One day, someone confidently said, “An MCP server is basically a tool.” Another person immediately disagreed and replied, “No, no — MCP is more like a client.” Before that debate could settle, someone else joined the conversation and said, “Actually, MCP is just a protocol.”

REST Assured provides specifications to solve this problem. Instead of defining the same settings in every test, common configurations and specifications can be created once and reused throughout the test suite.

This continues the direction the previous release set when we moved NFC, biometrics, and cryptography into the framework core. The full background on that earlier set is in NFC, Crypto, Biometrics, And A New Build Cloud.

Code Patterns in Spring Boot With Kafka

Spring for Apache Kafka gives me two primitives that feel pleasantly old Spring KafkaTemplate for sending and @KafkaListener for receiving. That template/listener model is intentionally similar to other Spring integration tech, which keeps application code focused on domain logic instead of raw client plumbing. 

Call them phantom APIs: routes, handlers, and parameters that show up in production but never appear in a spec, a ticket, or a design review. Some get hand-built by a developer in a hurry and are forgotten. Increasingly, though, they're a byproduct of AI code generation — Copilot, Cursor, an internal fine-tuned assistant, whatever your shop has standardized on — quietly scaffolding an admin route, a debug handler, or a permissive query path because that pattern showed up often enough in training data to feel "normal." Nobody asked for it. Nobody reviewed it with fresh eyes, because by the time a human glances at the diff, the suggestion already looks plausible. That's the part that should worry you more than any single CVE: plausibility, not malice, is now the main vector.

If your platform provisions domains for users (whether that’s a website builder, a multi-tenant SaaS, or a developer tool that provides domain-at-checkout), email routing belongs in your provisioning pipeline, executed immediately after domain registration, without any user involvement.

This article walks through registering the server, the full tool catalog it advertises, how Foxit’s eSign and DocGen REST APIs carry the same agent session forward into signing and document generation, and a concrete four-step workflow you can reproduce with your own files.

In May 2026, AWS dropped a bombshell: Amazon Q Developer IDE plugins and paid subscriptions will reach end-of-support on April 30, 2027, with new signups blocked as of May 15, 2026. The successor? Kiro — AWS's next-generation AI IDE that reframes how engineers build software from scratch.

If you're a backend engineer who has been relying on Q Developer for code completion, inline chat, and security scanning inside VS Code or JetBrains, the clock is ticking. But before you begrudgingly migrate, it's worth understanding why this transition is happening, what Kiro actually offers, and whether the trade-offs are worth it — especially in production backend contexts like microservices, distributed systems, and observability pipelines.

The pieces are an OpenAPI client generator, a SQLite ORM, JSON and XML mappers, a component binder with validation, build-time SVG and Lottie transcoders, and a declarative router with deep links. All ride on a single build-time codegen pipeline: a Maven-plugin pass that reads annotations or declarative source files at build time and emits typed Java that compiles into your binary. No reflection, no service loader, no Class.forName. The "How it works" section at the end of this post covers the codegen plumbing once you have seen what it powers.

Quick Intro

• Sign up at https://x.ai/api.
• Generate an API key from the console.
• Install pip install xai-sdk.
• Set env var: export XAI_API_KEY="your_key_here".
• Models list: https://docs.x.ai/developers/models.

I'll share some samples in Python.

This article will discuss the structure and usage of Kotlin Coroutines in detail. We will examine how to maximize Kafka message processing performance using Structured Concurrency principles and Resource Throttling techniques.

That distinction is worth sitting with. Because if authentication failed, you'd know. You'd see it in the logs. The SIEM would fire. The investigation would start in an obvious place.

Navia didn't catch it for more than three weeks after the attacker had already stopped. The company published a breach notice on March 13, 2026. Individual notification letters went out on March 18 — eighty-six days after the intrusion began.

For the past two years, engineering teams integrated LLMs via the path of least resistance: layering models on top of existing architectures. For human-facing use cases, this works. Humans provide implicit context, tolerate minor latency, and intuitively course-correct errors.

To solve this, we built a local LLM‑powered agent that runs entirely on our own servers, normalizes chaotic report data, filters out useless noise, enriches descriptions from Jira, and generates a clean list of actual accomplishments. In this article, we break down the architecture and explain why a CPU‑only, on‑premise approach is practical for enterprise clients who prioritize data privacy.

Before you write off “Agent” as just another industry buzzword, consider this: true agency isn’t just about complex LLM reasoning; it’s about autonomous execution. An agent bridges the gap between manual human effort and automated consistency, stepping in to handle what used to require our click-by-click attention. 

It had processed 214 expense entries across a 77-minute window. Every API call returned a 200. Every authorization token was correctly scoped. The workflow orchestrator logged nominal completion. The audit trail was clean, timestamped, and signed.

This article is about how that works, when it makes sense, and when it doesn't. It also covers the parts that surprised me, because the approach has real downsides and I want to be honest about them up front.

The following comprehensive blueprint serves as a definitive Data Patterns and Practices Library to help you align your infrastructure with proven engineering methodologies.

Spring’s TestContext framework can cache and reuse ApplicationContext between test classes, but only if the effective test configuration is the same. If the configuration differs, Spring has to create another context. In large enterprise applications, this can become expensive very quickly.