Building AI-Powered iOS and Android Apps with the Gemini API — Image Recognition, Voice Analysis, Chat, and Monetization
Architecture patterns, cost optimization, and monetization for adding image recognition, voice analysis, and AI chat to iOS/Android apps with the Gemini API — updated with Gemma 4 routing and the mandatory API key restriction change.
Integrating Gemini 3.2 Pro Function Calling into iOS/Android Apps: Production Design Patterns
A practical guide to integrating Gemini 3.2 Pro Function Calling into iOS and Android apps. Includes working SwiftUI, Kotlin, and Python code, plus production patterns proven in a real indie wallpaper app — cost, latency, staged rollout, and regression testing.
Localizing App Store Keyword Fields with Gemini 2.5 Flash — A Month of Notes Across 40 Apps
Operational notes from a month of using Gemini 2.5 Flash to draft the 100-character App Store keyword field across 40 wallpaper apps and several locales — CJK character counting, deduping against the title, prohibited terms, per-locale quirks, and what actually moved the needle.
Designing a Semantic Clustering Pipeline for App Reviews with Gemini Embeddings
How I cluster 10,000+ app reviews from my indie apps using Gemini Embeddings to compute improvement priorities. The three-layer pipeline and cost design that emerged from a year of running it.
Two Weeks of Classifying Half a Year of App Store Reviews with Gemini File API
I ran half a year of App Store Connect reviews through the Gemini File API for two weeks straight, asking it to classify and summarize them. Here is what worked, where Batch Mode fit better, and which sharp edges took me a few days to round off.
Gemini API Streaming Works Locally but Buffers in Production — Fixing Cloud Run, Vercel, and Cloudflare
Streaming responses flow token-by-token in local dev, then arrive as one big blob in production. A walkthrough of the five most common causes — Cloud Run timeouts, Vercel runtime mismatch, Cloudflare Workers proxying, server-side text() pitfalls, and client-side decoding — with the fixes I use on my own sites.
Evolving Gemini API Structured Output Schemas in Production — Design Notes from an Indie App
How I rebuilt the JSON contract layer for a Gemini-powered recommendation feature in a long-running indie app — Dual-Emit, Sunset protocol, and a Python compatibility checker.
Rebuilding My Wallpaper App's Color Variations on Nano Banana 2 — Life After the Imagen 4 Shutdown, and Why Resolution Decides the Margin
How I moved my wallpaper app's color-variation pipeline onto gemini-3.1-flash-image (Nano Banana 2): where Imagen 4 users have to go now, the current Interactions API call shape, a two-stage design derived from published per-resolution pricing, and a script that finds retired model IDs still sitting in your code.
Building Automatic Wallpaper Category Classification with Gemini Vision
An indie developer shares how they implemented automatic wallpaper image classification with the Gemini Vision API — including accuracy results, real pitfalls, structured-output tips, and a cost comparison with GPT-4o Vision.
Gemini API asyncio Patterns for Production: How I Cut Processing Time by 80% in My Indie App Backend
A hands-on report on integrating Gemini API asyncio into a production backend. Covers Semaphore-based rate limiting, exponential backoff, partial failure handling, and what breaks when you move the same pipeline to the current google-genai SDK.
Auto-generating Japanese and English Release Notes from git log with Gemini API — A Real Implementation from Beautiful HD Wallpapers v2.1.0
I realized I was spending 1–2 hours per release writing notes in multiple languages. Here's how I automated that with Gemini API and git log — tested on Beautiful HD Wallpapers v2.1.0, with code you can run today.
Diagnosing INVALID_ARGUMENT Errors in Gemini API Batch Image Analysis
When using the Gemini API to analyze multiple images at once, INVALID_ARGUMENT errors can be surprisingly hard to diagnose. This guide covers the three root causes—MIME type mismatches, inline data size limits, and contents structure errors—with code to fix each one.