It was the first time an electric vehicle led the field at NASCAR's most famous race. Chevrolet made history at the 67th Daytona 500 by introducing the 2025 Blazer EV SS as the official pace car. This marked the first time an electric vehicle led the field at NASCAR's most iconic race, a striking symbol of how the automotive world is shifting toward electrification while still honoring its racing heritage. The Blazer EV SS isn't just any electric SUV; it's the quickest SS model Chevrolet has ever built, and it turned heads both on and off the track. JOIN THE FREE "CYBERGUY REPORT": GET MY EXPERT TECH TIPS, CRITICAL SECURITY ALERTS AND EXCLUSIVE DEALS, PLUS INSTANT ACCESS TO MY FREE "ULTIMATE SCAM SURVIVAL GUIDE" WHEN YOU SIGN UP!

The Cybertruck answers a question no one in the auto industry even thought to ask: what if there was a truck that a Chechen warlord couldn't possibly pass up – a bulletproof, bioweapons-resistant, road rage-inducing street tank that's illegal to drive in most of the world? Few had seen anything quite like the Cybertruck when it was unveiled in 2019. Wrapped in an "ultra-hard, 30X, cold-rolled stainless steel exoskeleton", the Cybertruck was touted as the ultimate doomsday chariot – a virtually indestructible, obtuse-angled, electrically powered behemoth that can repel handgun fire and outrun a Porsche while towing a Porsche, with enough juice leftover to power your house in the event of a blackout. At the launch, Tesla's CEO, Elon Musk, said the truck could tackle any terrain on Earth and possibly also on Mars – and all for the low, low base price of 40,000. "Sometimes you get these late-civilization vibes [that the] apocalypse could come along at any moment," Musk said.

Tesla's quarterly results are in, and it seems the panic alarm finally went off. Analyst expectations for Tesla's first quarter of 2025 were already pretty grim, but Tesla handily beat them with a 71 percent drop in profit and a 20 percent drop in car sales, with the only thing keeping the company in the green for the quarter being 595 million in carbon credit sales. While the sales drop can partially be attributed to the launch of Tesla's updated Model Y, it's now obvious that Musk's political engagement, which includes running the so-called Department of Government Efficiency (DOGE), has been giving potential Tesla buyers pause. DOGE, which is supposed to be cutting unnecessary government expenses, has instead wreaked havoc inside the federal government, with fairly little to show for it. Musk's endorsement of far right political options probably isn't helping either, especially in Europe, where Tesla sales have plummeted in recent months.

Over the weekend, protesters gathered at Tesla showrooms in hundreds of cities across the world to demonstrate against Elon Musk laying waste to the US government in alliance with Donald Trump. One sign in Manhattan read: "Burn a Tesla, save democracy." Protesters are using the commercial democracy of consumer products to influence US political democracy. In New York City, several hundred anti-Tesla protesters gathered outside the EV company's Manhattan showroom on Saturday. Sophie Shepherd, 23, an organizer with Planet Over Profit, explained that the rally was not about protesting electric cars.

Recent advances in constrained reinforcement learning (RL) have endowed reinforcement learning with certain safety guarantees. However, deploying existing constrained RL algorithms in continuous control tasks with general hard constraints remains challenging, particularly in those situations with non-convex hard constraints. Inspired by the generalized reduced gradient (GRG) algorithm, a classical constrained optimization technique, we propose a reduced policy optimization (RPO) algorithm that combines RL with GRG to address general hard constraints.

Two-sided matching markets describe a large class of problems wherein participants from one side of the market must be matched to those from the other side according to their preferences.

Tesla Chief Executive Elon Musk told employees to hold on to their stock and stay optimistic amid a series of blows to his company's reputation that have sent shares plunging. Since Musk began his prominent role in the Trump administration in January, Tesla stock has taken a hit as protests against the electric vehicle brand have erupted across the country. Tesla shares rose 5% Friday to close at 248.71 but have dropped 34% this year. With Chief Executive Elon Musk playing a prominent role in the Trump administration, many Tesla drivers are no longer happy about supporting the car brand. Tesla vehicles, dealerships and charging stations have become targets for vandalism as distaste grows for Musk and his Department of Government Efficiency, or DOGE.

Federated learning (FL) is a machine learning paradigm that allows multiple FL participants (FL-PTs) to collaborate on training models without sharing private data. Due to data heterogeneity, negative transfer may occur in the FL training process. This necessitates FL-PT selection based on their data complementarity. In cross-silo FL, organizations that engage in business activities are key sources of FL-PTs. The resulting FL ecosystem has two features: (i) self-interest, and (ii) competition among FL-PTs.

This paper addresses the critical challenge of optimizing electric vehicle charging station placement through a novel data-driven methodology employing causal discovery techniques. While traditional approaches prioritize economic factors or power grid constraints, they often neglect empirical charging patterns that ultimately determine station utilization. We analyze extensive charging data from Palo Alto and Boulder (337,344 events across 100 stations) to uncover latent relationships between station characteristics and utilization. Applying structural learning algorithms (NOTEARS and DAGMA) to this data reveals that charging demand is primarily determined by three factors: proximity to amenities, EV registration density, and adjacency to high-traffic routes. These findings, consistent across multiple algorithms and urban contexts, challenge conventional infrastructure distribution strategies. We develop an optimization framework that translates these insights into actionable placement recommendations, identifying locations likely to experience high utilization based on the discovered dependency structures. The resulting site selection model prioritizes strategic clustering in high-amenity areas with substantial EV populations rather than uniform spatial distribution. Our approach contributes a framework that integrates empirical charging behavior into infrastructure planning, potentially enhancing both station utilization and user convenience. By focusing on data-driven insights instead of theoretical distribution models, we provide a more effective strategy for expanding charging networks that can adjust to various stages of EV market development.