GEMINI LABJP
NANOLITE — Nano Banana 2 Lite is here: Google's fastest and most cost-efficient Gemini Image model, made for running lightweight image generation cheaplyOMNIFLASH — Gemini Omni Flash is in public preview, a natively multimodal model that lets enterprises and developers build custom, dynamic video workflowsAGENTS — Managed Agents expand with background: true for async server-side runs and polling, remote MCP server integration, and refreshing credentials across interactionsMEMORY — The Memory Bank IngestEvents API is generally available, decoupling event ingestion from memory generation so you can stream content continuouslyTHROUGHPUT — Provisioned Throughput now lets you submit up to seven pending orders for the same model and regionDEPRECATE — Image generation models shut down on August 17, and the Grok 4.1 family on the Gemini Enterprise Agent Platform on August 20NANOLITE — Nano Banana 2 Lite is here: Google's fastest and most cost-efficient Gemini Image model, made for running lightweight image generation cheaplyOMNIFLASH — Gemini Omni Flash is in public preview, a natively multimodal model that lets enterprises and developers build custom, dynamic video workflowsAGENTS — Managed Agents expand with background: true for async server-side runs and polling, remote MCP server integration, and refreshing credentials across interactionsMEMORY — The Memory Bank IngestEvents API is generally available, decoupling event ingestion from memory generation so you can stream content continuouslyTHROUGHPUT — Provisioned Throughput now lets you submit up to seven pending orders for the same model and regionDEPRECATE — Image generation models shut down on August 17, and the Grok 4.1 family on the Gemini Enterprise Agent Platform on August 20
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Gemini API16AdMob5Monetization3Production3Gemini 2.5 Pro3iOS3Files API2multimodal2CI2Gemini Flash2Cost Optimization2Gemini 3.22
Gemini API/2026-05-24Advanced

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.

Workspace/2026-05-24Intermediate

Running NotebookLM and Gemini Deep Research in Parallel for a Firebase SPM Migration: Two Weeks of Notes

Notes from two weeks of running NotebookLM and Gemini Deep Research side by side, while researching the CocoaPods-to-SPM migration of an iOS wallpaper app portfolio with 50M+ cumulative downloads.

Gemini API/2026-05-22Advanced

A Gemini API Control Plane for Indie Developers Running an App Portfolio

When you run several apps (wallpaper, healing, manifestation) on Gemini API, keys scatter and per-app cost attribution disappears. This is the three-layer control-plane architecture I have used for twelve months, with the traps that only show up over time.

Gemini API/2026-05-21Advanced

Designing Event-Driven AI Workflows with Gemini API and Cloud Pub/Sub — Notes from an Indie Developer

An implementation memo on wiring Gemini API into Cloud Pub/Sub event-driven workflows. Using an app-review analysis pipeline as the running example, the article covers retry policy, dead-lettering, idempotency, and cost guardrails — from the perspective of someone running it solo.

Gemini API/2026-05-20Advanced

Surfacing AdMob Floor Price Candidates from Weekly Reports with Gemini 2.5 Pro — A Six-App Indie Operations Note

A practical pipeline for moving AdMob floor price tuning from gut feel to data, using Gemini 2.5 Pro to read weekly CSV exports. Notes from operating six wallpaper apps in parallel, with Function Calling to produce structured candidate values.

Gemini API/2026-05-19Advanced

Designing an Image Pipeline with Gemini Files API and Cloudflare R2 — Notes from Running a Wallpaper App

Notes from rebuilding the image processing pipeline of a wallpaper app around Gemini Files API and Cloudflare R2. Covers the 48-hour TTL, idempotent retries, and cost monitoring, with implementation code and 30 days of numbers.

Gemini Advanced/2026-05-19Intermediate

One Month of Letting Gemini 2.5 Pro Help With Apple Privacy Manifests — Indie Developer Notes

Notes from one month of using Gemini 2.5 Pro to help maintain PrivacyInfo.xcprivacy across an indie iOS app catalog. What worked, what didn't, and the workflow I settled on.

Gemini API/2026-05-15Intermediate

3 Gemini API Embedding Errors I Hit Building a Wallpaper App — and How I Fixed Them

Three real Gemini API Embedding errors encountered while building an auto-categorization feature for a wallpaper app with 50M+ downloads: INVALID_ARGUMENT, RESOURCE_EXHAUSTED 429, and poor RAG precision — with working code fixes.

Gemini Advanced/2026-05-13Intermediate

What Happens When You Show Your Own Artwork to Gemini Vision — An Honest Review from a Maker and a Developer

I fed my own art images into Gemini Vision to test what it reads and what it misses. An honest, indie-developer look at where it's genuinely useful for running a wallpaper app, and where it still falls short.

Updates/2026-05-06Intermediate

Gemini API Developer Update for May 2026 — What Changed and What You Should Do

A developer-focused roundup of Gemini API changes in May 2026. Covers Gemini 3.2 impressions, the June Gemini 2.0 Flash deprecation deadline, and what to prepare before Google I/O 2026.

Gemini Basics/2026-05-04Advanced

Gemini 3.2 Developer Monetization Blueprint — Building First-Mover Advantage with the New Model

With Gemini 3.2 reshaping the AI services market, here's how indie developers and small teams can raise client rates, design profitable own-products, and build first-mover positioning in a specific vertical — written from a working operator's perspective.

Gemini Basics/2026-04-30Intermediate

Google AI Pro vs Ultra: Which Should an Indie Developer Pick? 3 Months of Side-by-Side Use

After running Google AI Pro and Ultra side by side for three months as an indie developer, here's a clear decision framework that the price tables don't show — focused on Veo limits, Deep Think frequency, Mariner workflows, and operational stability.