AI is a contributor without context: it knows only what’s written down and follows only what’s enforced. Four security layers for agentic development, learned the hard way on Apache Struts and an LLM-powered Slack bot, plus a checklist for Monday.
AI made writing code faster, but the way we choose features, approve releases and measure success barely changed. A story of three years, five features and a Jira dashboard called “Production line”, plus three questions to ask before building any feature.
AI makes coding cheap, but it doesn’t replace engineering judgment. Here are six ways AI agents can quietly undermine your SDLC, and what you can do about them.
We ran a council of AI reviewers on a real Strapi PR that shipped two bugs. It caught both, every time. Here’s how to build your own, and when it’s worth the extra tokens.
JEP-544 brings AOT code compilation to Project Leyden. See how caching JIT-compiled code cuts JVM startup time, and how it compares to CRaC in containers with 1 and 4 vCPUs.
A practical comparison of k6, Gatling, and JMeter for HTTP load testing — covering scripting models, CI integration, workload accuracy, and what the second week of maintenance actually looks like.
FinTech is short for financial technology — but the term covers a lot of ground. This guide answers ten questions that help you get your bearings, from what FinTech is and who the biggest players are to how it is regulated and whether AI will replace it.
Virtual threads are good for I/O operations” is a popular buzzword, but what does it actually mean? Through a series of simple experiments on JDK 21 and 25, I explore when virtual threads truly unmount and when they block just like platform threads.
A step-by-step walkthrough of a repo trade on the Canton Network — from asset creation through agreement, settlement, and repurchase — connecting the Daml contract model to Java application code.
A deep dive into the FinTech ecosystem: the five key participant groups that shape it, how they connect, and why tokenization and synchronized finance are redefining the infrastructure layer.
When software engineering emerged in the mid-20th century, shaped by pioneers such as Alan Turing, John von Neumann, Grace Hopper, Edsger Dijkstra, and later Fred Brooks, software was largely about logic, mathematics, and precise instructions for a machine. Programs were smaller, the gap between the implementation and the knowledge required to understand was relatively short, and much of the truth about a system could be found directly in its source code. The code was the documentation.
Not so long ago, I wrote an article about OWASP ASVS 5.0 (a comprehensive security checklist aiming for web applications). I mentioned there that AI security was intentionally left outside of its scope. There is now a brand new OWASP document specifically for filling this gap: AISVS. I've looked at this standard. Is it actually helpful when building an AI-enabled system?
