36 viestiä tägattu "AI"

Earlier this year, Azul, a Java-focused company, released its 2026 State of Java Survey & Report. Based on responses from over 2,000 Java professionals worldwide, Azul revealed that developers are focused on managing cloud computing, securing apps, and controlling AI output.

Java remains a dominant force in development, with 64% of respondents reporting that more than half of their apps or workloads are built with Java or run on a Java Virtual Machine (JVM). For developers who wish to bring their apps to the browser with minimal changes to their architecture, webforJ, a full-stack Java framework, offers a strategic approach to modernizing apps quickly and efficiently.

You ask a chatbot about two plans, then follow up with "and what about the second one?" A good assistant knows what you mean. A forgetful one asks you to start over, because each request reached the model on its own, with nothing from the conversation before it.

Spring AI chat memory fixes that on the server. Spring AI keeps the recent messages for each conversation and adds them to every request, so the model sees the whole thread. In a webforJ app, the one thing left for you to decide is which conversation belongs to which user, and a session-scoped bean handles that in a few lines.

Ask a general AI assistant for a search field that waits until the user stops typing, and you'll often get a raw thread and a sleep call. It compiles, but it isn't how a webforJ app should do it, and you end up rewriting it by hand.

Agent skills for Java fix that. Also called Claude Code skills for Java, they teach the assistant how the framework expects things to be done. webforJ ships eight skills in its AI plugin, and Claude Code loads the right one when your prompt matches the task, so the same request comes back with a Debouncer instead.

Sending every prompt to a cloud model isn't always an option. Maybe the data can't leave the building, maybe you want to experiment without an API bill, or maybe you just want a chatbot that keeps working when the network doesn't.

Ollama in a Java Spring Boot UI covers all three. Ollama runs the model on your own machine, Spring AI talks to it like any other provider, and a webforJ view shows the chat, so the whole app stays in Java.

Ask an AI assistant for a Java method and it will often hand you one that sounds right and does not exist. An MCP server for Java developers fixes that. The webforJ MCP server lets AI coding assistants look up webforJ documentation, APIs and design tokens while they write code, so the assistant asks the server instead of guessing a method name. You connect it once, and it works with Claude Code, VS Code, Gemini CLI and OpenAI Codex CLI. In this guide you connect Claude Code, check an API answer against the server, and see a prompt for each job the server does.

To extract data from an image in Java, upload the file with the webforJ Upload component, send the bytes to a vision-capable model through Spring AI, and ask for a typed result. Spring AI maps the answer to a Java record, so your view gets the vendor, invoice number, date, and total as fields. You do not need a separate OCR library or a text parsing step.

Every few weeks a new AI coding tool claims the top spot, and Java teams end up debating Claude Code against Cursor against Copilot. For day-to-day work on a real Java web app, that debate matters less than it seems.

The best AI coding tools for Java developers are the ones that know the framework you build with. Claude Code, Cursor, Gemini CLI and Codex all write competent Java. The bigger gap is between an assistant that guesses at your framework and one that looks it up. With webforJ, one plugin install gives any of them the real docs, project scaffolding, theme generation, and token validation.

To stream ChatGPT responses in a Java web app, send each chunk from the model into a webforJ MarkdownViewer. The component renders Markdown as it arrives, keeps the newest text in view, and gives you stop() and whenRenderComplete() for the moments when the user cancels or the answer ends. The model call stays in a plain service, and the view only appends text.

webforJ 26.00 is here, our first major version since the 25.x series kicked off, and a big one. It brings a top-to-bottom design system refresh, new tricks for the Dialog and TabbedPane components, and a clean break from deprecated APIs, all paired with automated tooling that does most of the upgrade work for you. The highlights are below, and as always the GitHub release overview has the complete picture.

AI tools are changing the way people work, and it's easy to get left behind. They can be very powerful, but require some configuration and practice to really unlock their potential. While you're still doing things the way you always have, your peers are excitedly talking about how their autonomous AI agents are building and testing apps, completely transforming what it means to be productive and efficient, and what it means to "code." Maybe you occasionally use an AI as a fancy search engine or research tool, but it's certainly not doing your work for you. You might find yourself wondering: what are they doing differently?

As AI coding tools, assistants, and agents become increasingly powerful, professional engineers and casual vibe coders alike can go from concept to compilation faster than ever before. On top of that, meta-prompting systems like get-shit-done and Auto-Claude automate entire development workflows, so that the AI doesn't just write the code, but verifies it as well.

AI tools certainly accelerate output, and are very impressive at first glance. But what impact are they having on code quality? Can the open source ecosystem withstand the flood of AI-generated PRs? How can developers use AI without sacrificing understanding and quality?

Research into these questions is still emerging, but the current findings suggest that for anything that requires security, maintainability, and performance, it's best not to put too much trust in AI-written code.

This is why we've made a strategic choice at webforJ: AI-assisted development, but human-owned code.

Version 25.11 of webforJ is live! In this release, we've focused on polishing how users see your app, with built-in visual transitions, AI-ready components, and faster ways to prototype and learn about webforJ. See some of the most exciting highlights below, and as always, see the GitHub release overview for a more comprehensive list of changes.