GEMINI LABJP
SEARCH — File Search grounding now adds media_id for visual citations and page numbers, so you can trace the exact sourceAPI — Event-driven Webhooks replace polling for the Batch API and long-running operationsDEPRECATION — Two image preview models (e.g. gemini-3.1-flash-image-preview) shut down June 25; migrate dependent automation nowMODEL — Gemini 3.5 Flash is GA, beating 3.1 Pro on nearly every benchmark while running 4x fasterAGENTS — Managed Agents are in public preview on the Gemini API, running autonomous agents in isolated Linux sandboxesSTUDIO — Google AI Studio can now generate Android apps from natural-language promptsSEARCH — File Search grounding now adds media_id for visual citations and page numbers, so you can trace the exact sourceAPI — Event-driven Webhooks replace polling for the Batch API and long-running operationsDEPRECATION — Two image preview models (e.g. gemini-3.1-flash-image-preview) shut down June 25; migrate dependent automation nowMODEL — Gemini 3.5 Flash is GA, beating 3.1 Pro on nearly every benchmark while running 4x fasterAGENTS — Managed Agents are in public preview on the Gemini API, running autonomous agents in isolated Linux sandboxesSTUDIO — Google AI Studio can now generate Android apps from natural-language prompts
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Gemini Dev/2026-06-24Intermediate

Folding a Local Gemma 4 into Daily Work — Practical Notes on the Ollama API and Response Speed

Taking a local Gemma 4 you can now run interactively and folding it into real work: how to hit Ollama's local API from a script, tricks to improve perceived response speed, and a two-tier fallback that automatically routes to the cloud Gemini API — code included.

Gemini Dev/2026-06-24Beginner

Running Gemma 4 Locally on Windows — A Hands-On LLM in Two Commands with Ollama

How to run Google's lightweight open model Gemma 4 locally on a Windows laptop. With Ollama, you go from install to running in effectively two commands. Plus how to split work between the cloud Gemini API and a local Gemma.

Gemini Advanced/2026-04-09Advanced

Gemma 4: From Edge E2B to Cloud 31B—Choosing the Right Model and Implementation Patterns

Comprehensive exploration of Google DeepMind's Gemma 4 family (E2B/E4B/26B A4B/31B). Master MoE architecture, 256K context windows, native thinking mode, and multimodal capabilities. Learn edge deployment strategies, production implementations, and fine-tuning best practices.