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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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Gemma 4

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Ollama7local LLM6OpenCode3Local LLM3Gemma2API2Gemini2Android Studio2Android development2Function Calling2Edge AI2premium1
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/2026-05-06Advanced

Production-Grade Gemma 4 + Ollama + Android Studio — Task Routing, Fine-Tuning, Team Deployment, and CI Integration

A deep-dive into running Gemma 4 locally for Android development at production scale. Covers model-routing proxies, LoRA fine-tuning for project-specific patterns, Docker Compose team setup, and GitHub Actions AI code review integration.

Gemini Dev/2026-05-06Intermediate

Running Gemma 4 Locally in Android Studio via Ollama — Setup, Performance, and Real-World Development Experience

A hands-on guide to connecting Android Studio's local LLM feature with Gemma 4 via Ollama. Covers MacOS setup, model selection, practical coding experience, and when local AI makes more sense than cloud APIs.

Gemini Advanced/2026-05-05Intermediate

Gemma 4 × OpenCode Advanced Guide: Building a Production-Ready Local AI Dev Environment

Move beyond 'it works' with Gemma 4 and OpenCode. A deep guide to model selection, context management, prompt design, and hybrid cloud-local workflows for real-world development.

Gemini Basics/2026-05-05Beginner

Gemma 4 × OpenCode: Build a Free Local AI Coding Environment in 10 Minutes

Combine Google's open Gemma 4 model with the OpenCode terminal agent and you get a fully local, zero-cost AI coding environment ready in 10 minutes. Here's the setup and an honest take on the experience.

Gemini API/2026-05-04Advanced

Judging Gemma 4 and Nemotron 3 Nano Omni on 100 of My Own Images, Not a Benchmark Score

Heron-Bench and JMMMU headline scores are the wrong input for an adoption decision on local Japanese multimodal models. Using a wallpaper classifier as the case, here is how to build a 100-image eval set, weight errors by what they actually cost, and catch regressions when you re-quantize.

Gemini Dev/2026-05-04Intermediate

Gemma 4 26B A4B + OpenCode: Build a Free, Local Coding Agent on Your Mac or Linux Box

Apache 2.0–licensed Gemma 4 26B A4B paired with OpenCode finally puts a local coding agent within reach. Here is the practical setup walkthrough — choosing between Ollama, LM Studio, and vLLM, plus the agent configs I actually use.

Gemini Advanced/2026-04-28Intermediate

Building Local Agents with Gemma 4's Function Calling

Learn how to implement private, on-premises AI agents using Gemma 4's dedicated Function Calling tokens without relying on cloud APIs.

Gemini Dev/2026-04-12Intermediate

Building Agentic Systems with Gemma 4: Mastering Function Calling

A practical guide to implementing Function Calling with Gemma 4 for building reliable agentic systems. Learn how Gemma 4 differs from other open models, structured JSON output, and system prompt optimization with code examples.

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.

Updates/2026-04-11Intermediate

Run Gemma 4 Locally: Building Zero-Cost AI Apps

A complete guide to running Gemma 4 locally and building AI apps with zero API costs. Covers setup on MacBook and Raspberry Pi, production architecture design, and leveraging the 256K context window.