, and Scaling Laws Fabrizio Boncoraglio, Vittorio Erba, Emanuele Troiani, Yizhou Xu, Florent Krzakala, Lenka Zdeborová
https://t.co/CKWt5qGyOR

A Solvable High-Dimensional Model Where Nonlinear Autoencoders Learn Structure Invisible to PCA While Test Loss Misaligns With Generalization
Vicente Mendes, Lorenzo Bardone, Cédric Koller, Jorge Medina Moreira, Vittorio Erba ⋅ Emanuele Troiani, Lenka Zdeborova
https://t.co/rgDY2ieGJR

Deep networks learn to parse uniform-depth context-free languages from local statistics
Jack T. Parley, Francesco Cagnetta, Matthieu Wyart
https://t.co/yrqjxZTrAp

Demystifying LLM-as-a-Judge: Analytically Tractable Model for Inference-Time Scaling
Indranil Halder, Cengiz Pehlevan
https://t.co/ERCgkiOyIt

On the Existence of Consistent Adversarial Attacks in High-Dimensional Linear Classification
Matteo Vilucchio, Lenka Zdeborova, Bruno Loureiro
https://t.co/KDpelqRLla

Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisation
Zier Mensch, Lars Holdijk, Samuel Duffield, Maxwell Aifer, Patrick Coles, Max Welling, Miranda C. N. Cheng
https://t.co/ikqMhKQOfJ

Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability
Shobhita Sundaram, John Quan, Ariel Kwiatkowski, Kartik Ahuja, Yann Ollivier, Julia Kempe
https://t.co/wjgSrAbJ25

Thursday

Deriving Neural Scaling Laws from the Statistics of Natural Language
Francesco Cagnetta ⋅ Allan Raventos ⋅ Surya Ganguli ⋅ Matthieu Wyart
https://t.co/b6nKYqNulh

Symmetry in language statistics shapes the geometry of model representations
Dhruva Karkada, Daniel Korchinski, Andres Nava, Matthieu Wyart, Yasaman Bahri
https://t.co/Y58fJt9qmk

A Random Matrix Perspective on the Consistency of Diffusion Models
Binxu Wang, Jacob A Zavatone-Veth, Cengiz Pehlevan
https://t.co/snM0EAxv3Q

Hyperparameter Transfer with Mixture-of-Expert Layers
Tianze Jiang, Blake Bordelon, Cengiz Pehlevan, Boris Hanin
https://t.co/dIe32NuQTS

Analytic Bijections for Smooth and Interpretable Normalizing Flows
Mathis Gerdes, Miranda C. N. Cheng
https://t.co/kWCw1EHGn4

Efficient RL Training for LLMs with Experience Replay
Charles Arnal, Vivien Cabannnes, Taco Cohen, Julia Kempe, Remi Munos
https://t.co/EjmNErAFpC

Embedding Trust: Semantic Isotropy Predicts Nonfactuality in Long-Form Text Generation
Dhrupad Bhardwaj, Julia Kempe, Tim G. J. Rudner
https://t.co/MknrTodSaX

What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT
Yunzhen Feng, Julia Kempe, Cheng Zhang, Parag Jain, Anthony Hartshorn
https://t.co/S18yTP5fy5

From Kepler to Newton: Inductive Biases Guide Learned World Models in Transformers
Ziming Liu, Surya Ganguli, Andreas Tolias
https://t.co/H7eFnYtYQC

[PlanB @100trillionUSD]

Bitcoin metrics used to cost thousands per year.
BlockHorizon just opened everything up, 100% FREE data & charts:
✅ All metrics, daily updates
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Same data as other providers. No paywall
👉 https://t.co/KEK5tp3wT9

[leigh france @FengETH9]

@100trillionUSD This is awesome, thanks man

[leigh france @FengETH9]

Naomi serving justice

[ESPN @espn]

Naomi Osaka defeats the world No. 1 Aryna Sabalenka to advance to the quarterfinals‼️

[大冰要抄底（每天送1000） @dawda5152]

股票跌，币圈涨，
股市和币圈在玩高低切换，
下周美股继续跌，同时带跌63000的大饼回调到60000附近，
那些59000追空的傻子这两天可以找机会在63000附近加空补仓空单拉均价了
把均价拉到61000才能解套赚钱。

下周美股跌完，币圈跌到60000以后
7月中旬看币圈大涨下一次再涨就是7月中旬的10000刀的大涨7月中旬下旬看涨到68000-70000。
大冰的QQQ空单会空到下周跌完了
然后平空QQQ纳斯达克空单，扭头去抄底做多BTC的60000-68000-70000。

具体交易逻辑看前面两天的帖子
我说了太多遍行情逻辑了，累了。

[leigh france @FengETH9]

@dawda5152 大饼6000见，哥们儿别急着空

[leigh france @FengETH9]

在雪峰上，一起温暖筑梦成长

[Avalanche_中文🔺 @Avalanche_CN]

Team1 招募FAQ来了！🙋
FAQ 1：Team1 是什么❓在招什么样的人问号❓
Team1 是 Avalanche 面向全球社区招募的社区共建网络。
Team1希望找到真正愿意参与 Avalanche 生态建设的人，并且和全世界的社区连接在一起。
在中文区，也是一样，我们欢迎以下才能的社区朋友加入Team1
⚡️开发者
⚡️内容创作者
⚡️活动组织者
⚡️设计师、视频、摄影创作者
⚡️社区运营、BD等

不论你是Crypto native 很久的老鸟，还是大学生学生、刚入圈的新人，这里不会论资排辈，而是放大你的闪光点和潜力，用全球社区组织为你搭建平台来展现能力。

你对 Avalanche 感兴趣，愿意学习，愿意持续贡献，并且能找到自己参与生态建设的方式，Team1 就是一个很好的入口。
——欢迎申请 Team1

[憨厚的麦总 @Michael_Liu93]

这两天steam夏促，折扣还挺大的，怒买了三十多款游戏。

终于懂了为什么投资腰斩要去足疗店充卡买1w送1w了🤡

[leigh france @FengETH9]

哇塞！飞机上俯瞰这雪山云海和超蓝天空，自由的感觉瞬间爆棚，心跳都加速啦～太治愈太燃了

[火币HTX六爷｜火币赚币 @HTX_Molly]

【昨晚被两个用户感动到了，文末撒福利💙】

昨天不是什么特别的日子，但两个用户不约而同发来的长文让它变得极度特别。

最近这段时间，我们经历了很多风波，也承担了很多压力。

因此，当我看到他们发来的信息时，心里像是突然被打了两剂强心剂，又干劲满满了！

说明我们的服务真的走到火伴心里，帮他们解决了问题，得到了世界最重要的东西：信任。

过去两年，
很多人都说火币的口碑在慢慢变好。

没有什么秘诀。

是无数火币人，一座城市一座城市地跑，一个问题一个问题地解决。

面对一次又一次 FUD，我们没有选择沉默，也没有选择逃避。

能解释的，我们第一时间解释；
能解决的，我们第一时间解决；
遇到卡点，我们就陪着用户，想尽一切办法解决。

慢慢地，真心换来了信任，
火伴的支持让火币克服了一个又一个困难；

当然，我们离最好的自己还有距离。
还有很多地方需要继续努力，也还有很多问题需要继续改进。

但火币已经走过了 13 年。

经历过牛市，也经历过熊市；
经历过鲜花，更经历过风雨。

直到今天，我们依然坚持站在这里。

因为我们始终相信：
平台真正的价值，不是在行情好的时候，而是在用户需要你的时候。

谢谢每一位一直陪伴火币、相信火币的火伴。
也谢谢每一位批评过我们、督促我们变得更好的用户。

未来，无论市场如何变化，HTX 的每一位客服、每一位同事，都会继续把用户放在第一位。

火伴在，火币就在。❤️

⸻

最后，感恩不能只停留在嘴上。

🎁 福利来了：

① 转发 + 评论 「和火币一起穿越牛熊」，随机抽 66 位火伴，每人送 6U。

② 转发并分享 你对火币客服最真实的评价或建议（无论夸奖还是批评），我们将挑选 6 条最有建设性的建议，每人送 66U。

你们的在乎，让我们变得更好🫶

[leigh france @FengETH9]

@HTX_Molly 看到整个团队都在努力，我觉得这种氛围值得很多人继续支持下去 想要得奖
和火币一起穿越牛熊

[leigh france @FengETH9]

晨光轻抚，牛油果与荷包蛋的温柔相拥，奶香如梦。愿你每晨以简单之美，蓄力前行，拥抱丰盈一天