All Articles
Why 'contents must alternate between user and model' Won't Go Away in the Gemini API — and How to Fix It
A focused guide to the Gemini API's 'contents must alternate between user and model' error — what really triggers it, why role names from OpenAI break it, and how to fix Function Calling and system_instruction pitfalls with copy-pasteable code.
Why count_tokens Lies: 5 Reasons Your Gemini API Bill Is Higher Than You Estimated — A Reconciliation Playbook
count_tokens said 1,200 tokens. Cloud Console billed you for 4,800. I made the same mistake building my first indie app on Gemini. This guide walks through the five hidden contributors — thinking, tools, multimodal, history, caching — and how to reconcile them with reproducible code.
Speaker Diarization with Gemini API: Meetings and Podcasts
Use the Gemini API's multimodal audio understanding to label who said what in meeting recordings and podcasts — with a working Python example and prompt design tips.
Vertex AI Agent Engine × Gemini 2.5 Pro — Production Deployment for Managed Agents
Deploy ADK-based agents powered by Gemini 2.5 Pro on Vertex AI Agent Engine. Covers the trade-offs vs Cloud Run, sessions, tool calls, tracing, and a realistic cost model.
Sharpening Gemini CLI and Code Assist Context with .geminiignore — A
Use .geminiignore to clean up the context window for Gemini CLI and Code Assist, improving answer quality and cutting token usage at the same time.
Putting an AI That Answers Phones Into Production: Building a Phone Voice Agent With Gemini Live API and Twilio Media Streams
Bridge Twilio Voice and Gemini Live API over WebSocket to build a phone-answering AI agent that holds up in production. Full code, interruption handling, function calling, deployment notes, and per-minute cost math.
A Blueprint for Production-Grade Structured Output with Gemini API
A practical blueprint for running Gemini API's Structured Output reliably in production. Covers schema design, error handling, and performance optimization end-to-end.
Google AI Pro vs Ultra: Which Should an Indie Developer Pick? 3 Months of Side-by-Side Use
After running Google AI Pro and Ultra side by side for three months as an indie developer, here's a clear decision framework that the price tables don't show — focused on Veo limits, Deep Think frequency, Mariner workflows, and operational stability.
Ship a Production Gemini Agent in 30 Minutes with Mastra and TypeScript
Mastra keeps the lightness of Vercel AI SDK while adding the agent primitives you actually need in production. This guide walks through building, debugging, and deploying a Gemini-powered Mastra agent end-to-end, including the Cloudflare Workers gotchas that bit me first.
Production-Grade PII Redaction for the Gemini API — Detection, Masking, and Audit Logging That Actually Pass a Privacy Review
Are you piping raw user text straight into the Gemini API? This guide walks through detection, masking, and audit-log design so you can keep PII out of model traffic and pass GDPR, SOC 2, and customer privacy reviews — with code you can ship today.
Migrating to @google/genai: Seven Errors That Will Eat Your Afternoon
A field-tested guide to the seven errors you are most likely to hit when migrating from @google/generative-ai to @google/genai, with copy-paste fixes for Node.js and TypeScript codebases.
Building an LLM-as-Judge Evaluation Pipeline with Gemini — Production-Grade Design and Implementation
A practical guide to building an LLM-as-Judge evaluation pipeline using Gemini 2.5 Pro / 3 Pro as the judge. Covers Pointwise / Pairwise judging, bias mitigation, human-correlation measurement, and cost optimization, with working Python code for production use.