Tootfinder

Kraken Technology, which designs and builds autonomous maritime platforms, such as uncrewed subsurface vessels, raised a $175M Series B at a $1B valuation (John Reynolds/Tech.eu)
https://tech.eu/2026/07/09/maritime-defence-startup-kraken-technology-…

Maritime defence startup Kraken Technology hits unicorn status
British-founded maritime defence startup Kraken Technology today said it had achieved unicorn status. The startup, founded in 2020 by former speedboat racer Mal Crease, said it had raised $175m in a Series B funding round at a $1bn valuation, according to a press release. The funding round was led by Digital Transformation Capital Partners (DTCP), with support from the British Business Bank, NATO Innovation Fund (NIF), Rheinmetall, Inocea group as well as VC firms Hico Ventures, Thesiger Capita…

On Thursday June 25, 2026,
the Supreme Court, in a 6-3 decision,
granted Trump the power to terminate the Temporary Protective Status (TPS)
of hundreds of thousands of Haitians and Syrians living in America.
The ruling clears the path for Trump to extend his mass-kidnappings and deportations to over 350,000 people from Haiti.
Trump and his admin’s focus specifically on Haitian migrants isn't by chance.
What brought them into his focus was one of the …

Following the Trail: From Blood Tribe to the Supreme Court
How a neo-nazi campaign targeting Haitians in Ohio led all the way to the Supreme Court.

AI-Generated Interactive Fiction for Educational Use: A Pilot Study of Perceived Comprehensibility, Coherence, and Engagement
Finn Rogosch, Andreas Schrader
https://arxiv.org/abs/2608.10818 https://arxiv.org/pdf/2608.10818 https://arxiv.org/html/2608.10818
arXiv:2608.10818v1 Announce Type: new
Abstract: Generative artificial intelligence (AI) can produce educational content at scale, including interactive and narrative learning experiences, but technical generation alone is not sufficient: scenarios that are confusing, narratively inconsistent, or unengaging are unlikely to be useful in practice. This paper presents a pilot user-centred evaluation of AI-generated interactive fiction (IF) for educational use in higher education. Using a previously described domain-agnostic pipeline and a shared STEM content base, we generated a controlled pool of scenarios and asked participants (N = 22, STEM higher-education) to play one generated episode and rate it on narrative clarity, story-content coherence, engagement, and length acceptance. A free-text prompt captured open feedback. Narrative clarity and length acceptance were rated positively, engagement sat near the neutral mid-point of the scale, and story-content coherence was the weakest dimension by a clear margin. Qualitative feedback points to quiz integration as the bottleneck. Artificial in-fiction motivation for quiz prompts and abrupt setting changes were reported. Feedback also pointed to missing story-level consequences for wrong answers. From these observations, we derive concrete design implications that can inform larger follow-up studies, including later work on learning effectiveness.
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SynAct: A Reasoning-Acting Large Language Model Agent for Adaptive Synthesis Optimization
Fangzhou Liu, Peiyi Han, Jiawei Liu, Yuan Pu, Zhuolun He, Rongliang Fu, Tsung-Yi Ho, Bei Yu
https://arxiv.org/abs/2608.12751 https://arxiv.org/pdf/2608.12751 https://arxiv.org/html/2608.12751
arXiv:2608.12751v1 Announce Type: new
Abstract: Logic synthesis transforms RTL designs into gate-level netlists, where PPA results are highly sensitive to the choice of optimization commands, making synthesis tuning both high-dimensional and expensive. Previous approaches fall into two categories: automated methods, which perform black-box search over fixed action spaces with limited decision-level interpretability, and LLM-based methods, which typically generate static scripts upfront and cannot adapt to evolving circuit states. We present SynAct, an adaptive closed-loop LLM reasoning--acting agent that iteratively diagnoses live synthesis reports and reasons over the current circuit state, retrieved tool knowledge, and historical optimization experience to issue targeted commands. SynAct focuses on improving timing, particularly worst negative slack (WNS), while maintaining balanced area and power trade-offs. Experiments on a commercial synthesis tool across 14 designs show that SynAct reduces average WNS to 27% of that from bootstrap synthesis.
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Anthropic releases findings from a pilot that let three external researchers run studies on Claude usage; one study found users delegate high-stakes tasks (Anthropic)
https://www.anthropic.com/research/enabling-independent-research

Enabling independent research on how people use Claude
Earlier this year, we ran a pilot giving external researchers access to aggregate, real-world Claude usage data. Three research groups designed their own studies for Anthropic Insights, our privacy-preserving analysis tool. In this post, we share high-level results from those studies and what we learned running this pilot.

It is not clear how quickly Haitians and Syrians will become vulnerable to removal from the United States,
but the ruling from the Supreme Court on Thursday makes them deportable.
Their work permits will expire, and they will lose their jobs and driver’s licenses.
The long-awaited ruling landed like a bomb on the Haitian community in South Florida,
the largest in the country.
It quickly reverberated through Massachusetts; New York; Springfield, Ohio; and other…

‘Nowhere to Go’: T.P.S. Ruling Plunges Many Migrants Into Limbo
The decision renders 1.3 million people from more than a dozen countries, many who have lived in the United States for decades, vulnerable to deportation.

The US supreme court on Thursday ruled
in favor of the Trump administration’s bid to strip temporary protected status (TPS) from hundreds of thousands of Haitians and Syrians,
who were legally in the US and protected from deportation.
People with TPS are given the permission to live and work in the US because the Department of Homeland Security (DHS) deemed their home countries to be unsafe due to war, political instability or natural disasters.
In the past year, Tru…

US supreme court allows Trump administration to strip Haitians and Syrians of protected status
Decision effects hundreds of thousands of people who have permission to live and work in the US because their home countries are unsafe

From Continuous Dynamics to Practical Gradient-Based Samplers
James Chok
https://arxiv.org/abs/2608.05425 https://arxiv.org/pdf/2608.05425 https://arxiv.org/html/2608.05425
arXiv:2608.05425v1 Announce Type: new
Abstract: Gradient-based Markov chain Monte Carlo methods are often introduced as a catalog of algorithms: Hamiltonian Monte Carlo (HMC), the Metropolis-adjusted Langevin algorithm (MALA), the No-U-Turn Sampler (NUTS), and several underdamped variants. This presentation obscures the common structure of the methods and, more importantly, the reasons why a sampler that is correct in principle may be ineffective in practice. We develop a unified account, beginning with exact continuous-time dynamics that represent idealized sampling methods and for which Metropolis adjustments are not required. Numerical discretization makes the dynamics computationally feasible but introduces bias. Metropolis adjustment removes the asymptotic bias by converting numerical errors into rejection, leading to HMC, MALA, NUTS, and the Metropolis-adjusted kinetic Langevin algorithm (MAKLA).
The second half of the paper presents geometric design choices that determine practical performance, namely, although MAKLA and NUTS have nice theoretical properties, their sampling efficiency may be slow in practice. Importantly, a fixed mass matrix can whiten globally anisotropic targets, often fixing sampling inefficiency in Bayesian posteriors with large data. Whereas hierarchical posteriors introduce their own problem, causing state-dependent variation in the Hessian (e.g., Neal's funnel). We explain how a randomized step size can be used effectively to sample from such a distribution. The resulting paper is both a tutorial on the mechanics of gradient-based sampling and a set of practical recipes to improve sampler performance.
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It’s been at least 75 days since Senator Mitch McConnell has been seen in public.
Yet on Thursday
—despite McConnell supposedly being weeks into an intensive rehabilitation routine
—his team blatantly refused to provide a progress report.
The last time his office shared details regarding the 84-year-old’s health status was three weeks ago on August 6,
when a press release purportedly penned by McConnell himself explained that he had been discharged from a rehabil…

Desirée Townsend (@Cheering4Change) on X
New video provided by Alysia McMillian gives us a glimpse into Senator Mitch McConnell’s health condition, with the appearance of two MCombo lift recliner chairs designed to help the elderly get in and out of a chair.