2026 Predictions and New Year Kickoff 🎉
Join Dan Vega and DaShaun Carter for the Season 5 premiere of Spring Office Hours! In this kickoff episode, your favorite Spring experts look ahead to 2026 with their predictions. The hosts discuss what's on the horizon for the Spring ecosystem, including Spring Framework 7, Spring Boot 4, and the rapidly evolving Spring AI landscape. From Java's continued evolution to cloud-native innovations and the growing role of AI in developer workflows, Dan and DaShaun share their insights on the trends that will shape the year ahead. Whether you're planning your learning roadmap or curious about where the industry is heading, this episode is the perfect way to start 2026.
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Show Notes
More from Season 5
Community Potluck
This week is a potluck episode. We're setting the planned topics aside and answering your questions. We'll dig into the new JSON API coming to the JDK and what it means for Jackson. We'll talk about taking Spring AI apps to production, from guardrails to cost control. We'll cover keeping up with CVEs and upgrades, and finish with a lightning round of career questions. Bring your questions to the live chat, or send one ahead of time at springofficehours.io/ask.
Hugging Face for Java Developers with Pratik Patel
Dan and DaShaun are joined by Java Champion Pratik Patel to talk about Hugging Face for Java developers. Hugging Face hosts over a million open models, but most Java developers have never used one. They discuss what Hugging Face actually is, how to find a model worth using, the ways you can run open models from Java today, and when an open model beats a paid API.
dev2Next, Native JSON and a Week of New Models
We’re back from Dev2Next with a recap. Dan shows off two things he built last week. One is Kiln, a warm new theme for IntelliJ. The other is a set of demos for the JSON API coming to JDK 28. Then we dig into a busy week of model releases. Meta released the weights for Muse Spark Open, NVIDIA fit a 74B model into 24GB, and DeepSeek shipped a faster V4.1 Flash. Local inference is getting real, but RAM and VRAM prices keep climbing. We’ll talk about when running models on your own hardware makes sense, and how to point Spring AI at a model on your own machine. We’ll also answer your questions live.