Running Gemini Chat History on Redis — Field Notes on Not Losing Conversation State in Production
Keep a Gemini ChatSession in process memory and it evaporates on every redeploy or scale event. Here is how I back it with Redis in production, covering token budgets, concurrent sends, SDK coupling, and graceful degradation, with the code I actually run.
Catching Deprecated Gemini Models in CI ― A Guard for Back-to-Back Shutdown Deadlines
When shutdowns and deprecations pile up, build a CI check that mechanically finds stale Gemini model strings across your repo. Includes a deprecation registry, a scanner, and a days-remaining warn/fail tier you can copy and run.
Harden the Layer Before Gemini Sees User Media — A Validation Pipeline You Can Actually Run
Piping user-uploaded images and video straight into Gemini walks you into MIME spoofing, EXIF leaks, decompression bombs, and video that isn't ready yet. Here's the validation layer—magic-byte sniffing, Files API state polling, and cleanup—built up in working code.
Don't Break When the Default Model Moves: A Startup Capability-Probing Layer for Gemini
Pinning a model name breaks on deprecation; trusting the default breaks when the weights swap silently. This is the design I settled on: probe what the served model can actually do at startup, then build every request from that answer. Includes runnable Python.
When the Default Model Silently Upgrades: Catching Prompt Regressions in Numbers
Gemini 3.5 Flash is now the default and you can no longer turn it off. Assuming your responses can shift without you touching the prompt, here is how to bundle prompt, model, and sampling into one variant and catch regressions with canaries and an LLM judge — in working code.
Building Web Apps with Gemini — Prompt Design and Pitfalls in Google AI Studio
How to structure your prompts when asking Gemini to build web apps in Google AI Studio — and the pitfalls I actually ran into as an indie developer.
How a Deep Think Verification Step Tripled My API Bill, and How thinking_level Got It Back
After wiring API-accessible Gemini 3 Deep Think into my output-verification step, my projected monthly cost jumped roughly 3x. Here is the implementation record of capping it with thinking_level and a cost guardrail, then settling on a two-stage design with Flash.
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.
Switching Image Models Quietly Degrades Quality — A Gate That Catches It Without Manual Review
When you move image generation from preview to GA models, the API keeps returning 200 and quality slips silently. This is the three-layer gate I built to detect that drift without staring at every image: deterministic property checks, multimodal embedding similarity, and a Gemini judge, wired together in Python with thresholds and a cutover procedure.
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.
Getting Ready for Gemini in Chrome's Auto Browse — Structuring a Web App Agents Can Actually Operate
Before Gemini's auto browse reaches Android Chrome, here is how I reshaped my own web app so an agent can reliably operate it — pinning down action targets, the accessibility tree, JSON-LD, and guarding destructive actions, all with implementation code.