◉GEMINI LABJP
●NANOLITE — Nano Banana 2 Lite is here: Google's fastest and most cost-efficient Gemini Image model, made for running lightweight image generation cheaply●OMNIFLASH — Gemini Omni Flash is in public preview, a natively multimodal model that lets enterprises and developers build custom, dynamic video workflows●AGENTS — Managed Agents expand with background: true for async server-side runs and polling, remote MCP server integration, and refreshing credentials across interactions●MEMORY — The Memory Bank IngestEvents API is generally available, decoupling event ingestion from memory generation so you can stream content continuously●THROUGHPUT — Provisioned Throughput now lets you submit up to seven pending orders for the same model and region●DEPRECATE — Image generation models shut down on August 17, and the Grok 4.1 family on the Gemini Enterprise Agent Platform on August 20●NANOLITE — Nano Banana 2 Lite is here: Google's fastest and most cost-efficient Gemini Image model, made for running lightweight image generation cheaply●OMNIFLASH — Gemini Omni Flash is in public preview, a natively multimodal model that lets enterprises and developers build custom, dynamic video workflows●AGENTS — Managed Agents expand with background: true for async server-side runs and polling, remote MCP server integration, and refreshing credentials across interactions●MEMORY — The Memory Bank IngestEvents API is generally available, decoupling event ingestion from memory generation so you can stream content continuously●THROUGHPUT — Provisioned Throughput now lets you submit up to seven pending orders for the same model and region●DEPRECATE — Image generation models shut down on August 17, and the Grok 4.1 family on the Gemini Enterprise Agent Platform on August 20
← Back to Blog

Introducing Gemini Lab

gemini-labannouncementlaunch

What is Gemini Lab?

Gemini Lab is a bilingual knowledge base dedicated to Google's Gemini family of large language models. We cover everything from basic usage guides to advanced API development and prompt engineering techniques, all available in both Japanese and English.

Gemini Lab is part of a family of sister sites built on a shared technology stack. Alongside Claude Lab for Claude AI, Antigravity Lab for Google's experimental coding tools, and Rork Lab for mobile app development, our mission is to provide well-organized, practical knowledge about modern AI and development tools.

Why a Dedicated Gemini Knowledge Base?

Since the release of Gemini 2.0 in late 2024, Google's AI platform has evolved at a remarkable pace. Gemini 2.5 Pro and Flash introduced thinking models and enhanced multimodal processing in 2025. Now in 2026, the Gemini 3 series and the latest 3.1 Pro and Flash-Lite models have significantly expanded the lineup.

Keeping up with this rapid evolution requires systematic information organization. While official documentation is comprehensive, practical guides with real-world examples remain scarce. Gemini Lab was created to fill this gap with hands-on tutorials and in-depth technical content.

Content Categories

We currently publish articles across four main categories.

Gemini Basics — Introductory content for those getting started with Gemini, including overviews of what Gemini is, how to choose between Pro and Flash, and business use cases.

Developer Tools — Guides for developer-facing tools such as Gemini CLI, Google AI Studio, and Gemini Extensions, covering setup, workflows, and best practices.

API / SDK — Implementation-focused articles on the Gemini API, including quickstart guides, streaming, multimodal input, and logging and dataset management.

Advanced Usage — Deep dives into Gemini's cutting-edge features like Function Calling, Grounding with Google Search, and text-to-speech synthesis.

Technical Highlights

Gemini Lab is built with Next.js 16 and TypeScript, hosted on Cloudflare Workers. Articles are managed in MDX format with full bilingual support. Code blocks feature syntax highlighting powered by rehype-pretty-code and shiki, optimized for both dark and light themes.

We've also implemented llms.txt for AI search engine compatibility and comprehensive JSON-LD structured data for enhanced SEO.

What's Next

The Gemini ecosystem continues to expand rapidly. We plan to cover topics including detailed Gemini 3.1 Pro and Flash-Lite benchmarks, practical Gemini CLI workflows, integration patterns with Firebase and BigQuery, agent-building tutorials, and comparisons with other leading LLMs.

This blog will also serve as a hub for site updates and the latest Gemini news. Stay tuned, and thank you for visiting Gemini Lab!