snake
Longest snake ever measured is over 23.5 feet long
Environment Animals Wildlife Endangered Species Longest snake ever measured is over 23.5 feet long Nicknamed the'Baroness,' this python is longer than two great white sharks. The Baroness may be as much as 10 percent longer than initially measured. Breakthroughs, discoveries, and DIY tips sent six days a week. A snake in southwest Indonesia has shattered the Guinness World Record for the longest serpent ever spotted in the wild. Nicknamed "Ibu Baron" (the Baroness), the giant female reticulated python () discovered in late 2025 measures 23-feet-and-8-inches from head to tail--about the same length as a regulation soccer goal.
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King cobras take the train in India
Earth's largest venomous snakes are hitching a rides to places they don't belong. Breakthroughs, discoveries, and DIY tips sent six days a week. The king cobra () isn't a difficult snake to spot. A fully grown adult easily reaches over 13 feet long, making them the largest venomous snakes in the world. But despite their size and iconic appearance, at least one vulnerable species in India is sneaking aboard trains and accidentally arriving into new and dangerous habitats.
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Vine-inspired robotic gripper gently lifts heavy and fragile objects
In the horticultural world, some vines are especially grabby. As they grow, the woody tendrils can wrap around obstacles with enough force to pull down entire fences and trees. Inspired by vines' twisty tenacity, engineers at MIT and Stanford University have developed a robotic gripper that can snake around and lift a variety of objects, including a glass vase and a watermelon, offering a gentler approach compared to conventional gripper designs. A larger version of the robo-tendrils can also safely lift a human out of bed. The new bot consists of a pressurized box, positioned near the target object, from which long, vine-like tubes inflate and grow, like socks being turned inside out.
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SnAKe: Bayesian Optimization with Pathwise Exploration
Bayesian Optimization is a very effective tool for optimizing expensive black-box functions. Inspired by applications developing and characterizing reaction chemistry using droplet microfluidic reactors, we consider a novel setting where the expense of evaluating the function can increase significantly when making large input changes between iterations. We further assume we are working asynchronously, meaning we have to decide on new queries before we finish evaluating previous experiments. This paper investigates the problem and introduces'Sequential Bayesian Optimization via Adaptive Connecting Samples' (SnAKe), which provides a solution by considering large batches of queries and preemptively building optimization paths that minimize input costs. We investigate some convergence properties and empirically show that the algorithm is able to achieve regret similar to classical Bayesian Optimization algorithms in both the synchronous and asynchronous settings, while reducing the input costs significantly. We show the method is robust to the choice of its single hyper-parameter and provide a parameter-free alternative.