Jul 25, 2026

Why Large? Tiny LMs & Agents on Edge/Robotics — Cormac Brick, Google

Cormac Brick from Google AI Edge discusses how the increasing constraint of DRAM cost on edge devices necessitates the development and deployment of increasingly smaller AI models. He outlines the work of his team in optimizing models like Gemma, achieving 2.9 bits per weight for a 2 billion parameter model capable of running on a Raspberry Pi at 7.6 tokens/second, or on an NPU at 31 tokens/second decode for vision tasks. The talk delves into 'tiny models' (50M-500M parameters) that extend AI to older devices and enable features like robust voice-to-function calling via fine-tuning with synthetic data, exemplified by an offline voice dictation app.

May 20, 2026

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

Patrick Löber from Google DeepMind provides a technical walkthrough of the Gemini API's "any-to-any" capabilities. The session covers multimodal understanding of complex documents, video, and audio; an agentic loop using function calling to trigger native image and speech generation; and the real-time, audio-to-audio Live API.

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.

Dec 29, 2025

Agentic Al in SW Development: Evolving Patterns & Protocols • Bhuvaneswari Subramani • GOTO 2025

Bhuvaneswari Subramani details the "Agentic Shift" in AI by presenting an evolutionary journey through seven foundational system design patterns. The talk progresses from simple conversational clients to sophisticated, multi-agent systems, covering key patterns like Retrieval-Augmented Generation (RAG), Self-Correcting RAG, and the Model Context Protocol (MCP), explaining how each pattern adds new layers of context, action, and autonomy.

Sep 29, 2025

Beyond Chatbots: How to build Agentic AI systems with Google Gemini // Philipp Schmid

A deep dive into the evolution from static chatbots to dynamic, agentic AI systems. Philipp Schmid of Google DeepMind explores how to design, build, and evaluate AI agents that leverage structured outputs, function calling, and workflow orchestration with Google Gemini, covering key agentic patterns and the future of AI development.

Sep 03, 2025

Build Hour: Built-In Tools

Built-in tools like web search, file search, and code interpreter allow developers to extend model capabilities out-of-the-box. This summary covers the concepts, compares them to function calling, and details a demo of building a data exploration dashboard using multiple tools in concert.