The Era of AAP: Ai Augmented Programming using only Java by Stephan Janssen

Java is becoming a first-class citizen in AI programming with new frameworks like JLama and Llama3.java enabling local LLM inference

AAP (AI Augmented Programming) tools can now run large language models locally with up to 131,000 token context windows, eliminating the need for cloud services

DevOps Genie plugin for IntelliJ provides open-source AI coding assistance while keeping code private and running models locally

GPU support through projects like Babylon and TornadoVM will enable faster matrix multiplications and LLM inference directly in Java

Model sharding allows distributing large models across multiple machines, making it feasible to run billion-parameter models on consumer hardware

Token caching and streaming responses (20 tokens/second) improve the user experience when working with LLMs

RAG (Retrieval Augmented Generation) capabilities are evolving from basic to advanced implementations using semantic search and graph databases

Panama Vector API improvements enable faster matrix multiplications natively in Java, especially on ARM processors

Local LLM deployment eliminates privacy concerns around sending proprietary code to cloud services

The combination of local models, Java frameworks, and IDE integration creates a complete AI-assisted development environment that can increase developer productivity