◉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 cheaply●OMNIFLASH — Gemini Omni Flash is in public preview, a natively multimodal model that lets enterprises and developers build custom, dynamic video workflows●AGENTS — Managed Agents expand with background: true for async server-side runs and polling, remote MCP server integration, and refreshing credentials across interactions●MEMORY — The Memory Bank IngestEvents API is generally available, decoupling event ingestion from memory generation so you can stream content continuously●THROUGHPUT — Provisioned Throughput now lets you submit up to seven pending orders for the same model and region●DEPRECATE — Image generation models shut down on August 17, and the Grok 4.1 family on the Gemini Enterprise Agent Platform on August 20●NANOLITE — Nano Banana 2 Lite is here: Google's fastest and most cost-efficient Gemini Image model, made for running lightweight image generation cheaply●OMNIFLASH — Gemini Omni Flash is in public preview, a natively multimodal model that lets enterprises and developers build custom, dynamic video workflows●AGENTS — Managed Agents expand with background: true for async server-side runs and polling, remote MCP server integration, and refreshing credentials across interactions●MEMORY — The Memory Bank IngestEvents API is generally available, decoupling event ingestion from memory generation so you can stream content continuously●THROUGHPUT — Provisioned Throughput now lets you submit up to seven pending orders for the same model and region●DEPRECATE — 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 API/2026-05-06Intermediate

One Month with Gemini 2.5 Flash: An Indie Developer's Honest Cost and Performance Report

Real cost, speed, and quality data from running Gemini 2.5 Flash across three indie apps for a full month. Includes free-tier usage patterns, Flash vs Pro decision criteria, and cost-minimizing Python code.

◈ Gemini API/2026-05-03Intermediate

Common Gemini API Long Context Problems and How to Fix Them

Struggling with Gemini API's 1 million token context window? This guide covers the most common issues — ignored middle content, slow responses, unexpected costs, and unstable output — with practical code fixes.

◈ Gemini API/2026-04-12Advanced

Gemini API Production Performance Tuning — A Triple Optimization Strategy for Latency, Throughput, and Cost

Learn how to simultaneously optimize latency, throughput, and cost in production Gemini API deployments. Covers Flex/Priority inference, Context Caching, intelligent model routing, and async batch processing with working code and benchmark results.

⬡ Gemini Advanced/2026-04-12Advanced

Gemma 4 MoE vs Dense: Architecture Selection and Performance Optimization Guide

Deep dive into Gemma 4's 26B MoE vs 31B Dense: measured benchmarks, use-case selection criteria, quantization strategies, and deployment configurations from edge to cloud.

◈ Gemini API/2026-03-27Advanced

Gemini 3.1 Flash High-Speed Inference API: Implementation Techniques for Streaming, Function Calling & Batch Processing

Master the technical architecture of Gemini 3.1 Flash and understand how fast inference works. Learn optimal implementation patterns for streaming, function calling, and batch processing with code examples. Make data-driven model selection decisions by comparing Flash with Pro models.