Self-Healing Architecture for Gemini Computer Use — Production Patterns That Keep Browser Automation Alive Beyond Day Three
Gemini Computer Use looks magical in demos but breaks daily in production: vanishing elements, surprise modals, network jitter, off-by-four-pixel clicks. This guide builds a five-layer self-healing architecture in Python that classifies failures and recovers them automatically, with working code you can drop into your agent loop today.
Measuring Classification Confidence with Gemini API Logprobs — A Practical Walkthrough
Use the Gemini API responseLogprobs option to extract per-token confidence scores, then turn them into an auto-vs-review gate for classification — with working Python code and the threshold thinking behind it.
Custom Gemini API Agent Loop Without ADK — A Complete Production Guide to Tool Calling, Memory, and Parallel Execution
Build production-grade AI agents using Gemini API directly without Google ADK. This guide covers custom agent loops, tool calling patterns, sliding window memory, parallel execution, and battle-tested error recovery strategies.
Build an Auto-Documentation Pipeline with Gemini API and GitHub Actions
Tired of outdated docstrings and READMEs? This guide shows you how to build a CI pipeline that uses Gemini API and GitHub Actions to automatically suggest documentation updates on every Pull Request.
Gemini API × Langfuse — A Production Playbook for LLM Observability
A practical, production-grade guide to wiring Gemini API into Langfuse — tracing architecture, cost attribution, LLM-as-Judge on live traffic, PII masking, and sampling — with runnable code.
Parallel Function Calling in Gemini API: Production Patterns, Pitfalls, and Monitoring
A production guide to Parallel Function Calling in the Gemini API: DAG tool design, partial failure handling, rate limits, and monitoring — with working code.
Defending Gemini API Apps from Prompt Injection: A Multi-Layer Production Architecture
A four-layer prompt injection defense for Gemini apps: sanitized input, hardened prompts, structured output, and a moderator LLM — with runnable Python.
Resilient Gemini API Services in Production — Circuit Breakers, Bulkheads, and Fallback Models That Keep Your App Alive
A production-ready resilience playbook for Gemini API: circuit breakers, bulkheads, jittered retries, and model fallback chains — with working Python so your service stays up even when the upstream doesn't.
Gemini × DSPy: Retire from Prompt Craftsmanship — Automated Prompt Optimization
A hands-on implementation guide for combining Stanford's DSPy framework with Gemini to end the era of hand-written prompts. Covers Signatures, Modules, Optimizers, LLM-as-a-Judge metrics, and production pipelines — all with working code.
Don't Let Your Gemini Prompts Silently Rot — A Practical Regression Testing Playbook with Pytest
Ever tweaked a prompt and watched production quality quietly degrade? This article walks through testing Gemini API prompts with Pytest, combining snapshot tests and LLM-as-Judge to catch regressions automatically — all from the perspective of an individual developer running things solo.
to Production Architecture for Gemini API 2026— Design Patterns for Building Scalable, Reliable AI Systems
A comprehensive guide to production-grade design patterns for Gemini API. Covers resilient API clients, multi-layer caching, multi-tenant design, observability, and cost control with complete code examples.
Building a Git Commit Message Generator with Gemini API — A Python Developer's Guide
Build a Python tool that reads git diffs and generates meaningful commit messages automatically using the Gemini API. Includes working code, clipboard integration, and Git hook setup.