I tried 10 viral AI image generation prompts in both Nano Banana Pro and ChatGPT Images to see which one is better. Here are the results: Nano Banana Pro ChatGPT Images 1. Selfie
GENERATIVE AI
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AI manages everything for Scobleizer, including his X posts
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Wait until the AI haters learn that AI cooks my toast, makes me dinner, drives my car, runs my house, cleans my floors, entertains me, and that I have lists of 7,600 AI companies here on X. And that AI put this post on my feed. Happy holidays! 🙂
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Open source world model? Christmas 2025 is dope!
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Open source world model?
— Robert Scoble (@Scobleizer) 17 décembre 2025
Christmas 2025 is dope! https://t.co/5lB0hOOy8vOpen source world model? Christmas 2025 is dope!
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AI Artists and Filmmakers Improve with Better Tools and Stories
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I have helped judge a few of these kinds of contests and every time the work improves. Which makes sense, AI Artists and Film Makers are getting way better tools, but they also are telling better stories.
— Robert Scoble (@Scobleizer) 17 décembre 2025
Added to my AI Artist's lists. https://t.co/Y7KEGkGLNvI have helped judge a few of these kinds of contests and every time the work improves. Which makes sense, AI Artists and Film Makers are getting way better tools, but they also are telling better stories. Added to my AI Artist's lists.
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Does AI Have Imagination? Technical Facts Explained
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Does AI have imagination?
This seems to be the debate my original post has sparked. My take is: yes, but not in the way human beings imagine. I’m presenting the technical facts below. You can arrive at your own conclusion. When people say “AI has no imagination,” they’re -
AI imagination debate: technical nuances and probabilistic generation
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Since this is sparking a debate, and many people are claiming that AI has no imagination – here are some technical nuances. When people say “AI has no imagination,” they’re usually confusing experience with capability. Technically, AI generates stories using probabilistic
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Apple Research Advances Score Distillation for Flow Matching
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Score Distillation of Flow Matching Models – Apple Machine Learning Research The University of Texas at Austin, Apple
Paper: https://
machinelearning.apple.com/research/score
-distillation
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Page:
https://
yigu1008.github.io/SiD-DiT/ -
Apple Unifies Diffusion and Flow Matching for Faster Image Generation
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What if you could speed up a cutting-edge AI image generator without retraining it from scratch? Apple presents a new method that unifies two major AI image models (diffusion & flow matching) under a simple statistical rule. This lets them directly apply a powerful
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Canvas-to-Image: AI Generates Images from Your Imagination
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What if you could paint your imagination directly onto a canvas and have an AI generate the exact image you envisioned?
— 机器之心 JIQIZHIXIN (@jiqizhixin) 17 décembre 2025
Canvas-to-Image does just that.
It unifies text, subject references, poses, and layouts into a single canvas, training a diffusion model to reason across… pic.twitter.com/0fEfJl42jDWhat if you could paint your imagination directly onto a canvas and have an AI generate the exact image you envisioned? Canvas-to-Image does just that. It unifies text, subject references, poses, and layouts into a single canvas, training a diffusion model to reason across
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Agentic platform recommendations: genspark_ai, flowith, ManusAI, Pokee_AI
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A bunch. How about an agentic platform like @genspark_ai or @flowith or @ManusAI or @Pokee_AI or similar?