Tutorial: Antigravity & AI Studio Using Gemini APIs | Future of Data and AI | Agentic AI Conference

Why It Matters

By democratizing high‑performance multimodal AI and slashing inference costs, Google’s AI Studio accelerates product innovation and expands access to advanced generative capabilities for both startups and large enterprises.

Key Takeaways

• Google DeepMind unveiled multiple Gemini models for multimodal AI tasks.
• AI Studio now supports free API keys, usage monitoring, and low-cost inference.
• Gemini Flashlight can analyze 2‑hour videos for under $0.35.
• Build feature lets users generate custom apps, including database‑backed image recognizers.
• New LIIA model enables multilingual music generation with karaoke integration.

Summary

The video showcases Google DeepMind’s latest AI Studio platform and the suite of Gemini APIs released over the past six months, highlighting models such as Gemini 3.1 Flash Live, Flashlight, Nanobanana 2, Embeddings 2.0, LIA 3, Genie 3, and the open‑source Gemma 4 family. Paige Bailey walks through how developers can access AI Studio via ai.dev, generate free API keys, monitor usage, and experiment with multimodal inputs—text, audio, video, and images.

Key insights include the unprecedented multimodal capability of Gemini models, which can ingest and output across text, code, images, audio, and video. Cost efficiency is emphasized: the tiny Gemini 3.1 Flashlight processes a 2‑hour YouTube lecture for roughly 33 cents, while token pricing remains a fraction of a dollar. The platform also integrates database and OAuth services, enabling rapid app creation without extensive infrastructure.

Demonstrations feature real‑time video segmentation, multilingual transcription, and the LIIA music‑generation model that produces songs with lyrics in Hindi, Arabic, and other languages, even supporting karaoke‑style lyric highlighting. The new “Build” tool lets users describe an app—such as a bookshelf image recognizer that enriches data via Google Search and stores results in Firebase—and the model generates the full codebase automatically.

These capabilities lower barriers for engineers, data scientists, and creators, allowing them to prototype sophisticated AI‑driven products at near‑zero cost. Enterprises can embed multimodal intelligence into workflows, while independent developers gain a turnkey stack for building, deploying, and scaling AI applications.

Original Description

This session, demonstrated by Paige Bailey, AI Developer Relations Lead at DeepMind, shows developers how to go from idea to production faster using Google AI Studio and the Gemini APIs. You’ll explore how Gemini’s multimodal capabilities and developer tooling remove the friction from building intelligent applications; so you can focus on what you’re creating, not the infrastructure underneath.

In this session, you’ll learn to:

- Build and prototype AI-powered applications using Google AI Studio and the Gemini API suite.

- Leverage multimodal inputs, long context, and Gemini’s latest features to accelerate development workflows.

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