In this episode of the Colaberry AI Podcast, we dive into a powerful shift underway in the AI industry—the move from bigger models to smarter, more efficient architectures. Recent reports suggest that Meta is internally testing Avocado, a next-generation AI model that may achieve up to 100× efficiency gains over Llama 4. Developed under the leadership of Alexander Wang, Avocado signals a possible pivot toward closed-source strategies and ultra-cost-efficient scaling designed to serve Meta’s billions of users worldwide.
At the same time, innovation is accelerating in specialized domains. Researchers from Google and Peking University have introduced Paper Banana, a multi-agent framework that automates the creation of scientifically accurate diagrams, plots, and publication-ready visuals. This system highlights how AI is becoming deeply embedded in academic and research workflows—not as a writing assistant, but as a precision design and data-translation tool.
Further leaks indicate that a new Gemini 3 Pro checkpoint is being evaluated, showcasing advanced capabilities in generating precise user interfaces, complex SVG graphics, and structured visual layouts. This points to a growing emphasis on AI-powered design automation across professional software environments.
Together, these developments underscore a critical trend: the next generation of AI will be defined not by sheer parameter count, but by efficiency, domain specificity, and architectural intelligence.
🎯 Key Takeaways:
⚡ Meta Avocado prioritizes extreme efficiency over raw scale
🤝 Closed-source, cost-effective AI may dominate consumer platforms
🔄 Paper Banana automates scientific diagrams using multi-agent systems
📜 Gemini 3 Pro advances UI and SVG generation accuracy
🌍 Smarter architectures are replacing “bigger is better” thinking
🧾 Ref:
Meta Avocado, Paper Banana & Gemini 3 Pro – YouTube
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