“gemini”
Context Budgeting in Agent RAG: What to Include, What to Drop
Master context window management for agent RAG systems with strategies for token budgeting, relevance scoring, compression, and dynamic context allocation using Gemini API patterns.
Flutter AI & Agentic Development in 2026: From Vibe Coding to GenUI
How Dart and Flutter are embracing AI in 2026. From Agentic Hot Reload and MCP servers to GenUI and the A2UI protocol, discover how Flutter is redefining app development for the AI era.
Realtime Voice Agents with Google ADK: Building Low-Latency Conversational AI
Build production-ready voice agents using Google Agent Development Kit with streaming audio, tool calling, and natural turn-taking.
Building a Voice Agent with the Gemini Live API
Step-by-step guide to building real-time voice agents using the Gemini Live API with WebSocket streaming, VAD, and interrupt handling.
Safety Settings in the Gemini API: Blocking Harmful Content Before It Ships
Configure Gemini API safety settings to block harmful content at the API level. Set category thresholds, handle safety ratings, and build production-ready content filtering.
Handling Barge-In: Turn-Taking and Interruption for Voice Agents
How to implement barge-in, turn-taking, and interruption handling in voice AI agents — with VAD architectures, Gemini Live API patterns, and production benchmarks.
Google Gemini API Computer Use: Build Agents That See, Reason, and Act
Google brings computer use as a built-in tool in Gemini 3.5 Flash, enabling developers to build AI agents that control browsers, mobile, and desktop environments through screenshots and UI actions.
Building a Personal Research Assistant with Long-Context Agents
Learn how to build a personal research assistant using long-context AI agents that can process entire document collections, synthesize findings, and deliver structured research reports.
AI-Driven Full-Stack Architecture: Token Economics of Building with Rust and Next.js
Explore the token economics of building full-stack apps with Rust and Next.js under AI-assisted workflows. Learn codebase tokenization patterns, agent loop costs, and optimization techniques.
Building an AI-Powered Fitness Tracker with GenKit
Build an AI fitness tracker in Flutter: use the Gemini API (google_generative_ai package) for personalized workout plans, JSON structured output, and activity summaries, with GenKit flows as an alternative architecture.
Building an AI Pair Programmer: From IDE Plugin to CLI
An AI pair programmer is an agent with file tools and a loop. Learn the architecture, then build a minimal CLI pair programmer in Python with Gemini function calling.
Tools, Function Calling, and the Agent Execution Loop
The agent loop is a model deciding which tools to call, observing results, and iterating. Learn function declarations, tool_choice modes, and parallel calling.
Prompt Caching Strategies to Cut LLM API Costs
Cached input tokens are ~90% cheaper than fresh ones. Learn implicit vs explicit caching, TTL design, and how to structure prompts so your cache hits.
Gemini 3 for Accessibility: Real-time Multimodal Translation for the Inclusive Web
Accessibility is a multimodal problem: captions for the deaf, narration for the blind, sign language for signers. Learn to build an inclusive web layer with Gemini 3 that translates between speech, text, sign, and braille in real time — privacy-first and in the browser.
Gemini 3 in EdTech: Personalized Tutors that Adapt to Real-time Student Affect
The best tutor notices when you are confused, bored, or stuck — and changes how it teaches. Learn to build an adaptive Gemini 3 tutor that reads student affect in real time, adapts difficulty with cognitive-science models, and asks questions instead of handing out answers.
Building a Collaborative Gemini 3 Editor: Real-time Writing and Fact-Checking
Stop copying text between a doc and a chat window. Learn to build a collaborative editor where a Gemini 3 agent is a first-class co-author — reading, editing, and fact-checking the shared document through CRDTs in real-time.
The "Persona-Shift" Pattern: Dynamic Expert Simulation in Gemini 3
A static persona limits an agent to one role. Learn the Persona-Shift pattern: dynamic expert switching in Gemini 3 agents that lets a single model act as planner, critic, and specialist across a workflow — without persona drift.
Gemini 3 in LegalTech: Automating Complex Contract Audits with High-Precision Reasoning
Contracts are full of computational clauses that probabilistic models get wrong. Learn to build a production-grade contract audit system that pairs Gemini 3 extraction with a deterministic rule engine to eliminate the "reasoning cliff" and the hallucination risk.
Green AI: Optimizing Gemini 3 Reasoning for Low-Power Infrastructure
AI inference is primarily a data-movement problem, not a compute problem. Learn practical strategies to reduce the energy cost of Gemini 3 reasoning: token budgeting, model selection, quantization, speculative decoding, and measuring impact per prompt.
Gemini 3 in Cyber-Defense: Real-time Threat Hunting and Automated Mitigation
Threat hunting is a reasoning problem, not a pattern-matching problem. Learn to build a Gemini 3-powered defensive agent that hunts threats across SIEM data, maps behavior to MITRE ATT&CK, and automates mitigation with human-in-the-loop gates.
Agentic Game Design: Procedural World Building with Gemini 3 Multimodal Guidance
Games are multimodal by nature: maps, sprites, audio, and physics all have to agree. Learn to build an agent that designs and builds 3D game worlds with Gemini 3, using a constrained schema compiler and a generate-see-correct vision loop.
Agents in VR/AR: Navigating 3D Spaces with Gemini 3 Native Spatial Reasoning
Spatial reasoning is what lets an agent understand the difference between "on the table" and "next to the table." Learn to build VR/AR agents that perceive 3D space with Gemini 3, ground actions with bounding boxes and trajectories, and act through WebXR.
Gemini 3 Robotics: Bridging the Gap Between Reasoning and Physical Motion
Reasoning and motion used to live in different worlds. Learn the two-brain robotics pattern: an embodied reasoning model that plans and coordinates, and a vision-language-action model that moves.
Multi-Modal RAG for Video: Using Gemini 3 to Build Searchable 4K Video Knowledge Bases
Transcription-only video search throws away the pixels. Learn to embed video natively with Gemini Embedding 2, chunk with overlap, truncate with Matryoshka, and return trimmed clips on a text match.
Cost Optimization in AI Development: Managing API Bills and Resource Usage
Learn how to optimize AI development costs by managing token usage, choosing the right models, and implementing automated monitoring.
Full-Stack Dart in 2026: Building AI APIs with Shelf and Gemini
Discover how to build a high-performance backend API with Dart Shelf, integrated with dartantic_ai and Gemini.
Building Your First AI-Powered Flutter App with the Gemini API and GenKit
Step-by-step guide to integrating the Gemini API into a Flutter application using GenKit for seamless AI-powered features.
Power Up Your Terminal: Build a Natural Language CLI Tool with Typer and an LLM
Learn how to build a custom CLI tool that understands natural language commands using Python, Typer, and the Gemini API.
Optimizing Agent Performance: Context Caching with Gemini in the Google ADK
Learn how to leverage context caching in the Google Agent Development Kit (ADK) with Gemini 2.0+ models to significantly reduce latency and costs for token-heavy agent interactions.