How a Single @Transactional Annotation Can Crash Your Spring Boot App Under Load

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A common but dangerous pattern in Spring Boot applications involves placing the @Transactional annotation on methods that also make external HTTP calls, such as payment gateway requests. When this happens, a database connection is borrowed from the HikariCP pool at the start of the transaction and held open for the entire duration, including the time spent waiting for the third-party API to respond. Since external calls can take seconds, even moderate traffic can exhaust all available connections in the pool, causing new requests to time out and the application to become unresponsive. The outage can appear misleading because CPU, memory, and database metrics all look healthy, masking the real bottleneck. Developers can fix this by using programmatic transaction boundaries, domain events with @TransactionalEventListener, or the Transactional Outbox pattern to ensure database connections are not held during external I/O.

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DSPy 2.6 replaces manual prompt engineering with declarative AI pipeline compilation

DSPy 2.6, a framework originally developed at Stanford University, automates the process of prompt creation for large language models by letting developers define task logic rather than writing manual instructions. The framework uses an optimizer called MIPROv2, which takes a small training dataset and an evaluation metric to automatically generate, test, and select the most effective prompts for a given model. This approach addresses a long-standing fragility in handcrafted prompting, where minor model updates or unexpected inputs could cause error rates to spike dramatically and force engineering teams to restart optimization cycles from scratch. DSPy 2.6 structures AI development around modular components and declarative signatures, separating program logic from the underlying prompt text entirely. The release is being positioned as a shift from ad hoc prompt tuning toward a more systematic, compiler-like methodology for building reliable AI pipelines.

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ProgrammingDEV Community ·

Bootstrap Added to Blazor Task Tracker Without Touching App Logic

A developer tutorial series has concluded with its final installment, applying Bootstrap CSS styling to a previously functional but visually plain Blazor Task Tracker app. Bootstrap, a pre-built CSS framework, was used to add layout, spacing, and UI components such as navbars, cards, and buttons without altering any underlying C# logic. The series spanned five parts, beginning with a WPF implementation, progressing through MVVM refactoring, Razor rendering, and Blazor interactivity, before arriving at this styling phase. The final post demonstrates a key software principle: separating presentation from business logic, meaning visual changes required no modifications to the code driving the app's functionality. The complete series serves as a practical walkthrough of transitioning a desktop task management app to a styled, interactive web application using Microsoft's Blazor framework.

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ProgrammingDEV Community ·

BitNet b1.58 Brings 100B-Parameter AI Inference to Ordinary CPUs

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ProgrammingDEV Community ·

FlashAttention-4 targets memory bottlenecks in NVIDIA Blackwell B200 GPUs

FlashAttention-4 (FA4), developed by Tri Dao's lab in close collaboration with NVIDIA, has been released as a production specification optimised for the Blackwell B200 and GB200 accelerator architecture. The update addresses critical performance bottlenecks that emerged as large language models such as DeepSeek 4.1 and GPT-6 Astra began operating with context windows exceeding one million tokens. Earlier versions of the algorithm suffered synchronisation stalls and memory bandwidth saturation on Hopper-generation hardware, wasting up to 32% of available clock cycles. FA4 resolves this by introducing Asymmetric Kernel Pipelining via a hardware Tensor Memory Accelerator, splitting workloads between dedicated Producer and Consumer Warps running asynchronously. The release also adds native support for 4-bit quantisation formats FP4 and MXFP4, enabling fuller utilisation of the B200's 20 PFLOPS FP4 compute capacity and 8 TB/s memory bandwidth.

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