Antigravity CLI allows you to customize both the terminal window title and the bottom status line. This is done by passing a JSON payload of the current agent state to external shell scripts via standard input.

In this post, I will explain how I set up my environment, the specific scripts I use, and how to configure the CLI to load them.

The Principle

Both the title and the status line operate on the same principle:

My friend Leonard and I were collaborating over the weekend on some new updates for the Groovy Web Console. This console is an online playground where Apache Groovy users can run Groovy scripts online, with different versions of the language, from Groovy 3 up to the experimental Groovy 6. Additionally, there’s a specific integration with the Spock testing framework, which allows users to run tests written with the framework.

Here’s what the old console (well, the current one at the time of this writing) looks like:

Google recently announced Managed Agents in the Gemini API at Google I/O. This feature allows developers to run autonomous agents that “reason, plan, use tools, and execute code inside isolated cloud sandboxes”.

Rather than requiring developers to manually build, secure, and scale the execution environment (including sandbox containers, network routes, and runtime engines), the Gemini API handles this infrastructure. This is powered by the Antigravity agent running on Gemini 3.5 Flash.

This article shows how to implement these agentic capabilities in Java using the Gemini Interactions SDK.

At Devoxx France 2026 and JNation 2026, I had the pleasure of presenting a session on Agentic Design Patterns. In this talk, I explore how to move beyond basic LLM wrappers to build reliable, scalable, and sophisticated AI agent systems.

In the coming weeks, I’ll be blogging about some of these patterns, that I implemented using LangChain4j and ADK for Java.

Abstract

It’s time to dive into the deep end, far from “hello world” demos. To build your multi-agent systems, you often start by assembling classic bricks: sequential or parallel flows, or loops. The basics!

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Google just released Gemini 3.1 Flash Text-to-Speech (TTS), a new expressive TTS model that you can steer with audio tags and scene descriptions.

I wanted to see how it worked with the Gemini Interactions SDK for Java.

Expressive control

The model sounds natural out of the box, but the real benefit is the control you have over expressiveness. By defining “Audio Profiles”, “Scene Details”, and “Director’s Notes” in your prompt, you can control the character’s pacing, tone, and environment.

A few days ago, Max Rydahl Andersen published a fascinating article about nanocode: a minimalist Claude Code alternative implemented in just 260 lines of Java (inspired from a 250-line Python equivalent). It was a masterclass in “leanness,” using raw HTTP calls and Jackson JSON parsing, an OpenRouter or Anthropic LLM endpoint, to create an autonomous coding loop.

I loved the concept, but I had a very practical motivation to take it in a different direction: I don’t have a Claude subscription. 😃

Today, DeepMind announced the release of Gemma 4, a very impressive and powerful new version of the Gemma family of models. As I’ve been contributing to ADK Java a fair bit recently, I was curious to see how I would configure ADK Java agents to work with Gemma 4.

In this article, we’ll explore 3 paths:

• Calling the AI Studio API surface directly,
• Calling Gemma 4 hosted via a vLLM instance thanks to the LangChain4j bridge.
• Calling Gemma 4 locally via Ollama

With the appropriate model weights format, we’ll also be able to run Gemma 4 locally via Ollama. But that’s for another day.

Since I started using Model Context Protocol (MCP) to equip my AI agents with useful tools, I’ve been looking for ways to quickly build and iterate on local servers. A few weeks ago, I shared how to easily build a local MCP server in Java with a custom skill in Gemini CLI. Today, I wanted to put that skill to the test by creating a Wikipedia MCP server.

What’s impressive is that I didn’t even have to leave my terminal or read documentation. The entire process was a conversation with Gemini CLI, leveraging its ability to search the web, find libraries, and even check migration guides!

I’m happy to echo here the release of ADK for Java v1.0, Google’s Agent Development Kit framework to build AI agents in Java. I spent a lot of time on this project. I also wrote the announcement blog post on the Google for Developers blog. And I’ve recorded this YouTube video highlighting some of the new features of the framework, in which I’m demonstrating some of them via an app I built: my Comic Trip agent (pun intended).

Generative AI isn’t just about text or images (with Nano Banana) but it’s also great at generating videos (with Veo 3). And now with the recently released Lyria 3 model from DeepMind, you can create some engaging and creative music with lyrics (generated, or your own) or invent a calming instrumental track to loop in the background of your online TikTok or YouTube Shorts.

And of course, if you’re a Java developer like me, you can do all that in Java!