Project Leyden brings AOT compilation and persistent caching to standard JVM Java — no native binary required. Learn how it works internally, where it wins, where it fails, and how to use it correctly in production.
A definitive side-by-side comparison of Jackson 3 and Jackson 2 covering the package namespace rename, JacksonException unchecked change, API changes, performance tuning, custom serializer renames, Spring Boot 4 integration, and new features — with annotated code examples for both versions.
A complete, step-by-step guide to migrating from Jackson 2.x to Jackson 3.1.2 — covering the tools.jackson group ID and package rename, the unchecked JacksonException change, removed APIs, ObjectMapper to JsonMapper builder migration, custom serialiser renames, and Spring Boot 3/4 coexistence.
Three identical Spring Boot 4.1.1 endpoints — I/O-bound and CPU-bound — re-benchmarked on JDK 25 under platform threads, virtual threads, and WebFlux. Real numbers, a benchmark-methodology bug caught and fixed along the way, an event-loop-starvation failure mode isolated CPU benchmarks miss, and a decision framework: if your service does X, pick Y, because Z.
The definitive 8086 register reference: all 14 registers explained with size, purpose, aliases, mandatory roles, and typical instructions. Includes a bit-by-bit FLAGS table, addressing mode summary, and a "which register for which task" decision guide that maps every common programming task to the correct register — with links to worked assembly examples throughout.
A JEP-by-JEP deep dive into Java 25 LTS — the features that will actually change how you write and profile Java. Each section includes a before/after code example, an explanation of what the JVM does differently, the pitfall to avoid, and a copy-paste AI prompt to retrofit your existing codebase. Covers JEP 495, 494, 485, 492, 488, 491, 454, and JFR method profiling.
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.
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.
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.
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.