May 23, 2026

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

A comprehensive overview of Google DeepMind's latest advancements, featuring Paige Bailey demonstrating Gemini 1.5 Flash's cost-effective video analysis and AI Studio's single-prompt app generation. Guillaume Vernade showcases a full generative media pipeline, turning a public domain book into an illustrated, animated, and scored project using Gemini, Nano Banana, VO, and LIA. Ian Valentine closes with the power of Gemma 4, demonstrating on-device, multi-agent code generation and debugging without cloud APIs.

May 22, 2026

AI on Android: Ask me Anything — Florina Muntenescu & Oli Gaymond, Google DeepMind

Android provides a comprehensive AI strategy through AI Core, which manages the on-device Gemini Nano model. Developers can use the ML Kit GenAI APIs for easy access, with a hybrid inference option to fall back to the cloud for broader device support, ensuring both performance and reach.

May 11, 2026

MLX Genmedia — Prince Canuma, Arcee

A tour of MLX, the on-device AI framework for Apple Silicon. This talk explores real-world applications from real-time vision and multimodal omni models to sub-100ms speech synthesis and video generation, all running locally. It highlights breakthrough techniques like Turbo Quant for 1M context and showcases community projects in robotics and native apps, arguing for a future where powerful AI runs without the cloud.

May 05, 2026

Accelerating AI on Edge — Chintan Parikh and Weiyi Wang, Google DeepMind

A deep dive into Google's AI Edge stack for on-device AI, covering the new Gemma 4 models, the LiteRT framework for cross-platform deployment, and practical use cases in agent skills, tool calling, and hardware acceleration on CPUs, GPUs, and NPUs.

May 03, 2026

TLMs: Tiny LLMs and Agents on Edge Devices with LiteRT-LM — Cormac Brick, Google

Cormac Brick from Google's AI Edge team details the dual trends of on-device AI: large, system-level models like Gemma 4 enabling complex agent skills, and fine-tuned tiny LLMs for high-performance, in-app tasks. The summary covers the architecture of on-device function calling, the engineering trade-offs for edge deployment, and the practical workflow for fine-tuning and deploying models under 1B parameters on platforms like Android and iOS.

Apr 29, 2026

Everything I Learned Training Frontier Small Models — Maxime Labonne, Liquid AI

Maxime Labonne from Liquid AI shares a playbook for post-training frontier small models (under 1GB) for on-device deployment. The talk breaks down the LFM2.5 recipe, which includes on-policy preference alignment and agentic reinforcement learning, and addresses unique challenges at the 1B scale, such as capability interference and 'doom loops', offering concrete solutions to build efficient models for tasks like data extraction and tool use.