If you’ve recently vibe-coded and exported a Gemini-powered app from Google AI Studio to host it online on Google Cloud Run, you might have noticed a server/ directory containing a Node.js application. This isn’t just a simple file server; it’s a clever “transparent proxy” designed to solve a classic problem in frontend AI development:

How do I use my API key without leaking it to the browser?

In this post (although vibe-coding is supposed to be all about not looking at the code at all) we’ll dissect exactly how this architecture works, why it’s safer than a client-side key, and where its security limits lie.

A few days ago, LangChain4j 1.11.0 was released, and with this version, a few notable enhancements to the support of the Gemini model family have landed. Let’s dive in!

New Image Generation Models (Gemini 2.5 & 3.0 Preview, aka 🍌 Nano Banana)

Back in 2019, I spent significant time researching Webhooks. In particular, I was interested in best practices, pitfalls, design patterns, and approaches for implementing Webhooks in a reliable, resilient, and effective way.

Everything is distilled in that article: Implementing Webhooks, not as trivial as it may seem

It likely took me a full week to dive deep into this subject, finding sources and experimenting with design patterns myself. But nowadays, AI makes it easier to dive deeper into topics, explore unfamiliar aspects, and share findings with your team.

The Agent Client Protocol (ACP) allows you to connect external AI agents directly into IDEs and text editors that support that protocol (like JetBrains’ IntelliJ IDEA, PyCharm, or WebStorm, as well as Zed). This means you can bring the power of the Gemini CLI directly into your editor, allowing it to interact with your code, run terminal commands, and use Model Context Protocol (MCP) servers right from the AI Assistant chat window.

First of all, dear readers, let me wish you a happy new year! This is my first post on this blog for 2026. I’m looking forward to continuing sharing interesting content with you.

During my holiday break, I wanted to put my recent Java implementation of the Gemini Interactions API to the test. I implemented and released it with the help of Antigravity. My colleague Shubham Saboo and Gargi Gupta wrote a tutorial on how to build an AI research agent with Google Interactions API & Gemini 3. I thought this was a great opportunity to replicate this example in Java using my Interactions API Java SDK.

Google and DeepMind have announced the Interactions API, a new way to interact with Gemini models and agents.

Here are some useful links to learn more about this new API:

• An announcement is available on Google’s Keywords blog:
  Interactions API: A unified foundation for models and agents
• A more detailed article is available on Google’s developers blog:
  Building agents with the ADK and the new Interactions API
• The newly released Gemini Deep Research agent is now available via the Interactions API as well:
  Build with Gemini Deep Research
• The official documentation of the Interactions API.

About the Interactions API

The Rationale and Motivation

The Interactions API was introduced to address a shift in AI development, moving from simple, stateless text generation to more complex, multi-turn agentic workflows. It serves as a dedicated interface for systems that require memory, reasoning, and tool use. It provides a unified interface for both simple LLM calls and more complex agent calls.

What a wild title, isn’t it? It’s a catchy one, not generated by AI, to illustrate this crazy week of announcements by Google. Of course, there are big highlights like Gemini 3 Pro, Antigravity, or Nano Banana Pro, but not only, and this is the purpose of the article to share with you everything, including links to all the interesting materials about those news.

Gemini 3 Pro

The community was eagerly anticipating the release of Gemini 3. Gemini 3 Pro is a state-of-the-art model, with excellent multimodal capabilities, advanced reasoning, excellent at coding, and other agentic activities.

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.

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.

MCP (Model Context Protocol) is making a buzz these days! MCP is a protocol invented last November by Anthropic, integrated in Claude Desktop and in more and more tools and frameworks, to expand LLMs capabilities by giving them access to various external tools and functions.

My colleague Philipp Schmid gave a great introduction to MCP recently, so if you want to learn more about MCP, this is the place for you.

In this article, I’d like to guide you through the implementation of an MCP server, and an MCP client, in Java. As I’m contributing to LangChain4j, I’ll be using LangChain4j’s mcp module for the client.