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Common Side Effects airs Sundays at 11:30pm on Adult Swim — next day on Max.

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Is a 1953 independently made American black-and-white science fiction/comedy film, produced by A.D. Nast, Jr., Arch Oboler, and Sidney Pink, written and directed by Arch Oboler, and starring Hans Conried, Gloria Blondell, Billy Lynn, and Edwin Max. The film was distributed by United Artists.

Directed by Arch Oboler.
Screenplay by Arch Oboler.

Hans Conried as Kerry West.
Janet Warren as Carolyn West.
Billy Lynn as Coach Trout.
Edwin Max as the Television Deliveryman.
Gloria Blondell as the Bill Collector.
Evelyn Beresford as Old Lady Motorist.
Bob Jellison as the TV Shop Owner.
Norman Field as the Doctor.
Stephen Roberts as Head Treasury Agent.
Connie Marshall as Susie.
William Phipps as Student.
Lenore Kingston as Offended Phone Operator #2
Alice Backes as Offended Phone Operator #1
Brick Sullivan as Cop.

🌆 Step into a world of neon lights, towering skyscrapers, and rain-soaked streets in this stunning animated journey inspired by the iconic Blade Runner universe. Experience a cyberpunk dystopia brought to life through mesmerizing visuals and immersive animation. Whether you’re a fan of the original movie or just love futuristic worlds, this video will transport you straight into a world of high-tech chaos and mystery. Get ready to explore a world where the lines between humanity and technology blur. 🔮💻 #bladerunner #cyberpunk #animation #neonlights #dystopian #futureworld #animationart #aiart #fantasy #sciencefiction #cinematicai #cinema #midjourney #hailuoai #synthwave #darkwave #hailouai Wanna Try out Hailou? https://hailuoai.video?invite-code=13666198 Like what i do and want to show some love? https://buymeacoffee.com/feartube

Common Side Effects airs Sundays at 11:30pm on Adult Swim — next day on Max.

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About Adult Swim:
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UCLA doctoral student Yilin Wong noticed that some tiny dots had appeared on one of her samples, which had been accidentally left out overnight. The layered sample consisted of a germanium wafer topped with evaporated metal films in contact with a drop of water. On a whim, she looked at the dots under a microscope and couldn’t believe her eyes. Beautiful spiral patterns had been etched into the germanium surface by a chemical reaction.

Wong’s curiosity led her on a journey to discover what no one had seen before: Hundreds of near-identical spiral patterns can spontaneously form on a centimeter square germanium chip. Moreover, small changes in experiment parameters, such as the thickness of the metal film, generated different patterns, including Archimedean spirals, logarithmic spirals, lotus flower shapes, radially symmetric patterns and more.

The discovery, published in Physical Review Materials, occurred fortuitously when Wong made a small mistake while attempting to bind DNA to the metal film.

We introduce PokéChamp, a minimax agent powered by Large Language Models (LLMs) for Pokémon battles. Built on a general framework for two-player competitive games, PokéChamp leverages the generalist capabilities of LLMs to enhance minimax tree search. Specifically, LLMs replace three key modules: player action sampling, opponent modeling, and value function estimation, enabling the agent to effectively utilize gameplay history and human knowledge to reduce the search space and address partial observability. Notably, our framework requires no additional LLM training. We evaluate PokéChamp in the popular Gen 9 OU format. When powered by GPT-4o, it achieves a win rate of 76% against the best existing LLM-based bot and 84% against the strongest rule-based bot, demonstrating its superior performance. Even with an open-source 8-billion-parameter Llama 3.1 model, PokéChamp consistently outperforms the previous best LLM-based bot, Pokéllmon powered by GPT-4o, with a 64% win rate. PokéChamp attains a projected Elo of 1300–1500 on the Pokémon Showdown online ladder, placing it among the top 30%-10% of human players. In addition, this work compiles the largest real-player Pokémon battle dataset, featuring over 3 million games, including more than 500k high-Elo matches. Based on this dataset, we establish a series of battle benchmarks and puzzles to evaluate specific battling skills. We further provide key updates to the local game engine. We hope this work fosters further research that leverage Pokémon battle as benchmark to integrate LLM technologies with game-theoretic algorithms addressing general multiagent problems. Videos, code, and dataset available at this https URL.