AI Studio

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.

Any-to-Any: Building Native Multimodal Agents - Patrick Löber, Google DeepMind

May 20, 2026 · 16:21

Patrick Löber, a member of the technical staff at Google DeepMind, explains how to build native multimodal agents using the Gemini API ecosystem, covering multimodal understanding, native image and speech generation, and real-time interaction via the Live API. He demonstrates constructing a NotebookLM clone as an agentic system where a reasoning Gemini model decides whether to generate an infographic or podcast-style audio using function calls to specialized models like Nano Banana for images and a text-to-speech model for speech. The episode details practical implementation: uploading PDFs, video, and audio files; using context caching to reduce costs by 90%; and generating infographics or multi-speaker audio directly from prompts. Löber highlights that native generation models understand world context—like drawing arrows on a map to produce the Golden Gate Bridge—and that the Live API enables audio-to-audio interactions with a single architecture, supporting multiple languages and accents.

Let's go Bananas with GenMedia — Guillaume Vernade, Google DeepMind

May 18, 2026 · 1:17:14

Guillaume Vernade from Google DeepMind demonstrates the full GenMedia stack—Nano Banana for images, Veo for video, Lyria for music, and TTS—by illustrating an open-source book live. The core insight is that Gemini acts as the prompt engineer for every other model, and this works partly because the gen media models were trained on prompts written by Gemini. He shows how to generate character portraits, chapter scenes, animated video clips using images as first frames, and distinct music per chapter, with Lyria Realtime allowing continuous music that responds to new prompts like a DJ. A new interactions API caches context server-side to make chained multi-turn calls cheaper. For TTS, he uses a trick: assigning different speaking styles (e.g., long poetic pauses vs. breathless stutter) to the same two voices to create four distinct-sounding characters.

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.

Gemma 4 Deep Dive — Cassidy Hardin, Researcher, Google DeepMind

Apr 27, 2026 · 19:03

Cassidy Hardin, a researcher at Google DeepMind, details the Gemma 4 family of open-source models, claiming they set a new precedent for small-scale performance. The family includes two on-device effective models (E2B and E4B) with Per Layer Embeddings (PLE) stored in flash memory, and two larger models: a 26B mixture-of-experts (MoE) with 128 total experts (8 active) and a 31B dense model that ranked #3 on the LM arena leaderboard, outperforming models 20x its size. Architectural innovations include interleaved local/global attention (5:1 ratio, sliding windows of 512/1024 tokens), grouped query attention (8 queries per key-value in global layers), and native multimodal support with variable aspect ratios and resolutions for vision encoders (550M params for large, 150M for small) plus a 305M parameter conformer for audio. All models are released under Apache 2.0 license, available for self-hosting on Hugging Face and Ollama, or cloud deployment on Vertex AI.

Building in the Gemini Era – Kat Kampf & Ammaar Reshi, Google DeepMind

Dec 15, 2025 · 17:57

Kat Kampf and Ammaar Reshi from Google DeepMind present Gemini 3 and Nano Banana Pro, arguing that these models enable anyone to build complex, aesthetic applications through natural language alone. Gemini 3 achieves state-of-the-art results in one-shot UI design and agentic tool calling, with SweBench outperformance. Nano Banana Pro integrates Google Search for world knowledge and renders text accurately, handling up to 14 consistent people per image. They demonstrate vibe coding in AI Studio, building a personalized comic book with precise text, laptop stickers grounded in search, and a 3D racing game that scaled to 23 players live. Upcoming full-stack runtime adds backend support and automatic database integration, further democratizing software creation.

AI Engineering with the Google Gemini 2.5 Model Family - Philipp Schmid, Google DeepMind

Jul 11, 2025 · 1:44:51

In this workshop, Philipp Schmid from Google DeepMind demonstrates AI Engineering with the Gemini 2.5 model family, focusing on using Gemini 2.5 Flash via a free API tier for hands-on coding tasks including text generation, multimodal processing of images, audio, and PDFs, function calling with structured outputs, and integration with MCP servers. The session covers setting up API keys in AI Studio, uploading files via the Files API (free for 1 day), and controlling thinking budgets (0–24,000 tokens) to manage cost and reasoning depth. Schmid shows how Gemini natively processes videos at 1 frame per second for accurate timestamp extraction and how PDFs are handled by combining OCR text with image understanding. He introduces native tools like Google Search with grounding metadata, code execution, and URL context, and explains how MCP servers can be used seamlessly with the Gemini SDK for tool calling. The workshop also covers parallel vs sequential function calling, the Agent Development Kit (ADK), and the upcoming asynchronous function calling for the Live API, providing a practical path from simple generation to agentic workflows.

A year of Gemini progress + what comes next — Logan Kilpatrick, Google DeepMind

Jul 10, 2025 · 11:58

Logan Kilpatrick, head of product for Google AI Studio at DeepMind, announces the final update to Gemini 2.5 Pro, which achieves state-of-the-art results on Aider and HLE benchmarks. He details Google's 50x increase in AI inference over the past year, driven by merging research and product teams into DeepMind. The episode outlines Gemini's evolution toward a universal assistant that unifies Google products, with upcoming features including proactivity, native audio and video capabilities (Veo), and smaller models. Kilpatrick also previews developer-focused updates: a SOTA embeddings model, a deep research API, and Veo 3 and Imagine 4 in the API, alongside repositioning AI Studio as a dedicated developer platform.

Veo 3 for Developers — Paige Bailey, Google DeepMind

Jun 21, 2025 · 20:37

Paige Bailey, engineering lead for Google DeepMind's DevRel team, presents Veo 3, the latest video generation model that natively produces synchronized audio—dialogue, sound effects, and music—from text and image prompts, marking a leap in coherence, physics simulation, and cinematic control. She demonstrates Veo 2's creative controls like reference-powered videos, outpainting, and character consistency, and shows how video generation has evolved from 2023's choppy clips to Veo 3's polished raccoon dancing in front of pyramids. A key demo replicates a Chick-fil-A commercial: Veo 2 required stitching multiple tools, while Veo 3 produced the full ad from a single prompt. The models are accessible via Vertex AI private preview and Gemini Ultra plans.