South Carolina mother Justine Marie May faces homicide by child abuse charges after her two young daughters were found dead during a welfare check in Charleston County.

Food Can the Real Restaurant Phone Be Saved? Across the industry, A.I. is poised to take over reservations by phone. But what if parrying with a grouchy maître d' is part of what makes dining out great? Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. The first time I called Kat--the conversational A.I. receptionist fielding calls at fried-chicken-and-ramen joint Katsubō, in Charleston, South Carolina--I heard the convincing din of restaurant background noise before she addressed me in her youthful (cheeky, even?) voice.

Science Archaeology Civil War shipwreck remains in'fantastic' shape on ocean floor The USS Monitor was an ironclad ship nicknamed a'Yankee cheesebox.' A bathymetric view of USS Monitor, looking at the stern of the wreck with the boilers and inner framework of the armor belt captured by Northrop Grumman using μSAS . Breakthroughs, discoveries, and DIY tips sent six days a week. One of the most famous shipwrecks in United States history has received a glow-up, courtesy of stunningly detailed, underwater 3D scanning technology. The National Oceanic and Atmospheric Administration (NOAA) recently released highlights from its 2025 survey of the USS Monitor, the iconic prototype ironclad warship that sank during the Civil War .

Even as AI capabilities improve, most enterprises do not consider agents trustworthy enough to work on production data. In this paper, we argue that the path to trustworthy agentic workflows begins with solving the infrastructure problem first: traditional lakehouses are not suited for agent access patterns, but if we design one around transactions, governance follows. In particular, we draw an operational analogy to MVCC in databases and show why a direct transplant fails in a decoupled, multi-language setting. We then propose an agent-first design, Bauplan, that reimplements data and compute isolation in the lakehouse. We conclude by sharing a reference implementation of a self-healing pipeline in Bauplan, which seamlessly couples agent reasoning with all the desired guarantees for correctness and trust.

At an airport in South Carolina on Thursday, US representative Nancy Mace called police officers "fucking incompetent" and berated them repeatedly, according to an incident report. Nancy Mace, the South Carolina Republican congresswoman, unleashed a tirade against law enforcement at the Charleston International Airport on Thursday, WIRED has learned. According to an incident report obtained by WIRED under South Carolina's Freedom of Information Act, Mace cursed at police officers, making repeated derogatory comments toward them. The report says that a Transportation Security Administration (TSA) supervisor told officers that Mace had treated their staff similarly and that they would be reporting her to their superiors. According to the report, officers with the Charleston County Aviation Authority Police Department were tasked with meeting Mace at 6:30 am to escort her from the curb to her flight and had been told that she would be arriving in a white BMW at the ticketing curb area.

As unmanned aerial vehicles (UAVs) become increasingly prevalent in both consumer and defense applications, the need for reliable, modality-specific classification systems grows in urgency. This paper addresses the challenge of data scarcity in UAV audio classification by expanding on prior work through the integration of pre-trained deep learning models, parameter-efficient fine-tuning (PEFT) strategies, and targeted data augmentation techniques. Using a custom dataset of 3,100 UAV audio clips (15,500 seconds) spanning 31 distinct drone types, we evaluate the performance of transformer-based and convolutional neural network (CNN) architectures under various fine-tuning configurations. Experiments were conducted with five-fold cross-validation, assessing accuracy, training efficiency, and robustness. Results show that full fine-tuning of the EfficientNet-B0 model with three augmentations achieved the highest validation accuracy (95.95), outperforming both the custom CNN and transformer-based models like AST. These findings suggest that combining lightweight architectures with PEFT and well-chosen augmentations provides an effective strategy for UAV audio classification on limited datasets. Future work will extend this framework to multimodal UAV classification using visual and radar telemetry.

This study presents a methodology to treat performance-based seismic design as an inverse engineering problem, where design parameters are directly derived to achieve specific performance objectives. By implementing explainable machine learning models, this methodology directly maps design variables and performance metrics, tackling computational inefficiencies of performance-based design. The resultant machine learning model is integrated as an evaluation function into a genetic optimization algorithm to solve the inverse problem. The developed methodology is then applied to two different inventories of steel and concrete moment frames in Los Angeles and Charleston to obtain sectional properties of frame members that minimize expected annualized seismic loss in terms of repair costs. The results show high accuracy of the surrogate models (e.g., R2> 90%) across a diverse set of building types, geometries, seismic design, and site hazard, where the optimization algorithm could identify the optimum values of members' properties for a fixed set of geometric variables, consistent with engineering principles.