A Provenance Gate That Never Asks Gemini to Name the Country
How I rebuilt the pre-publish provenance check for a ukiyo-e wallpaper app, moving from asking the model to name a country to asking it only to list the marks physically present on the paper. Includes the working code, the decision table that lives on my side, and why an abstain path had to come before accuracy.
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.
Testing Gemini Vision for Wallpaper Auto-Classification — Real Accuracy Numbers and Pitfalls
A hands-on record of automating wallpaper image classification with Gemini Vision — moving from 67% to 87% accuracy, and using a confidence threshold to cut human review down to 15% of the batch.
Gemini API × React Native Operational Notes — Indie Mobile App in Production
Operational notes from running Gemini API inside a React Native/Expo indie mobile app on iOS and Android: real device pitfalls, AdMob coexistence, Cold Start mitigation, AsyncStorage TTL design, and cost realities at 65,000 monthly requests
I Asked Gemini to Grade My App Store Screenshots. Everything Scored 78–85.
Ask Gemini Vision to grade App Store screenshots out of 100 and good candidates and deliberately broken ones both land at 78–85. Here is how I measured the judge's discrimination power, dropped absolute scoring, and rebuilt it as debiased pairwise comparison.
Structured Product Image Analysis with the Gemini API — A Production Pipeline Built on Thousands of Photos
Turn a one-off image analysis script into a production pipeline that auto-generates tags, descriptions, and categories at scale — covering structured output, resumable batches, measured cost, and model routing learned from real indie-developer operation.
Apple Vision Framework × Gemini API: Hybrid Image Recognition — Cutting Wallpaper App Cloud Inference Costs by 70%
How I built an on-device prefilter with Apple Vision Framework to cut Gemini Vision API calls by more than half in my iOS wallpaper app. Real cost, accuracy, and latency numbers, with the gotchas an indie developer hits along the way.
Wallpaper Classification on the Gemini API — Migrating Off the Legacy SDK, and Tuning Resolution, Confidence, and Cost
Implementation notes from automating wallpaper classification with the Gemini API: migrating to @google/genai, resizing to a 1024px long edge, receiving results via responseSchema, confidence thresholds, retrying only on 429/5xx, and tracking real cost with usageMetadata.
Gemini 2.0 Multimodal and Live API: The
A deep dive into Gemini 2.0's two flagship capabilities: multimodal processing and the Live API. Covers image, video, and audio handling alongside real-time streaming conversation — everything you need to build rich, interactive applications with the Gemini API.