How to Auto-Generate Meeting Notes with Gemini — A Practical Guide to Google Meet × Workspace AI
Learn how to automatically create and summarize meeting notes using Gemini. From Google Meet transcription to Gemini-powered summarization in Google Docs and full automation with Apps Script, this guide covers practical techniques to streamline your workflow.
Building Production Semantic Search with Gemini Embeddings API — Design, Implementation, and Operations
A comprehensive guide to building production-grade semantic search with Gemini Embeddings API. Covers vector DB selection, reranking, recommendation engines, and cost optimization with practical code.
Deep Dive into Gemini's Speech-to-Speech Translation — Technology Architecture and Developer Applications
Comprehensive technical exploration of Gemini 2.5's speech-to-speech translation. Learn the end-to-end architecture, Native Audio API implementation, low-latency techniques, and production deployment patterns.
Gemini × Android Studio: AI-Powered App Development— Code Assist & Agent Mode
Master Gemini's integration with Android Studio. From Code Assist basics to Agent Mode, learn how to leverage AI for Kotlin/Jetpack Compose code generation, debugging, and test automation.
Automating Multilingual Translation and Localization with Gemini API
Learn how to automate multilingual translation and app localization using Gemini API. Covers Python implementation, glossary management, batch processing, and quality checks.
Long-Term Memory and Session Persistence with Gemini API — Design Patterns for Production Chatbots
Master the design patterns for long-term memory management, session persistence, and token budget control essential for building production-grade chatbots with Gemini API.
Automate Document Summarization and Meeting Notes with Gemini API
Learn how to build an automated document summarization and meeting notes system using the Gemini API and Python. Covers text, PDF, and audio file processing with practical code examples.
Gemini × VS Code: The Complete AI Coding Assistant Setup Guide
Learn how to set up Gemini Code Assist in VS Code from installation to practical coding workflows. A step-by-step guide to supercharging your development with AI assistance.
Applying TurboQuant to RAG and Vector Search — New Uses for KV Cache Compression
Google's TurboQuant compression technology extends beyond LLM inference to RAG pipeline vector databases. Learn how embedding vector compression can improve memory efficiency, search speed, and scalability for large-scale RAG systems.
Gemini × Cursor Integration Guide — How to Use Gemini Models in Your AI Editor
Learn how to set up and use Google Gemini models in the Cursor AI editor. This guide covers API integration, prompt techniques, and practical tips for code completion, chat, and Composer features.
Building RAG Agents with Gemini × LlamaIndex — From Document Search to Multi-Step Reasoning
Learn how to build high-accuracy RAG (Retrieval-Augmented Generation) agents using Gemini API and LlamaIndex. A step-by-step guide covering index construction, query engines, and agent design.
Gemini File Search API — Build AI Responses Grounded in Your Own Data Without RAG
Learn how to use Gemini File Search API to build AI responses grounded in your own documents without vector databases or RAG pipelines, with production-ready implementation patterns.