To thrive with AI, leaders must foster continuous discovery, robust evaluation, and agile experimentation, moving away from static processes and empowering teams to adapt quickly in an ever-changing landscape.
AI dependencies change rapidly, risking app stability. Use a small, independent "canary" app to test upgrades before main deployment. Pair with AI agents for validation and strong production monitoring to catch issues early and minimize disruption.
This blog explains how Google ADK for Java helps enterprises get the most out of the Java ecosystem.
A widespread breach of school platforms highlights how BOLA vulnerabilities expose sensitive data. Learn what BOLA is, why traditional security misses it, and how to secure APIs with robust relationship checks.
Learn how to build a secure Spring Boot MCP client that enables AI agents to connect with your legacy inventory server, discover and use tools at runtime, and implement robust security measures against prompt injection and other risks.
Learn how to wrap legacy Spring Boot apps as MCP servers using Spring AI, enabling AI agents to interact with your backend via a unified protocol. The guide covers setup, adapter design, and practical tips for fast AI integration.
This blog covers safe software releases using strategies like canary releases and feature flags, stressing testing, monitoring, fast rollbacks, and a blameless culture. It highlights leadership, checklists, and AI-driven remediation as future trends.
When you share engineering metrics with executives, link each one to a key business goal. First, find out which outcomes matter most to your leaders, such as cutting costs, speeding up delivery, or improving product quality. Next, figure out which engineering work supports those goals and choose metrics that show
William Zinsser’s "On Writing Well" teaches that clear, simple writing connects with readers. Cut out jargon, focus on clarity, and rewrite for conciseness. The book’s advice makes non-technical writing approachable and enjoyable for everyone.
Obsessing over engineering metrics can harm team culture and block innovation. Use metrics for reflection, not as strict targets. Separate AI and human outputs, prioritize psychological safety, and focus on meaningful improvement over chasing numbers.
A team works best when it can quickly find and fix system issues. Speed and accuracy both matter. Success also depends on how much risk the organization is willing to take and how much technical debt stands in the way of fixing big security problems. 💡The answer is not to
1. Our Core Principle We use AI to help us do more, but we don’t let it replace our own judgment. We are fully responsible for every line of code, every design choice, and every customer interaction, even if AI helped us. 2. Roles & Responsibilities We’ve set