GEMINI LABJP
LOGS — Developer logs for the Interactions API are now viewable in the AI Studio dashboard, so you can trace each execution step for supported callsOMNI — Gemini Omni Flash (public preview) generates 3–10s, 720p videos from text or animates a still image, then lets you refine the result conversationallyINTERACT — The Interactions API is GA and recommended for all new projects — one interface for text, multimodal understanding, tool use, and agentic workflowsSTATE — Use previous_interaction_id to keep conversation state server-side. Execution steps are observable, and long-running tasks support background executionNOTEBOOK — NotebookLM has been renamed Gemini Notebook, reportedly used by over 30M people and 600K organizationsMODEL — Gemini 3.5 Flash is the widely available latest model. The flagship Gemini 3.5 Pro remains unconfirmed officially — treat reports cautiouslyLOGS — Developer logs for the Interactions API are now viewable in the AI Studio dashboard, so you can trace each execution step for supported callsOMNI — Gemini Omni Flash (public preview) generates 3–10s, 720p videos from text or animates a still image, then lets you refine the result conversationallyINTERACT — The Interactions API is GA and recommended for all new projects — one interface for text, multimodal understanding, tool use, and agentic workflowsSTATE — Use previous_interaction_id to keep conversation state server-side. Execution steps are observable, and long-running tasks support background executionNOTEBOOK — NotebookLM has been renamed Gemini Notebook, reportedly used by over 30M people and 600K organizationsMODEL — Gemini 3.5 Flash is the widely available latest model. The flagship Gemini 3.5 Pro remains unconfirmed officially — treat reports cautiously
ARTICLES

All Articles

All (733) Gemini Basics (57) Dev Tools (106) API & SDK (405) Advanced (110) Workspace (37) Updates (18)
API & SDK/2026-05-31Intermediate

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 byte counting, deduping against the title, prohibited terms, and what actually moved the needle.

Advanced/2026-05-31Advanced

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.

API & SDK/2026-05-30Advanced

Preserving Gemini 3 Thought Signatures So Multi-Turn Function Calling Doesn't Degrade

When you build function calling on Gemini 3 thinking models, reasoning quality often drops from the second turn onward. The cause is usually a dropped thought signature. Here is how to keep it and verify the effect.

API & SDK/2026-05-30Intermediate

Why Gemini 2.5 Pro Rejects thinkingBudget: 0 (and How to Fix It)

Setting thinkingBudget to 0 on Gemini 2.5 Pro returns a 400 INVALID_ARGUMENT error. Here is why the per-model thinking budget ranges differ, how to minimize thinking on Pro the right way, and when to switch to Flash, with Python and JavaScript examples.

API & SDK/2026-05-30Intermediate

Two Months of Turning App Store Connect Daily Sales into a Slack Digest with Gemini 2.5 Flash

Notes from two months of running App Store Connect Sales/Trends data through Gemini 2.5 Flash and posting a short morning digest to Slack. Why Flash beat Pro for this job, how AdMob and store revenue stopped colliding, and what a single 'normal/check' label changed.

API & SDK/2026-05-30Advanced

Propagating a Time Budget Through a Multi-Stage Gemini Pipeline

A field memo on killing DEADLINE_EXCEEDED errors in an in-app help search by carrying a single request-wide deadline through the embed, search, and generate stages — sizing maxOutputTokens from the remaining budget and reserving a fallback budget so a breach returns a partial answer instead of an error.

API & SDK/2026-05-29Advanced

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.

Dev Tools/2026-05-29Intermediate

Treating a 0.5B Local LLM as a 'Front-Line Router' — Gemini Nano Next to Qwen 0.5B

Qwen2.5 0.5B reads as 'too weak for daily chat' when you give it the wrong task. As a mobile-app developer with 50M cumulative downloads behind me, I find it useful to put Gemini Nano next to Qwen 0.5B and think about the routing layer instead.

API & SDK/2026-05-29Intermediate

Why HTTP Referrer Restrictions on Your Gemini API Key Cause 403 Errors in Production

Walks through why a Gemini API key with HTTP referrer restrictions can suddenly return 403 PERMISSION_DENIED in production. Covers the exact referrer string format, SDK behavior differences, how to safely route around the limitation with a tiny edge proxy, and how it differs from the CORS error you hit when calling straight from the browser.

Advanced/2026-05-29Intermediate

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.

API & SDK/2026-05-29Advanced

Layering Gemini API Response Caches in Three Tiers — How I Split Memory, Redis, and Context Cache

Notes from running a three-tier cache (in-memory, Redis, Gemini Context Cache) in front of the Gemini API for six weeks across a wallpaper app — actual hit rates, billing impact, and the invalidation traps that ate me alive.

Advanced/2026-05-28Intermediate

Cursor's New Model on Coding Agent Index, and Why I Still Pick Gemini as My Center of Gravity

A third-party evaluator, Coding Agent Index, recently rated a new Cursor-developed model as 'frontier-class performance at one-tenth the cost.' I walk through how a solo developer who keeps Gemini at the center of their stack should read that ranking, and where to add the new model without churning the rest of the workflow.