AI brings your books to life

Ever wanted to step inside the pages of Frankenstein? Today, we explore how Character AI is turning classic novels into interactive worlds, alongside OpenAI’s leap into biology. 😃

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What’s up? Character.AI has launched c.ai Books, a new feature that lets users step inside classic novels and interact with their characters and worlds like a game.

Want the details? CAI Books is available on mobile and web through CAI Labs. It launches with more than 20 public domain classics, including Alice in Wonderland, Pride and Prejudice, Dracula, and Frankenstein. Users can choose to play as a character from the book or insert their own persona into the story. There are different ways to experience each title: one mode follows the original plot, while another lets users go off script and shape the story however they want. Character AI also includes alternate universe remixes, so people can completely reimagine a book’s setting, tone, or genre.

Why is this important? This could give classic literature a fresh audience by turning reading into something more interactive and accessible. Instead of replacing books, it may help more people feel curious about stories they might otherwise ignore.

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What happened? OpenAI has introduced GPT-Rosalind, a new AI model built specifically to help scientists with biology, drug discovery, and medical research.

How does it work? GPT-Rosalind is built to act more like a research assistant for scientists. It can read across papers, databases, genetic information, protein data, and experimental results, then reason across all of that to help with tasks like finding patterns, generating hypotheses, planning follow-up experiments, and interpreting results. OpenAI also says it is especially designed to use outside scientific tools well, which matters because life science research often requires moving between many databases and specialized software. Through a new plugin, it can connect to more than 50 scientific tools and data sources, helping researchers pull together evidence instead of doing that work manually across scattered systems.

Why is this significant? Drug discovery is slow, expensive, and often takes more than a decade, so even small improvements early in the process could have major effects later. If this model truly helps researchers generate better ideas and design stronger experiments faster, it could speed up scientific discovery and eventually help bring new treatments to patients sooner.

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Hi — I’m Hunter, a PhD candidate whose work has appeared in major academic journals and popular tech outlets. I founded FryAI to make staying ahead of AI clear, accessible, and fun.