02 Apr 2026
Modern Spring applications often degrade into tightly coupled systems as they grow. Clear module boundaries, feature-oriented packaging, and strict dependency direction are essential to prevent this. By keeping business logic independent from Spring, limiting application context size, structuring projects into build-time modules, and using events to decouple behavior, teams can build systems that scale in complexity without sacrificing testability, maintainability, or development speed.
31 Mar 2026
On-device machine learning on iOS delivers real value only when it is treated as part of the user experience, not just a technical dependency. By combining background model updates, local personalization, and graceful fallbacks, developers can build adaptive systems that improve over time. Streamed inference enables faster, more responsive interfaces, while privacy-first, offline-capable pipelines increase reliability and user trust without relying on servers.
26 Mar 2026
Deterministic replay debugging brings control to Android apps plagued by flaky crashes and elusive race conditions. By recording user events, lifecycle callbacks, and async boundaries, developers can replay executions with identical timing and behavior. Injecting clocks, dispatchers, and controlled schedulers makes concurrency deterministic, enabling reliable reproduction of bugs that normally vanish under a debugger and significantly improving testability and architecture.
24 Mar 2026
Accessibility in apps is no longer optional. Legal requirements and real user impact expose how everyday engineering decisions break accessibility in production apps. Missing semantics, broken focus management, inaccessible custom components, and unannounced dynamic content are common failure points. The solution lies in enforcing accessible defaults, building components with constraints, testing with screen readers, and treating accessibility bugs as critical defects rather than edge cases.
19 Mar 2026
A deep dive into persistence beyond basic repositories, focusing on how Spring applications really behave under load. It explains JPA’s persistence context and dirty checking, clarifies transaction boundaries and propagation, and shows practical, non-folklore solutions to the N+1 problem. It also provides clear guidance on when to choose JPA, JDBC Template, R2DBC, or NoSQL to improve performance, correctness, and long-term maintainability in real-world systems.