Building with Gemini 3, AI Studio, Antigravity, and Nano Banana | The Agent Factory Podcast

Key Concepts

• Gemini 3 Pro: The latest iteration of Google's Gemini model series, focusing on enhanced coding capabilities and the ability to act on reasoning.
• Nano Banana Pro: The next generation of Google's NanoBanana models, likely for more specialized or efficient AI tasks.
• Anti-gravity: A new IDE (Integrated Development Environment) for developers, built by DeepMind, designed for AI development.
• AI Studio: A platform for developers to build and deploy AI applications, featuring tools like "Build" and "Annotate."
• Gemini Live: An API for real-time, conversational AI interactions.
• Vending Bench: A benchmark for evaluating an AI model's ability to run a passive business, measuring profitability.
• Elmarina & Webdev Arena: Benchmarks for evaluating AI model performance in specific domains (likely related to coding or web development).
• Pre-training & Post-training: Two key phases in model development. Pre-training involves exposing the model to vast amounts of data (internet, synthetic data like code and video footage). Post-training refines the model with specific use-case examples and techniques like reinforcement learning.
• Tool Use & Function Calling: Capabilities that allow AI models to interact with external tools and execute specific functions, crucial for agent-like workloads.
• Multimodality: The ability of an AI model to understand and process information from multiple types of data, such as text, images, audio, and video.
• Google Cloud: Google's cloud computing service, used for deploying and managing AI applications.
• OpenAI Compatibility Layer: A feature that allows developers to easily switch from using OpenAI APIs to Gemini APIs with minimal code changes.

Gemini 3: The Next Evolution in AI Models

Gemini 3 represents the next iteration in Google's Gemini model series, building upon its predecessors with a strong emphasis on enhanced coding abilities and the capacity to execute actions based on its reasoning. This evolution is characterized by significant improvements in tool use and function calling, making it highly suitable for agent-style workloads and enabling more composite architectures where different models can interact with systems to accomplish tasks.

Evolution of Gemini Models:

• Gemini 1: Focused on understanding diverse content types, including video, images, audio, text, and code, all simultaneously.
• Gemini 2: Introduced the concept of reasoning, planning, and step-by-step thinking with detailed thought traces and "thinking tokens."
• Gemini 3: Prioritizes becoming significantly better at coding and acting upon instructions.

Model Development: Pre-training and Post-training

The development of Gemini 3 involves two critical phases:

1. Pre-training: This phase focuses on exposing the model to an extensive amount of data. This includes the entirety of the internet and a significant amount of synthetic data. Examples of synthetic data include video game footage and synthetically generated code that is then run to verify its utility against descriptions.
2. Post-training: This phase refines the model by providing specific examples of desired use cases. This includes curated examples of users employing multiple tools to complete tasks, extensive multi-turn conversations, and scenarios involving website edits. Techniques like reinforcement learning are also incorporated.

Benchmarking and Performance

Gemini 3 Pro has demonstrated impressive performance across various benchmarks:

• Vending Bench: This benchmark evaluates a model's ability to run a passive business, specifically a vending machine. Gemini 3 Pro achieved approximately $5462 per vending machine annually, showcasing its strategic and long-term decision-making capabilities. The speaker expressed interest in developing a "laundromat bench" as well, highlighting the potential for AI in managing passive businesses.
• Elmarina: Gemini 3 is the first model to surpass 1500 on this benchmark, achieving a score of around 151.
• Webdev Arena: The model performs exceptionally well in web development. The "Build" feature in Replit, which assists in creating user interfaces, is powered by Gemini 3.
• Reasoning and Multimodality: Gemini 3 is considered state-of-the-art in reasoning and multimodality.
• Voxel Art Experiences (Minecraft): The model shows significant improvement in creating voxel art, with noticeable differences compared to Gemini 2.5 Pro in demonstrations. It excels at game creation, including those with complex mechanics and appealing user interfaces.

Introducing Anti-gravity: An AI-Native IDE

Anti-gravity is a new IDE developed by DeepMind, specifically designed for AI development. It integrates Gemini 3 Pro directly, allowing developers to build applications more intuitively.

Key Features of Anti-gravity and AI Studio's "Build" Feature:

• Intuitive Interface: Developers can simply describe the app they want to create, and the IDE generates the code.
• App Gallery: Provides a collection of remixable apps created by the team, including comic book creators, product mockups, and simulation games.
• "I'm Feeling Lucky" Feature: Offers ready-made app ideas for users who are unsure where to start.
• Autofix Capability: The system attempts to automatically fix errors in the generated code, though this is triggered less frequently as models improve.
• Code Generation and Prompt Creation: The IDE generates code and also writes prompts for the AI models, often producing better prompts than manual creation.
• Directory Structure: Organizes generated files into a clear directory structure.
• Integration with Latest Models and Features: Benefits from insights into the most recent models and API capabilities.
• React and React Native Support: Generates React apps that can be accessed on mobile devices via React Native.
• GitHub Integration: Allows saving projects to public or private GitHub repositories.
• "Annotate" Feature: Enables users to circle features on the app interface and add comments for design modifications, similar to redlining with a designer.
• Task List and Implementation Plan: The AI generates a task list and an implementation plan, providing documentation and grounding for the development process.

Example: Building an Insurance Cataloging App

A demonstration showcased the creation of an insurance cataloging app using AI Studio's "Build" feature. The app was designed to:

• Use the webcam and microphone for a conversational interface.
• Catalog user-shown objects, describing them, noting their condition, and estimating their value using Google Search grounding.
• Present the cataloged items in a table format.
• Display statistics about the insured objects.
• Incorporate a "Nordic theme" with an "IKEA vibe."
• Utilize the Gemini Live API for the video conversation.

The process involved the AI interpreting a complex prompt, generating code, and then debugging and refining the application. The final app, named "Nordic Shield," successfully cataloged items, provided descriptions and conditions, and used Google Search to estimate values, with a follow-up agent performing the search.

Nano Banana Pro: Generative Media Capabilities

Nano Banana Pro is highlighted for its advanced generative media capabilities. Examples of its applications include:

• Image Generation: Creating images of popular physicists and computer science figures (e.g., Claude Shannon, Richard Feynman, Carl Sagan) with high quality.
• Game Asset Generation: Used in a hackathon project for generating all game assets and design patterns for games.
• Orthographic Blueprints: Generating detailed blueprints for real-life locations, such as Neuschwanstein Castle, from various views with high text reliability.
• Physics Explainers: Creating detailed explanations of physics concepts.
• Resolution and Aspect Ratio Control: Users can specify resolution (1K, 2K, 4K) and aspect ratios for generated media.

The combination of Nano Banana Pro with other Gemini models allows for complex workflows, such as researching information, generating images, and then animating them into explainers for presentations.

Deployment and Logging with Google Cloud

The generated applications can be deployed to Google Cloud, providing a unique URL and access to logging and insights.

Google AI Studio Logging Features:

• JSON Blobs: Records all incoming JSON data from app interactions.
• Usage and Billing: Tracks API errors, requests per day, and potential rate limit issues.
• Project Management: Allows viewing and managing different projects and generative language client keys.

Personal Project: Website Redesign with Anti-gravity and Gemini 3

The host demonstrated a personal project where they used screenshots of a new website design and their old website's code. These were fed into Anti-gravity with Gemini 3 Pro to redesign the existing website to match the new aesthetic.

• Process: The AI interpreted the design philosophy from the images and applied it to the codebase.
• Outcome: The redesigned website adhered well to the brand guidelines, featuring interactive elements like "jiggling pills" and improved organization of resources.
• Benefits: This process highlights the power of multimodal AI in understanding visual design and translating it into functional code, significantly reducing manual effort for developers, especially those less proficient in specific coding languages like TypeScript.

Conclusion: A Unified AI Stack

The discussion emphasized that Google's advancements extend beyond individual models to encompass their entire AI stack, from hardware and compilers to machine learning frameworks and end-to-end deployment solutions like Anti-gravity and AI Studio. This integrated approach ensures the development of highly capable models for diverse use cases, making it easier to build and deploy powerful AI applications.