Tootfinder

An analysis based on current valuations of OpenAI and Anthropic suggests ~$370B of philanthropic assets tied to the two AI companies are poised to become liquid (Nan Ransohoff/Nan's Substack)
https://nanransohoff.substack.com/p/the-third-wave-of-american-phila…

The third wave of American philanthropy
AI is about to generate hundreds of billions in new philanthropic funding. We have real work to do to make the most of it.

Analysis: OpenAI and Anthropic employees are donating to campaigns more heavily and cohesively than Google, Meta, and Airbnb employees did post-IPO (Alexandra Lindsay/The San Francisco Standard)
https://sfstandard.com/2026/07/18/ai-s-…

AI’s new political donor class is already outspending Big Tech’s last one
Campaign filings show AI employees donating faster, more heavily, and more in sync than three prior tech-IPO donor classes. Here’s where the money is going.

Impressive real-world numbers of EDF 🇫🇷 (with its smart charging subsidiary DREEV) for charging a fleet of electric waste collection trucks:
10 MWh of charging for 10 trucks, at a cost of minus €129, by collecting otherwise wasted energy and providing grid support
#EV #Flexibility

Millions of homes in London, Essex and Kent at risk of sinking as climate crisis worsens https://www.theguardian.com/environment/2026/jun/11/millions-homes-london-essex-and-kent-sinking-climate-crisis-subsidence

Good point-by-point analysis of the MOU between Iran and the USA. Total defeat for Trump big win for Iran.
#usa #iran #mou

Midweek Update #15: The MOU Is Out
Hi all,

Long-horizon prediction of three-dimensional wall-bounded turbulence with CTA-Swin-UNet and resolvent analysis
Bo Chen, Yitong Fan, Jie Yao, Weipeng Li
https://arxiv.org/abs/2605.17888 https://arxiv.org/pdf/2605.17888 https://arxiv.org/html/2605.17888
arXiv:2605.17888v1 Announce Type: new
Abstract: Long-horizon prediction of three-dimensional (3D) wall-bounded turbulence with machine-learning methods remains a challenging task, due to the rapid accumulation of autoregressive errors and the substantially computational cost. To address these challenges, we present a hybrid machine-learning framework, in which a channel-time-attention Swin-UNet (CTA-Swin-UNet) and a multi-time-scale fusion correction (MTFC) strategy are developed to predict the turbulent flow fields in a wall-parallel plane, with affordable computational cost. Then, 3D flow fields are reconstructed via a resolvent-based spectral linear stochastic estimation (SLSE), rooting from the predicted planar flow. Results show that the CTA-Swin-UNet outperforms the baseline models (LSTM, FNO and traditional Swin-UNet) in both single-step prediction and autoregressive rollouts, indicating the effectiveness of introducing the CTA module into the Swin-UNet architecture. At the same temporal interval, the CTA-Swin-UNet remains stable for approximately 150 rollout steps, while the baseline models fail within 20 to 50 rollout steps. After introducing the MTFC strategy, a longer horizon upto 300 steps is achieved. Using the resolvent-based SLSE reconstruction further recovers the 3D flow structures and energy spectral distributions from the predicted planar inputs, which demonstrates that the proposed framework provides an effective and computationally efficient approach for long-horizon autoregressive prediction of 3D wall-bounded turbulence.
toXiv_bot_toot

Trial lawyers, with big liability litigation earnings, are among the most active lobbyists against autonomous vehicles, even as data shows clear safety benefits (Alex Tabarrok/Marginal Revolution)
https://marginalrevolution.com/margina

Trial Lawyers Lobby Against Autonomous Vehicles - Marginal REVOLUTION
Roughly 37,000–40,000 Americans die in auto accidents every year. We now have large‑scale, real‑world evidence—from Waymo and a joint analysis with Swiss Re—that driverless operations can be substantially safer than matched human driving within their current operating domains. The latest data show that over 220 million miles driven, Waymo vehicles–in Los Angeles, San Francisco, Phoenix, […]

Financial markets are rewarding companies that are well positioned to benefit from widespread adoption of artificial intelligence (AI)
with higher returns,
according to a new Yale-led analysis of 380 trillion AI tokens,
one of the largest datasets of real-world AI consumption ever studied.
The difference in stock returns between companies with the most and least AI exposure is substantial
— about 0.64% per week,
a financial benefit the researchers call the…

Analysis of 380 trillion AI tokens reveals how the technology is transforming financial markets
A study of a massive dataset of real-world AI consumption shows on how AI is affecting businesses, workers, and financial markets.

Great analysis about preventing mobile missile launches.
#iran

Square Peg in a Round Hole: AirPower against Mobile Targets and Missiles
A Case Study of Operation Crossbow, Scud Hunting, and Iran

No way out. Good analysis on Trump and Iran.
#trump #iran

No Way Out
Trump has trapped the US in Iran and the costs are rising