AI & Machine Learning Archives

Ankur in AI & Machine Learning

Spring AI 1.x to 2.0: The Migration Guide (What Breaks, and What Breaks Silently)

A complete Spring AI 1.x to 2.0 migration guide, verified against Spring AI 2.0.0 on Spring Boot 4.1. Boot 4 is mandatory and Jackson moves to 3. internalToolExecutionEnabled and toolNames are removed outright, not renamed. Chat memory becomes stricter: 2.0 removes the remaining default-ID and builder-based configuration paths, while explicit per-request conversation IDs — introduced during the 1.x line — become the only safe migration target. Plus the changes that produce no compile error at all.

Ankur in AI & Machine Learning

Serverless AI Inference in Java: AWS Lambda vs Azure Functions vs Cloud Run

A hands-on comparison of serverless Java AI inference on AWS Lambda, Azure Functions, and Google Cloud Run — with real benchmark data, cost tables, a clear winner recommendation, and code you can run.

Ankur in AI & Machine Learning

TypeScript vs Java vs Python in the AI Era: Who Owns Which Layer?

Python, TypeScript, and Java each claim dominance in the AI era — and they are all right, for different layers. This definitive guide maps which language owns the model layer, the orchestration layer, and the backend layer, with real code showing how all three work together in a complete AI system.

Ankur in AI & Machine Learning

AI-Native Backend Design: Rethinking Microservices, Databases, and APIs in 2026

Traditional microservices, REST APIs, and relational databases were designed before AI became a first-class backend concern. This reference guide covers the five core principles of AI-native backend design — prompt-driven APIs, vector vs SQL decisions, event-driven pipelines, AI observability, and where current microservice patterns break.

Ankur in AI & Machine Learning

Java vs Python for AI in 2026: The Hidden Backend War Nobody Talks About

Python rules AI research. Java rules enterprise production. In 2026, they're fighting for the same backend. This deep technical comparison — covering latency, memory, scalability, the GIL problem, and hybrid architecture patterns — settles where each language actually wins.

Ankur in AI & Machine Learning

From Copilot to Autonomous Agents: How AI Will Replace CRUD APIs (Java Perspective)

AI is shifting from code assistant to autonomous agent — and that shift has profound consequences for how Java backend systems are architected. Will REST APIs disappear? Explore the Java perspective on agent orchestration, intent-driven backends, and what CRUD actually becomes in an AI-native world.

Ankur in AI & Machine Learning

Spring AI + LangChain4j: Building Production-Ready AI Microservices in Java

A comprehensive production guide to building AI-powered microservices in Java using Spring AI and LangChain4j — covering RAG pipelines, observability, cost optimization, and real-world architecture patterns.

Ankur in AI & Machine Learning

LLM Gateway for Java Microservices — Complete Runnable Code & Demo

Complete runnable LLM Gateway project: full directory structure, docker-compose, pom.xml with Spring Boot 3.4.5 and Spring AI 1.1.0, all source files, build instructions, and five end-to-end I/O demos covering normal routing, cache hits, rate limiting, and provider failover.

Ankur in AI & Machine Learning

Spring AI RAG in Java — Complete Runnable Code & End-to-End Demo

Complete runnable Spring AI 1.1.0 RAG project: full directory structure, docker-compose, pom.xml, all source files, build instructions, and an end-to-end PDF ingestion and query walkthrough.

Ankur in AI & Machine Learning

Implementing Vector Embeddings and Semantic Search in Pure Java

A complete implementation of a semantic search engine in pure Java — no external libraries. Covers TF-IDF vectorisation, cosine similarity, and document ranking by meaning. Explains the mathematics behind vector embeddings, how they power RAG and AI search systems, and provides a working query engine with annotated code and sample output. The same architecture used by Pinecone, Weaviate, and Elasticsearch — built from scratch.