Long LLM inference times can frustrate users. Learn how to use Operational Transparency and Firebase AI Logic to stream "thinking" steps, turning the black box into a glass box and keeping users engaged.
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We’re diving deep into the latest paradigms in AI development, starting with the difference between traditional context files (Gemini.md) and the new "Agent Skills" dynamic. We also share a story about using the Vertex AI Prompt Optimizer to automate our YouTube descriptions. It took 5 hours and nearly 100 million tokens, but the results were surprisingly consistent. Finally, we geek out on the Model Context Protocol (MCP), experimenting with exposing Flutter application state as local tools using Unix sockets.
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AGENTS.md is dead weight. Discover the automated workflow for building lean, token-saving Agent Skills.
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In this next episode of our "untitled" podcast, Nohe and Rody take a "tech walk" to discuss the evolving landscape of AI development tools. We dive deep into the differences between the linear workflows of Gemini CLI and the asynchronous, project-level capabilities of Anti-Gravity. We also geek out on home lab setups—discussing the shift from Docker Compose to Kubernetes (K3s) on Raspberry Pi clusters—and share a game-changing workflow using NotebookLM to generate context files for your AI agents. Finally, we explore Stitch for generative UI, including how to instantly create shaders and animations from simple screenshots.
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Learn how to use the Vertex AI Prompt Optimizer to automatically tune your prompts to get better results by iterating on your prompts and then running an evaluation on the outputs assessing their quality to see if it has improved.
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Learn how to improve your AI outputs to a more reliable output through continuous improvement.
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I used AI to rebuild my blog into something better. Come along as I show you how I show you why and how I did it.
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In this masterpiece, Nohe and the crew unwrap some of the most requested feature requests from the Firebase community - yes, that's you! This episode unpacks major announcements including the comprehensive integration of Gemini AI across the development workflow, the General Availability of Firebase Data Connect with managed PostgreSQL, and the arrival of real-time updates for Remote Config on the web.
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Nohe breaks down how he built a "Campaign Agent" for Dungeons & Dragons/Pathfinder. This system uses a loop of specialized agents to help manage game sessions.
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Learn how to secure AI agents and tool calls to prevent prompt injection and unauthorized data access using authorized application context.
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The Firebase Extension for Gemini CLI brings AI capabilities directly into your development environment! This video walks you through practical examples of using the Gemini CLI to configure Firebase services and deploy your applications efficiently.
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Learn how to secure AI endpoints from abuse and prohibitive costs using Firebase App Check, replay-protected tokens, and rate limiting.
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Add AI-powered review suggestions to your app that automatically suggest star ratings as users type.
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Level up your AI applications with real-time web content using Google Search Grounding! This video shows you how to ground your AI applications with real-time, publicly available web content, ensuring more accurate and current answers.
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Discover the power of Firebase's Model Context Protocol (MCP) Server! Learn how this experimental tool connects AI applications with enterprise data and tools, streamlining communication and enabling secure AI actions on behalf of users.
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Firebase Studio, formerly Project IDX, is a cloud-based environment for building, testing, and deploying production-quality applications. Discover how to use Firebase Studio's App Prototyper to quickly build AI applications.
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In this deep dive, Alexander Nohe takes a look at Imagen for image generation and presents a challenge to those viewers to come up with a prompt for generating their own images. Learn more about Imagen and the Vertex AI SDKs here.
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Learn how to use Firestore's new Vector Embedding support to power semantic search and recommendations in your Firebase app! In this episode of Firebase Deep Dives, Nohe covers: -What vector embeddings are and how they work -Why you would want to use vector embeddings in your app -How to generate vector embeddings using Vertex AI and Cloud Functions -How to query your Firestore data using vector embeddings and k-nearest neighbors search
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Integrate the Gemini API with Firebase to add generative AI capabilities to your apps using Firebase Extensions.
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Nohe and Khanh are on a mission to build a Flutter with @Firebase app. In 24 hours, they’ve built a video sharing app that uses AI (via the PaLM API) to automatically generate video summaries for users. Watch this episode of Learning to Fly to learn more!
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