241 tutorials live · 241 planned · free forever
The practical curriculum for Java developers who want to build real AI systems — Spring AI, LangChain4j, RAG pipelines and autonomous agents, explained with code you can actually run.
AI engineering, enterprise backend, Spring Security and interview preparation — each in dependency order, so no tutorial assumes something you have not covered.
Complete classes with imports and configuration — not fragments that quietly omit the hard part.
Full walkthroughs of deployable systems with architecture, tests and Kubernetes manifests.
Every concept is framed in terms you already know: beans, starters, streams and back-pressure.
The curriculum
Each roadmap is sequenced so every topic assumes only what came before it. Start where your experience already is.
From Spring Boot developer to Agentic AI engineer
Seventy-five topics in dependency order, from your first ChatClient call to multi-agent systems running in production. Nothing here assumes Python, a data-science background, or a GPU.
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From Spring Boot novice to enterprise architect
Sixty-eight topics covering every layer of modern enterprise Java: Spring Boot internals, REST API design, microservices, RabbitMQ and Kafka, DevOps, and the system-design patterns senior interviews actually ask about.
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From basic authentication to zero-trust architecture
Forty topics covering every authentication mechanism, every authorisation model, every major attack class and the compliance frameworks that govern them — ending in a zero-trust platform design.
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From core Java recall to production war stories
Fifty-eight topics covering what Java interviews actually test: the language semantics behind the stock questions, collection and JVM internals, concurrency, and nine real production incidents — memory leaks, CPU spikes, pool exhaustion and load collapse — told as stories you can retell in a room.
58 of 58 published
Start reading
Deep, single-topic guides with runnable Spring Boot code.
The Java 17-to-21 features that matter most for AI work: records, sealed classes, pattern matching, text blocks and virtual threads — each shown with a concrete AI use case.
Read tutorialFunctional Java refreshed for AI work: streams for document pipelines, Optional for safe metadata access, and CompletableFuture for concurrent model calls — with practical examples.
Read tutorialProject Reactor for AI developers: Mono, Flux, back-pressure and WebFlux — and the one place they are genuinely the right tool, streaming LLM tokens to a browser.
Read tutorialMicroservices patterns that matter for AI systems: API gateway, circuit breakers around model calls, the saga pattern for agent workflows, and where an AI service fits in the topology.
Read tutorialContainerize and deploy a Spring Boot AI application: a production Dockerfile with layered JARs, Kubernetes deployment with secrets for API keys, health probes and resource limits.
Read tutorialMaven and Gradle for Java AI projects: managing Spring AI and LangChain4j versions with BOMs, multi-module layout for projects with separate ingestion and serving, and dependency hygiene.
Read tutorialBuild something real
Complete applications you can deploy — architecture, code, tests and infrastructure.
A Spring Boot HR platform that answers policy questions from your own documents, screens résumés against a role, and handles leave requests through a tool-using agent — the project that turns phase 1 and 2 into something you can demonstrate.
An agent that investigates incidents, analyses logs, reviews pull requests and proposes remediation — with every write action behind a human approval gate. The project where autonomy meets consequences.
A multi-agent research platform where an orchestrator dispatches to specialists — filings analyst, news analyst, quantitative analyst — and reconciles their findings into a cited report. The capstone project.
An OAuth 2.1 authorization server with MFA and WebAuthn, a gateway that enforces OPA policy on every request, dynamic credentials from Vault, and a hash-chained audit trail — the project that turns the Spring Security roadmap into a running platform.
Twelve Spring Boot services, an orchestrated saga across payment and inventory, Kafka for events and RabbitMQ for commands, all deployed to Kubernetes with tracing and dashboards — the project that turns the Enterprise Backend roadmap into a system you can run.
A Spring Boot service that fails on demand in eight documented ways, with a k6 harness, JFR and heap-dump tooling wired in, so you can debug a real memory leak and a real CPU spike instead of reading about them.