All Articles
When Gemini Computer Use Acts on a Stale Screen and Fails Quietly — Field Notes on Guarding the Loop
A Computer Use agent will click based on a screenshot taken moments ago, miss the real target, and throw no error. These are field notes on measuring those silent misclicks and stopping them with an observe-act-verify loop.
When Gemini's Maps Grounding Quietly Fails in Production — Field Notes on Attribution, Billing Boundaries, and Fallbacks
An operations-focused look at the pitfalls that surface after you ship Grounding with Google Maps on Gemini: detecting silent grounding misses, meeting the attribution requirement, knowing which responses are billed, and building fallbacks for latency and staleness.
Restarting a Long Agent Run From Where It Broke — A Step-Ledger Design for Gemini 3.5 Flash Long-Horizon Tasks
Gemini 3.5 Flash is good at long-horizon tasks, but when a 40-step run dies on step 29, you usually start over. An append-only step ledger gives you resume, idempotency, and audit in one place. Here is the design with working Python and measured results.
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.
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.
A Minimal Autonomous Agent with Gemini — Tool-Loop Design Lessons
Building an autonomous agent from a minimal setup with the google-genai SDK's automatic function calling — plus the step limits, tool allowlists, and retry decisions learned from automating real blog operations.
Pre-Screening Wallpaper App Submissions with Gemini Vision: A Two-Week Field Memo
Before submitting a new batch of wallpapers, I spent two weeks running Gemini's image understanding as a first-pass filter for store review risk. What it caught, what it missed, and where a human still has to decide.
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.
The Day You Switch Gemini Embedding Models: Designing a Zero-Downtime Reindex
Upgrade your embedding model and every vector you ever stored becomes incompatible. Here is a dual-index design for re-embedding hundreds of thousands of vectors without downtime, complete with a resumable reindex job and a query-side abstraction layer.
Three Weeks Rewriting 40 App Store Descriptions in Gemini Advanced Canvas
Notes from three weeks of rewriting 40 App Store descriptions in Gemini Advanced Canvas. What I let the AI handle, what I always touched by hand, and the small ASO effects I observed across my wallpaper and well-being apps.