All Articles
Gemini 2.5 Flash vs Pro: The Practical Selection Guide — Real Benchmarks and a Hybrid Routing Implementation
Benchmark-driven guide to choosing between Gemini 2.5 Flash and Pro. Real measurements of speed, cost, and reasoning quality, plus a hybrid routing implementation and context caching patterns that cut costs by up to 80%.
Optimizing App Store Screenshots with Gemini Vision API: A Complete Guide for Indie Developers
A complete Python implementation guide for using Gemini's multimodal Vision API to automatically analyze, score, and improve App Store screenshots. Four production-ready tools with full error handling.
Six Months with Gemini Deep Research: An Honest Review of What Actually Works
An honest, personal review of Gemini Deep Research after six months of daily use. What it does well, where it falls short, and how to get the most out of it.
Analyzing Your Entire Codebase with Gemini 2.5 Pro — A Complete Guide to Automated Architecture Reviews, Tech Debt Detection, and Refactoring Suggestions
Use Gemini 2.5 Pro's 1M-token context window to analyze an entire codebase at once. This guide walks through building a Python pipeline for automated architecture reviews, tech debt scoring, and refactoring suggestions.
Gemini API × PWA Complete Implementation Guide — Service Workers, Offline AI, and Web Push Notifications for App Store-Quality Web Apps
Build production-grade AI web apps by combining Gemini API with Progressive Web App techniques — Service Worker caching, IndexedDB for AI context persistence, offline fallbacks, and Web Push notifications with AI-generated content.
Gemini API Truncated Responses: Fix max_output_tokens, FinishReason & Streaming Issues
Learn why the Gemini API cuts off responses mid-generation and how to fix it. Covers max_output_tokens configuration, reading FinishReason values, and fixing incomplete streaming implementations with working Python examples.
Getting the Most from Google NotebookLM: Capabilities, Limits, and Practical Use Cases
A practical deep dive into Google NotebookLM — the source-grounded AI notebook powered by Gemini. Learn how its chat, podcast generation, and notebook guide features work, when to use it over regular Gemini, and what to watch out for.
Building a RAG System With the Gemini API: From Embeddings to Production Deployment
A complete implementation guide for RAG systems using the Gemini Embedding API and Gemini 2.5 Pro. Covers chunk strategy, vector store setup, query expansion, reranking, hallucination mitigation, async optimization, and evaluation.
Build a Personalized Recommendation System with Gemini Embedding API — Real-Time Content Recommendations from User Behavior
Learn how to build a real-time personalized recommendation system using Gemini Embedding API. Covers system design, user profile modeling, cosine similarity ranking, caching, and production scaling — with complete Python code.
Running Gemini 2.5 Pro in Production: A Practical Implementation Guide
A production-focused guide to Gemini 2.5 Pro: streaming API, Context Caching for 75% cost reduction, Thinking budget control, multi-turn conversation management, and complete error handling patterns.
Gemini 2.5 Pro vs 3.1 Pro: Should You Upgrade? — A 3-Month Comparison with Real Code
A practical 3-month comparison between Gemini 2.5 Pro and 3.1 Pro. Learn the real differences in cost, speed, and reasoning performance — with code examples and clear guidance on when to upgrade.
Making Gemini's 1M Token Context Window Actually Work: Patterns for Long-Input Accuracy
A large context window doesn't automatically mean better results. This guide covers the 'lost-in-the-middle' problem and four practical patterns for getting reliable answers from long Gemini inputs.