A viral red carpet moment shone light on a group of hunky Instagram influencers--and the followers who are too horny to care that they're not real. With his deep brown eyes, wide grin, and almost comically chiseled body, Jae Young Joon is the platonic ideal of a hunky male influencer. On Instagram, where he has more than 320,000 followers, he regularly posts himself trying on sheet masks at home, enjoying soju and karaoke with his friends, or posing in front of the Ferris wheel at Coachella . Occasionally, he'll promote his music, including his recent LP Pressure Release which features a BDSM-inspired album cover, his back muscles rippling underneath a harness and chains. It's an impressive online presence, and Jae's fans eat it up: his comments are filled with fire and heart-eye emoji and people praising his music.

This Scammer Used an AI-Generated MAGA Girl to Grift'Super Dumb' Men A med student says he's made thousands of dollars selling photos and videos of a young conservative woman he created using generative tools. Like many medical school students, Sam was broke. The 22-year-old aspiring orthopedic surgeon from northern India got some money from his parents, but he says he spent most of it subsidizing his licensing exams, and he's still saving up to hopefully emigrate to the US after graduation. So he started searching for ways to make additional money online. Sam, who requested a pseudonym to avoid jeopardizing his medical career and immigration status, tried a few things, with varying degrees of legitimacy and success.

Morphology-control co-design concerns the coupled optimization of an agent's body structure and control policy. This problem exhibits a bi-level structure, where the control dynamically adapts to the morphology to maximize performance. Existing methods typically neglect the control's adaptation dynamics by adopting a single-level formulation that treats the control policy as fixed when optimizing morphology. This can lead to inefficient optimization, as morphology updates may be misaligned with control adaptation. In this paper, we revisit the co-design problem from a game-theoretic perspective, modeling the intrinsic coupling between morphology and control as a novel variant of a Stackelberg game. We propose Stackelberg Proximal Policy Optimization (Stackelberg PPO), which explicitly incorporates the control's adaptation dynamics into morphology optimization. By modeling this intrinsic coupling, our method aligns morphology updates with control adaptation, thereby stabilizing training and improving learning efficiency. Experiments across diverse co-design tasks demonstrate that Stackelberg PPO outperforms standard PPO in both stability and final performance, opening the way for dramatically more efficient robotics designs.

The EV manufacturer is supported by a robust online community. But Elon Musk's politics and overblown hype about Full Self-Driving are turning some loyalists away. This month, Tesla customers erupted in outrage over what some called a " bait and switch " by the electric vehicle manufacturer. Initially, the company had offered to transfer the Full Self-Driving feature, which is now only available through a subscription model but could once be purchased for a "lifetime" fee that ran as high as $15,000, to any new Tesla purchased by March 31. The deal was most tempting for drivers already enticed by a new base Cybertruck model that cost just $59,990, a price that CEO Elon Musk soon clarified would only last for 10 days, leaving potential buyers a very small window to make up their minds. Then Tesla quietly amended the language of the FSD transfer agreement, stipulating that customers would need to take delivery of a Tesla by March 31 in order to swap their FSD from their last vehicle to the next.

The optimal policy in various real-world strategic decision-making problems depends both on the environmental configuration and exogenous events.

Neural Information Processing Systems http://nips.cc/

Navigation guided by natural language instructions presents a challenging reasoning problem forinstruction followers.

We evaluate through thousands of human-agent interactions, demonstrating 15.4% absolute improvement in instruction execution accuracy