Genie

Prompt to Pipeline: Building with Google's Gen Media Stack — Paige & Guillaume, Google DeepMind

May 23, 2026 · 1:54:35

Paige Bailey, Guillaume Vernade, and Ian Valentine from Google DeepMind demonstrate the company's full generative media stack—from Gemini 3.1 Flash Light's video analysis at $0.25 per million tokens to Genie 3's playable world models and Gemma 4's on-device agentic coding—showing developers how to build multimodal apps without cloud APIs. Paige shows AI Studio's Build feature creating a bookshelf scanning app with Firestore and OAuth. Guillaume walks through a workshop using Nano Banana 2 for character portraits, VO 3.1 Lite for video generation at $0.05 per image, LIA 3 for chapter scores, and text-to-speech with distinct voices. Ian runs Gemma 4's 26B mixture-of-experts model on a MacBook to generate 10 SVGs in parallel and build a game from a spec, all locally. The episode argues that Google's models absorb common agent patterns, making custom fine-tunes less necessary.

Build & deploy AI-powered apps — Paige Bailey, Google DeepMind

Apr 29, 2026 · 1:03:20

Paige Bailey, engineering lead for developer relations at Google DeepMind, demonstrates how the company's latest multimodal models and AI Studio platform enable rapid prototyping of AI-powered apps at minimal cost. She showcases Gemini 3.1 Flash Lite analyzing YouTube dinosaur videos for under a penny, the new Build feature constructing a fully functional bookshelf cataloger with Firebase database and Google login, and Gemini Live providing real-time multilingual screen interaction. Other demos include Genie 3 generating playable world models from text prompts, NanoBanana 2 performing cost-effective image editing, Veo 3.1 Lite producing stock footage (e.g., a "vegan basketball food truck" featuring Chef Curry), Lyria 3 creating custom music tracks, and Gemma 4 offering open-source multimodal models that run on mobile devices. Bailey emphasizes that the entire stack—grounded by Google Search and code execution—allows developers to ship ideas that would have been startups years ago, with all code exportable for production use.

Building Generative Image & Video models at Scale - Sander Dieleman, Google DeepMind

Apr 21, 2026 · 40:46

Sander Dieleman, a research scientist at Google DeepMind, explains the behind-the-scenes process of building generative image and video models at scale, focusing on diffusion models. He emphasizes that data curation is often underrated and more impactful than model tweaking. Dieleman details how latent diffusion models use learned autoencoders to compress pixels into smaller representations, enabling training on high-resolution video. He describes diffusion as 'spectral autoregression' that generates images coarse-to-fine, and notes that guidance—amplifying the difference between conditional and unconditional predictions—massively improves sample quality at the cost of diversity. On sampling, he contrasts deterministic and stochastic approaches, and covers distillation techniques like consistency models that reduce steps. Dieleman also discusses control signals beyond text, such as camera motion in video generation, and the importance of post-training for conditioning.

How Google DeepMind is researching the next Frontier of AI for Gemini — Raia Hadsell, VP of Research

Apr 18, 2026 · 20:37

Google DeepMind VP of Research Raia Hadsell presents three non-language-model frontiers of AI: Gemini Embeddings 2, an omnimodal embedding model that unifies text, video, audio, and PDFs into a single semantic vector for fast retrieval; weather forecasting models including GraphCast (spherical GNN for 15-day forecasts), GenCast (probabilistic model 97% more accurate than benchmarks), and FGN (directly predicts cyclones, used by the US National Hurricane Center); and Genie world models that create interactive, memory-rich 3D environments from prompts, enabling real-time dynamic changes for gaming and education.