LangChain4j has crossed an important line:
from “interesting library” → production Java AI infrastructure.

I wrote a long-form guide with 50 LangChain4j interview questions, covering:
• AI Services
• RAG & embeddings
• Tools & agents
• Memory & context limits
• Observability, cost, and security

Written from a production Java perspective — not Python demos.

https://www.the-main-thread.com/p/langchain4j-interview-questions-java

#Java #LangChain4j #AIinJava #EnterpriseJava #RAG #LLM #SoftwareArchitecture #DevExperience

#Java apps can now run transformer-based AI models directly in the JVM - no Python, REST wrappers, or microservices required!

Integrate #ONNX-powered inference with:
• Tokenizer support
• GPU acceleration
• Modular deployment
• Observability

🔗 Read more in the #InfoQ article by Syed Danish Ali: https://bit.ly/4htllhT

#SoftwareArchitecture #AIinJava

🔧 Spring AI feels like your familiar Spring Boot buddy—smooth, seamless, and zero friction. Meanwhile, LangChain4j is the Swiss Army knife: chainable prompts, chat memory, RAG—you name it. Choose wisely. #AIinJava #ChatMemory

#AI is here to stay - and it’s a HUGE opportunity for #Java & #Spring developers!

#SpringAI 1.0 is now available - a comprehensive solution for AI engineering in Java, shaped by rapid advances in the AI space.

📘 In this #InfoQ article, Josh Long examines the new Spring AI 1.0 project and explores how it can be used to integrate AI more effectively!

🔗 Learn more: https://bit.ly/4lTYBc3

#SoftwareDevelopment #AIinJava

We're finally bringing ASCII art to life on Saturday's stream!
Think retro-style scenes, fully generated by the AI.
Fantasy + LLMs + Java.
Any ideas for scenes you'd love to see in ASCII?
#AsciiArt #TextGame #LLM #AIinJava