Horny CEO Solutions is a parody service page for Lovescape, presenting an AI adult companion as the discreet answer to executives’ lonely nights, business travel and long hours. Its pitch borrows the language of enterprise software and corporate risk management: the companion is available on demand, learns a user’s preferences, answers questions, and offers conversation, games and stories. She supposedly has no interest in company roadmaps or money, and requires no meetings, board approval or calendar invites. The joke is that a fantasy companion can provide attention without the scandals and scrutiny associated with powerful executives’ private lives.
The page extends that premise through mock product features and specifications. It promises zero effects on share prices, investigations, headlines, shareholder letters or PR crises, alongside unlimited patience, instant responses and constant availability. Images and descriptions emphasize a soft, discreet, “board-safe” design, while the FAQ advises users not to expense it and clarifies that the offering is not a real company. The figures are explicitly labeled satire; the characters are AI-generated adults, and Lovescape is described as real. Overall, the page uses corporate jargon and the specter of executive scandals to advertise an AI companion while making clear that its “enterprise-grade” promises are a joke.
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Researchers tested 16 LLMs on all 55 TIS-100 puzzles, giving each one attempt and time to refine its code. The models found 133 new performance tradeoffs across 18 puzzles and set three outright cycle records, though human players improved on most solutions within days. (tis-100.wincon.dev)
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AI is increasingly becoming a gateway through which people find information. Audiences still resist having AI produce their news. (niemanlab.org)
Nvidia holds a central position in the AI industry, making disruption to the company potentially catastrophic for the wider ecosystem. The race for advanced AI is also intensifying economic and geopolitical conflict, while concentrated corporate power and infrastructure face growing political resistance. (france24.com)
LLM inference can require reading about 26 GB of model weights to generate each token from a 13-billion-parameter model. The explanation shows how model size drives memory bandwidth demands during text generation. (youtube.com)
Deletion Tuning matches full fine-tuning across three LLM post-training methods by zeroing fewer than 0.05% of model weights. Its compact masks also preserve prior-task accuracy better than full fine-tuning, LoRA, and random sparse tuning. (arxiv.org)
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