Trusting Gemini Structured Output in Production — Schema Design, Double Validation, and Bounded Retries
Gemini's structured output guarantees parseable JSON, not correct values. Notes on schema design with @google/genai, why propertyOrdering matters, a Zod double-validation layer, handling MAX_TOKENS truncation, and a bounded-retry extraction pipeline.
Keeping Gemini API's Default-Model Shift From Becoming an Incident — Pinning Model IDs and Detecting Silent Upgrades in Production
When the default model quietly moves up, your output length, reasoning behavior, and cost change with zero code edits. This guide shows how to pin model IDs in a single source of truth and verify the effective model from the response to detect default changes.
Controlling Image Tokens with the Gemini API media_resolution Setting — Tuning Batch Image Classification by Measurement
media_resolution, introduced in the Gemini 3 line, switches how many tokens an image input consumes across three levels. Through real batch-classification measurements, this guide shows how to balance cost and accuracy by assigning the right tier per task.
Gemini's GA Image Models Won't Output Exact Device Resolutions — A Wallpaper Pipeline That Fixes Aspect Ratio and Safe Areas
After switching to the GA image models, your wallpapers no longer fit the screen. Here's how to crop one master image into every device resolution and cut your generation count to a fraction, with full Pillow code.
Where to Adopt Gemini 3.5 Flash GA First — Per-Workload Evaluation and a Staged Rollout with a Model Router
How I migrated production workloads to Gemini 3.5 Flash GA in stages: a per-workload evaluation harness, measured results, an env-based model router, and rollback design.
The Morning Gemini Generated Fine but the Publish Crashed — A 'Generation Outbox' So Expensive Output Is Never Lost
Generation succeeds, then the process dies right before publishing. The expensive output is gone, and you pay for the same generation again. Here is a 'generation outbox' that persists the output first and turns publishing into an idempotent follow-up, plus what it did for me during the June outage.
Reconciling Orphaned Gemini Files API Uploads Across a Fleet of Apps
Files API uploads quietly expire after 48 hours. Here's how I keep orphaned files and quota under control across six apps, using reconciliation against my own database and a scheduled cleanup job — written up as production notes from running wallpaper apps.
Recording Provenance for Gemini Output — Designing for Reproducibility and Audit
Before you lose track of which model and prompt produced an output months later: how to stamp provenance metadata onto Gemini generations so quality investigations and model migrations stay reproducible.
A Lightweight Gemini Backend with Bun and Hono — Reclaiming the Small Tools of Indie Development
Has your Node and Express Gemini backend grown heavy with dependencies and build times? Here is how I moved one to Bun and Hono — folding streaming, rate limiting, cost caps, testing, and self-hosting into a single light runtime — along with the pitfalls I hit in production.
Stopping Gemini API Config Drift — Codifying Model IDs and Safety Settings to Catch Cross-Environment Gaps
Most of those puzzling per-app bugs come from drift in model IDs and safety settings between environments. This guide shows how to codify your Gemini config and snapshot the effective settings to detect cross-environment gaps.
Measuring the Economics of Each Gemini-Powered Feature — So You Can Keep It, Fix It, or Retire It
Gemini API costs are visible at the account level, but the profitability of an individual feature never shows up on its own. This guide shows how to tag every request, build a per-feature cost ledger, join it with revenue signals from AdMob and in-app purchases to compute contribution margin, and decide whether to keep, fix, or retire each feature — with the code I actually run.
Trimming Gemini Embeddings from 3072 to 768 Dimensions: A Matryoshka Approach to Cutting Vector DB Cost and Latency
gemini-embedding-001 returns 3072-dimensional vectors, but thanks to Matryoshka representation you can keep only the leading dimensions with almost no quality loss. This is a design for trimming to 768 to cut vector DB storage and latency, including the re-normalization pitfall and coarse-to-fine search code.