Multimodal ai

Google DeepMind Developers: How Nano Banana Was Made

Google DeepMind Developers: How Nano Banana Was Made

Google DeepMind's Oliver Wang and Nicole Brichtova discuss the creation of the Nano Banana image model, its viral success driven by character consistency, and the future of AI in creative work, from user interfaces and 2D/3D world models to the next frontier of video generation and high-fidelity reasoning.

Is AI Slowing Down? Nathan Labenz Says We're Asking the Wrong Question

Is AI Slowing Down? Nathan Labenz Says We're Asking the Wrong Question

Nathan Labenz argues that AI progress is not slowing down but is instead manifesting in less obvious but more powerful ways, such as advanced reasoning and multimodal capabilities. He deconstructs the debate around GPT-5's perceived impact, highlights the revolutionary potential of AI agents in science and engineering, and discusses the tangible effects on job automation. The conversation also explores the rise of robotics, the challenges of emergent AI behaviors like reward hacking, and concludes with a call for a collective, positive vision to steer this transformative technology.

How LiveKit Became An AI Company By Accident

How LiveKit Became An AI Company By Accident

Russ d'Sa, CEO of LiveKit, recounts the company's unexpected journey from a pandemic-era open-source WebRTC project to becoming a crucial infrastructure provider for AI voice interfaces, most notably for OpenAI's ChatGPT. He details the serendipitous moments that led to this pivot and shares his vision for LiveKit as the nervous system for a multimodal AI future.

Introducing gpt-realtime in the API

Introducing gpt-realtime in the API

An overview of the new GPT-realtime speech-to-speech model and the general availability of the Real-Time API, detailing its architecture, advanced capabilities like image input and multilingualism, training methodology, and new enterprise-ready features.

Building Production-Grade RAG at Scale

Building Production-Grade RAG at Scale

Douwe Kiela, CEO of Contextual AI, explains the evolution from basic RAG to "RAG 2.0", an end-to-end, trainable system. He argues that this system-level approach, which integrates optimized document parsing, retrieval, reranking, and grounded models, is superior to relying on massive context windows alone and is a fundamental tool for next-generation AI agents.