In this article, I’ll guide you through the process of programmatically interacting with a web browser using the new Computer Use model in Gemini 2.5 Pro. We’ll accomplish this in Java ☕ leveraging Microsoft’s powerful Playwright Java SDK to handle the browser automation.

The New Computer Use Model

Unveiled in this announcement article and made available in public preview last month, via the Gemini API on Google AI Studio and Vertex AI, Gemini 2.5 Pro introduces a pretty powerful “Computer Use” feature.

Continuing my journey with Javelit, after creating a frontend for “Nano Banana” to generate images and a chat interface for a LangChain4j-based Gemini chat model, I decided to see how I could integrate an ADK agent with a Javelit frontend.

The Javelit interface for an ADK search agent

The key ingredients of this interface:

• a title (with some emojis 😃)
• a container that displays the agent’s answer
• a text input field to enter the search query

The ADK agent

For the purpose of this article, I built a simple search agent, with a couple of search tools:

Yesterday, I uncovered the Javelit project in this article where I built a small frontend to create and edit images with Google’s Nano Banana image model.

Javelit is an open source project inspired by Streamlit from the Python ecosystem to enable rapid prototyping and deployment of applications in Java.

Today, I want to show you another example of Javelit. This time, I’m creating a chat interface using LangChain4j with the Gemini chat model.

What we want to build

Have you ever heard of Javelit? It’s like Streamlit in the Python ecosystem, but for the Java developer! I was lucky that the project creator reached out and introduced me to this cool little tool!

Javelit is a tool to quickly build interactive app frontends in Java, particularly for data apps, but it’s not limited to them. It helps you quickly develop rapid prototypes, with a live-reload loop, so that you can quickly experiment and update the app instantly.

Large Language Models (LLMs) are all becoming “multimodal”. They can process text, but also other “modalities” in input, like pictures, videos, or audio files. But models that output more than just text are less common…

Recently, I wrote about my experiments with Nano Banana 🍌 (in Java), a Gemini chat model flavor that can create and edit images. This is pretty handy in particular for interactive creative tasks, like for example a marketing assistant that would help you design a new product, by describing it, by futher tweaking its look, by exposing it in different settings for marketing ads, etc.

In previous articles, I explored how to create an MCP server with Micronaut by vibe-coding one, following the Model Context Protocol specification (which was a great way to better understand the underpinnings) and how to create an MCP server with Quarkus.

Micronaut lacked a dedicated module for creating MCP servers, but fortunately, recently Micronaut added official support for MCP, so I was eager to try it out!

Note: For the impatient, you can checkout the code we’ll be covering in this article on GitHub.

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Yesterday, we went bananas 🍌 creating and editing images with Nano Banana, in Java. Now, what about generating videos as well, still in Java, with Veo 3?

Especially since this week, Google announced that Veo 3 became generally available, with reduced pricing, a new 9:16 aspect ratio (nice for those vertical viral videos) and even with resolution up to 1080p!

In today’s article, we’ll see how to create videos, in Java, with the GenAI Java SDK. We’ll create videos either:

By now, you’ve all probably seen the incredible images generated by the Nano Banana model (also known as Gemini 2.5 Flash Image preview)? If you haven’t, I encourage you to play with it within Google AI Studio, and from the Gemini app. or have a look at the @NanoBanana X/Twitter account which shares some of its greatest creations.

As a Java developer, you may be wondering how you can integrate Nano Banana in your own LLM-powered apps. This is what this article is about! I’ll show you how you can use this model to:

A few days ago, Google DeepMind released a new embedding model based on the Gemma open weight model: EmbeddingGemma. With 308 million parameters, such a model is tiny enough to be able to run on edge devices like your phone, tablet, or your computer.

Embedding models are the cornerstone of Retrieval Augmented Generation systems (RAG), and what generally powers semantic search solutions. Being able to run an embedding model locally means you don’t need to rely on a server (no need to send your data over the internet): this is great for privacy. And of course, cost is reduced as well, because you don’t need to pay for a remote / hosted embedding model.

I often find myself staring at a wall of text online. It could be a lengthy technical article, a detailed news report, or a deep-dive blog post. My first thought is often: “Is this worth the time to read in full?” On top of that, for my podcast, Les Cast Codeurs, I’m constantly gathering links and need to create quick shownotes, which is essentially… a summary.

My first attempt to solve this was a custom Gemini Gems I created: a personalized chatbot that could summarize links. It worked, but I often ran into a wall: it couldn’t access paywalled content, pages that required a login, or dynamically generated sites that I was already viewing in my browser. The solution was clear: I needed to bring the summarization to the content, not the other way around. The idea for a Chrome extension was born.